Conversation with Satya Nadella, CEO of Microsoft

20 Jan 2026 08:30h - 09:00h

Session at a glance

Summary

This discussion between BlackRock CEO Laurence Fink and Microsoft CEO Satya Nadella focuses on the transformative impact of artificial intelligence as it moves from experimental technology to foundational infrastructure for businesses and societies worldwide. Nadella describes AI as a platform shift comparable to the internet, mobile, or cloud computing, representing a continuation of the 70-year arc of digitizing information about people, places, and things to build analytical and predictive capabilities. He emphasizes that AI’s true value lies not in the technology itself, but in its diffusion and practical application across industries, countries, and organizations to create economic surplus and improve outcomes in healthcare, education, and business productivity.

The conversation highlights how AI is already transforming workflows, with Fink noting that processes at BlackRock that previously took 12 hours now complete in minutes, enabling the firm to manage $14 trillion in assets with unprecedented efficiency. Both leaders stress that successful AI adoption requires fundamental organizational changes, including new mindsets, skill development, and workflow redesign, with information flowing more horizontally rather than through traditional hierarchical structures. They discuss the critical importance of infrastructure, particularly reliable power grids and data centers, as prerequisites for AI diffusion, noting that token production costs are dropping by half every three months.

Regarding global adoption, Nadella observes that while technical expertise is becoming more evenly distributed worldwide, the United States shows more aggressive enterprise adoption compared to other regions. The discussion addresses concerns about AI creating a “bubble,” with both leaders arguing that widespread diffusion and practical application across industries will determine whether AI delivers genuine economic value. They conclude that the future will likely involve multiple AI models working together, with organizations’ competitive advantage coming from how they orchestrate these models with their proprietary data and context to achieve specific business outcomes.

Keypoints

Major Discussion Points:

– AI as a Platform Shift and Its Transformative Potential: Nadella describes AI as representing a fundamental platform shift comparable to or greater than the web, mobile, or cloud computing. He emphasizes that AI continues the historical arc of computation – digitizing artifacts and building analytical/predictive power – but with significantly enhanced reasoning capabilities that can transform documents into websites or applications seamlessly.

– The Critical Importance of AI Diffusion and Democratization: Both leaders stress that AI’s success depends on widespread diffusion across economies, companies, and societies rather than remaining concentrated among tech firms. Fink raises concerns about AI applications being heavily weighted toward educated economies, while Nadella argues that broad accessibility of AI “tokens” (computational outputs) is essential to prevent this technology from becoming a bubble.

– Organizational Transformation and Workflow Revolution: The discussion explores how AI fundamentally changes organizational structures and workflows. Nadella provides personal examples of how AI has inverted information flow in organizations, flattening hierarchies and requiring new approaches to mindset, skillset, and context engineering within companies.

– Infrastructure Requirements and Global Competitiveness: A significant focus on the infrastructure needed for AI adoption, particularly energy/power grids and data centers. The conversation addresses Europe’s challenges with energy dependence and emphasizes that countries and regions must focus on global competitiveness rather than just local protection, with Nadella advocating for Europe to leverage its strengths in industrial and financial sectors.

– Data Sovereignty and Enterprise Control: The discussion challenges traditional notions of data sovereignty, with Nadella arguing that “firm sovereignty” – a company’s ability to embed its tacit knowledge in models it controls – is more critical than geographic data location. He emphasizes the importance of companies maintaining control over their intellectual property and comparative advantages in the AI era.

Overall Purpose:

The discussion aims to explore AI’s transition from experimental technology to foundational infrastructure, examining how it will reshape business, society, and global competitiveness. The conversation seeks to address practical questions about implementation, diffusion, and the structural changes required for successful AI adoption across different scales of organizations and economies.

Overall Tone:

The tone is consistently optimistic and forward-looking, with both participants displaying confidence in AI’s transformative potential. The conversation maintains a collaborative, strategic perspective throughout, with Fink asking probing questions about challenges and risks while Nadella provides detailed, thoughtful responses. There’s an underlying urgency about the need for rapid adoption and diffusion, but the tone remains constructive rather than alarmist, focusing on solutions and opportunities rather than dwelling on potential negative consequences.

