Join the conversation with Satya Nadella, CEO of Microsoft, at the World Economic Forum Annual Meeting 2026 in Davos on scaling AI, tech leadership, and global innovation.
In Davos, Satya Nadella framed generative AI as the next major “platform shift,” extending a decades-long arc of computation that turns the world into digital artifacts that software can reason over. He argued the near-term trajectory is clear: AI is moving from assistance (autocomplete) to chat-based guidance to “agent mode,” and toward increasingly autonomous systems—still grounded in “human agency.” The bigger opportunity, he said, is not the novelty of models but broad-based productivity: “The real question… is, how do you ensure that the diffusion of AI happens and happens fast?” Diffusion requires both supply and demand. On supply, nations need “a ubiquitous grid of… energy and tokens,” with “token factories” spreading like electricity; competitiveness will correlate with “tokens per dollar per watt.” On demand, organizations must redesign workflows and structures as information flows flatten: Copilot can invert briefing processes by instantly providing a “360” view and enabling faster cross-functional sharing. Leaders must pair mindset, skilling, guardrails, and “context engineering” so AI has the firm’s tacit knowledge. Nadella predicted a multi-model future: advantage will come from orchestrating closed, open, and proprietary models with enterprise data to create controllable IP and avoid “leaking enterprise value” to external model providers.
I want to talk about AI, and I really want to, because I think this is on everybody's mind more than almost any other subject today. Related to intersection of business, technology, society. So, Satya, you know, we're we're moving AI from something that was experimental, something that we always talked about in the future. And now it's, it's today, and it's now more foundational. And it's not just foundational for companies, but it really is becoming now foundational for countries and throughout society. And I think, you know, you have an advantage over so many other people, you know, being at the forefront of this technology transition. So, with that, I wanted to ask a few questions related to that. You have described that AI as a platform shift. And what does what does that mean? Question one where do you see that shift going in the next few years? And importantly, the third part of my question would be fast forwarding a few years, five years, what's going to seem obvious in hindsight that feels less clear today.
Now, first of all, it's great to be back here, Larry. And it's, I had a chance, in fact, yesterday when you put out the letter, to kick off the forum and read it and, and in there, you sort of had this one line of, really, I think when it comes to AI, the real question in front of all of us is, how do you ensure that the diffusion of AI happens and happens fast? I mean, I think you had that line of how do the models, the data and the infrastructure spread more evenly to create surplus everywhere? If you sort of think about it, the the way I come at this is not that, this has always been the arc of computation, right. You can sort of take it in the last 30 years or the last 70 years. It's always been about, can you digitize artifacts on about people, places and things and then build analytical and predictive power? Right. That's what the mainframes did. That's what the minicomputer did. That's what the client server era did. That's what the web era did. The mobile cloud did. So it depends irrespective of which paradigm or platform, it has been one continuous arc of saying, let's make better sense of this world, by reasoning about it in digital form. Because in some sense, once you have these artifacts in digital form, you can use a more malleable resource like software, right? Which doesn't have the same type of, I'll call it, marginal cost economics associated with it. That allows us to then build a more insight in more, more capability. And in that context, AI, I would say, is of the same class, at least, like the web or the internet, or mobile or PC or the cloud or, and maybe even greater. And so to me right now where we are is, you know, let's take just what's happened with software engineering, right? Which is one is knowledge work. You know, you could say it's elite knowledge work. It started off, you know, in fact, my own belief in this generation of AI and its capability, really got built up when I first saw GitHub Copilot do code completions, right. So for the longest time, we had the dream that if you're a software developer, can you predict the next word or the next, line of code? And suddenly it started working with these models. Then you said, okay, if I can do that, then can I actually go and bring back, you know, the flow for a software developer by going to a chat session and asking any question, and it comes back with answers that then you can use in your coding flow. Right. So that was the next thing. Then you said, well if that's working, can I assign it small tasks. That was the agent mode. Now you have complete autonomous agents where you can give it your entire project. Right? It can work
24 over seven.
