The Rise of the Full-Stack Builder and Hyper-Leveraged Generalist with Microsoft CEO Satya Nadella

The world is going to be very skeptical of tech and tech companies that say, "Trust us, we've got it. The future is going to be glorious." You kind of have

to deliver tangible benefits because it's too important this time around.

It's too much of the economy for it not to be the case. True ambition is about making the impossible possible. I take

great inspiration from sort of the people who were managing the Azure network. We built in the last 15 months

network. We built in the last 15 months more Azure capacity than we built in the first 15 years. I mean it's crazy. Our

job is not to do Azure networking. Our

job is to build the agentic system that does Azure networking, right? The way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much.

Maybe the next big startup could be someone who builds a new university, a new pedagogy even of how to get someone to go through a curriculum and find

economic opportunity. That's highly

economic opportunity. That's highly valuable.

Please welcome Swix Saragoa Allad Gil and chairman and chief executive officer of Microsoft Satinadella.

Hello son.

Uh, I'm so excited to be here. Welcome

to a crossover episode of No Priors in Lane Space with Sat Nadella. Um,

congratulations on an amazing build.

No, thank you so much and it's great to be with both of you. I listen to both of you or both the podcast all the time.

It's great to be on it.

Thank you so much. So you're just talking about um these amazing uh announcements from across the Microsoft estate all morning for I think three hours. What is the uh what's the most

hours. What is the uh what's the most important reflection or takeaway you have? I I'd say there are uh perhaps the

have? I I'd say there are uh perhaps the the biggest one for me is let's sort of conceptualize this more as

an ecosystem play as opposed to a single model or even a single platform right I mean you know whenever I at least for me having grown up at Microsoft having seen

whatever four major platform shifts u I sort of fall into that um camp where a platform is defined by fundamentally its ability to create more value about the

platform versus what's captured in the platform. And so if you you view what's

platform. And so if you you view what's happening right now, I think this morning's keynote was how can any company whether it's an AI native

company or a traditional enterprise company participate as a firstass participant where they can point to AI they created.

Right? It's not that they don't use other people's AI. Of course they will.

But to me, what's the path? What's the

recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you

look like? What is valuable? How do you do that? That's it. That's sort of our

do that? That's it. That's sort of our job to do.

Yeah.

Ecosystem strategy is uh very complicated, right? Because you end up

complicated, right? Because you end up building certain components, partnering for certain components, supporting them.

You just announced this big suite of models. Like tell us a little bit about

models. Like tell us a little bit about the uh training strategy for Microsoft.

So, so the thing that we wanted to do with the MAI models was to build and as Mustafa talked about first of all a great lineage right starting with

pre-training uh with very good data quality uh doing all the abilations making sure because in in some sense it's become even harder

to build a clean lineage model just because there's so much stuff out there um that you truly need to ablate out to be able to have a fantastic pre-trained model. In fact, that's one of the

model. In fact, that's one of the challenges of a lot of the openw rate models is they look great on one benchmark or two, but they're not great on practice. So, that's why in fact even

on practice. So, that's why in fact even in our FDES are are pretty gone really excited about these MAI models because

how the heck can a small 5D model hill climb? uh and it goes back a little bit

climb? uh and it goes back a little bit to what I think is ultimately the key thing to do which is try to pursue finding that cognitive core. Uh so to me

starting with a clean lineage then creating that ability for companies to be able to use this right not just as a generalist but to create their own

specialist by building this hill climbing scaffold around it right so it's not just the model but you have a hill climb scaffold around it then you will start building your rle you will

start collecting the traces most importantly you'll have private eval because we know all the eval out there are good, interesting, but they're not really that critical at this point because they all can be maxed. And so

the point is each company will have its own private eval. And so that end to end platform story around our models is sort of uh what I think is interesting. And

then the one other thing Sarah since you brought that up is I do feel there's a new frontier like people talk about the frontier and you're operating at the frontier. Um, interestingly enough, if

frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in in in fact the the Lando Lakes demo we showed was pretty cool. We used whatever GPD 55,

pretty cool. We used whatever GPD 55, right? Then you collected a bunch of

right? Then you collected a bunch of traces and then you took a 5B reasoning model and achieved higher. Uh, so that is another aspect of what it means to appear, you know, operate at the

frontier.