Speakers

– Laurence Fink: Chairman and CEO of BlackRock (mentioned as being “at BlackRock” and processing “$14 trillion of other people’s money”)

– Satya Nadella: CEO of Microsoft (mentioned as being “at Microsoft” and references to joining the company in 1992)

Additional speakers:

None identified in the transcript.

Full session report

Discussion Report: AI as Foundational Infrastructure – A Conversation Between Laurence Fink and Satya Nadella

Executive Summary

This discussion between BlackRock Chairman and CEO Laurence Fink and Microsoft CEO Satya Nadella at the World Economic Forum in Davos explores artificial intelligence’s evolution from experimental technology to foundational infrastructure. The conversation examines AI’s transformative impact on business operations, productivity, and global competitiveness, while addressing critical challenges around equitable diffusion, infrastructure requirements, and organizational adaptation.

Fink describes dramatic productivity improvements at BlackRock, where processes that previously required 12 hours now complete in minutes, enabling the management of $14 trillion in assets with unprecedented efficiency. Both leaders emphasize that AI’s success depends on rapid and widespread diffusion across industries and economies, not just technological sophistication.

AI as a Platform Shift and Foundational Infrastructure

Nadella positions AI as potentially the most significant platform shift in decades, comparing it to previous technological revolutions including the web, mobile computing, and cloud infrastructure. He describes AI’s fundamental capability as enhanced reasoning that enables seamless transformation between different formats—converting documents into websites or applications through intelligent processing rather than manual recoding.

As Nadella explains: “You can take a document, you can turn it into a website, you can turn it into an app. And the reason why you can do that is because there’s reasoning happening in that transformation.” This represents a qualitative leap beyond previous automation technologies.

Fink acknowledges that AI has definitively moved from experimental to foundational status. He notes that “without this technology, we could not function” at BlackRock’s current scale, managing hundreds of thousands of mandates across their $14 trillion in assets.

The conversation reveals AI’s evolution through concrete examples, particularly in software development. Nadella describes his experience with GitHub Copilot as his first “belief-building” moment with AI, watching the technology progress from simple code completion to autonomous agents capable of working continuously while maintaining human oversight.

The Critical Imperative of AI Diffusion

Both speakers identify AI diffusion as the determining factor between genuine transformation and speculative bubble. Nadella articulates this principle clearly: “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.”

This perspective reframes AI discussion from technological capabilities to economic distribution. Success requires demonstrating real-world outcomes across sectors—from AI-accelerated drug trials in healthcare to improved educational outcomes and enhanced productivity in traditional industries.

Nadella provides a compelling example of AI diffusion in action: a rural Indian farmer using early GPT technology to navigate farm subsidy applications in local language, demonstrating AI’s potential to democratize access to complex systems. This example illustrates how AI can bridge gaps between sophisticated bureaucratic processes and individual users regardless of education level or geographic location.

However, Fink raises concerns about current AI applications being “heavily weighted towards those who are educated or educated economies,” questioning whether this creates greater global polarization. Nadella acknowledges these challenges while arguing that AI tokens are more evenly accessible globally than previous technologies, though success depends critically on capital investment, policy environments, and infrastructure.

Infrastructure Requirements and Energy Considerations

Both speakers identify energy infrastructure as fundamental to AI accessibility and regional competitiveness. Cheap power and robust grid infrastructure emerge as prerequisites for effective AI adoption, with energy constraints significantly impacting which regions can compete in the AI economy.

Nadella emphasizes that society will only grant “social permission” to use energy for AI if it demonstrably improves outcomes: “We will have social permission to use energy to create these tokens only if we can show that these tokens are improving outcomes.”

The conversation addresses Europe’s particular challenges, including energy costs and the need to balance data sovereignty concerns with global competitiveness. Nadella argues that Europe should focus on leveraging its human capital and engineering capabilities to create globally competitive products rather than concentrating primarily on data protection.

Economic Impact and Productivity Transformation

The discussion provides specific evidence of AI’s immediate economic impact. Fink describes how AI has revolutionized BlackRock’s operations: “Things that took 12 hours to compute now take minutes.” This transformation enables instantaneous management of complex financial operations at unprecedented scale.

Nadella emphasizes that AI’s economic value must translate into measurable productivity gains, with competitiveness increasingly determined by “tokens per dollar per watt” efficiency. He notes that token pricing has been “dropping by half every three months,” making AI capabilities increasingly accessible.

