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Podcast

Satya Nadella — Microsoft’s AGI Plan & Quantum Breakthrough

Dwarkesh Podcast

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  • Value Creation in Tech
    • Satya Nadella observed that business model shifts are more challenging than tech trends.
    • Accurately predicting where value will be created is crucial for success. Transcript: Dwarkesh Patel You think about what actually, which decisions ended up being the long-term winners in the 80s and 90s and which ones didn’t, and especially when you think about, you know, you were At Sun Microsystems, they had an interesting experience with the 90s.com bubble. People talk about this data center build out as being a bubble, but at the same time, we have the internet today as a result of what was built out then. What are the lessons about what will stand the test of time? What is an inherent secular trend? What is just ephemeral? What stands out? Satya Nadella Yeah, it’s actually, it’s interesting. I mean, I think the, if I sort of go back, even at least the four big transformations that I’ve been part of, right? If you say the client and the client server, so that’s the birth of the graphical user interface and the x86 architecture, basically even allowing us to build servers. It was very clear to me, I remember going to what was PDC in 91. In fact, I was at Sun at that time. And in 91, I went to Moscone, went to basically that’s when Microsoft first described Win32 interface. And I said it was pretty clear to me what was going to happen where the server was also going to be an x86 thing, right? So that when you have the scale advantages accruing to something, that’s the secular bet you have to place, right? And so, what happened in the client was going to happen on the server side, and then you were able to then actually build client server applications, so the app model, and it became clear. Then the web was the big thing for us, which we had to deal with in starting. In fact, as soon as I joined Microsoft, I think, what is it? Like the browser, the Netscape browser or the Mosaic browser came out, what, December, November of 93, right? I think is when, you know, Andreessen and crew sort of had that. And so that was a big game changer. I mean, in an interesting way, just as we were getting going on what was the client server wave, and it was clear that we were going to win it as well, we had the browser moment. And so we had to adjust. And we did a pretty good job of adjusting to it, right? Because the browser was a new, I’d say, app model. And we were able to embrace it with everything we did, right? Whether it was HTML in Word or build a new thing called the browser ourselves and compete for it and then build a web server on our server stack and sort of go after it. Except, of course, we missed what turned out to be the biggest business model on the web, because we all assumed the web is all about being going to be distributed. Who would have thought that search would be the biggest winner in organizing the web? And so that’s where we obviously didn’t see it and Google saw it and executed super well. So that’s kind of one lesson learned for me is like, hey, you got to really not only get the tech trend right, you also have to get where is the value going to be created with that trend. And these business model shifts are probably tougher than even the tech trend changes. Where is the value going to be created in AI? That’s a great one. (Time 0:01:41)
  • AI and Hyperscalers
    • Hyperscalers will benefit from AI’s increased compute demands, similar to the shift from client-server to cloud.
    • The AI model landscape will likely not be winner-take-all due to open-source alternatives and government regulation. Transcript: Satya Nadella So I think the, at least in my current thing is, there are two places where I can say with some confidence. One is the hyperscalers do well, right? Because the fundamental thing is, if you sort of go back to even how Sam and others describe it, I mean, like if, you know, intelligence is log of compute, whoever can do lots of compute Is a big winner. And the other interesting thing is, if you look at underneath even any AI workload, like take chat GPT. It’s not like everybody’s excited about what’s happening on the GPU side. It’s great, but it’s like the ratio, like, in fact, I think of my fleet even as a ratio of the AI accelerator storage to compute. And at scale, you got to grow it. And so that infrastructure need for the world is just going to be exponentially growing, right? So in fact, it’s mana from heaven to have these AI workloads, because guess what? They’re more hungry for more compute, right? Not just for training, but we now know for test time. And as I said, test time, like here’s an interesting thing. When you think of an AI agent, it turns out the AI agents is going to exponentially increase compute usage because you now are not even bound by just one human invoking a program. It’s one human invoking programs that invoke lots more programs. And so that’s going to create massive, massive demand and scale for compute infrastructure. So our hyperscale business, Azure business, I think that’s like, and other