Yeah. I I think uh I first of all I have to congratulate you on basically building a frontier neolab inside of Microsoft in two years. Um I'm wondering you know you have all this AI strategy that you're rolling out. I'm wondering

what do you know now that you wish you would tell yourself two years ago or two to three years ago. Three years for the Jensen partnership, two years for uh Mi.

Yeah. I mean I think the the thing when that I reflect quite a bit right which is sort of obviously I got into all this when I got excited by the the scaling laws paper and you know when you know even the open AI partnership came about

when those folks said hey we're going to really throw a lot of computer transformers uh and they've helped right the thing that I always look back and say wow these things um do have

capability that they're climbing up I mean this you know this crude way of saying it is intelligence is log of compute kind of works. Now what I think

we underestimated perhaps is the real world complexity of deploying these so that they actually deliver the value in the real world.

Right? So the outcomes as measured by any benchmark is interesting important but the true eval is when people out

there are able to do unique things that they only can value and it's very measurable right that I wish we had sort of even like had more in our

consciousness right which is as an industry because right now I think when people say wow I don't want a token max it's an artifact of us not having

thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there but I'm glad we are here.

What are some of the use cases that you've seen that have created the most value for your customers because I know that people talk a lot about code and I think it's pretty clear that that's something that's having very large scale impact. Are there other areas that you

impact. Are there other areas that you find in common that your customers are really benefiting? Yeah, I think yeah to

really benefiting? Yeah, I think yeah to your point obviously coding is now got but it's interesting by the way ladish to you even talk about the coding right which is coding is worked so well that

we now have to rebuild the IDE right I mean it's kind of nuts to see what we launched is like oh my god I have these 100 agent sessions I the cognitive load

it transfers back to me as a human is so excessive that now I need a new UI oh by the way I like the the chat as the only

artifact is also impossible. So that's

why we need a canvas. So it's kind of interesting for all the things about where is software needed or where is UI needed. Uh you kind of need that even

needed. Uh you kind of need that even for code right in a fully agentic world.

But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we we showed with auto u um autopilot, right, on what you see with claws is a

good one because if you sort of think about a lot of human capital is doing the glue work, right? If you now can

augment that with tokens slash agents that are longunning, durable, right?

then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh so you can like I'm positive that 6 months from

now we'll all be saying oh wow like all through night the night there was a bunch of stuff that all these autopilots that I have working on my behalf with my

delegated authority so to speak right I can sort of given even my identity did a bunch of work then of course I'll need my new ad to say what did you do like am

I did I do this work and so on so I think that that's where compressing of workflows uh complete leading of tasks. Uh that's

where I think a lot of the value gets created.

I think you raised a really interesting point which is there's the actual agent is doing the code and then there's a harness around it and that's the environment that's the context that's everything you're setting up as a developer around actually a coding

agent. What is the harness for the

agent. What is the harness for the enterprise? Is there an equivalent

enterprise? Is there an equivalent concept for broader productivity work or how do you think about that concept sort of that's right. So, so in some sense you

that's right. So, so in some sense you kind of want the harness to define the models the the data uh and the tools and

so that you have a loop across those three and so what we are trying to first of all make sure is each of our products that we build right whether it's GitHub copilot or the security cop the stuff we

showed with mdash or even the discovery for science it doesn't matter all of them are multimodel harnesses um with tools tools access so that you can do this progressive uh disclosure of

tools even so that they're token efficient. Uh and then you're feeding it

efficient. Uh and then you're feeding it with very rich context because that's sort of the other hard lesson we've learned in the last two years is oh my

god the amount of work you need to do to prep the context layer uh such that your plan can execute in the most efficient

way is where the magic is. So we have in our case we have the GitHub harness which essentially we're using across all our products. It's available in foundry

our products. It's available in foundry and we're open like you can use your llama harness whatever or you can use the um uh you know any open harness or any harness of yours and train with your

tools and multiple models and your context. And so that's the pitch because

context. And so that's the pitch because right now a lot of dialogue is um hey if I train the harness plus tools and the model together you get eval. And what we

are proving out is and the best example of that is what we did with mdash right because when it launched um it found bugs or vulnerabilities that were not

found by mythos uh and so there is existence proof I would claim that you can have a multimodal harness uh that can in fact

be more uh performant in the real world.

