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Heat. [music] Heat. [music] Heat. Heat. [music] Heat. Heat. Heat. Heat. [music] Heat. Heat. [music] [music] Heat. Heat. [music] [music] >> [music] [music] >> Heat. Heat. [music] >> [music] [music] [music] [music] >> Heat. Heat. Heat. Heat. Heat. [music] >> [music] >> Heat. Heat. [music] >> [music] [music] >> I want to talk about AI, and I would really want to because I think this is on everybody's mind more than almost any other subject today, um, related to the intersection of business, technology, society.
Um, so Satya, um, 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. Um, and it's now more foundational. [snorts] And it's not just foundational for companies, but it 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. Um, so, um, with that, I wanted to ask a few questions related to that. You have described that AI is 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?
You know, first of all, um, it's great, uh, to be back here, uh, Larry, and it's, um, I had a chance, in fact, yesterday when you put out the letter, uh, to kick off the forum and read it. And, um, and in there, you sort of had this one line of, uh, 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 [clears throat] spread more evenly to create surplus everywhere? If you sort of think about it, the the way I come at this is not that, um, 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 mini computer did. That's what the client server era did. That's what the web era did. The mobile cloud era did. So it, like, it, it, depend, irrespective of which paradigm or platform, it has been one continuous arc of saying, let's make better sense of this world, um, 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. >> Um, which doesn't have the same type of, you know, I'll call it [clears throat] marginal cost economics associated with it that allows us to then build more insight and more, uh, more capability. And in that context, AI, I would say, is of the same class, at least like the web or the internet, um, 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, you know, is knowledge work. Um, you know, you could say it's elite knowledge work.
>> It started off, uh, you know, in fact, my own belief in this generation of AI and its capability, uh, 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 sort of word or the next, uh, line of code? Uh, and suddenly it started working with these models. Uh, 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, uh, 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. Uh, now you have complete autonomous agents where you can give it your entire project, right? It can work, uh, 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, uh, the software developer 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 if let's say in early 80s, if somebody had come to us and said, what, four billion people are going to wake up every morning and start typing, you would have said, why? Right? You know, we have a, like, we have a typist pool that's good enough, we don't need four billion people. But we, that's what happened, right? We invented this entire class of thing called knowledge work, where people started really using computers, uh, to go amplify what we were trying to achieve, uh, using software. I think in the context of AI, that same thing is going to happen.
>> Um, 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. Uh, 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, "Oh, I don't like the website. I want an app." It'll write more code to transform it. So that reasoning and cap, reasoning capabilities, that prediction capabilities, that ability to take action, remain long-term coherent, is all improving. Um, and our job though, is to parlay this, like take even what you at BlackRock are doing, right? When you're bring, taking something like say, Copilot plus Aladdin, and bringing those things [clears throat] 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 at 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, um, mandates. Um, we could do that instantaneously. And we, 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 too. Does, does surplus mean fewer workers? What do we mean by surplus? And so, you know, the, I'm going to tie that into my second question about AI diffusion.
>> Yeah. >> To me, the, 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, you know, can you describe how this process of diffusion, uh, across economies, across companies, across people and countries, how does that play out?
>> Yeah, I think that this is the real question, right? Because one of the, uh, things right now, the zeitgeist is a little bit about the admiration for AI in its abstract form or as as technology.
>> [clears throat] >> Uh, but I think we, as even a global community, um, have to get to a point where we're using this to do something useful, uh, that changes, uh, the outcomes of people and communities and countries and industries, right? Otherwise, I don't think, uh, this makes much sense, right? In fact, I would say we will quickly lose even the social permission, uh, to actually take something like, uh, 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, uh, 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, uh, around the world, is to just say that, like, let's make sure that the supply is there, which is, uh, everything from the chips on down, ultimately, ely 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, uh, 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, I loved, you know, I think Jobs had a nice metaphor, he called it the bicycle for the mind. Uh, Bill had a metaphor, which I remember, was like, 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, you know, 10x, 100x, 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 [clears throat] cheering award winner, uh, called Raj Reddy, who had this nice metaphor of AI, and he had this long before, uh, even generative AI. He said, either, either it's a cognitive amplifier, or it's a guardian angel, right? So if you think of AI as that, um, then that, 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, uh, you know, industry is better served across the payer, the provider, and, uh, and the patient, ultimately, right? That's an, 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? Um, and interestingly enough, I think if mobile has taught us one thing, is it, it's actually distinct from what happened in the PC, right? Uh, 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. Um, you know, right now, um, what's the model in, 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. 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. Um, and that needs to come back, right? People need to say, 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, it's, it's very [clears throat] easy to see how mobile and the diffusion of mobile, how it transformed economies, especially in the global south. How does, how do this, you know, to me, I, 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 [clears throat] we're not leaving major portions of society or the world behind? Because I think that's, that's [clears throat] going to be the big issue for us going forward.
