# LIVE: Microsoft CEO Satya Nadella in Conversation with Larry Fink at Davos WEF | AC1G *DRM News* · https://www.youtube.com/watch?v=9DsKNVPlDP4 ## Summary At Davos, BlackRock's Larry Fink interviews Microsoft CEO Satya Nadella about AI's place in the long arc of computing, its diffusion across firms and countries, and its organizational impact. Nadella argues AI is a platform shift at least as big as the web or mobile, that diffusion depends on token efficiency, capital, grids, and skilling, and that sovereignty now means firms controlling models that embed their tacit knowledge. He closes by predicting a multimodel world where orchestration and context engineering are a firm's real IP. ## Key points - Nadella frames AI as the latest stage in a 70-year arc of digitizing people, places, and things, comparable to or greater than the PC, web, mobile, and cloud. - He traces AI's evolution in software engineering from GitHub Copilot code completions, to chat, to agent mode, to fully autonomous agents working 24/7. - Fink says BlackRock now processes $14 trillion of other people's money, with computations that once took 12 hours now taking minutes thanks to AI. - Nadella argues diffusion is everything: tokens per dollar per watt must improve, and countries need capital investment, modernized grids, and broad skilling. - He warns AI will lose 'social permission' if tokens don't improve health, education, public sector efficiency, and private sector competitiveness. - A telltale sign of a bubble would be if only tech firms benefit; real success means AI-accelerated outcomes like drugs, healthcare, and finance across the world. - Nadella says firm-level sovereignty means embedding tacit knowledge in models you control, or you leak enterprise value to a model company. - He predicts a multimodel world where firms orchestrate closed and open source models with their own context and data as their IP. ## Notes ### The arc of computation - Satya Nadella frames AI within a 30–70 year arc of digitizing people, places, and things to build analytical and predictive power — from mainframes to mobile cloud. - He ranks AI as at least the class of the internet, mobile, PC, or cloud, and maybe greater. ### From Copilot to agents - His belief in this generation of AI built up when GitHub Copilot started doing code completions. - The progression: completions → chat in the coding flow → agent mode for small tasks → fully autonomous agents given entire projects, working 24/7 (though long-term coherence still needs work). - He compares to the early 80s: nobody predicted 4 billion people typing daily, which created knowledge work; AI will similarly amplify human agency. - Bill Gates' old question — what's the difference between a document, a website, and an application? — is finally answered by AI transforming one into another via code. ### BlackRock at scale - Larry Fink: computations that took 12 hours now take minutes, processing $14 trillion of other people's money with hundreds of thousands of mandates. - Fink: without AI, BlackRock could not function at its current scale. ### Diffusion is everything - Nadella: the zeitgeist admires AI in the abstract, but it must change outcomes for people, communities, countries, and industries — or it loses social permission to spend scarce energy on tokens. - Supply side: tokens per dollar per watt must monotonically improve; token factories will diffuse like electricity grids. - Demand side: every firm must use it; skilling broadly is the strongest correlate of diffusion. - He cites a rural Indian farmer using an early GPT-3/3.5 bot in a local language to reason over farm subsidies and complete a form. ### Bubble test - A telltale sign of a bubble would be if only tech firms benefit; real diffusion means AI-accelerated drugs, healthcare, and sector-wide gains. - Fink: countries and companies that diffuse technology fastest will be the ultimate winners. ### Organizational change - Copilot flattens information flow — Nadella gets a 360 brief on Larry Fink and shares it across functions, inverting the old trickle-up structure. - Formula: mindset → skill set → data set (context engineering). - Adoption is a barbell: small companies start fresh; large firms have data and relationships but face a bigger change-management challenge. ### Europe and sovereignty - Europe should worry less about data sovereignty and more about global competitiveness of its industrial and financial companies. - Firm sovereignty means embedding tacit knowledge in weights you control — otherwise you leak enterprise value to a model company. ### Multimodel future - Not one dominant model: firms orchestrate closed and open source models, distill their own, and feed them context — that orchestration is the IP. ## Transcript [00:00] 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. [clears throat] Right? That's what the mainframes did. That's what the mini computer did. That's what the client server error did. That's what the web error did. The mobile cloud error did. So it like 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 [00:55] 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 u marginal cost economics associated ated 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 [01:49] 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 u 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 [02:33] your entire project right it can work uh you know >> 24/7 >> it can work for 24/7 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 it's let's say in early 80s If somebody had come to us and said what 4 billion people are going to wake up every morning and start typing you would have said why right you know we [03:17] have a like we have a typist pool that's good enough we don't need 4 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 [04:01] 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 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 co-pilot [04:47] plus Aladdin and bringing those things together [clears throat] >> 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 instantaneous 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 [05:30] surplus everywhere and that's really the goal >> well surplus could be scary too does does surplus 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 [06:19] 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 are 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 [07:07] 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 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 [07:54] 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 [08:42] have. But 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 Turing award winner uh called Raj Ready 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 health care uh you know industry is better served [09:30] 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 the diffusion happens and the one 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, uh there used to be a real relationship between learning Excel skills or word skills and [10:25] 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 consumptionled. 