Satya Nadella on AI’s Business Revolution: What Happens to SaaS, OpenAI, and Microsoft?

Speaker A [00:00:00]:
All right, everybody, we're thrilled to have the one, the only Satya Nadella here, the third CEO of Microsoft, for an impromptu fireside chat with David Sachs, our czar of AI and crypto. Satya, third CEO of Microsoft, born in India. What an incredible story. Came here right after college, and you had a little round trip to pick up your wife. In your book, to bring her here, tell everybody briefly how that occurred.

Speaker B [00:00:35]:
Well, that's a great story of the labyrinth that is the immigration policies of the United States. I think my wife and I went to college together in India. I came here for grad school. We then got married. I got my green card, and she couldn't come join because we got married. So the story goes, basically, I had to give up my green card. So the funny thing is I went to the American Embassy in Delhi and I said, where's the line to give up my green card? And they said, there is no such line.

Speaker A [00:01:14]:
That would be a crazy thing to do in the 90s.

Speaker B [00:01:17]:
So it was a strange thing to give up your green card, get an H1 so that she could join. But it all worked out. So, you know, it's a long lost memory, but it was a way to work around it.

Speaker A [00:01:29]:
I wanted to ask you, having launched a copilot first with GitHub, then having a copilot on the desktop, you made a very bold move for Microsoft to put that in the Windows product, which I use every day on the desktop. But you did that before it really could recognize the file system and interact with applications. Got a little bit of a lukewarm reception. But now you've been doubling down. Doubling down. And there seems to be, in my estimation, three modalities for the knowledge workers. 

Elon's building at xai, what they're calling a human emulator. If you saw that leak this week. Yeah. Where they're just building employees and just putting them into their chat rooms and email. Then you have Claude came out with cowork this week. Incredibly powerful. People are kind of losing their minds over it. I've been playing with it for the last 40 hours. Truly impressive. What's your vision for Microsoft and how knowledge workers will actually put this to use? Because there seems to be a gap between, you know, playing around with ChatGPT and getting some interesting results and getting business results.

Speaker B [00:02:36]:
Yeah. So I think one of the most perhaps illustrative examples of trying to understand these various form factors is looking at coding, which is obviously a form of knowledge work, or probably the best example of knowledge work. And if you think about the journey coding has been. It started with essentially the next edit suggest, right? That was the first time, in fact my own belief in this entire generation of tech really sort of got formulated when I started seeing I think this. You know, there's a Codex model back in the day, it was pre GPT3.5. That's when Nexted Editions started working with some real accuracy. Then we went to chat, then we went to Actions and now to full autonomous agents. And then the autonomous agents can be both foreground background in the cloud or local. So that's all the form factors that exist today when you're coding. And interestingly enough, if you look at it, you use all of them, right? It's not like there's only one form factor. 

So that's I think probably one of the other lessons. So for example, when I'm in a cli, I can you go a foreground agent, background agent and then just literally go edit in Vs code right there. All happening in parallel, right? So that sort of shows how these form factors even compose. So then you bring that to knowledge work. To your point, we started with chat. Chat with reasoning, sort of goes beyond just request response because you now have that chain of thought where you can see it work. Now they're actions, right? 

Essentially either through computer use or through, you know, basically skills and agent calls. So you can do actions. So that's kind of the state of the copilot today. Now there is a way to think about the theory of the mind evolution, right? Because you need like if you remember, Jobs had the best line, I would say for PCs or computers was to say it's a bicycle for the mind. Bill had a line which I liked as well, which was it's information at your fingertips. We kind of need now a new concept metaphor for how we use computers in the AI age. You have one and the one I like actually came from the CEO of Notion, which I like. That manager of.

Speaker A [00:04:59]:
Incredible.

Speaker B [00:05:00]:
Yeah.

Speaker A [00:05:00]:
You haven't bought it yet.

Speaker B [00:05:01]:
I've not bought that. But it's both management, you know, basically a manager of infinite minds. That's a nice way to think about it, right? When you sort of really look at all the agents that you are working with, you kind of need to understand what I. In fact, the other term I like is we macro delegate and micro steer. In fact, you kind of need that in coding. You kind of have it, right? So you do a macro delegation and then I can in parallel give it instructions while it is doing work. 