>> Yeah. So, it's, it's, it's interesting, right? This is one of those times when, uh, by definition, and because of the rails that have been established, you know, as you said, right, which is >> uh, what's happened with mobile, as well as what's happened with, um, uh, you know, essentially connectivity, >> right? >> You have the ability [clears throat] to sort of deliver the tokens pretty evenly around the world. >> Right? >> A lot more so than, let's say, uh, the PC era or even the beginning of the mobile era, right? Because it took a long time for even the mo smartphone, in particular, to penetrate, um, 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? It's one, 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 GPT3 or 25, even, uh, 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, uh, 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, uh, who perhaps didn't have that, because the technology was so much more accessible. So, I, I do think it's in our hands, even in the global south, to use it, uh, to create, I would say, more of that opportunity where there isn't one. Um, but I think what the [clears throat] necessary conditions still are, do you have, uh, 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. >> Um, 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 [clears throat] you don't have a sophisticated sort, or rather, if you don't have a, 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, like, to me, a long-term scalable solution is to have, uh, you know, all of these token factories, part of the real economy, connected to the grid, connected to the telco network, delivering, uh, just like we delivered bits, you have to deliver tokens plus bits. Um, and that's kind of what's going to drive at scale, whether it's in the global south or in, uh, on the developed world.
>> So, 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 democratization of technology is, and the diffusion of that technology really does then transform the demand. And the, 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, it's, it's for this not to be a bubble, >> Yeah. >> 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. Uh, right? If, uh, all we talk about is what's happening to the technology side, that then that's by, you know, it's just purely supply side. >> Right? >> Uh, ultimately, if we are not talking about, wow, here is a drug comp, you know, drug that was sort of brought into the market that's super successful because it was, uh, AI accelerated the clinical trial. It's not even the magical molecule, right? It's kind of even the rest of what is, uh, needed in order to make something much more relevant, right? Um, and so the more we [clears throat] 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. Uh, right? Because that's, it's a narrow point in time, calculation. Is >> Right now, that's what we're seeing more. >> That's what we're seeing, you know, you know, in in the developed world, in particular. But remember, my capital, like, that, the one thing that, you know, is definitely we're spending a lot of it in the United States, but 50% of it is also all over the world. >> Right? >> Um, and so, interestingly enough, it depends on, uh, demand all over the world. And the demand all over the world will only be there if there is local surplus plus 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, I'm sure, um, Microsoft has evolved itself. So it 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, that which ultimately then creates that demand, which eliminates any fears and bubbles.
>> Yeah. Now, I think it's probably one of the, the, the big challenges with all of these new technologies is when work, work artifact, and workflow changes, uh, that means we, as firms, have to change how we work. In fact, I remember meeting, um, uh, the CEO of General Ali, you know, a few years back, and he was describing he had joined, um, uh, the firm, you know, pre [clears throat] era, and, uh, uh, and he was describing how, for example, they worked with their agents in the field, with faxes, inter-office memos, and, um, and, and suddenly [clears throat] 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 has changed, right? So similarly, I think with AI, uh, you are going to start seeing, uh, actual change in how workflow happens, right? I mean, even [clears throat] 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, uh, there used 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're doing with you as a client, what we're doing as a client of yours, and everything in [clears throat and cough] between, as an investment. It's 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. Uh, so the current structure may not make sense, um, 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 work, the workflow with the technology. Then that needs skill set. So you can't sort of talk about this in the abstract. You 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. Uh, 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, uh, put the guardrails that allow you to be able to trust it. So skills, uh, so mindset, skills, the other big consideration, uh, 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, uh, to take advantage. In fact, that's why I think you, 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'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, I think that what you're seeing is, it's easier, the, because if you have a green sort of, um, uh, 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%. And I think, 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 >> right? >> Uh, with, with what is possible, uh, you, 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, uh, um, you have know-how. But the bottom line is, if you don't translate that with a new production function, uh, 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, uh, where neither side, like whether you're a new entrant or an incumbent, can't take it as like, I, 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, I, I'm seeing there are two things [clears throat]. I would say, Larry, as I travel around the world, the quality of, um, whether it's the know-how, the software developers, the startups, um, 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, uh, for the first time, just because access to what's happening, uh, is there. That said, [clears throat] at scale, the commitment to using this, the risk capital being there, the large companies pushing it hard. I mean, I, you know, again, the US, you know, is, in fact, if I compare it, uh, take financial sector, if financial sector's adoption of the cloud versus AI, night and day, right? Because in an interesting way, it's much faster, uh, when it comes to AI versus it was with the cloud. And cloud, because for a variety of reasons, >> Regulatory issues too, until the regulators allowed banks to bring their data off campus, that was a big issue. >> Yeah. So I would say, I think wherever [clears throat] you know, so in the West, in particular, in the US, uh, 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 [clears throat] 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, it, the demand is costly.