The speakers discuss AI’s role as what Nadella terms a “cognitive amplifier” and “bicycle for the mind,” enhancing rather than replacing human capabilities. Nadella envisions AI providing access to “infinite minds” – the ability to tap into vast computational reasoning power to augment human decision-making.

Organizational Transformation and Workflow Revolution

Nadella describes how AI fundamentally alters organizational information flows: “a complete inversion of how information is flowing in the organization.” Traditional hierarchical structures where information trickles up through departments give way to flattened, horizontal information flows.

This transformation requires organizations to develop new approaches across three dimensions: mindset, skillset, and context engineering. Success depends on leadership commitment to redesigning workflows rather than simply implementing AI tools within existing processes.

The conversation reveals that AI adoption follows a “barbell pattern”—proving easier for small companies starting fresh and more challenging for large organizations requiring significant change management. Nadella notes that financial sector AI adoption has been “night and day” compared to cloud adoption, partly due to regulatory changes that previously prevented banks from moving data off-campus.

Data Sovereignty and Enterprise Control

The discussion introduces a framework for understanding sovereignty in the AI era, shifting focus from geopolitical data sovereignty to corporate control over AI models encoding organizational knowledge. Nadella identifies this as “the topic that’s least talked about, but I feel will be most talked about in this calendar year.”

He argues that true competitive advantage comes from controlling AI models that encode organizational knowledge rather than merely controlling where data is stored. Firms unable to maintain this control will experience “enterprise value leakage” to external model providers.

For Europe specifically, Nadella advocates to “build locally and think globally,” leveraging strengths in industrial and financial sectors while ensuring contributions remain globally competitive rather than focusing primarily on data protection within European borders.

Future AI Model Landscape

Both speakers envision a “multi-model world” where competitive advantage comes from orchestrating multiple AI capabilities rather than relying on single dominant providers. Nadella describes combining closed source, open source, and custom models with proprietary data and sophisticated orchestration capabilities.

This multi-model approach suggests that future AI competition will focus on application and integration rather than model dominance. Organizations will differentiate themselves through their ability to combine various AI capabilities with their unique context and business requirements.

Key Challenges and Considerations

While both speakers express optimism about AI’s potential, they acknowledge significant challenges. Fink consistently raises questions about ensuring AI benefits reach beyond educated populations and developed economies. The conversation highlights the tension between AI’s theoretical accessibility and practical barriers including infrastructure, capital requirements, and organizational capacity.

The speakers agree that countries and organizations achieving fastest AI diffusion will emerge as winners, regardless of whether they created the underlying technology. This emphasizes implementation and adoption strategies over pure technological development.

Conclusion

This discussion between Fink and Nadella provides insights into AI’s transition from experimental technology to foundational infrastructure. Their conversation moves beyond technological capabilities to examine practical implementation challenges, economic distribution concerns, and organizational transformation requirements.

The speakers emphasize that AI’s ultimate success will be measured by its ability to create broadly distributed economic value and improve outcomes across sectors and populations. Their focus on diffusion over creation, application over capability, and organizational transformation over technological implementation offers a framework for understanding AI’s practical impact on business and society.

As Nadella concludes, the goal is ensuring AI serves as a tool for augmenting human capability and improving outcomes rather than concentrating benefits among technology providers alone. The conversation suggests that achieving this vision requires coordinated attention to infrastructure, policy, organizational change, and equitable access considerations.

Session transcript

Laurence Fink

I want to talk about AI, 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, we’re moving AI from something that was experimental, something that we always talked about in the future, and now 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 is a platform shift.

And 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?

Satya Nadella

You know, first of all, it’s great to be back here, Laurence Fink. And I had a chance, in fact, yesterday when you put out the letter to kick off the forum and read it. 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 of the data and the infrastructure spread more evenly to create surplus everywhere, if you sort of think about it. 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 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 era did. 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, which doesn’t have the same type of, I’ll call it marginal cost economics associated with it, that allows us to then build more insight and more capability.

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, and maybe even greater. And so, to me, right now, where we are is, let’s take just what’s happened with software engineering, which is one, is knowledge work. You could say it’s elite knowledge work.