So that's sort of the state, even today of copilot or what have you. You bring up a little bit of one of the form factors I'm very excited about and you'll see us even in the next week even do things is while I'm sitting in GitHub copilot. It's not as if software developers sit in isolation, right? It's not like the only thing I work on is my repo. I attend meetings, I write specs or others have written specs that I'm implementing. I need to have my repo be consistent with that. So that means using either a straightforward MCP server or a skill I want to be able to call into my work iq, which is the copilot. Bring that in. That's the type of composition of knowledge work that will happen. Same thing with security. 

Say you're a security professional. You have lots of logs. How do you sort of really analyze them? You drop them into a file system, then write code on top of it, create a dashboard, what have you. Those are the types of knowledge work that we can enable. Then I think you bring up one more thing which is can you create, quote, unquote, digital employees, digital co workers or what have you? And it's all about credentials. Right. So today you could like, you can literally assign.

Speaker A [00:06:47]:
Are you working on that as well?

Speaker B [00:06:48]:
Yeah. So in fact, we introduced something called Agent365 as a way to give identities, in fact, extending the identities we have for humans today and the endpoint protection we have for their compute devices to agents.

Speaker A [00:07:02]:
So you might clone me working in the HR department or working in the marketing department and have a virtual version of me inside of office.

Speaker B [00:07:09]:
That's correct. So there are two sort of modalities there. One is you give every knowledge worker infinite minds. That's kind of one. And then you create even infinite minds independent of your identity. Because the identity is one of the key things you got to get right even for it to work. Right.

Speaker A [00:07:26]:
So permissions and decision making.

Speaker B [00:07:29]:
Permissions, decision making. And like one of the key things is who did what to whom is sort of the most important query in an organization. Right. At the end of the day, the organization needs to understand what work got done and what's the provenance of that work and how do you trace it back. Right. So therefore you kind of want either. If it's a human with a lot of agents, then it's really macro delegation, microsteering by the human whose identity was passed on. So it's delegation versus a separate identity.

Speaker A [00:08:01]:
And that was done by A level of management, product management that you've eliminated, that Alphabet's eliminated, Meta has started to eliminate in their organization. Four years ago you had the same number of employees you have at Microsoft now, but you put a $90 billion onto the top line of the revenue in that time. You, and you doubled your income during that time. So how did that happen? Is that automation of those jobs? Is it you were a little bit overstaffed. Unpack.

Speaker B [00:08:31]:
I think it's, it's actually you're pulling on a very interesting thread which is at some level what's the big structural change that needs to happen. In fact, I would say this is probably the biggest change in knowledge work since PCs. I mean I always, you know, think about like how did work happen pre PCs, right? I mean think about a multinational company like ours trying to do a forecast, right? Faxes went around, interoffice memos got sent and then you kind of created a, you know, a forecast. 

Then Suddenly, you know, PCs became standard issue. You put an Excel spreadsheet, put some numbers, sent it in email, everybody entered numbers and you had a forecast. So the work, the work artifact and the workflow all changed. That's what's happening. So for example, I'll give you at LinkedIn, we used to have product managers, we had designers, we had front end engineers and then we had back end engineers and so on. So what we did is we sort of took those first four roles and combined them in fact increased scope and said they're all full stack builders. So I like that because that's a structural change that allows for us to increase the change, both the work and the workflow between these functions and I.

Speaker A [00:09:46]:
Would assume the velocity because you don't have four people communicating and that throughput of ideas, which is one person and vibe coding.

Speaker B [00:09:52]:
Exactly. And there's a new workflow. So at the same time, as you can imagine, if to build an AI product today there's a complete new workflow, right? It starts with evals, right? So basically there's this eval to science, to infrastructure. And so evals are done by these full stack builders and what have you and product managers and new form the infrastructure is built by the systems engineers at the back end because they support the science that supports the product. So in some sense there's a new loop and you have to structurally change. 

