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All right, everybody. We're thrilled to have the one, the only Tata Nadella here, the third CEO of Microsoft for a
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uh impromptu fireside chat with David Saxs, Arzar of AI and crypto. Satia third, CEO of Microsoft, born in India.
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What an incredible story. came here right after college and uh you had a little round trip to pick up your wife
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in your book to to bring her here. Tell everybody briefly uh how that occurred. >> Well um uh you know so that's that's a
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great story of uh the uh the labyrinth that is the immigration policies of the United States I think. Um I my wife and
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I went to college together in India. I came here for grad school. We then got married. Uh I got my green card. Um and
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she couldn't come join because we got married. So the story goes basically I
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had to give up my green card. So the funny thing is I went to the American embassy in Delhi
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>> and I said where's the line to give up my green card? And they said there is no
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such line. Um >> that would be a crazy thing to do in the '9s. So it was a strange thing to give
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up your green card, get an H1 so that she could join, but it all worked out. So um you know it's a long lost memory
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but it was you know a way to work around it. Um I wanted to ask you uh having launched a co-pilot first with GitHub
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then having a co-pilot on the desktop. You made a very bold move for Microsoft to put that in the Windows product,
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which I use every day, on the desktop, but you did that before it really could recognize the file system and interact
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with applications. Got a little bit of a lukewarm reception, but now you've been
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doubling down, doubling down, and there seems to be, in my estimation, three modalities for knowledge workers.
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Elon's building at XAI, what they're calling a human emulator, if you saw that leak this week. Yeah.
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uh where they're just building employees and just putting them into their chat
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rooms and email. Then you have Claude came out with co-work this week. Incredibly powerful. People are kind of
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losing their minds over it. I've been playing with it for the last 40 hours.
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Truly impressive. What's your vision for Microsoft and how knowledge workers will actually put this
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to use because there seems to be a gap between you know playing around with chatbt and getting some interesting
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results and getting business results. >> Yeah. So I think it one of the most um
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perhaps illustrative examples um of trying to understand these various form factors is looking at coding which is
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obviously a form of knowledge work or uh probably the best example of knowledge work and if you think about the journey
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coding has been it started with u essentially uh uh the next edit suggest right that was the first time in fact my
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own belief in this entire uh generation of tech really sort of got formulated where I started seeing I think it was G
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you know there's a codeex model back in the day it was preGPT35 uh that's when next edits started
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working with some real accuracy then we went to chat then we went to actions and
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now to full autonomous agents and then the autonomous agents can be both foreground background in the cloud or
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local right so that's all the form factors that exist today when you're coding and interestingly If you look at
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it, you use all of them, right? It's not like there's only one form factor. So
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that's I think probably one of the other lessons. So for example, when I'm in a
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CLI, I can you go a foreground agent, background agent, and then just literally go edit in VS Code, right?
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There all happening in parallel, right? So that sort of shows how these form factors even composed. So then you bring
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that to knowledge work to your point. We started with chat. chat with reasoning sort of goes beyond just request
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response because you now have that chain of thought uh where you can see it work
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now they're actions right essentially either through computer use or through
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uh AP you know basically skills uh and agent calls so you can do actions so that's kind of the state of the copilot
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today now there is a way to think about you know the the theory of the mind evolution Right? Because you need like
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if you remember you know Jobs had the best line I would say for PCs or computers was to say if you it's a
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bicycle for the mind. Bill had a line which I liked as well which was it's information at your fingertips. We kind
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of need now a new concept metaphor for how we use computers in the AI age. And you have one
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>> and the one I like actually came from the CEO of notion which I you know that
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manager of >> incredible product. Yeah. >> You haven't bought it yet? I've not got
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that. Uh but the it's both management of you know basically a manager of infinite minds. That's a
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nice way to think about it right when you sort of really look at all the agents that you are working with. You
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kind of need to understand what I in fact the other term I like is we macro delegate and micro steer. In fact you
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kind of need that in in in coding you kind of have it right. So you do a macro delegation and then I can in parallel
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give it instructions while it is doing work. So that's sort of the state even
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today of co-pilot or what have you. you bring up a little bit of what one of one
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of the form factors I'm very excited about and you'll see us even in the next
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week even uh do things is while I'm sitting in GitHub copilot what it's not as if software developers
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sit in isolation right it's not like the only thing I work on is my repo I attend
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meetings um I write specs or others have written specs that I'm implementing uh I
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need to have my repo be consistent with that so that means using either a straightforward MCP server or a skill I
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want to be able to call into my work IQ which is the co-pilot bring that in that's the type of composition uh of
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knowledge work that'll happen same thing with security say you're a security
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professional you have lots of logs uh how do you sort of really analyze them you drop them into a file system then
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write code on top of it create a dashboard what have you those are the types of knowledge work that we can
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enable there I think you bring up one more thing which is can you create quote unquote digital employees digital
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co-workers or what have you and it's all about credentials right so the I today
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you could like you can literally assign >> are you working on that as well
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>> yeah so in fact we introduced something called agent 365 as a way to give
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identities in fact extending the identities we have for humans today uh and the endpoint protection we have for
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their compute devices to agents so >> so you might clone me working in the HR
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department or working in the marketing department and have a virtual correct >> version of me inside of office.