It started off, 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. 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? Suddenly, it started working with these models.

Then you said, okay, if I can do that, then can I actually go and bring back the flow for a software developer by going to a chat session and asking any question? It comes back with answers that then you can use in your coding flow. 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, you know. 24-7. It can work for 24-7.

I mean, it’s still, we’ve got some ways to go for these things to remain coherent long time, but nevertheless, it’s getting better and better. And interestingly enough, you look at it, the software developer still has got a lot of agency in it, right? That’s why I kind of still think that 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, let’s say in the 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?

We have a typist pool that’s good enough. We don’t need 4 billion people. But that’s what happened.

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, that 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, 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 reasoning and reasoning capabilities, that prediction capabilities, that ability to take action, remain long-term coherent is all improving.

Our job, though, is to parlay this, like take even what you at 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.

Laurence Fink

I could just tell you, at our firm, things that would take 12 hours to compute now take minutes. For us, processing $14 trillion of other people’s money with hundreds of thousands of different mandates, we could do that instantaneously. 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.

Satya Nadella

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.

Laurence Fink

Well, surplus could be scary, too. Does surplus mean fewer workers? What do we mean by surplus?

And so 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 can you describe how this process of diffusion across economies, across companies, across people and countries, how does that play out?

Satya Nadella

Yeah, I think that 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 technology. But I think we, as even a global community, have to get to a point where we’re using this to do something useful that changes the outcomes of people and 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. 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 are 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 mind. Bill had a metaphor which I remember was information at your fingertips, right?

These two metaphors were great, like, 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, 10x, 100x, right? So in some sense, 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 healthcare, you know, industry is better served across the payer, the provider, 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 I’ll mention, Laurence Fink, 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’s how you get a healthcare job, or here’s how you get a finance job, or here’s 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.

Laurence Fink

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, 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, more polarization?

How do we ensure 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 gonna be the big issue for us going forward.

Satya Nadella

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, you know, essentially connectivity.

You have the ability to sort of deliver the tokens pretty evenly around the world. 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 was 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 2.5 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. 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. And 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 ourselves. 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 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 app scale, whether it’s in the global South or on the developed world.

Laurence Fink

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 democratization of technology and 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.

Satya Nadella

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. If all we talk about is what’s happening to the technology side, then that’s by day, it’s just purely supply side.

Ultimately, if we are not talking about, wow, here is 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. 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 a narrow point in time calculation. Right now, that’s what we’re seeing more. That’s what we’re seeing in the developed world in particular.

But remember, my capital, the one thing that is definitely, we’re spending a lot of it in the United States, but 50% of it is also all over the world. 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.

Laurence Fink

So let’s drill down a little more. Because 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 and the utilization at the corporate level or at a government level, which ultimately then creates that demand, which eliminates any fears it bubbles?

Satya Nadella

Yeah, no, I think it’s probably one of the big challenges with all of these new technologies is when work artifact and workflow changes, that means we as firms have to change how we work. And in fact, I remember meeting the CEO of Generali a few years back. And he was describing, he had joined the firm pre-PC era.

And he was describing how, for example, they worked with their agents in the field with faxes, inter-office memos. And suddenly the PC showed up and people would then put a spreadsheet in an email and send it around. So the entire workflow and the work process changed.

So similarly, I think with AI, you are going to start seeing actual change in how workflow happens. I mean, even preparing, in fact, for me coming to Davos, whatever, 50 bilateral meetings I have preparing for those had a particular workflow, 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 co-pilot and say, hey, I’m meeting Laurence Fink, 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 what 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 workflow with the technology. Then that needs skillset. 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? So you can’t, again, you can’t just be afraid of it.

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 dataset that you’re feeding? Like context. 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 at some, you know, it’s going to, there are going to be firm-wide differences. There are going to, there could be sector-wide differences, but it’s going to fundamentally be because of the leadership will in an organization.

Laurence Fink

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?

Satya Nadella

I think that what you’re seeing is it’s easier, 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.

So is it a barbell then? It is a barbell. So small companies that are just starting use that platform.