hyperscalers, I think that’s a big thing. Then after that, it becomes a little fuzzy because you could sort of say, hey, there is a winner-take model. I just don’t see it because this, by the way, is the other thing I’ve learned is being very good at understanding what are winner-take markets and what are not winner-take markets is In some sense everything. Like I remember even in the early days when I was getting into Azure, I mean, Amazon had a very significant lead and people would come to me and investors would come to me and say, oh, it’s Game over. You’ll never make it. Amazon’s, it’s winner take all. And having competed against Oracle and IBM and client server, I knew that, look, the buyers will not tolerate winner take all, right? Structurally, hyperscale will never be a winner-take because buyers are smart. Consumer markets sometimes can be winner-take but anything there, the buyer is a corporation, an enterprise, an IT department, they will want multiple suppliers. And so, you got to be one of the multiple suppliers. And so, that, I think, is what will happen even in the model side. So, there will be open source. There will be a governor. Just like on Windows, one of the big lessons learned for me was if you have a closed source operating system, there will be a complement to it, which will be open source. And so to some degree, that’s a real check on what happens. And so I think in models, there is one dimension of maybe there’ll be a few closed source, there will definitely be an open source alternative. And the open source alternative will actually make sure the closed source winner take all is mitigated. So that’s kind of at least my feeling on the model side. And by the way, let’s not discount if this thing is really as powerful as people make it out to be, the state is not going to sit around and wait for private companies to go around and all Over the world. So it’s sort of, I don’t see it as a winner-take Then about that, I think it’s going to be the same old stuff, which is in consumer, in some categories, there may be some winner-take network Effect, right? After all, ChatGPT is a great example. I mean, it’s kind of like it’s an at-scale consumer property that has already got real escape velocity, right? I go to the app store and I see, you know, it’s always like there in the top five. And I say, wow, like that’s pretty unbelievable. So they were able to use that early advantage and parlay that into an app advantage. And so in consumer, that could happen. In the enterprise, again, I think there will be by category different winners. So that’s sort of at least how I analyze it. I (Time 0:05:07)
  • Measuring AI’s Impact
    • Satya Nadella believes increased AI usage should correlate with significant GDP growth.
    • He suggests a 10% growth target as a true measure of AI’s revolutionary impact. Transcript: Dwarkesh Patel Recently reported that your yearly revenue from AI is $13 billion. But if you look at your year-on growth on that, in like four years, it’ll be 10x that. You’ll have $130 billion in revenue from AI if the trend continues. If it does, what do you anticipate we’re doing with all that intelligence? Like this industrial scale use, is it going to be like through office? Is it going to be you deploying it for others to host? Is it going to be, you got to have the AGI to have $130 billion in revenue? What does it look like? Satya Nadella Yeah, I see the way I come at it, Dworkish, is it’s a great question because at some level, if you’re going to have this sort of explosion, abundance, whatever, commodity of intelligence Available, the first thing we have to observe is GDP growth, right? Before I get to what Microsoft’s sort of revenue will look like. I mean, there’s only one governor in all of this, right? Which is, this is where a little bit of, we get ahead of ourselves with all this AGI hype, which is, hey, you know what? Let’s first see if, let’s say, develop. I mean, remember, the developed world is what? 2% growth. And if you adjust for inflation, it’s zero. So, in 2025, as we sit here, I’m not an economist, at least I look at it and say, man, we have a real growth challenge. So the first thing that we all have to do is let, and when we say, oh, this is like the industrial revolution, blah, blah, blah. Oh, let’s have that industrial revolution type of growth. That means, to me, 10%, 7%, developed world, inflation adjusted, growing at 5%. That’s the real marker, right? So, it can’t just be supply side, right? It has to be. In fact, that’s the thing, right? I think a lot of people are writing about it. I’m glad they are, which is the big winners here are not going to be tech companies. The winners are going to be the broader industry that uses this commodity that, by the way, is abundant. Right. And suddenly productivity goes up and the economy is growing at a faster rate. When that happens, we’ll be fine as an industry. But that’s, to me, the moment, right? So it costs self-claiming some AGI milestone. That’s just nonsensical benchmark hacking to me. The real benchmark is the world growing at 10%. (Time 0:15:18)
  • Intelligence: Better and Cheaper
    • Satya Nadella highlights the importance of both improving intelligence and reducing its cost.