So a premise behind the uh training at the independent frontier labs is really you know we're going to have these models and we'll have an API business and we'll support enterprises and

startups but a firstparty product be it productivity or code or search drives the majority of revenue. That's a

different value equation than you're describing I think with the Microsoft ecosystem. uh if if that's the case tell

ecosystem. uh if if that's the case tell me if it's the case uh because obviously you have firstparty products and you have enablement products um what is the role of the develop like what's going to be hard and the set of skills and the

value capture the developer has in that world yeah so I think that there's always going to be the case that someone who is super successful and as a platform builder can also have

firstparty products it was true with Windows it was true uh with uh the the SAS side and the cloud side as well with us and others and so on. But the thing

that is is it should not be a limiter to other people achieving that same success, right? That I think is the core

success, right? That I think is the core difference which is the the network effects this time around around intelligence are such because they learn

from data and not really lots of data.

It's just the few samples that you have to see to understand what's novel about something. So that's why the game

something. So that's why the game becomes how to protect. So that's why I would say every company having private eval maybe the biggest IP right I think

about it like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces maybe one of the biggest drivers

uh of IP like so in other words another asset test is you have an eval that's private you're using a a G model A can

you switch it to model B and you climb up. If you can, then you're in control.

up. If you can, then you're in control.

If you can't, you're not in control. And

that's where even the harness decision becomes super important, right? So

therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that

an AI native startup needs, a SAS company needs or every enterprise needs.

Yeah, I think in a very real way you are Microsoft historically as an operating systems company and then become a cloud company. Maybe like the third act is

company. Maybe like the third act is that you're harness or eval whatever whatever the the sort of conglomerate of concepts that you want to put together. Um I I think like

enabling every company to have like frontier intelligence or what what I forget the the exact term that you use um is the is the mission right that is that is the platform promise that you

build with us you will get your intelligence uh for your data that's right that to to me that is the like if there was one tagline uh for

this entire developer conference is can everybody operate at the frontier with their frontier intelligence right to me that is soant important because

otherwise it I I don't know how you achieve stable equilibrium right which is how do I then go and say wow my company is going to have a terminal

value because I now know how to continuously compound on top of what's a platform that gets better right so when like Windows obviously came out Adobe

built autoes built u or even like take what Jensen said we built DX and he built you know CUDA on top of Um, right. I mean, I always say to

Um, right. I mean, I always say to Jensen, "God, I got the short end of that, right? I wish uh we had recognized

that, right? I wish uh we had recognized it." But nevertheless, but that idea

it." But nevertheless, but that idea that you can build a platform layer that someone else can then extend out um and build their own intelligence layer in this case, I think is everything, right?

Without it, why have a developer conference? I can just come and have you

conference? I can just come and have you all sort of just worship at the altar of one model. But that's not a developer

one model. But that's not a developer conference. uh backstage we you had a

conference. uh backstage we you had a discussion about what is IP or what is the value in a company it used to be the length of human experience at a company and now it's this other thing which is

the eval the experience in sort of applying agents to the company I just want you to like flesh that out a little bit more because yeah it's a great way to frame it right because at the end of the day every company is going to have both the human

capital that is still going to be super valuable uh because humans uh and their ability to find the gaps that exist at all times is going to be the way we all

will create value, right? I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as

token capital goes up, right? So let's

say a any corporation has lots of tokens and lot of human capital. The question

is how do you compound the two? So if

you have a like if you take in teams I have a bunch of agents doing work and a bunch of humans doing work and the traces between those that is really

important context of how that enterprise is creating value. Then that goes back to train not a generalist model but to tain the train the company veteran agent

uh right that is super valuable again right which is when a company says it should in fact go onto the balance sheet is how I think about it right that's so in fact they may be like human capital

was never possible to go put on a balance sheet uh because you then know how to capture the tacet knowledge whereas now I think you can with the agents that have learned through the

through time through all the traces. Uh

so that's what at least we think will happen.

I think the SEC is going to have to have accounting standards for token uh expertise.