And so a lot of what is happening inside a tech is that change which is I think going to be pretty massive. And at the same time a company like ours, I have to do everything. It's not like I can just so go live in the future. I have to make sure we're doing a fantastic job of doing hot patching on Windows is done with quality while at the same time building the evals that are improving copilot quality. Right. And so both of those have to be first class.

Speaker A [00:10:50]:
I assume this is the most challenging moment of your career because Microsoft was so dominant duopoly in some spaces. But you really weren't up against the competition level you're up against now. I was talking to Elon Musk, you know, and he was sort of saying, well, building cars was pretty easy because I was up against the legacy carmakers and now I'm up against. Just look at the set you're up against.

Speaker B [00:11:15]:
Yeah, it's a pretty intense time. I mean, so the way I always think is it's always helpful when you have a complete new set of competitors every decade because that keeps you fit. If I think about it, I joined Microsoft in 92 when I had Novell as the big existential competitor we had. And here we are in 2026. And you're absolutely right, it's a pretty intense time. I'm glad there's the competition, quite honestly. At the end of the day, when I look at it as a percentage of gdp, five years from now, where will tech be? It will be higher. So we are blessed to be in this industry. It's a lot of intense competition, but it's not so zero sum as some people make it out.

Speaker A [00:12:01]:
Pie is getting much bigger.

Speaker B [00:12:03]:
Much the TAM and the just the impact of this tech is going to be so massive. The question then of course is what is like I always go back to what's the brand identity? Microsoft has brand permission. We have what do customers expect from us? It's sometimes we kind of overthink somehow that every customer wants the same thing from all of the competitors. And finding that out, it's kind of a different take on the Peter Thiel thing, which is you gotta avoid competition by really understanding what customers really want from you versus thinking everybody's a competitor.

Speaker A [00:12:39]:
David.

Speaker C [00:12:39]:
Yeah, so there are a lot of heads of state here, obviously at Davos, as well as CEOs of Fortune 500 companies. And I think you got asked a question last night at the dinner about how they should think about AI and how to be successful. And I recall they used the word diffusion. And I was wondering if you could expand on those remarks because that really resonated with some of the policy work I've been doing.

Speaker B [00:13:01]:
No, absolutely. In Fact, what you all have been doing is to make sure in this context, the American tech stack is broadly used around the world and is trusted around the world. Because I think when I look back, David, to me, at the end of the day, you create the technology, but really the benefits come only by intense use. In fact, one of my favorite studies has always been this work that an economist I think out of Dartmouth did. His name is Diego Coman, where he studied basically what happened during the Industrial Revolution, how did countries get ahead? And the simple sort of takeaway from that was any country that brought the latest technology into their country and then did value add technology on top of it, right? So it's like, don't reinvent the wheel, bring the latest and then build on top of it. That's to me what happens when you have diffusion. 

So especially with general purpose technology like AI, it needs to spread, like right in our own country, in the United States, we now need, we have the tech. The question is, is it being used in health care, Is it being used in financial services, Is it being used in every sector of the economy by large businesses, small business, public sector? So to me, unless and until we see that diffusion and intense use, we're not going to have the success. And so that's the phase we are in. It's diffusing faster. And so some of the work, policy work you have done, and in general the good news here is the technology's there. The rails around cloud and mobile that were laid out make it possible for this thing to spread, right? It's not, you know, impossible to get the tokens. The question is, what are the use cases, how do, and how do you manage the change and all of that? 

You know, like one of the questions at least in Davos, is it's one thing for the west and the developed nations. What about the Global South? I think Global south has a huge opportunity too, quite frankly, because to me, like, let's say, you know, 40%, 50% of the GDP of most global south countries is public sector. So just imagine this tech making a difference in how the governments really parlay the taxpayer money into services for citizens. And if there's efficiency gains, that's probably a couple of points of GDP growth right there. And so I'm very optimistic that there's going to be a pull and that we should, as the United States, given the technology stack we have in Europe, in Asia, in South America, in Africa and everywhere else, get it to be broadly deployed.