    • Breakthroughs that enhance performance per token can drive greater demand, similar to cloud computing’s impact on server usage. Transcript: Dwarkesh Patel Yeah. I mean, speaking of prices coming down, you recently tweeted after the DeepSeek model came out about Jevons Paradox. And I’m curious if you can flesh out so jevon’s paradox occurs when there’s like the demand for something is highly elastic um is intelligence that bottlenecked on prices going down Because when i think about at least my use cases as a consumer it’s like intelligence is already so cheap it’s like two cents per million tokens like do i really need it to go down to 0.02 Cents? I’m just like really bottlenecked on it becoming smarter. And if you need to do charge me 100x, do 100x bigger training run. I’m happy for companies to take that. But maybe you’re seeing something different on the enterprise side or something. What is the key use case of intelligence that really requires you get a 0.002 cents per million tokens? Satya Nadella I mean, I think the real thing is the utility of the tokens, right? So which is in some sense, both need to happen. One is intelligence needs to get better and cheaper. And anytime there’s a breakthrough, like even what DeepSeek did or what have you, with the efficient frontier of, let’s say, performance per token changes, and the curve gets bent, And the frontier moves, that just brings more demand. And so that’s sort of how I look at it. And that’s what happened with cloud, right, by the way. Here’s an interesting thing. We used to think, oh, my God, we’ve sold all the servers in the client server era, except once we sort of started putting servers in the cloud, suddenly people started consuming more Because they could buy it cheaper and buy, it was elastic and they could buy it as a meter versus a license. And it completely expanded. Like, I mean, I remember like, you know, going, let’s say, to a country like India and sort of talking about, oh, here’s a SQL server. We sold a little, but man, cloud in India is so much bigger than anything that we were able to do in the server era. And that I think is going to be true. If you think about if you want to really have in the global south, in a developing country, if you had these tokens that were available for healthcare that were really cheap, that’ll Be the biggest change ever. (Time 0:21:38)
  • AI Deployment Challenges
    • Deploying AI capabilities faces challenges in change management and process adaptation.
    • Integrating AI into knowledge work requires new workflows, similar to how spreadsheets and email transformed forecasting. Transcript: Dwarkesh Patel I think it’s quite reasonable for somebody to hear people like me in San Francisco and think, look, they’re kind of silly. They don’t know what it’s actually like to deploy things in the real world. As somebody who works with these Fortune 500s and is working with them to deploy things for hundreds of millions, billions of people. What’s your sense on how fast deployment of these capabilities will be, even when you have working agents, even when you have things that can do remote work for you and so forth, with All the compliance and with all the inherent bottlenecks? Is that going to be a big bottleneck or is that going to move past pretty fast? It is going to be a real challenge because the real issue is change management or process change, right? Satya Nadella I mean, this is, here’s an interesting thing, right? Which is one of the analogies they use is just imagine how a multinational corporation like ours did forecast pre-PC and email and spreadsheets, right? I mean, faxes went around. Somebody then got those faxes and then did an inter-office memo that then went around and people entered numbers and then ultimately a forecast came maybe just in time for the next quarter. Then somebody said, hey, I’m just going to take an Excel spreadsheet, put it in email, send it around. People will go edit it and I’ll have a forecast. So the entire forecasting business process changed because the work, the work artifact and the workflow changed. That is what needs to happen with AI being introduced into knowledge work. In fact, when we think about even all these agents, the fundamental thing is there’s a new work and workflow. Like, for example, for me, even prepping for our sort of podcast, I go to my co-pilot and I say, hey, I’m going to talk to Dvarkesh about, you know, our quantum announcement and this new, You know, model that we built for game generation and just kind of give me like a summary of all the stuff that I should read up before going. And he knew like the two nature papers, he took that. In fact, I even said, hey, go give it to me in a podcast format. And so it sort of even did a nice job of two of us chatting about it. So that became, and in fact, then I shared it with my team, right? So I took it and put it into pages, which is our artifact, and then shared. So the new workflow for me is I think with AI and work with my colleagues, right? So that’s a fundamental change management of everyone who’s doing knowledge work, suddenly figuring out these new patterns of how am I going to get my knowledge work done in new ways. That is going to take time. It’s going to be something like in sales and in finance and supply chain. So for an incumbent, I think that this is going to be one of those things where, you know, let’s take one of the analogies I like to use is what manufacturers did with lean. I love that because in some sense, if you look at it, lean became a methodology of how one could take an end-to process in manufacturing and become more efficient, right? It’s that continuous improvement, which is reduce waste and increase value. That’s what’s going to come to knowledge. This is like lean for knowledge work in particular. And that’s going to be the hard (Time 0:24:51)
  • Satya Nadella’s AI Workflow
    • Satya Nadella uses Copilot to prepare for meetings by summarizing topics and generating podcast-style conversations.