Uh you're talking about the equilibrium state um and a stable equilibrium where companies have this compounding value and can see terminal value for themselves. Another challenge to you

themselves. Another challenge to you know the considered equilibrium of okay there are applications and workflows that are sort of common to a vertical or

a horizontal um and this was like the generation of SAS companies and you know Microsoft has lots of SAS properties as well and then there are things that are very specific to every enterprise that they're differentiated against um I'm

sure you have heard much and participate in much of the debate about the end of software because all these workflows are are cheap to generate now um do you think the equilibrium looks different

between what agents get built in enterprises versus in their vendors in the future.

Yeah. So I think what's happening there is see we we had a particular way we captured u I would say workflow in apps right

because we built a data model right we schematized some part of some business process we then built a bunch of business logic y and then we put a bunch of UI on top of it right so that's kind of what every SAS company

and a little configuration for like 20 20 years that was and that was it so interestingly enough.

Now you kind of get to relitigate that vertical stacking, right? So I still think for example that data model that you built underneath every SAS application is super good, right? Like

why reinvent it? Like I my general ledger better be a general ledger. I

don't need new schema creation. Uh in

fact that entity relationship uh is actually pretty good robust thing that I want to feed.

And you want to be stable.

That's right. Then same thing with business logic right if if you look at uh we have this product called PowerBI right it was like dashboards galore

people created the beauty underneath that dashboard is a very rich semantic model right someone took the pain to create a dashboard and do all the

measures and you want that that's business logic right I want that to be available to me so I think the challenge of the SAS business model is we packaged

one way we now have to learn how to unbundle these things and rebundle in new ways and discover new business models right I mean if you look at it what's happening today with Microsoft

365 is a great example right we have this thing called work IQ in fact like what we are realizing is oh my god like you if you look at in fact there's a

historical parallel to right we sold first exchange and sharepoint and uh you know before teams we had a thing called link server and what have you. And we

thought, oh, that's all going to move to the cloud. But little did we realize

the cloud. But little did we realize that, oh, the number of people who will use servers in the cloud is 10x, 100x, right? Because people were not buying

right? Because people were not buying servers. They were just buying a

servers. They were just buying a subscription.

Mhm.

The same thing is now happening with M365 because with work IQ, we've exposed what is perhaps the most important database in a company that never got

used as a database because it was only captive to our apps, right? It was all email operated on it, Teams operated on it Word Excel PowerPoint SharePoint.

But now, like this is one of the coolest things I get to do with work IQ. I go to a GitHub repo and I say, "Hey, I attended a bunch of design meetings last week related to this repo. Can you

capture all that and tell me what changes I should make?" I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base,

right? Previously you could never have

right? Previously you could never have thought of using M365 for something like that. So the value creation opportunity

that. So the value creation opportunity now in the agent world is in fact 10x more but it does require us to have for example there's going to be usage around

M365 right which is going to be perhaps more than even the end users and we have to even rearchitect like in fact like what I use to serve an inbox or a

mailbox cannot be used to serve an agent. uh and so that's sort of what

agent. uh and so that's sort of what we're doing. I don't believe in like

we're doing. I don't believe in like permanent business models for any of these domains but in the near term do you have a prediction between uh you

know outcomes based pricing token based pricing enterprise bundles yeah the way I think about this is always we've had like let's even take

the per user pricing the per user pricing is really an artifact of someone creating a budget needing certainty right because it's the

most important thing like somebody wants a budget they need a per user and and per user is just a set of entitlements to usage right that's kind of what it is and so

the way is the first bundling will be take some usage bundle it into per user stacks and you know then sell subscription so subscriptions I think are going to be there per user is going

to be there then the next big thing will be consumption so people will say I want consumption and it's also possible that people will say I don't even want to pay for any of the subscriptions or the

consumption. It's outcome. But remember,

consumption. It's outcome. But remember,

most people love outcomes until they have an outcome. Because once you have an outcome, it's like giving away royalty, right? I mean, like I I've

royalty, right? I mean, like I I've talked to customers who love, you know, outcome based pricing and I say, "I'm all in until they, oh my god, like what are you talking about? You're sharing in my outcome." No, no, no. I want you to

my outcome." No, no, no. I want you to go back to per user pricing and I want you to consumption price. Right? So, I

think that debate will go on. uh but and all all the all all of these business models have a particular time and a place versus one to rule them all and if anything if you're a SAS vendor or

you're a platform vendor having that flexibility and quite frankly we face this with GitHub right we just recently announced a per user pricing on GitHub