It can work for 24 over seven. I mean, it's still we've got some ways to go for these things to remain coherent long term, but nevertheless, it's getting better and better. And interestingly enough, you look at it, the software developers still is. Got a lot of agency in it. Right. So that's why I kind of still think that, you know, going and thinking of these as somehow living outside of the realm of human agency is probably not the right way to think about it. In fact, the way to perhaps conceive it, like, let's say, in early 80s, if somebody had come to us and said, well, 4 billion people are going to wake up every morning and start typing, you would have said, why, right? You know, we have like, we have a typist pool. That's good enough. We don't need 4 billion people. But that's what happened. Like we invented this entire class of thing called knowledge work, where people started really using computers, to go amplify what we were trying to achieve, using software. I think in the context of AI, the same thing is going to happen. It's not like, you know, what is hardcore coding is going to remain hardcore coding forever. It's just that the levels of abstraction are going to change, but we also are going to have code as output, just like documents. In fact, one of Bill's things at Microsoft from the day I joined in 92 always was what's the real difference between a document, a website, and an application? Right? It's the lack of sort of software that can transform itself. Interestingly enough, AI finally gives us that right, which is I can write a document, I can just say, no, I don't want it as a document, I want it as a website. It'll just transform that document using code into a website. I say, well, I don't like the website. I want an app. It'll write more code to transform it. So that is reasoning and reasoning capabilities, that prediction capabilities, that ability to take action, remain long term coherent is all improving. And our job though is to parlay this like take even what you had Blackrock are doing. Right. You're taking something like say Copilot plus Aladdin and bringing those things together to improve the productivity in the firm for the decisions you want to make right with your data.
I could just tell you from, in our firm things that would take 12 hours to compute now takes minutes for us processing $14 trillion of other people's money with hundreds of thousands of different mandates. We could do that instantaneously. And you know that to me, if it wasn't for the technology and AI today, we would not be able to function to the scale that we're operating.
That's right. And so to me, that one firm at a time, one country at a time, if we can really take these tokens and bend the curve of productivity, then there is surplus everywhere. And that's really the goal.
Well, surplus could be scary to to surplus mean fewer workers. What do we mean by surplus. And so you know I'm going to tie that into my second question about AI diffusion. Yeah. To me the whole realization of AI for any society and also for a more balanced world is making sure that it's diffused and accessible and available across the world. So what can you describe how this process of diffusion across economies, across companies, across people and countries, how does that play out?
Yeah, I think this is the real question, right. Because one of the things right now, the zeitgeist is a little bit about the admiration for AI in its abstract form or as, as, as technology. But I think we as even a global community, have to get to a point where we are using this to do something useful, that changes the outcomes of people in communities and countries and industries. Right. Otherwise, I don't think, this makes much sense. Right. In fact, I would say we will quickly lose even the social permission, to actually take something like, energy, which is a scarce resource, and use it to generate these tokens. If these tokens are not improving health outcomes, education outcomes, public sector efficiency, private sector competitiveness across all sectors, small and large. Right. And that to me is ultimately the goal. So therefore I think really diffusion is everything. And so the way it happens is let's sort of unpack this. On the supply side, what needs to happen in each country is the tokens per dollar per watt have to sort of monotonically get more efficient and better. Right. So to some degree, even what we're trying to do with the investments the two firms are doing around the world is to just say that, like, let's make sure that the supply is there, which is everything from the chips on down, ultimately to these token factories that get deployed everywhere. By the way, there's not one token factory. This token factory is the first thing that's going to be diffused all around the world. It's just like electricity, right? You just need a ubiquitous grid of, energy and tokens that then will power the rest of the economy. Right. So that's, I think, one side of it. Then the demand side of this is a little bit like every firm has to start by using it. If I look back, even, you know, when the PCs first came out or the personal computing era started, I loved, you know, I think jobs had a nice metaphor. He called it the bicycle for the mine. Bill had a metaphor, which I remember was like information at your fingertips. Right? These two metaphors were great, which allowed us to say, that's what it is. It's a tool that I will use to get information at my fingertips. I'll use it as a cognitive amplifier. Now, I think that's what we have. But, you know, ten x 100 x, right. So in some sense you as a as every knowledge worker, you now have access to infinite minds. That's the way I think about it. Right. So there's a Turing Award winner, called Raj Reddy, who had this nice metaphor of AI, and he had this long before, even generative AI. He said either it's a cognitive amplifier or it's a guardian angel. Right. So if you think of AI as that, then in the global workforce, right, when a doctor can get to a patient, spend more time with the patient because the AI is doing the transcription and entering the records in the EMR system, entering the right billing code so that the health care, you know, industry is better served across the payer, the provider, and, and the patient ultimately. Right. That's an outcome that I think all of us can benefit from. So I feel ultimately it's going to require real leadership on the private sector and the public sector to ensure that diffusion happens. And the one thing other thing I'll mention, Larry, is skilling. Right? So in some sense, the thing that diffusion is very strongly correlated to one thing alone, which is how broadly are people skilled in using this? And interestingly enough, I think if mobile has taught us one thing is it's actually distinct from what happened in the PC. Right. I remember even growing up in the Global South, there used to be a real relationship between learning Excel skills or word skills and getting a job. You know, right now, what's the model in mobile? It's kind of created the same opportunity, but it's been a lot more consumption led. It's these creator economy and what have you. But it has not been about sort of oh, wow, here is how you get a healthcare job or here is how you get a finance job. Or here is how you get, you know, you get ahead professionally. And that needs to come back, right? People need to say, oh, I pick up this AI skill and now I'm a better provider of some product or service in the real economy.