100%. I mean, in fact, I would say even for large organizations, there’s a fundamental challenge, right? Because unless and until your rate of change keeps up 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, I mean, large organizations have an inherent strength. You have the relationships, you have the data, you have know-how, 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 of 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 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 would say, Laurence Fink. As I travel around the world, the quality of whether it’s the know-how, the software developers, the startups, or even 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, again, the U.S., in fact, if I compare it, take financial sector. If financial sector’s adoption of the cloud versus AI, night and day, right? 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. We had regulatory issues, too. Until the regulators allowed banks to bring their data out of campus, that was a big issue.

I would say, I think, wherever … In the West, in particular in the U.S., there is clearly a real, I’d 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.

You mentioned about the power, the grid. Is that going to be one of the determinants of the accessibility? If you do not have cheap power, the demand is costly.

100%. So if you look at the tokens per dollar per watt, which I think in some sense I would claim that GDP growth in any place will be directly correlated. If you buy my entire argument that, look, we’ve got a new commodity.

It’s tokens. 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 why there’s tokens per dollar per watt. And by the way, there are many, many elements to this, which is it’s not just the production side. That’s why I think even having the grid is important.

Construction costs. So if you think about the total TCO, everything, it’s like, are you a cheap producer of energy? Can you build the data centers?

Then what’s the cost curve of the silicon in the systems? And by the way, look at the token pricing. Token pricing basically drops by a half every three months.

I mean, 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 whose prices are just going to monotonically come down in a pretty fast curve.

Laurence Fink

We’re sitting in Europe, and there is a real fear because Europe does not have its own power. It has to import mostly of its power. Do you have any messages for Europe related to this?

Satya Nadella

Yeah, I mean, I think there are two sets of things. One is, here we are in Switzerland, and I look at the pharma or the financial sector. Obviously, they do a big job in this country, as 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 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 and world class. You have to absolutely invest in producing, having the energy and the tokens here, which again, you’re attracting, like as I said, we are investing and others are investing. The data centers here.

So the question is, what’s that next generation of output that comes from here? I always think about the German Mittelstand. Whenever I go to a jeweler or a dentist in the United States, I’m surrounded by German Mittelstand.

Yes, totally. It’s just unbelievable engineering prowess of that country. And now the question, and by the way, that’s the point.

They are producing industrial products, which today are built into it all the intelligence as well. That data, so I know whenever we come to Europe, everyone’s 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. 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 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 in?

Laurence Fink

A leader. So do you think the whole idea around sovereignty of data, is that being misunderstood?

Satya Nadella

I think that when people talk about sovereignty, first of all, it’s very important, clearly.

Laurence Fink

Who owns the data?

Satya Nadella

And in a week like this, it’s more important. But that said, it is, you have to kind of think about how, what does sovereignty mean? For example, in the AI era, the topic that’s least talked about, but I feel will be most talked about in this calendar year, will be the sovereignty of a firm.

Just imagine, if you’re a 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. It’s like everybody’s talking about everything else that is sort of outside of that. Whereas the most important thing is, it really doesn’t matter if you have, 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’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 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 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.

Laurence Fink

One last question. I know we’re running out of time. In five years or 10 years, is there gonna be one dominant model that we’re all gonna be using?

And how is Microsoft preparing for this? Are we gonna be using one model for enterprise, one model for other trades?

Satya Nadella

You know, even in the last, whatever, three years, four years that we’ve been at it, the reality at this point is it’s a multi-model world. 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 gotta do 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 gonna be getting ahead.

Laurence Fink

Ladies and gentlemen, let’s thank Satya, my friend. Thank you. Thank you.

Well. Thank you. Thank you.

Thank you. Thank you. And hopefully this is the beginning of many great dialogue and conversations here at the World Economic Forum.

Thank you, everyone. Thank you. Thank you.

Satya Nadella

Speech speed

162 words per minute

Speech length

5012 words

Speech time

1853 seconds

AI represents a continuation of the computational arc spanning 70 years, focused on digitizing artifacts and building analytical power, but potentially greater than previous platforms like web, mobile, or cloud

Explanation

Nadella argues that AI follows the same fundamental pattern as previous computing paradigms – digitizing information about people, places, and things to build analytical and predictive capabilities. However, he positions AI as potentially having greater impact than previous platform shifts like the internet, mobile, or cloud computing.

Evidence

Nadella argues that AI models and their outputs are more readily available worldwide compared to earlier technology rollouts like PCs or smartphones. However, he notes that realizing this potential requires appropriate capital investment, supportive policies, and robust electrical grid infrastructure.