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>> That's correct. So, so there are two sort of modalities there. One is you
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give every knowledge worker infinite minds. That's kind of one and then you create even infinite minds independent
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of the your identity because the identity is one of the key things you got to get right even for it to work
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right. Right. So >> permissions >> and decision making >> permissions decision- making and like
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one of the key in things is who did what to whom is sort of the most important query in an organization right at the
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end of the day the organization needs to understand what work got done h and what's the provenence of that work uh
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and how do you trace it back right so therefore you kind of want either what if it's a human with a lot of agents
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then it's really macro delegation micro steering by the human whose identity was
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passed on. So it's delegation versus a separate identity. >> And that was done by a level of
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management, product management that you've eliminated, that Alphabets eliminated. Meta has started to
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eliminate in their organization four years ago. You had the same number of employees you have at Microsoft now, but
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you put a $90 billion onto the top line of the revenue in that time and you doubled your income during that time. So
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how did that happen? Is that automation of those jobs? Is it you were a little bit overstaffed?
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>> Unpack. >> I think it's it's actually you're pulling on a very interesting thread
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which is at some level what's the big structural change that needs to happen.
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In fact, I would say this is probably the biggest change in knowledge work since PCs. I mean I always you know
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think about like how did work happen precise right? I mean think about a multinational company like ours trying
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to do a forecast. uh right faxes went around, inter office memos got sent and then you kind of created a you know uh a
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forecast then suddenly you know PCs became standard issue uh you put an Excel spreadsheet put some numbers sent
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it in email everybody entered numbers and you had a forecast so the work the work artifact and the workflow all
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changed that's what's happening so for example I'll give you uh at LinkedIn
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uh we used to have product managers we had designers uh we had front-end engineers and then we had CIS backend
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engineers and so on. So what we did is we sort of took those first four roles and combined them in fact increased
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scope and said let's they're all full stack builders. So I like that because
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that's a structural change that allows for us to increase the change both the
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work and the workflow between these functions and I would assume the velocity because you don't have four
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people communicating and that throughput of ideas it's just one person and vibe
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coding. Exactly. And there's a new workflow. So what at the same time as you can imagine if to build an AI
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product today there's a complete new workflow right it starts with eval right
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so basically there's this eval to science to infrastructure and so eval are done by these full stack builders
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and what have you and product managers in the new form the infrastructure is built by the systems engineers at the
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back end because they support the science that supports the product. So in some sense there's a new loop uh and you
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have to structurally change and so a lot of what is happening inside a tech is that change uh which is I think going to
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be pretty massive uh and at the same time a company like ours I have to do everything. It's not like I can just so
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go live in the future. I have to make sure we're doing a fantastic job of doing hot patching on Windows is done
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with quality uh while at the same time building the evals that are improving co-pilot quality. Right? And so both of
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those have to be first class. >> I assume this is the most challenging moment of your career because Microsoft
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was so dominant duopoly in some spaces. Um but you really weren't up against the
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competition level you're up against now. I was talking to Elon, you know, and he
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was sort of saying, well, building cars was pretty easy. Uh because I was up against the legacy car makers and now
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I'm up against just look at the set you're up against. Yeah, it's it's a pretty intense time. I
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mean, so the way I I I always think is it's always helpful uh when you have a