because little you know GitHub copilot was constructed at a per user level before we understood even uh the intensity of usage of agents, right? It

was an interactive way for a developer to use code complete maybe task. It was

not like oh I launched 10,000 s you know agents that are going on all day right so that is what the adjustment is about so now that we really want there will

always be a per user but they will have to be a consumption meter how do you think about the durability of SAS more generally one thing I've observed is in a lot of enterprises internally there will be teams that

almost have agent euphoria they're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SAS vendors and saying we're not going to work with you anymore or we're considering an internal project and it seems like in six to nine months

maybe some of those people will come back and say actually we we can't rebuild everything. How do you think

rebuild everything. How do you think about what's durable in this world and what isn't? It's a I think we have to go

what isn't? It's a I think we have to go through one full budget cycle on this to really see the uh uh the sort of the

emergence of the equilibrium because at the end of the day there's marginal cost to even generating the app, right? In

fact, there can be even a a simple way to say it like if you should always acquire something if the marginal cost of building and maintaining uh something

on your own is higher. uh right that should be like it's a quantifiable right a quantifiable thing and the maintenance part is important right even like you got to remember like hey you know all

the security stuff that now AI will find you better fix them too fast of course there's a coding agent to help you with but then that burns tokens right so whose responsibility is it it's kind of

like a a cycle that you've got to think through and I think we've gone through the excitement that I can generate a lot of software I think the next thing would be what software do I really want to

generate? What software do I want to use

generate? What software do I want to use from others? How do I compose these two

from others? How do I compose these two into some agentic workflow that I have agency over? Right? Because I think

agency over? Right? Because I think there'll be very little tolerance for anybody who's inflexible uh at the vendor level. Uh but at the same time, I think that anyone who has

got that flexibility, shows up, delivers the value, we'll be back at again, right? with selling software but with

right? with selling software but with just different business models. In fact,

uh speaking about building software, um one of my favorite moments from I think a previous build maybe one or two years ago was they had a they there was a section of you building your own software. I'm curious if you're building

software. I'm curious if you're building anything now.

Yeah. So I I think the you know first of all let's face it right building software has made it possible for even the incompetence of a CEO of a company

like ours uh you can build. So thank

God. But that said, I I I I I do feel that, you know, something like um GitHub Copilot to me and especially the new

sessions app or the new app has just made it so much more possible for you to have agency over artifacts that you felt

you couldn't touch before, right? So to

for me as a CEO even to go to a codebase uh to be able to learn about it like I remember joining Microsoft long back you know first and then you say man everybody had to go in and look at you

know whatever cutler's malo or what have you to learn how to do good C uh C++ code um so now that ability to be more

full stack up and down is so good but that doesn't mean every one of us should be doing the same thing the question is how do you then have the ability to inspect things, learn things, see

things. Um, I think is just so much

things. Um, I think is just so much more. And so to me, what I'm building a

more. And so to me, what I'm building a lot of is these longunning foundry agents. Uh, right. So there's

agents. Uh, right. So there's

autopilots. So the easiest thing is to me, I think I just built one, uh, even last week where the idea was, hey, can I

have an agent that is continuously monitoring essentially my own chief of staff autopilot, right? We're going to have that obviously in uh Scout. That's

what uh uh we showed. But it was so easy and trivial to build. I took it work IQ.

I said take work IQ, go u and build a foundry longunning agent uh store all the memory in um uh using Rayfin, right?

Basically at my back end as a service and lo and behold, it built it. And not

only built it, I could say publish to teams and it published the damn thing to teams. So the ability uh to have a you know some end toend project like this

complete is just pretty miraculous.

How do you think uh that impacts the different types of engineering roles that exist in the future? Because right

now I think there's you know a dozen different types of engineers that you can be from QA front end etc. You know there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four

engineering roles. It'll be people who

engineering roles. It'll be people who are managing agents. It'll be forward deployed engineers or FTEEs. It'll be

security engineers and then people working on large scale infrastructure for a small number of services and then everything else just collapses into the agentic world. Do you think that's a

agentic world. Do you think that's a correct view of the world?

Yeah, I mean I think I think we'll have to experiment our way through it. But

what you said is what there are some very atscale things at LinkedIn. They

did structurally change uh and you know basically built up a new discipline called full stack builder. Right? Right?