Speaker C [00:15:50]:
One of the questions I get asked a lot about the AI race is how do you know if you're winning? Or how do you know if the United States is ahead of its global competitors? And the answer I give is market share. If we look around the world in five years and we see that American companies, American technology has say 80% market share, it means we did a good job. If you look around the world in five years and see that it's say Chinese chips and Chinese models that are being used all over the world, well means we probably lost. So you know, ultimately usage is the proof of the pudding is in the eating of it. I mean in this case the way that you know that you're succeeding is through market shares, through usage.

Speaker B [00:16:33]:
And I agree with that. But David, since you even worked at Microsoft for a few years.

Speaker C [00:16:39]:
One of.

Speaker B [00:16:40]:
The things that I'm very grounded on is always that Bill Gates line of a platform. So one of the things that I always think about is its market share, but it's also ecosystem effects, right? See what the United States always has done is not just about our market share or even the revenues to US companies. In fact one of the things I learned at Microsoft is whenever I did a country visit, the data I would first study is in let's say in the UK or in Switzerland or what have you is what is the total employment created in Switzerland in our channel that used to be like the number one thing in our country reports, right? And the total number, like the number.

Speaker A [00:17:20]:
Of IT workers, the number office workers.

Speaker B [00:17:25]:
So channel partners, these ISVs, so number of ISVs who are there. So we used to have a complete marker of how did the ecosystem around the platform get built one country at a time. And that is what the United States has always done. In fact the US tech stack, including in China got built because others built around our tech stack, the same thing is going to happen. So that's why I think the work you're doing around diffusion is about really increasing the size of the pie, the trust in the platform so that there is true economic opportunity, quite frankly.

Speaker C [00:18:02]:
Well, you're right and I remember actually your, you brought back some memories from this is about a decade ago when my company Yammer was acquired by Microsoft, we were part of the SharePoint group. And I remember that the, the product managers there were very proud of the fact that the revenue from the SharePoint ecosystem, meaning non Microsoft, the, the consulting community, the implementers who would go into companies influence SharePoint. I think their revenue was something like seven times greater than, than Microsoft's own software revenue. And I think in aggregate And I think, and I think Bill had a line about you're not an ecosystem or a platform until the revenue on top of your platform is some factor of your own revenue. 

That's right. And I think what's really important about this is when we talk about diffusion and obviously we want the United States to have this leading position, it doesn't mean it's bad for the rest of the world because they're able to build on top of those platforms and create even more value.

Speaker B [00:19:02]:
100%. In fact, that's sort of the most important point. Right? So this is not about American tech and American revenues to the United States. It's actually creating opportunity using a new platform everywhere. And in fact, you know, like I remember I worked on our database products in the 90s, you know, with SAP. In fact, the combination of SQL Server and R3 were successful on both sides. There's a lot talked about intel and Microsoft. But one of the other things that I grew up in, which has sort of been foundational in how I look at the world, is what we did with a European software company that is still, you know, a giant. And so that, you know, who knows what the next big AI app will be and where and what will happen. But I sort of go in with the attitude that there will be tech companies, maybe even top five tech companies that could emerge everywhere with even the American tech stack.

Speaker A [00:19:58]:
You have done some amazing acquisitions and you're quite a deal maker on top of being a technologist. It's probably the least reported aspect of your spectacular tenure and the massive growth you've had. But you did a deal with OpenAI and probably one of the most savvy slash controversial dealmakers of all time, Sam Altman. That deal was looked at as you're set up to get a windfall in cash, which you don't need, as Microsoft always nice, I'm guessing, if they ipo. But did you create potentially, and this was the criticism of it, an ultimate competitor to Microsoft? And how do you think about that? And how can Microsoft, which missed Steve Ballmer's biggest regret, missing the mobile revolution. How can you not have a Gemini and X, a Claude that is your own or in your mind, do you have that because you have the source code of OpenAI?

Speaker B [00:20:57]:
Yeah, I think that that's right. So when people say, where's your 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 of different things, right? One is to us, the most important thing when I look at what is Microsoft's strategy today. One is we want to build token factories, right? So our biggest business today is Azure business. And the Azure business, the TAM given what's going to happen is so huge that we now need to be fantastic at building these token factories. 