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complete new set of competitors every decade because that keeps you fit. Uh if you think about it, I joined uh
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Microsoft in '92 when I had Noel as the big existential competitor we had. Um
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and here we are in 2026. Uh and it you're absolutely right, it's a pretty
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intense time. I'm glad there's the competition. uh it's it's quite honestly
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at the end of the day when I look at it right as a percentage of GDP 5 years from now where will tech be right uh it
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will be higher so we're blessed to be in this industry it's lot of intense
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competition but it's not so zero sum as some people make it out >> it's getting much bigger
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>> much the TAM and the you know just the impact of this tech is going to be so
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massive um the question then of course is what is like I I always go back to what's the brand identity Microsoft has
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brand permission. We have what do customers expect from us. It's sometimes we kind of overthink somehow that every
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customer wants the same thing from all of the competitors and finding that out, right? It's kind of a different take on
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the Peter Theal thing which is you got to avoid competition by really understanding what customers really want
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from you uh versus thinking everybody's a competitor. >> David. >> Yeah. So there are a lot of heads of
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state here obviously at Davos as well as CEOs of Fortune 500 companies and I think you got asked a question last
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night at the dinner about how they should think about AI and how to be successful and I recall they used the
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word diffusion and I was wondering if you could expand on those remarks because that really resonated with some
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of the policy work I've been doing. >> No, absolutely. In fact, uh what you all
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have been doing to make sure in in this context of the American tech stack um is
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broadly used around the world and is trusted around the world because I think uh when I look back David to me um uh at
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the end of the day you create the technology but really the benefits come only by intense use. In fact, one
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of my favorite studies has always been this work that uh an economist I think out of Dartmouth did uh his name is
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Diego Comman where he studied uh basically what happened during the industrial revolution um how did
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countries get ahead? Um and the simple sort of takeaway from that was any country uh that brought the latest
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technology into their uh country and then did value add technology on top of it. Right? So it's like don't reinvent
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the wheel. Bring the latest and then build on top of it. That's to me uh what
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happens you know when you have diffusion. So especially with general purpose technologies like AI it needs to
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spread like right in our you know in our own country in the United States we now
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need we have the tech. The question is is it being used in healthcare? Is it being used um in financial services? Is
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it being used in every sector of the economy by large businesses, small business, public sector? Um so to me un
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unless and until we see that diffusion and intense use uh we're not going to
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have the success. Um uh and so that's the phase we are in it's f you know it's
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diffusing faster um and so some of the work policy work you have done um and in general all you know the good news here
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is the technologies there the rails around cloud and mobile that were laid out make it possible for this thing to
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spread right it's not you know impossible to get the tokens the question is what are the use cases how
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do and how do you manage the change in all of that um you know like one of the questions at least in Davos is it's one
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for the west and the developed nations. What about the global south? Um I think global south has a huge opportunity too
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quite frankly because to me like let's say you know 40% 50% of the GDP of most
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global south countries is public sector. So just imagine this tech making a difference in how the governments uh
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really parlay their taxpayer money into services for citizens and there's if
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there's efficiency gains that's probably couple of points of GDP growth right
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there and so I'm very optimistic that there's going to be a pull uh and that
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we should as the United States given the technology stack we have uh in Europe in
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Asia in you know in South America in Africa and everywhere get it to be broadly deployed.