So it's very easy to see how mobile and the diffusion of mobile, how it transformed economies, especially in the global South. How does how does this you know, to me, I just read a research report that said, the applications for AI so far are heavily weighted towards those who are educated or educated economies. And so does that create that, you know, more of a bifurcation, a more polarization? How do we ensure that that that diffusion is spread evenly? How do we make sure that we're not leaving major portions of society or the world behind? Because I think that's that's going to be the big issue for us going forward.
Yeah. So it's interesting, right? This is one of those times when, by definition, and because of the rails that have been established, you know, as you said, right. Which is, what's happened with mobile as well as what's happened with, essentially connectivity. Right? You have the ability to sort of deliver the tokens pretty evenly around the world, right? A lot more so than, let's say, the PC era or even the beginning of the mobile era. Right? Because it took a long time for even the smartphone in particular to penetrate, all of the world, whereas now it's not the case. Right? These models and their outputs are pretty much available everywhere. And so the question to me is what's the use cases that make sense. Right. In fact, one of the demos I always go back to, I think this is even in the beginning of 23, was a rural Indian farmer, was able to use a bot built on, I think, a very early GPT 3 or 2, five even, essentially to reason over some farm subsidies that he had heard about in a local language and had it even in that very early days, have it even show some agentic behavior. Right? Like go complete a form for me. So in some sense, it took, you know, it brought back agency to someone who perhaps didn't have that because the technology was so much more accessible. So I do think it's in our hands, even in the Global South, to use it to create, I would say, more of that opportunity where there isn't one. But I think what the necessary conditions still are. Do you have, the capital investment being put in, do you even have an environment for capital? Because in an interesting way, we are, for example, as hyperscalers investing all over. Right, including the Global South. So as long as there's an environment which attracts the capital investment.
And you see the demand.
And you see and then. Yeah, the demand is there. Yeah. And so the question is, how do you have a set of policies that allow for both the capital to come in for it to find nexus with? There are certain things, by the way, private capital can do certain things that public capital only can do. For example, the grid. Right? It's not I mean, grid in most countries is sort of fundamentally driven by governments, public and public. And so if you don't have a sophisticated or rather, if you don't have a real approach to modernizing the grid, that will hold things back. I mean, there's a lot of talk about behind the meter and so on. And yes, there's some amount of that we can do.
We can do that in the US. Many countries can't.
Exactly. And it's not long term scalable, right? I mean, to me, a long term scalable solution is to have, you know, all of these token factories, part of the real economy, connected to the grid, connected to the telco network, delivering, just like we delivered bits. You have to deliver tokens plus bits. And that's kind of what's going to drive an at scale, whether it's in the Global South or in on the developed world.
So many people talk about there may be an AI bubble. I mean, the most important thing that we see as an investor is the the, the democratization of technology is the diffusion of that technology really does then transform the demand. And the companies or the countries that diffuse it fastest are going to be the ultimate winners, not the technology creator.