    • He then shares these summaries with his team using Microsoft Pages, demonstrating a new workflow. Transcript: Dwarkesh Patel Work of management teams and individuals for doing knowledge work. And that’s going to take its time. Things Lean did is physically transformed what a factory floor looks like. It revealed bottlenecks that people didn’t realize until you’re really paying attention to the processes and workflows. You mentioned briefly how your own workflow has changed as a result of AIs. I’m curious if we can add more color to what would it be like to run a big company when you have these AI agents that are getting smarter and smarter over time? Satya Nadella Yeah, in fact, it’s interesting you asked it. I was thinking about it. For example, today, if I look at it, we are very email heavy. So I got in in the morning and I’m like, man, my inbox is full and I’m responding. And so I can’t wait for some of these co-pilot agents to kind of automatically populate my drafts so that I can start reviewing and sending. And so that’s kind of what. But literally, I do feel like I already have in Copilot, like at least 10 agents, right? I have, which I do at, because I query them as sort of different things for different tasks. And I feel like there’s a new inbox that’s going to get created, which is my millions of agents that I’m working with will have to invoke some exceptions to me, notifications to me, ask For instructions. So at least what I’m thinking is that there’s a new scaffolding, which is the agent manager is going to be that one. It’s not just a chat interface. I kind of need a smarter thing than chat interface to manage all the agents and their dialogue. So that’s why I think of this co-pilot as the UI for AI is a big, big deal. And each of us is going to have it. So basically think of it as there is knowledge work and there’s a knowledge worker, right? The knowledge work may be done by many, many agents, but you still have knowledge worker who is dealing with all the knowledge workers. And that I think is the interface that one has to build. Yeah, (Time 0:28:10)
  • Quantum Breakthrough
    • Microsoft’s quantum breakthrough involves creating topological qubits, which are more reliable due to their physical properties.
    • This is compared to the transistor moment, potentially enabling a million-qubit quantum computer on a small chip. Transcript: Dwarkesh Patel Okay, before we get to that, I want to keep asking you more about AI. But I really want to ask you about the big breakthrough in quantum that Microsoft Research has announced. So can you explain what’s going on here? Satya Nadella Yeah, this has been another 30-year journey for us. It’s unbelievable. I’m the third CEO of Microsoft been excited about quantum. I think the fundamental breakthrough here or the vision that we’ve always had is you need a physics breakthrough in order to build a utility scale quantum computer that works. And so, we took that path, you know, which was the path of sort of saying, look, the one way for having that less noisy or the more reliable qubit is to bet on a physical property that by definition Is more reliable. And that’s kind of what led us to those Majorana zero modes as the thing to go, which was theorized in the 1930s. And so the question was, can we actually physically fabricate these things? Can we actually build them? So the big breakthrough effectively, and I know you talked to Chetan, was that we now finally have existence proof and a physics breakthrough of our Majorana zero modes in a new phase Of matter effectively, right? So this is why I think we like the analogy of thinking of this as the transistor moment of quantum computing where we effectively have a new phase, which is the topological phase, which Means we can even now reliably hide the quantum information and measure it, and we can fabricate it. And so now that we have it, we feel like with that core foundational fabrication technique out of the way, we can start building a Majorana chip, that Majorana 1, which I think is going To basically be the first chip that will be capable of a million qubits physical. And then on that, thousands of logical qubits error corrected. And then it came on, right? So then you suddenly have now got the ability to build a real utility square quantum computer. And that, to me, now so much more feasible, right? Because without something like this, you will still be able to achieve milestones, but you’ll never be able to build a utility scale computer. And so that’s why we’re excited about it. Dwarkesh Patel Amazing. And by the way, I believe this is it right here. That is it, yeah. Satya Nadella I forget now, are we calling it Majorana? Yeah, that’s right, Majorana 1. And I’m glad we named it after that. And this is the… I mean, to think of the fact that we are able to build something like a million qubit quantum computer in a thing of this size is just unbelievable. And that’s, I think, the crux of it, which is unless and until we could do that, you can’t dream of building a utility-scale quantum computer. (Time 0:30:45)
  • Muse: AI Game Generation
    • Microsoft’s “Muse” model uses gameplay data to generate consistent and diverse game worlds adaptable to user modifications.