So they went and said, "Hey, let's bring uh people from design and product management, front-end engineering, all pull them together." Uh but also have an edge, right? It's not like the design

edge, right? It's not like the design person still doesn't have the design edge or the front-end person doesn't have the front-end edge, but you can give yourself bigger scope in roles so

that you're not confined to one role. Um

and then equally infrastructure has become very critical, right? So in other words like I mean RLEs I mean one thing we've realized is even for the Excel

team for example building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems uh and so you kind of need even new talent right

distributed systems people even in what was considered an enduser app team uh because it's a different skill set so yes infrastructure science is the other one obviously um so I think we'll see

how these evolve right where's the real I mean always the world will have a bunch of specialists um you know I think the generalist role is going to be the

most exciting right because the leverage of a generalist um is where we are going to see the maximum returns right when when you said

hey are you coding I'm now a g like what I basically translated knowledge work right which I did where I created a word

document or a spreadsheet or even uh and now I can build an app right it's in the same sentence uh right that idea that oh

wow my generalist skills have gotten higher leverage I think is what we're going to see across the board music to the ears of CEOs and VCs that are like a little dangerous and a lot of golden age for idea people

idea people man with a lot of agency if you take that idea of personal agency and you just zoom it out to the organizational context Um uh my partner Mike Renol who uh actually started his

career at Microsoft just wrote an essay where one of the big takeaways is it's an age where you can be much more ambitious and you need to be given the pace of the environment and how quickly

actually users and companies are open to adopting new technologies. Um how do you think about I feel silly asking this of somebody running a you know trillion dollar plus company already but how do

you think about how Microsoft can be more ambitious now?

It's a great question. Um I think um I think the the thing in these type of transitions

is to have a conceptual model of how work can change to go after outcomes that you could

hardly imagine previously. Right? In

fact, Kevin Scott has this nice line, right? which is um when you can make the

right? which is um when you can make the impossible like when you're making hard things easier that's sort of one point of leverage but true ambition is about

making the impossible possible. So now

the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build what was impossible and what can

we build and I'll give you one example of this right which is I take great inspiration from sort of the people who were managing the Azure network and they came to the this was from even last year

you know we were scaling you saw that I I talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years. I mean, it's crazy, right? It's pretty wild and it's the

right? It's pretty wild and it's the same team. So, they saw that and they

same team. So, they saw that and they said, "Bob, this just ain't going to work if we don't reconceptualize our work." So, they built essentially they

work." So, they built essentially they said, "Our job is not to do Azure networking. Our job is to build the

networking. Our job is to build the agentic system does that does Azure networking, right? These are the folks

networking, right? These are the folks managing the 500 plus fiber operators managing the van right all over and fiber operations ultimately is a

physical operation things get caught things get you know have to be repaired you know we have fancy words called devops and so on basically emails are coming in and you got to go respond to

them take care of it so they built this agentic system they even have a character for it it's called miles and it sort of does all this stuff right they started sort was screaming for more

tokens and so on and so they were saying look I we don't need headcount we need tokens in order to be able to manage uh our operation that reconceptualization

of what their work is right they they basically took their work and made it meta right that meta work is now their new work right in the 80s if somebody had come to

us and said four billion people are going to get up in the morning and start typing my model would have been we need four billion typists But we're not doing typing, we're doing

knowledge work. So that to me I think is

knowledge work. So that to me I think is it right which is whether it's Microsoft or whether it's any organization is to give ourselves permission to do new

types of meta cognition meta work using these new tools to change the outputs that matter uh and then really make the impossible possible. So completing that

impossible possible. So completing that dot or the the connective tissue across those I think is where a lot of the enterprise value will get created.

Should we talk about data centers?

Yeah, please ask.

Oh, okay. Well, uh we this leads nicely into the data center buildout. I always

think just I'm just impressed at the sheer scale of the buildout from Microsoft but also everyone else that this is redefining what it means to be a hyperscaler and I just feel like that

that that is had unprecedented scale on finances uh on the way you run the company but also the communities that are that are impacted um just talk a bit more about what you're seeing on the ground like when you visit your

Yeah I think there are two aspects of it obviously the the buildout is uh extraordinary um you know nothing like this has happened and it's great to be

one of the participants in it. Uh but

you brought up the other part right I think at this point it's clear that unless we as an industry

uh are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways uh at the community level. Right? because

this is not just a a campaign um right it has to be real where people are saying look this is not changing the prices on energy for me in fact if

anything it's bringing down prices because long-term there's going to be a better grid there is going to be more energy water consumption is in fact not sort of uh in fact water is being

replenished right you got to really you know educate folks on truly what's happening the the closed loop systems we're building. We have to invest in the

we're building. We have to invest in the training, the jobs, the tax base. In

fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.