Evidence

Example of rural Indian farmer using early GPT models to reason over farm subsidies in local language and complete forms

Major discussion point

AI Diffusion and Global Accessibility

Topics

Development | Digital access | Infrastructure

For AI not to be a bubble, benefits must spread beyond tech firms to show real-world outcomes like AI-accelerated drug trials and improved productivity across sectors

Explanation

Nadella argues that AI will only avoid being a bubble if its benefits extend far beyond technology companies to create tangible improvements in healthcare, education, and other sectors. He emphasizes the need for real-world applications that demonstrate clear value.

Evidence

Example of AI-accelerated drug clinical trials, need for improvements in health outcomes, education outcomes, and public sector efficiency

Major discussion point

Economic Impact and Productivity Transformation

Topics

Economic | Development | Sustainable development

Agreed with

– Laurence Fink

Agreed on

The critical importance of AI diffusion and democratization for avoiding a bubble and ensuring broad benefits

AI success requires translating tokens into economic growth, with GDP growth directly correlated to tokens per dollar per watt efficiency

Explanation

Nadella presents a framework where AI tokens become a new economic commodity, and a country’s or company’s success depends on efficiently converting these tokens into economic value. He argues that GDP growth will be directly linked to how efficiently tokens can be produced and utilized.

Evidence

Major discussion point

Future AI Model Landscape

Topics

Economic | Digital business models | Intellectual property rights

Laurence Fink

Speech speed

145 words per minute

Speech length

760 words

Speech time

313 seconds

AI has moved from experimental to foundational for companies, countries, and society, requiring understanding of where this shift is heading

Explanation

Fink positions AI as having transitioned from a future concept to a present reality that is becoming foundational across multiple levels of society. He emphasizes the need to understand the trajectory and implications of this transformation, particularly looking ahead five years to identify what will seem obvious in hindsight.

Evidence

Agreed with

– Satya Nadella

Agreed on

The critical importance of AI diffusion and democratization for avoiding a bubble and ensuring broad benefits

AI dramatically improves operational efficiency, reducing 12-hour computations to minutes and enabling management of $14 trillion with hundreds of thousands of mandates instantaneously

Explanation

Fink provides concrete examples from BlackRock’s operations showing AI’s transformative impact on computational efficiency and scale management. He argues that without AI technology, his firm could not operate at its current scale of managing $14 trillion across hundreds of thousands of different investment mandates.

Evidence

Specific example of 12-hour computations reduced to minutes, managing $14 trillion with hundreds of thousands of mandates instantaneously

Major discussion point

Economic Impact and Productivity Transformation

Topics

Economic | Digital business models | Development

Agreed with

– Satya Nadella

Agreed on

AI delivers dramatic productivity improvements and operational efficiency gains

Energy constraints and power costs will significantly impact which regions can effectively compete in the AI economy

Explanation

Fink raises concerns about energy requirements as a limiting factor for AI adoption, particularly highlighting Europe’s dependence on imported power. He questions how regions without cheap, abundant energy will be able to compete effectively in an AI-driven economy.

Evidence

Europe’s dependence on imported power, energy as a determinant of AI accessibility

Major discussion point

Infrastructure and Energy Requirements

Topics

Infrastructure | Economic | Development

Agreed with

– Satya Nadella

Agreed on

Energy infrastructure and power costs are critical determinants of AI competitiveness

Agreements

Agreement points

AI represents a fundamental platform shift that is now foundational rather than experimental

Speakers

– Satya Nadella
– Laurence Fink

Software development demonstrates AI’s evolution from code completion to autonomous agents working 24/7, while maintaining human agency

AI dramatically improves operational efficiency, reducing 12-hour computations to minutes and enabling management of $14 trillion with hundreds of thousands of mandates instantaneously

Summary

Both speakers provide concrete examples of AI’s transformative impact on productivity, from software development workflows to financial operations, demonstrating significant time and efficiency improvements.

Topics

Economic | Future of work | Digital business models

Similar viewpoints

Both speakers acknowledge the risk of AI creating or exacerbating inequality between developed and developing regions, while recognizing that AI has better potential for global accessibility than previous technologies if proper infrastructure and policies are in place.