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>> You one of the questions I get asked a lot about the AI race is how do you know
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if you're winning or how do you know if the United States is ahead of its global
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competitors? And the answer I give is market share. You know, if we look around the world in 5 years and we see
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that American companies, American technology has say 80% market share, it means we did a good job. If we look
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around the world in five years and see that it's say Chinese chips and Chinese
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models that are being used all over the world, well means we probably lost. So you know ultimately usage is the proof
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of the pudding is in the eating of it. I mean the in this case the way that you know that you're succeeding is through
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market shares through usage. >> I and I I would agree with that. But David, since you even worked at
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Microsoft for a few years, um you you know, one of the things that I'm very grounded on is always uh that Bill Gates
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line of a platform, right? So, one of the things that I always think about is it's market share, but it's also
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ecosystem effects, right? See, what the United States always has done is not just about our market share or even um
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the revenues to US companies. In fact, one of the things I learned at Microsoft is whenever I did a country visit, the
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data I would first study is in let's say in the UK or in Switzerland or what have
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you is what is the total employment created in Switzerland uh in our channel that used to be like the number one
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thing uh in our country reports right and the total number >> that be like the number of IT workers
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the number >> office workers channel so channel partners we so number of ISVs
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uh who were there. So we used to have a complete marker of how did the ecosystem
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around the platform get built one country at a time and that is what the United States has always done. In fact
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the US tech stack >> including in China got built because others built around our tech stack the
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same thing is going to happen. So that's why I think the work you're doing around
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diffusion, >> right, >> is about really increasing the size of the pie, the trust in the platform so
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that there is true economic opportunity quite frankly. >> Well, you're right and and I remember
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actually you you brought back some memories from this is about a decade ago when my company Yammer was acquired by
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Microsoft. we were part of uh the SharePoint group and I remember that the um the product managers there were very
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proud of the fact that the revenue from the SharePoint ecosystem meaning non-Microsoft
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the um the consulting community the implementers who would go into companies implement SharePoint I think their
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revenue is something like seven times greater than Microsoft's own software revenue and I think
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>> in aggregate >> in aggregate and I think and I think Bill had a line about you're not an
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ecosystem or platform until the revenue on top of your platform is some, you know, factor of your own revenue. And
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and I and I think that I think what's really important about this is when we
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talk about diffusion and obviously want the United States to have this leading position, it doesn't mean it's bad for
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the rest of the world because they're able to build on top of those platforms
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and create even more value. >> 100%. In fact, that's sort of the most important point, right? So this is not
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that this is not about uh American tech um and America revenues to the United States. It's actually creating
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opportunity using a new platform everywhere. And in fact you know the you know like I remember I worked on our
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database products uh in the '90s u you know with SAP in fact the combination of
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uh SQL Server and R3 were successful on both sides. There's a lot talked about
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Intel and Microsoft, but one of the other things that I grew up in which has sort of been foundational in how I look
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at the world is what we did with a European software company that is still uh you know a giant. And so that you
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know who knows what the next big AI app will be and where and what will happen. But uh I I sort of go in with the
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attitude that there will be tech companies uh maybe even top five tech companies that could emerge everywhere
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with even the American tech stack. you have um done some amazing acquisitions and you're quite a dealmaker on top of
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being a technologist. It's probably the least reported aspect of your spectacular tenure and the massive
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growth you've had. But you did a deal with OpenAI and probably one of the most savvy
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slashcontroversial dealmakers of all time, Sam Alman. That deal was looked at as
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you you you're you're set up to get a windfall in cash which you don't need as
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Microsoft. always nice I'm guessing if they IPO but did you create potentially
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and this was the criticism of it an ultimate competitor to Microsoft and how do you think about that and how can
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Microsoft which missed Steve Bombber's biggest regret missing the mobile revolution how can you not have a Gemini