>> 100%. So if you sort of look at the tokens per dollar per watt, right? Which I think in [clears throat] some sense, I would claim that GDP growth in any place will be directly correlated. Like, you, if you sort of buy my entire argument that look, we've got a new commodity, its tokens. >> Right? >> And the job of every economy, uh, and every firm in the economy is to translate these tokens into economic growth. Then if you have a cheaper commodity, it's better. Uh, and so that's sort of what, why there's tokens per dollar per watt. And by the way, there are many, many elements to this, right? Which is, uh, it's not just, um, the production side. That's why I think even having the grid is important. Um, uh, construction costs, right? So if you, like, if you think about the total TCO, everything, it's like, how are you a cheap producer of energy? Can you build the data centers? Uh, then what's the cost curve, uh, of the silicon and the systems? Uh, the, and by the way, look at the token pricing, right? Token pricing basically drops by, you know, a half, uh, every 3 months. I mean, this is a, so how, so that, 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 co, Europe does not have its own power. It has to import mostly of its power. Um, do you have any messages for Europe related to this?
>> Yeah, I mean, I think so there are two sets of things, right? One is, you know, here we are in in Switzerland, and I look at, uh, the, the pharma or the financial sector, you know, obviously Switzer, uh, they, they do do a big job in in this country, as in in Europe, but they're also international brands and international operations. So one thing that, uh, whenever I think about Europe is, the Europeans are producing products and services that actually are going everywhere in the world. And so therefore, uh, 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. Uh, but European economy is thrives, and has, you know, thrived in the last, whatever, 200 years, 300 years. The miracle of the West is fundamentally because of what has happened in Europe, uh, 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, you, again, I go back to the human capital. Here is just fantastic and world-class. Uh, you have to absolutely invest in, uh, producing, uh, you know, having the energy and the tokens here, which again, you're attracting, like, as I said, we're investing, and others are investing, uh, the, 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 Mittelstand. Whenever I go to a jeweler or a dentist in the United States, I'm surrounded by German Mittelstand. Right. Totally.
>> Um, it's just unbelievable engineering prowess of that country. Uh, 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 the 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?
>> Um, and so that's, I think, what needs to change. Uh, you 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, uh, 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, uh, 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, I think that the, when people talk about sovereignty, first of all, it's very important, clearly, and who owns, >> and in a week like this, it's more important. >> Uh, but that said, it is, you have to kind of think about how, what is sovereignty mean? Like, for example, in the AI, the topic that's least talked about, but I feel will be most talked about in, in this, uh, this calendar, 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, that, 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 like, 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. Yes, uh, 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, uh, and control over the models. And it's not a one-way enterprise value transfer. Um, and so, to me, I think sovereignty requires real thought on what is it, um, 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. Uh, there is comparative advantage in firms that needs to be preserved, even in the AI era. That's what'll give you real sovereignty.
>> One last question. I know we're running out of time. Um, 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? 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, um, the, the reality at this point is, it's a multimodal world, right? I mean, the, in fact, if you think about it, the, in 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? Uh, so think of these models, uh, 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? Yes.
>> Right. So it's that three parts. So can I bring in all the models, by the way, uh, 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. Uh, 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, in 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, uh, thank Satya, my friend. Thank you. [applause] for, and hopefully, this is the beginning of many great dialogue and conversations here at the World Academic Forum. Thank you, everyone.
>> Thank you. Heat. Heat. [music] >> [music] >> Heat. Heat. >> [music]