    • This model responds to real-time input, marking a significant advancement in AI-driven game generation. Transcript: Dwarkesh Patel Or remember, the world is going to be growing at 10%. So we’ll be fine. Let’s dig into the other big breakthrough you’ve just made. And it’s amazing that you have both of them coming out the same day in your gaming world models. I’d love if you can tell me a little bit about that. Satya Nadella I think we’re going to call it Muse, is what I learned, is they’re going to be the model of this world action or human action model. And this is very cool. See, one of the things that, you know, obviously Dolly and Sora have been unbelievable in what they’ve been able to do in terms of generative models. And so one thing that we wanted to go after was using gameplay data. Can you actually generate games that are both consistent and then have the ability to generate the diversity of what that game represents and then are persistent to user mods, right? So that’s what this is. And so they were able to work with one of our game studios. And this is the other publication in Nature. And the cool thing is what I’m excited about is bringing, and so we’re going to have a catalog of games soon that we will start sort of using these models or we’re going to train these models To generate and then start playing them. And in fact, when Phil Spencer first showed it to me, where he had an Xbox controller and this model basically took the input and generated the output based on the input, and it was consistent With the game. And that to me is a massive, massive moment of, wow, it’s kind of like the first we saw ChatGPT complete sentences or Dolly draw or Sora, this is kind of one such moment. (Time 0:42:45)
  • Microsoft’s Gaming Vision
    • Dwarkesh Patel notes Microsoft’s investment in gaming IP could create a unified experience across game worlds using AI.
    • Satya Nadella explains that gaming is a core focus for Microsoft, with AI enhancing its potential, much like CGI did. Transcript: Dwarkesh Patel We’ll superimpose videos of what this looks like atop this podcast so people can get a chance to see it for themselves. I guess it’ll be out by then so they can also watch it there. This in itself is incredible. You, through your Spanish CEO, have invested tens, hundreds of billions of dollars in building up Microsoft gaming and acquiring IP. And in retrospect, if you can just merge all of this data into one big model that can give you this experience of visiting and going through multiple worlds at the same time, and if this Is a direction gaming is headed, like a pretty good investment we have made. Did you have any premonition about this or a good coincidence? Satya Nadella No, I mean, I wouldn’t say that we invested in gaming to build models. We invested, quite frankly, I want to, here’s an interesting thing about our history. We built our first game before we built Windows, right? Flight Simulator was a Microsoft product long before we even built Windows. So, gaming has got a long history at the company and we want to be in gaming for gaming’s sake. And that’s, I always start by, I hate to be in businesses where there are means to some other end. They have to be ends on to themselves. And then, yes, we are not a conglomerate. We are a company where we have to bring all these assets together and be better owners off by adding value, right? So, for example, cloud gaming is a natural thing for us to invest in because that’ll just expand the TAM and expand the ability for people to play games everywhere. Same thing with AI and gaming. We definitely think that it can be helpful in maybe changing. It’s kind of like the CGI moment even for gaming long-term. And it’s great as the biggest world’s largest publisher, this would be helpful. But at the same time, you’ve got to produce great quality games. I mean, you can’t be a gaming publisher without sort of first and foremost being focused on that. But the fact that this data asset is going to be interesting, not just in gaming context, but it’s going to be a general action model and a world model, it’s fantastic. I mean, like, you know, I think about gaming data as perhaps, you know, what YouTube is perhaps to Google, gaming data is to Microsoft. And so therefore, I’m excited about that. (Time 0:44:51)
  • Microsoft’s Three Big Bets
    • Satya Nadella identifies AI, quantum computing, and mixed reality as Microsoft’s three big bets.