Speakers

– Satya Nadella
– Laurence Fink

Arguments

Current AI applications are heavily weighted toward educated economies, raising concerns about leaving major portions of society behind

AI tokens are more evenly accessible globally than previous technologies, but success depends on capital investment, policy environment, and grid infrastructure

Topics

Development | Digital access | Economic

Both speakers understand that AI adoption requires fundamental organizational transformation, not just technology implementation, with success depending on leadership commitment to changing workflows and developing new capabilities.

Speakers

– Satya Nadella
– Laurence Fink

Arguments

AI fundamentally changes workflow and information flow within organizations, flattening traditional hierarchical structures and requiring redesign of organizational processes

Successful AI adoption requires the right mindset, skillset, and dataset context engineering, with firm-wide differences emerging based on leadership commitment

Topics

Economic | Future of work | Digital business models

Unexpected consensus

Europe’s competitive strategy should focus on global markets rather than data protectionism

Speakers

– Satya Nadella
– Laurence Fink

Arguments

Europe should focus on global competitiveness of its products and services rather than just protecting European data, leveraging its world-class human capital

The most important sovereignty issue is firms controlling their tacit knowledge in AI models to prevent enterprise value leakage

Explanation

Unexpectedly, both speakers, despite representing major global technology and financial firms, advocate for Europe to focus on global competitiveness rather than data sovereignty concerns. This consensus is significant as it challenges prevailing European policy discussions about data protection and suggests a more market-oriented approach.

Topics

Economic | Digital business models | Development

The future AI landscape will be multi-model rather than dominated by a single provider

Speakers

– Satya Nadella
– Laurence Fink

Arguments

The future will be a multi-model world where success depends on orchestrating multiple models with context engineering and proprietary data

Explanation

Despite Nadella representing Microsoft, which has significant investments in OpenAI and could benefit from model dominance, he advocates for a multi-model future. This unexpected consensus suggests both speakers see competitive advantage coming from orchestration and application rather than model monopolization.

Topics

Economic | Digital business models | Intellectual property rights

Overall assessment

Summary

The speakers demonstrate remarkably high consensus across all major discussion points, agreeing on AI’s foundational importance, the critical need for rapid diffusion, infrastructure requirements, productivity benefits, and competitive strategies. Their alignment spans technical, economic, and policy dimensions.

Consensus level

Very high consensus with significant implications for AI policy and business strategy. The agreement between a major technology CEO and financial services leader suggests broad industry alignment on AI’s transformative potential and the conditions necessary for successful adoption. This consensus could influence policy discussions around AI infrastructure investment, diffusion strategies, and competitive positioning globally.

Differences

Different viewpoints

Definition and implications of ‘surplus’ in AI adoption

Speakers

– Laurence Fink
– Satya Nadella

Arguments

Fink raises concern: ‘Well, surplus could be scary, too. Does surplus mean fewer workers? What do we mean by surplus?’

Nadella frames surplus positively: ‘if we can really take these tokens and bend the curve of productivity, then there is surplus everywhere. And that’s really the goal.’

Summary

Fink expresses concern that AI-generated surplus might lead to job displacement and questions what surplus actually means for workers. Nadella views surplus as an unqualified positive outcome that should be the goal of AI adoption.

Topics

Economic | Future of work | Development

Unexpected differences

Optimism vs. caution about AI’s social impact

Speakers

– Laurence Fink
– Satya Nadella

Arguments

Fink consistently raises concerns about inequality, job displacement, and leaving populations behind

Nadella maintains an optimistic view of AI as fundamentally beneficial and accessible

Explanation

Given their positions as leaders of major technology-adjacent companies, one might expect more alignment on AI’s benefits. However, Fink consistently raises cautionary questions about AI’s social implications while Nadella maintains a more uniformly positive outlook. This is unexpected as both are business leaders who should theoretically benefit from AI adoption.

Topics

Economic | Future of work | Development

Overall assessment

Summary

The disagreements are relatively minor and primarily center on framing and emphasis rather than fundamental opposition. The main areas of disagreement involve the implications of AI-driven productivity gains for workers and the current state of AI accessibility across different populations.