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 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, oh, I pick up this AI skill and now I'm a better provider of some product or service in the real economy. So it's [cough] it's very easy to see how mobile and the diffusion of mobile how it transformed economies especially in the global south. How does how does this you know to me I I I just [11:11] read a research report that said um 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 that diffusion is spread evenly? How do we make sure that we're not [clears throat] leaving major portions of society or the world behind because I think that's that's going to [clears throat] 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 [12:00] essentially connectivity >> right >> you have the ability to sort of deliver the tokens pretty evenly around the world >> right >> a lot more so than let's say uh the PC C 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 [12:50] 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 uh 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 necessary [clears throat] conditions still are do you have uh the capital investment being put in uh do you even have an environment for capital because in an interesting way we are for example as hyperscalers investing all [13:39] over right including the global south uh 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 where there are certain things by the way private capital can do certain things that public capital only can do for example the grid right it's not I mean grid in most countries is sort of fundamentally driven by governments >> public >> and public and so if you [clears throat] don't have a sophisticated sort or rather if you don't have a a real approach to modernizing the grid uh that [14:19] 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 an app 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 [14:59] 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. about the technology creator. >> That's that's you know it's it's it's for this not to be a bubble >> y >> by definition it requires that the benefits of this are much more evenly spread. I mean I think a tellt tell 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 day you know it's just purely supply side >> right [15:51] >> uh ultimately if we are not talking about wow here is a drug comp you know drug that was sort of uh 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 [clears throat] we have uh 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 [clears throat] curve and bring local surplus and economic growth all around the world. [16:37] Not just economic growth driven by capital expenses. uh right because that's it's a narrow point in time uh calculation. right now that's what we're seeing more >> that's what we're seeing in you know you know in in the developed world in particular uh but remember my capital like that the one thing that you know is definitely we spending a lot of it in the United States but 50% of it is also all over the world >> right >> um and so interesting 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 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, [17:21] governments are going to have to evolve. 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. No, I think it's probably one of the the the big challenges with all of these new technologies is when uh work work artifact and work flow changes. uh that means we as firms have to change how we work. In fact, [clears throat] I [18:12] 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, prec >> [clears throat] >> era and uh uh and he was describing how, for example, they worked with their agents in the field uh with faxes, inter office memos and um and and suddenly the PC showed up and people would then put a spreadsheet in an email and send it around and the entire workflow and the work process 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 uh had a particular [19:00] workflow right which is uh 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 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 between [clears throat] 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 [19:41] 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 [20:32] need to think about changing the work the workflow with the technology then that needs skill set. [clears throat] 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 guard rails 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 guard rails that allow you to be able to trust it. So skills uh so mindset skills the other big consideration um really is how do you make sure you have the data set that you're feeding like context like it's [21:20] 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 department 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 [22:05] going to be at some you know it's going to there are going to be firmwide differences. They're going to there could [clears throat] be sectorwide 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 [22:48] are just starting use that platform >> 100%. And I think in fact I would say even for large organizations there's [clears throat] a fundamental challenge right because unless and until your rate of change keeps up with >> right >> uh with with what is possible uh you're you're going to get schooled by someone small being able to achieve scale because of these tools. So but I think scale like large organizations have an inherent strength. You have the relationships, you have the data, you have um you have knowhow, 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 [23:29] 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 I would say Larry as I travel around the world the quality of um whether it's the knowhow the software developers the startups [24:15] 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 at [clears throat] 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 [25:10] with the cloud and cloud because for a variety of reasons, >> regulatory issues too until the regulators allowed banks to bring their data off out of 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. Uh 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 [clears throat] you you mentioned about the power the grid is that going to be one of the determinants of of the accessibility if you do not have cheap power it the demand is costly >> 100%. So if you sort of look at the [25:59] tokens per dollar per watt right which I think in some [clears throat] 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 uh [26:45] everything it's like the 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 uh 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 uh 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 [27:37] 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 Switz they're uh they they do do a big job in 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 [28:18] 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 middlear [29:00] whenever I go to a jeweler or a dentist in the United States I'm surrounded by German middle right totally >> um it's just unbelievable engineering provice 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 you US and the rest of the world uh as opposed to just thinking that somehow by protecting Europe you're going to be [29:50] 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 that when people talk about sovereignty first of all it's very important clearly [30:38] and in a week like this it's more important >> uh but that said uh >> it is you have to kind of think about how what is sovereignty mean like for example in the AI era the topic that's least talked about but I feel will be most talked about in in this uh this calendar year will be the sovereignty of a firm. Just imagine if your firm you're not able to embed the tacit knowledge of the firm in a set of weights in a model that you control. By definition, you have no sovereignty. That means you're leaking enterprise value to some model company somewhere. In fact, 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 [31:31] 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 uh 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 solve problems. But the one problem that will only be solved is by you having much more sovereignty over the you know tacid knowledge uh and control over the models and it's not a one-way enterprise value transfer. Um and so to me I think [32:13] 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. uh 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 5 years or 10 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? So 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 3 [33:00] years, four years that we've been at it, um the the reality at this point is it's a multimodel 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 orchest 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 [33:54] 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 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 [34:43] 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.