That's that's, you know, it's, for this not to be a bubble, by definition, it requires that the benefits of this are much more evenly spread. I mean, I think a telltale sign of if it's a bubble would be if all we're talking about are the tech firms. Right. If, all we talk about is what's happening to the technology side, then that's by, you know, it's just purely supply side, right? Ultimately, if we are not talking about, wow, here is a drug, a drug that was sort of brought into the market that's super successful because it was, AI accelerated the clinical trial. It's not even the magical molecule. Right. It's kind of even the rest of what is needed in order to make something much more relevant. Right. And so the more we have, and by the way, it's happening. Right. So I'm not sort of saying that that's why I'm much more confident that this is a technology that will, in fact, build on the rails of cloud and mobile, diffuse faster and bend the productivity curve and bring local surplus and economic growth all around the world, not just economic growth driven by capital expenses. Right. Because that's it's a narrow point in time calculation.
Right now. That's what we're seeing.
That's what we're seeing. You know, in the developed world in particular. But remember my capital, the one thing that you know, is definitely is spending a lot of it in the United States, but 50% of it is also all over the world. Right. And so, interestingly enough, it depends on, demand all over the world, and the demand all over the world will only be there if there is local surplus all over the world. And so that's sort of the way I see the equation.
So let's drill down a little more as AI diffuses. Obviously organizations, companies, governments are going to have to evolve. I'm now getting to the demand side. So how do you think the structure of organizations is changing in an AI world across roles, across teams management? I'm sure, Microsoft has evolved itself, so it'd probably be good to tell the audience, how do you see this diffusion occur in the utilization at the corporate level or at a government level, which ultimately then creates that demand, which eliminates any fears and bubbles?
Yeah. No, I think it's probably one of the big challenges with all of these new technologies is when, work, work artifact and work flow changes. That means we as firms have to change how we work. In fact, I remember meeting, the CEO of Generali, you know, a few years back, and he was describing he had joined, the firm, you know, pre-pcv era and, and he was describing how, for example, they worked with their agents in the field, with faxes, interoffice memos, and, and suddenly the PC showed up and people would then put a spreadsheet in an email and send it around, and the entire workflow and the work process changed. Right. So similarly, I think with AI, you are going to start seeing, actual change in how workflow happens, right? I mean, even in fact, for me coming to Davos, you know, whatever 50 bilateral meetings I have preparing for those had a particular workflow, right, which is there is to be my field team would prepare notes and that would come to my HQ and that would get further refined. And nothing had really changed. Right? Since I joined in 92 to essentially even a few years back. Whereas now I just go to copilot and say, hey, I'm meeting Larry, please give me a brief and it comes back and gives me. By the way, the one nice thing is it gives me a 360, right? It knows what we are doing with you as a client, what we are doing as a client of yours and everything in between as an investment. So it captures even information unlike anything else. In fact, what I do is I take that and immediately share that back with all my colleagues across all the functions. Right? Think about it. It's a complete inversion of how information is flowing in the organization. It's not like this classic we have an organization, we have departments, we have these specializations. And the information trickles up. No, no, no, it actually it flattens the entire information flow. So once you start having that you have to redesign structurally. So the current structure may not make sense, because you want people to be able to work in a way that allows them to have this information flow freely. So all this leads me to if I had to sort of say, what's the formula? The formula. I think it starts with the mindset. So the mindset we as leaders should have is we need to think about changing the work, the workflow with the technology, then that needs skill set. So you can't sort of talk about this in the abstract. You've got to use it like so. If I'm not using the.
You have to trust it.
You have to trust it, you have to use it. You have to learn even how to put the guardrails to trust it. Right. You can't again, you can't just be afraid of it. It's going to it's going to be diffused. So the question is, as a firm, you have to use it to learn how to even, put the guardrails that allow you to be able to trust it. So skills, so mindset skills. The other big consideration, really, is how do you make sure you have the data set that you're feeding, like context, like it's kind of like you have a new intelligence layer, but the intelligence layer is only as good as the context you give it. So people describe it even as context engineering. But that is what firms do, right? If you think about what do firms do, it's all about the tacit knowledge we have by working as people in various departments and moving paper and information. So the question is, how do you really have this AI also have that context. So these are sort of some of the new things that have to percolate throughout an organization to take advantage. In fact, that's why I think you're going to see that challenge of why am I not seeing immediate results in productivity? Because you have to do the hard work. In fact, that's why it's not going to be some, you know, it's going to there's going to be firm wide differences. They're going to there could be sector wide differences, but it's going to fundamentally be because of the leadership will in an organization.