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an XAI a claude that is your own or in your mind do you have that because you have the source code of open AI
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>> yeah I think that that's right so when when people say uh where is your
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foundation model? I mean at the end of the day we do have the IP but that said I think you bring up a couple different
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things right one is to us the most important thing when I look at what is Microsoft's uh strategy today one is we
00:21:14
want to build token factories right so our biggest business today is Azure business and the Azure business the TAM
00:21:20
given what's going to happen is is so huge that we now need to be fantastic uh
00:21:25
at building these token factories and um that's means a heterogeneous fleet of
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infrastructure and that every hyperscaler has always done which is use software to make
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maximum use of it and for TCO and utilization. So that's one side of it. Then there's the app server business
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right which is everybody we you talked about like if everyone's going to be building agents have infinite minds have
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these RL gyms have eval what have you there's an entire just like every platform has had an app server this one
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has an app server that's what we're doing with foundry and what have you
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right so there's an app server business in that app server one of the things
00:22:01
that structurally now is pretty clear is anyone building any application or any company is going to use not one model
00:22:08
but all the models Right? Why would I not? Right? Which is in fact I will orchestrate for any given task even
00:22:15
multiple models. Right? There's this one nice thing that we came out in our healthcare practice called the decision
00:22:21
orchestrator. What it proves is that by assigning roles, right? So investigator,
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data analyst, domain expert, just giving even prompted roles to models and then orchestrating them gets better results
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than any one single frontier model. Am I right to read into that then that you're
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bullish on the open- source models and think large language models will largely be commoditized and that's not where the
00:22:47
value will occur. In fact, the way I think about it is that just like what happened
00:22:51
>> and Apple thinks that too by the way. >> By the way, what the way you think about
00:22:54
what happened in the database market, right? You know, I used to be like everything is just a SQL database until
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it was not, right? There was I mean, think about it. There dock databases, there is no SQL databases. The
00:23:05
proliferation of databases, right? Who would have thought that the database market would have such a richness to it
00:23:12
>> or that it could ever be open source? That was >> that's true. I mean talk about Postgress
00:23:16
or what has happened even with which is open but there are even companies that have backed it and so so
00:23:20
to me that's what's going to happen and to me a model is like the database
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market you know it's it's got it's going to differences but I sort of somehow
00:23:28
think that uh it's not there are definitely going to be frontier models that are closed source you know there
00:23:33
going to be open source models that are going to be uh uh frontier class in fact
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if anything I think in this next year what'll be probably a big part of the discussion is what's the future of a
00:23:46
firm? A firm should be able to take the tacet knowledge it has and embed it inside a weights in a model that they
00:23:57
control. Right? So when somebody asks me how many models should be there, I'll
00:24:01
say as many models as firms in the world. Right? That's sort of the an extreme way. Uh because because to me
00:24:07
that's how I think this you know this knowledge economy becomes an AI economy.
00:24:12
>> Are you secretly and you can say it here since we're on allin working on an LLM to
00:24:18
exist on the Windows desktop because that you are you have it like today there's a five silica model which is
00:24:25
completely resident using NPUs and of course using uh GPUs in fact the largest installation
00:24:33
um of high power in fact it's one of the fascinating the workstation is back I'm
00:24:36
one of the most if you went to see >> which is great for Microsoft because you
00:24:41
you have a nice desktop business. >> Absolutely. And so we and in fact we think that that form factor especially I
00:24:47
mean I I always say this which is u you know I started my career on a command line. Who knows I may just end it in a
00:24:54
command line. >> Well you started at Sun which was the original 5 $10,000 workstation. Do you
00:24:59
see a time where you'll be meeting with your customers here and advocating a 10
00:25:03
$20,000 desktop machine that has an LLM and the hardware? You can you can put a DGX card and you can have like just a
00:25:12
fantastic machine and the models I and by the way you know we are one architecture tweak away from even having
00:25:18
some kind of a distributed model architecture right even ane architecture that shows knows how to really
00:25:25
distribute itself right that's the type of breakthrough that can completely change uh what hybrid AI may look like
00:25:32
but we're absolutely committed and focused on making the PC a great place for local models uh and local models
00:25:40
that then do even a lot of the prompt processing and call into the cloud, right? So there's a whole lot of work
00:25:45
that can happen and that's sort of definitely something that's underway.
00:25:48
>> Yeah, I think that the cloud co-work has kind of shown the power of tapping into
00:25:52
the local file drive and be able to use that. That that brings up another point.
00:25:56
you you got me thinking about Yammer and for people who don't know um you know
00:25:59
Yammer's claim to fame this is about 15 years ago was that it pioneered a lot of
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um well it used a lot of consumer growth tactics to attack enterprise software I'm wondering as you think about
00:26:09
enterprise adoption of AI how do you think it's going to spread over the next
00:26:15
year it feels like we're at sort of a a a critical point do you think it's going
00:26:19
to be top down is it going to come from the CEO directing a team giving them a strategic transformation
00:26:26
project and they're going to do an RFP or do you think it's going to spread
00:26:29
bottom up in the enterprise through AI native employees who are adaptable who are using the tools in their own lives
00:26:37
and they start to bring these things to work and start accomplishing amazing things.