Disagreement level

Low to moderate disagreement level. The speakers largely align on AI’s transformative potential and the need for broad diffusion, but differ in their assessment of current challenges and social implications. Fink takes a more cautious, questioning approach while Nadella is more optimistic. These differences suggest healthy debate about implementation approaches rather than fundamental disagreement about AI’s value or direction.

Partial agreements

Similar viewpoints

Both speakers acknowledge the risk of AI creating or exacerbating inequality between developed and developing regions, while recognizing that AI has better potential for global accessibility than previous technologies if proper infrastructure and policies are in place.

Speakers

– Satya Nadella
– Laurence Fink

Arguments

Current AI applications are heavily weighted toward educated economies, raising concerns about leaving major portions of society behind

AI tokens are more evenly accessible globally than previous technologies, but success depends on capital investment, policy environment, and grid infrastructure

Topics

Development | Digital access | Economic

Both speakers understand that AI adoption requires fundamental organizational transformation, not just technology implementation, with success depending on leadership commitment to changing workflows and developing new capabilities.

Speakers

– Satya Nadella
– Laurence Fink

Arguments

AI fundamentally changes workflow and information flow within organizations, flattening traditional hierarchical structures and requiring redesign of organizational processes

Successful AI adoption requires the right mindset, skillset, and dataset context engineering, with firm-wide differences emerging based on leadership commitment

Topics

Economic | Future of work | Digital business models

Takeaways

Key takeaways

AI represents a fundamental platform shift comparable to or greater than the web, mobile, or cloud, continuing the 70-year arc of computation focused on digitizing and analyzing the world

The critical success factor for AI is diffusion speed – companies and countries that diffuse AI fastest will be the ultimate winners, not necessarily the technology creators

AI can dramatically transform productivity, reducing computation times from hours to minutes and enabling unprecedented scale of operations

For AI to avoid being a bubble, benefits must spread beyond tech firms to create real-world outcomes across all sectors, including healthcare, education, and public services

AI adoption follows a barbell pattern – easier for new small companies starting fresh, more challenging for large organizations requiring significant change management

The future will be a multi-model world where competitive advantage comes from orchestrating multiple AI models with proprietary data and context engineering

Energy infrastructure and power costs will be critical determinants of AI accessibility and regional competitiveness

True sovereignty in the AI era means firms controlling their tacit knowledge in AI models to prevent enterprise value leakage to external model companies

Europe should focus on global competitiveness of its products and services rather than just data protection, leveraging its world-class human capital and engineering prowess

Resolutions and action items

Organizations must change their mindset, develop AI skillsets, and implement context engineering to successfully adopt AI

Firms need to orchestrate multiple AI models (closed source, open source, and custom) with their proprietary data to maintain competitive advantage

Countries need to invest in both capital infrastructure (data centers) and grid modernization to support AI diffusion

Leadership commitment is essential for successful organizational AI transformation, requiring workflow redesign and structural changes

Skilling and training programs are necessary to ensure broad AI adoption across all levels of the workforce

Unresolved issues

How to prevent AI from creating greater polarization between educated and less-educated populations globally

How to ensure equitable AI diffusion in regions with limited energy infrastructure or capital investment capacity

The specific mechanisms for balancing data sovereignty concerns with the need for global competitiveness

How to address the change management challenges for large organizations adopting AI at scale

The long-term implications of AI on employment and workforce structure beyond productivity gains

How to maintain coherence in autonomous AI agents working over extended periods

Suggested compromises

Europe should balance its leadership in privacy and AI safety regulations with building local AI capabilities and thinking globally about competitiveness

Organizations should adopt a gradual approach to AI implementation, starting with specific use cases while building trust and establishing guardrails

Countries should pursue hybrid approaches combining private capital investment with public infrastructure development, particularly for grid modernization

This historical perspective challenges Europe’s inward-looking approach to AI regulation and data sovereignty. It reframes European competitiveness as fundamentally dependent on global relevance rather than regional protection, offering a strategic rather than defensive mindset.

Impact

This comment elevated the discussion from technical implementation to grand strategy, connecting AI adoption to Europe’s historical economic success. It challenged the audience to think beyond regulatory frameworks to competitive positioning in a global AI economy.

Overall assessment

Explanation

Nadella claimed this correlation exists but didn’t provide supporting data or detailed analysis of this relationship

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.