Do you see the applications being used across large companies and medium and small companies, or is it still the domain of mostly the large companies at this moment?
I think that what you're seeing is it's easier the because if you have a green sort of, you know, if you start fresh, it's easier to adopt these tools and you construct your organization knowing that these tools exist.
Is it a barbell then?
It is a barbell.
So small companies that are just starting to use that platform 100%.
And I think in fact, I would say even for large organizations, there is a fundamental challenge, right? Because unless and until your rate of change keeps up with. Right, with what is possible, you're going to get schooled by someone small being able to achieve scale because of these tools. So but I think scale large organizations have an inherent strength. You have the relationships, you have the data, you have, you have knowhow. But the bottom line is, if you don't translate that with a new production function, then you really will be stuck. And so therefore, the change management challenge for large organizations is going to be bigger. The structural challenge for small organizations or how to overcome scale issues is going to be harder. So it's sort of the two sides in an interesting way. It's going to be a very competitively intense world, where neither side like whether you're a new entrant or an incumbent, can't take it as like, I can just coast.
What about country to country? Are you seeing big differences in how the applications are being used? Is it is AI still the domain of developed countries, or is it becoming rapidly a domain of all countries?
I'm seeing there are two things I'd say, Larry, as I travel around the world, the quality of, whether it's the know how the software developers, the startups, or even large or large organizations, it's not that different. It's fascinating. You can show up in Jakarta, you can show up in Istanbul, you can show up in Mexico City. It's not that different than showing up even in, say, Seattle or San Francisco, right? It's not for the first time just because access to what's happening, is there. That said, at scale, the commitment to using this, the risk capital being there, the large companies pushing it hard. I mean, you know, again, the US, you know, is in fact, if I compare it, take financial sector in financial sectors, adoption of the cloud versus AI night and day. Right. Because in an interesting way, it's much faster, when it comes to AI versus it was with the cloud and cloud because for a variety of reasons.
Regulatory issues to until the regulators allowed banks to bring their data out of campus, that was a big issue.
Yeah. So I would say I think wherever, you know, in the West, in particular in the US, there is clearly a real, I would say, more of an energy around it in terms of going and using it. But it's sort of a lot more uniformly spreading around the world than any technology, at least I've seen.
But are you are you mentioned about the power, the grid? Is that going to be one of the determinants of of the accessibility if you do not have cheap power, if the demand is costly, 100%.
So if you sort of look at the tokens per dollar per watt, right. Which I think in some sense I would claim that GDP growth in any place will be directly correlated. Like if you sort of by my entire argument that, look, we've got a new commodity. It's tokens, right? And the job of every economy, and every firm in the economy is to translate these tokens into economic growth. Then if you have a cheaper commodity, it's better. And so that's sort of what why this tokens per dollar per watt. And by the way, there are many, many elements to this, right. Which is it's not just, the production side. That's why I think even having the grid is important, construction costs. Right. So if you if you think about the total TCO, everything, it's like the. Are you a cheap producer of energy? Can you build the data centers? Then what's the cost curve of the silicon and the systems? And by the way, look at the token pricing, right? Token pricing basically drops by, you know, a half, every three months. I mean, this is a so, so that's why I think you can sort of really plot how you use the tokens to create surplus, knowing that you have a commodity that's whose prices are just going to monotonically come down in a pretty fast curve.
We're sitting in Europe, and there is a real fear because of the Europe does not have its own power. It has to import most of its power. Do you have any messages for Europe related to this?
Yeah. I mean, I think there are two sets of things, right? One is, you know, here we are in Switzerland, when I look at, the pharma or the financial sector, you know, obviously they're, they do do a big job in this country, as in, in Europe, but they're also international brands and international operations. So one thing that, whenever I think about Europe is the Europeans are producing products and services that actually are going everywhere in the world. And so therefore, European competitiveness is about the competitiveness of their output globally, not just in Europe. I think sometimes when you come to Europe, there's a lot of conversation about just Europe. But European economy is, thrives and has thrived in the last, whatever, 200 years, 300 years. The miracle of the West is fundamentally because of what has happened in Europe, is because they were able to produce things that the world needed. And so I would say that's number one. And in order to do that again, I go back to the human capital here is just fantastic. In world class, you have to absolutely invest in, producing, you know, having the energy and the tokens here, which again, you're attracting, like, as I said, we are investing and others are investing here in Europe, the data centers here. So the question is what's that next generation of output that comes from here. Right. I always think about the German middle start whenever I go to a jeweler or a dentist in the United States, I'm surrounded by German middle start, right?