00:26:41
>> Yeah. No, I think you know like all things David I think it's both the top
00:26:45
down bottom up right. uh the that the reason I say that top down is if I look at the ROI uh of uh applying AI in
00:26:53
customer service uh or in supply chain or in HR self-service those are the easy projects where uh IT and CXOs can make
00:27:04
calls and that's where you'll see the first drop of uh real AI adoption but
00:27:09
the bottom up is what ultimately will happen right I mean with even with the PCs in fact if you think back at the
00:27:16
lawyers brought word in and then finance bought Excel in and then email came and
00:27:21
then it became standard issue. That's what's happening right now. So for
00:27:24
example, these agents when I sort of talk about everybody's building agents,
00:27:29
they are figuring out a way to go create these things that are changing workflow
00:27:35
and removing drudgery in their work. Right? That's sort of the beginning of what is a bottomup transformation. Um I
00:27:43
you I was in fact the thing that I'm most excited about is this bottomup change even at Microsoft for example we
00:27:49
manage something like 500 odd fiber operators around the world in in Azure today and by the way I not myself
00:27:57
realized it a lot of it you know it's called DevOps but it's a it's a physical
00:28:00
asset things get cut and when you sort of say DevOps that means you literally are emailing people and saying hey what
00:28:07
happened to that fiber cut how do we repair it so there's a lot of back and forth so this network the the person who
00:28:13
runs our global network basically has built to your point about these person they're just digital employees
00:28:19
essentially that are doing all of that devops uh and so that's and there's a
00:28:25
completely bottoms up uh where you see the tools it's kind of like hey I have
00:28:29
the new way to build agents it's there I'm going to use it to create levels of
00:28:34
automation uh that remove drudgery improve efficiency improve quality and that ultimately is a skilling thing
00:28:41
which is sort of the big issue which is um and skilling is not mystical it's
00:28:46
just by doing right so it's not like I go to a class per se it's like the
00:28:50
diffusion of the tools uh and using the tools and that I think is what's really
00:28:55
going to be happening >> and and we're in a very interesting moment empowering an existing employee
00:29:00
with these tools is so much easier than hiring and mentoring and bringing up the
00:29:06
next generation so it feels like we're in a little bit of an indigestion moment
00:29:10
at Microsoft, do you think who's going to have my job >> in 30 or 40 years, if the company stays
00:29:17
the same size? Because given your technology first approach, there's really no reason to ever add another
00:29:24
Microsoft employee at the pace this is going and you haven't for four years.
00:29:29
So, how you may have swapped some in and out and changed the texture of it. So, how do you think about maybe this next
00:29:37
generation? What advice would you have for these college graduates who maybe don't have an offer for Microsoft right
00:29:42
now? And you used to spend a lot of time on that building that group, but maybe you don't have that luxury now. Do you
00:29:48
think about it ever? >> No, I I mean it's a great question. I you know there's a little bit of a
00:29:52
debate what happens to early in career and how is college recruiting. I still am a big believer in uh college
00:29:58
recruiting because at the end of the day um this is going to change the curve by
00:30:05
which anyone can pick up proficiency in a codebase. Let's just it takes sort of
00:30:10
just regular CS hiring. uh what has changed is perhaps for someone who comes in new into a team and to be able to
00:30:19
ramp up thanks to all of uh the markdowns, the skills, uh the fact that I can go ask the agent. I mean, think
00:30:28
about it, right? It's like having an unbelievable mentor who is getting you onboarded onto a codebase faster. So in
00:30:36
some sense the productivity curve uh of a college hire is going to be much steeper than it ever before. So I think
00:30:44
there might be a difference. In fact, one of the things we're experimenting with is a different type of
00:30:48
apprenticeship, right? Which is you take somebody who's an IC senior dev have
00:30:53
like a cohort of college uh hires working with them because it's a new way of working. It's like I remember like
00:31:00
all you know everybody who joined Microsoft would say go how how did you know whatever um Cutler implement Malik
00:31:06
or what have you right he would go try to read uh his code to understand uh what great craftsmanship looks like
00:31:14
nowadays I think that great craftsmanship uh comes by looking at even how the 10x 100x engineers use AI
00:31:22
to build great quality products uh and that is what these new college grads will learn and learn faster and so
00:31:30
that's a beneficial thing for a company like us because at the end of the day
00:31:33
you know until we saw longevity or something we need people to come into the workforce be successful at Microsoft
00:31:40
so we are very committed but we are also making sure that the scopes of the jobs
00:31:45
make sense for what the aspirations of people are going to be both who are currently in the workforce and people
00:31:50
who are entering the workforce. Okay, on that note, Sache Nadella, >> thank you so much.