And that means a heterogeneous fleet of infrastructure and that every hyperscaler has always done, which is use software to make maximum use of it and for TCO and utilization. So that's one side of it. Then there's the app server business, right? Which is everybody you talked about like if everyone's going to be building agents, have infinite minds, have these RL gyms, have evals, what have you, there's an entire. Just like every platform has had an app server, this one has an app server. That's what we're doing with Foundry and what have you. So there's an app server business in that app server. One of the things that structurally now is pretty clear is anyone building any application or any company is going to use not one model, but all the models. 

Why would I not, right? Which is in fact I will orchestrate for any given task, even multiple models. There's this one nice thing that came out in our healthcare practice called the decision orchestrator. What it proves is that by assigning roles, so investigator, data analyst, domain expert, just giving even prompted roles to models and then orchestrating them gets better results than any one single frontier model.

Speaker A [00:22:38]:
Am I right to read into that then, that you're bullish on the open source models and think large language models will largely be commoditized and that's not where the value will accrue.

Speaker B [00:22:47]:
In fact, the way I think about it is that just like what happens.

Speaker A [00:22:51]:
Apple thinks that too, by the way.

Speaker B [00:22:52]:
By the way, the way you think about what happened in the database market, right. You know, I used to be like everything is just a SQL database until it was not, right? There was, I mean think about it, that doc databases, there is no SQL databases, the proliferation of databases, right? Who would have thought that the database market would have such a richness to.

Speaker A [00:23:11]:
It or that it could ever be open source. That was mindful.

Speaker B [00:23:14]:
I mean talk about Postgres or what has happened even with Mongo, which is open, but there are even companies that have backed it. And so, so to me that's what's going to happen. To me a model is like the database market, it's got its going differences, but I sort of somehow think that there are definitely going to be frontier models that are closed source you know, there are going to be open source models that are going to be frontier class. In fact, if anything, I think in this next year what will be probably a big part of the discussion is what's the future of a firm. 

A firm should be able to take the tacit knowledge it has and embed it inside a weight in a model that they control. So when somebody asked me how many models should be there, I'll say as many models as firms in the world. That's sort of an extreme way because to me that's how I think this knowledge economy becomes an AI economy.

Speaker A [00:24:12]:
Are you secretly, and you can say it here, since we're on all in working on an LLM to exist on the Windows desktop, because that you have.

Speaker B [00:24:22]:
It like today there's a 5 silica model which is completely resident using NPU's and of course using GPU's. In fact the largest installation of high power. In fact, it's one of the fascinating. The workstation is back. I'm one of the most. If you went to see great for.

Speaker A [00:24:39]:
Microsoft because you have a nice desktop business.

Speaker B [00:24:42]:
Oh, absolutely. And so we, and in fact we think that that form factor is especially. I mean I always say this, which is, you know, I started my career on a command line. Who knows, I may just end it in a command line.

Speaker A [00:24:54]:
Well, you started at sun, which was the original 5, $10,000 workstation. Do you see a time where you'll be meeting with your customers here and advocating a 10, $20,000 desktop machine that has an LLM and the hardware you.

Speaker B [00:25:08]:
Can, you can put a DGX card and you can have like just a fantastic machine and the models. And by the way, you know, we are one architecture tweak away from even having some kind of a distributed model architecture, even an MOE architecture that knows how to really distribute itself. That's the type of breakthrough that can completely change what hybrid AI may look like. But we're absolutely committed and focused on making the PC a great place for local models and local models that then do even a lot of the prompt processing and call into the cloud. So there's a whole lot of work that can happen. And that's sort of definitely something that's in our way.

Speaker C [00:25:48]:
Yeah, I think that the Claude cowork has kind of shown the power of tapping into the local file drive and be able to use that. That brings up another point. You got me thinking about Yammer. And for people who don't know Yammer's claim to fame, this is about 15 years ago was that it pioneered a lot of, well, it used a lot of consumer growth tactics to attack enterprise software. I'm wondering, as you think about enterprise adoption of AI, how do you think it's going to spread over the next year? 