What's the tax base that's there in the community and and all this has to be real um and and if that is the case, then we will have permission. If it is not, we won't have permission. It's as

simple as that, right? Which is uh we we I think we have to take it as an industry pretty seriously. Uh I think it's good for communities to be skeptical, ask the hard questions, for

us to do the hard work, earn that. Um

but at the end of the day, if this if we can really be the produc I've always felt like in human history, if you use a lot of energy but also create a lot of

value for society, the story has been fantastic. If you don't do that, it's

fantastic. If you don't do that, it's not been that great. And this time around, I'm a firm believer that ultimately if you do have a token economy that drives productivity, that

drives economic growth, that drives broadspread um you know participation, better health outcomes, um then I think we'll be in a great place. Uh and that's

at least what we all have to be focused on.

Yeah. It makes me think actually that with all these initiatives that you're doing, might be easier to see ROI in a communities first before in enterprise.

Yeah, I I mean I think both sides in fact it comes back together. It has to be the people in the communities are going to be employed are going to be participants uh in the real economy

right that's I think the question is like if we if the broad economy is doing well and the communities are doing well the dots get connected is sort of the

market forces are such that we will connect the dots and that I think is it like you got to be able to see the evidence you can't be about any one company uh but it has to be broad economic growth and broad e you

community permission.

Yeah.

Talk about what is most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI?

So you're saying what's the the what have you updated most on in terms of societal impact of AI? I think the um the the most u

critical thing is the first question we even started with which is we need to tell the story and make it real that

everybody has a real shot to participate as a first class participant in this new economy right that's kind of I think in

the next 12 months 18 months months. We

need a way for people to say, "Oh, wow.

I get it." Right? There's going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen.

Whether it's the benefits like health outcomes or my ability to create a startup or my ability to run my local

sort of uh store more efficiently, it's just happening and I see that uh benefit myself, right? that to me you know

myself, right? that to me you know earning that permission in a path dependent way we can't wait see the one thing you I've now learned is I think

the world is going to be very skeptical of tech and tech companies that say trust us we've got it the future is

going to be glorious uh you kind of have to deliver tangible benefits um and quite frankly politicians winning

elections uh because they have advocated for that.

That will be at least my adjustment because without it um thinking that somehow because it's too important this time around it's too much of the economy

for it not to be the case. So one very simple framework I have for you know what are what is going to be the broad benefit of AI um beyond the communities

just working in technology are are sort of wealth creation that's going to happen in a ton of different companies startups and large companies then you have healthcare you you had amazing demos today there are companies

like open evidence I think that is happening um education seems like another one that's an obvious good where we haven't seen as much impact as I'd expect you have hypothesis on why that

might be or if it'll come. Yeah, I mean I think this is where again how we think about education, how you know recently I met with uh the founders of Alpha School and learned a lot about what they were

going and going about and it's fascinating to listen uh to how to even rethink uh what is education really look like because I think it's actually very important uh and I'm not saying anything

traditionally being done is less important right I was even looking at the uh it's fascinating to see I I I forget the which Stanford class it was uh the the Asian guidelines for CS

something uh because you still need people to learn uh like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus

saying hey fix my training run uh so I think learning concepts is important it's going to be critical but the way we create the incentives what are the

credentials how we value those credentials what is the employment opportunity for those credentials so I think that there's a complete change

that has to happen uh given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So

I think interestingly enough maybe the next big startup and success story could be someone who builds a new university

um or a new um pedagogy even of how to get someone to go through a curriculum and find economic opportunity. Uh that's

highly valuable.

Well that has felt uh perhaps impossible for a long time but it's a great note to end on and something that might be possible. Yeah. Thank you Sasha.

possible. Yeah. Thank you Sasha.

Thank you so much. Thank you. We

appreciate it. Thank you all.

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