Yeah, totally.
It's just unbelievable engineering prowess of that country. And now the question and by the way, that's the point, that they are producing industrial products which today are built in into it all the intelligence as well, that data. Right. So I know whenever we come to Europe, everyone's like talking about sovereignty and data, this data that guess what? Europe actually should be much more concerned about access to their industrial companies, their financial services companies of data from us and the rest of the world, as opposed to just thinking that somehow by protecting Europe, you're going to be competitive. You are only going to be competitive if the products coming out of Europe are globally competitive. Right. And so that's I think, what needs to change. I know Europe has led in privacy. That's fantastic. Has led in many aspects of even safety around AI and what have you. And that's a feature that's great. But you also have to complement it with by building locally and then also thinking globally, what's the contribution this continent will make to the rest of the world, which it has historically been a leader.
A leader. So do you think the whole idea around sovereignty of data is that being misunderstood?
I think that when people talk about sovereignty, first of all, it's very important, clearly.
Who owns the data.
And in a week like this, it's more important. But that said, it is. You have to kind of think about how what is sovereignty mean. Like, for example, in the AI era, the topic that's least talked about, but I feel will be most talked about in, in this, this calendar year will be the sovereignty of a firm. Just imagine if your firm, you're not able to embed the tacit knowledge of the firm in a set of weights in a model that you control. By definition, you have no sovereignty. That means you're leaking enterprise value to some model company somewhere. In fact, it's sort of fascinating that nobody's talking about that, right? It's like everybody's talking about everything else that is sort of, you know, outside of that. Whereas the most important thing is it really doesn't matter if you, in fact, the data center where it runs is the least important thing, quite honestly. But even there, first of all, the data centers, all are all over. Just because speed of light is a real constraint. And so therefore the data centers will be spread, you will have digital, you'll be able to encrypt everything. You'll be able to have the keys with you. All of these are much more technically solved problems. But the one problem that will only be solved is by you having much more sovereignty over the, you know, tacit knowledge and control over the models. And it's not a one way enterprise value transfer. And so to me, I think sovereignty requires real thought on what is it? You know, control of destiny means that your, your ability to produce something that is unique is preserved. David Ricardo was not wrong. There's comparative advantage in countries, there is comparative advantage in firms that needs to be preserved even in the AI era. That's what will give you real sovereignty.
One last question. I know we're running out of time. In five years or ten years, is there going to be one dominant model that we're all going to be using or are. And how is Microsoft preparing for this, or are you going to be are we going to be using one model for for enterprise, one model for other other traits?
You know, even in the last whatever, three years, four years that we've been at it. The, the reality I at this point is it's a multi-model world, right? I mean, the in fact, if you think about it, the both there are going to be multiple models. And the trick is really how do you take advantage of these multiple models and in fact build your own model by distilling these. Right. So think of these models that you orchestrate to build your own model. And more importantly, you do what is described as orchestration or harness engineering. So the IP of any application or any firm is how do you use all these models with context engineering or your data. Right. So it's that three parts. So can I bring in all the models by the way. Which is closed source, open source, build my own model, orchestrate them and feed it my data to change the trajectory of some outcome that I care about. That's it. That's the entire picture. So you can do it in like, oh, I produce a particular product or service. First I got to do better. Better job in sales or better job in R&D or better job in finance or what have you. And you take that outcome and then you say, can I use all the models, orchestrate them and feed it my context? And then as a result of it, the reasoning traces are really leading to some capability and models that I control as my IP. As long as firms can answer that question, they're going to be getting ahead.
Ladies and gentlemen, let's thank Satya my friend.
Thank you for.
And hopefully this is the beginning of many great dialogue and conversations here at the World Economic Forum. Thank you everyone.
Thank you.