It feels like we're at sort of a critical point. Do you think it's going to be top down? Is it going to come from the CEO directing a team, giving them a strategic transformation project and they're going to do an rfp? Or do you think it's going to spread bottom up in the enterprise through AI native employees who are adaptable, who are using the tools in their own lives and they start to bring these things to work and start accomplishing amazing things?

Speaker B [00:26:41]:
Yeah, no, I think, you know, like all things, David. I think it's both the top down, bottom up, right? The reason I say that top down is if I look at the ROI of applying AI in customer service or in supply chain or in HR self service, those are the easy projects where IT and CxOs can make calls and that's where you're seeing the first drop of real AI adoption. But the bottom up is what ultimately will happen, right? I mean even with the PCs. In fact, if you think back at it, the lawyers brought word in and then finance bought Excel in and then email came and then it became standard issue. That's what's happening right now. So for example, these agents, when I sort of talk about everybody's building agents, they're figuring out a way to go create these things that are changing workflow and removing drudgery in their work. That's sort of the beginning of what is a bottom up transformation. In fact, the thing that I'm most excited about is this bottom up change. 

Even at Microsoft, for example, we manage something like 500 odd fiber operators around the world in Azure today. And by the way, I'd not myself realized it, a lot of it, it's called DevOps, but it's a physical asset. Things get cut. And when you sort of say DevOps, that means you literally are emailing people and saying hey, what happened to that fiber cut? How do we repair it? So there's a lot of back and forth. So this network, the person who runs our global network basically has built to your point about these person, they're just digital employees essentially that are doing all of that DevOps. And so that's, and there's a completely bottoms up where you see the tools. 

It's kind of like hey, I have the new way to build agents. It's there. I'm going to use it to create levels of automation that remove drudgery, improve efficiency, improve quality. And that ultimately is a skilling thing, which is sort of the big issue, which is. And skilling is not mystical, it's just by doing right. So it's not like I go to a class per se. It's like the diffusion of the tools and using the tools. And that I think is what really going to be happening.

Speaker A [00:28:56]:
And we're in a very interesting moment. Empowering an existing employee with these tools is so much easier than hiring and mentoring and bringing up the next generation. So it feels like we're in a little bit of an indigestion moment at Microsoft. Do you think who's going to have my job in 30 or 40 years if the company stays the same size? Because given your technology first approach, there's really no reason to ever add another Microsoft employee at the pace this is going. And you haven't for four years. So how may have swapped some in and out and change the texture of it. So how do you think about maybe this next generation? What advice would you have for these college graduates who maybe don't have an offer for Microsoft right 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 think about it ever?

Speaker B [00:29:49]:
I mean, it's a great question. You know, there's a little bit of a debate what happens to early in career and how is college recruiting? I still am a big believer in college recruiting because at the end of the day this is going to change the curve by which anyone can pick up proficiency in a code base. Let's just. It takes sort of just regular CS hiring. What has changed is perhaps for someone who comes in new into a team and to be able to ramp up thanks to all of the markdowns, the skills, the fact that I can go ask the agent. I mean, think about it, right? It's like having an unbelievable mentor who is getting you onboarded onto a code base faster. So in some sense, the productivity curve of a college hire is going to be much steeper than it ever before. 

So I think there might be a difference. In fact, one of the things we're experimenting with is a different type of apprenticeship, which is you take somebody who's an IC senior dev, have like a cohort of college hires working with them because it's a new way of working. It's like I remember like everybody who joined Microsoft would say go, how did whatever Cutler implement Malloc or what have you, he would go try to read his code to understand what great craftsmanship looks like nowadays. I think that great craftsmanship comes by looking at Even how the 10x100x engineers use AI to build great quality products. And that is what these new college grads will learn and learn faster. 

And so that's a beneficial thing for a company like us, because at the end of the day, until we saw longevity or something, we need people to come into the workforce, be successful at Microsoft. So we are very committed. But we are also making sure that the scopes of the jobs make sense for what the aspirations of people are going to be, both who are currently in the workforce and people who are entering the workforce.

Speaker A [00:31:52]:
Okay. On that note, Satya Nadella, thank you so much.