    • These bets represent breakthroughs in business logic, systems, and user interfaces, respectively. Transcript: Dwarkesh Patel Yeah. When you write your next book, you got to have some explanation of why those three pieces all came together around the same time, right? Like there’s no intrinsic reason you would think quantum and AI should happen in 2028 and 2025 and so forth. That’s right. Satya Nadella But at some level, I kind of look at it and say, the simple model I have is, hey, is there a systems breakthrough? And to me, the systems breakthrough is the quantum thing. Is there a business logic breakthrough? That’s kind of like AI to me, which is like, can the logic tier be fundamentally reasoned differently? And, you know, instead of, you know, imperatively writing code, can you have a learning system? And that’s sort of the AI one. And then the UI side of it is presence. Yeah. (Time 0:49:09)
  • Trust and the Legal Framework for AI
    • Satya Nadella believes establishing trust and a robust legal framework are crucial for deploying advanced AI.
    • He emphasizes the need to address liability and societal implications before considering AI as a new species. Transcript: Dwarkesh Patel But we just zoom out and consider this statement you’ve made. You think about like you as somebody, as a hyperscaler, as the person doing research in these models as well, providing training, inference research for building a new species, like In the grand scheme of things, how do you think about this? Do you think we’re headed towards superhuman intelligence in your time as CEO? I think even Mustafa uses that term. Satya Nadella In fact, he’s used that term more recently around what this new species. The way I come at it is you definitely need trust. I think the one thing that before we kind of claim it is something as big as a species, the fundamental thing that I think that we’ve got to get right is that there is real trust, whether It’s personal or societal level, trust that’s baked in. That’s the hard problem. Because I think the biggest rate limiter to the power here will be how does our legal, call it infrastructure, we’re talking about all the compute infrastructure. How does the legal infrastructure evolve to deal with this? Like, entire world is constructed with things like humans owning property, having rights, and being liable. Like, that’s the fundamental thing that one has to sort of first say, okay, what does that mean for anything that now humans are using as tools? And if humans are going to delegate more authority to these things, then how does that structure evolve? Until that really gets resolved, I think just talking about sort of the tech capability, I don’t think is going to happen. Dwarkesh Patel As in like, we won’t be able to deploy these kinds of intelligences until we figure out how to. Satya Nadella Because at the end of the day, there is no way, like today you cannot deploy these intelligences unless and until there’s someone indemnifying it as a human. That’s, I think, to your point, that’s one of the reasons why I think about like even the most powerful AI is essentially working with some delegated authority from some human. You can sort of say, oh, that’s all alignment, this, that, and the other. And that’s why I think you have to sort of really get these alignments to actually work and be verifiable in some way. But I just don’t think that you can deploy intelligences that are out. So for example, this AI takeoff problem may be a real problem, but before it is a real problem, the real problem will be in the courts because the courts, I mean, like no society is going To allow for some human to say AI did that. Yes. (Time 0:50:17)
  • AI Alignment and Safety
    • Satya Nadella advocates for allocating compute resources to address AI alignment challenges and create observable runtime environments.
    • This includes governing AI’s scope and scale to prevent unintended harm and build social permission for deployment. Transcript: Dwarkesh Patel The alignment side, so two years ago, you guys released Sydney Bing. And just to be clear, I think given the level of capabilities at the time, I think it was like sort of like a charming, endearing, kind of funny example of misalignment. But that was because at the time, it was like chatbots, they can go think for 30 seconds and give you some funny slash inappropriate response back. But if you think about that kind of system that can like, I think to a New York Times reporter, try to get him to like leave his wife or something. If you think about that going forward, and you have these agents that are for hours, weeks, months going forward, just like autonomous swarms of AGIs who could be in similar ways misaligned And just screwing stuff up, maybe coordinating with each other. Just what’s your plan going forward to like when you get the big one, you get it right? Satya Nadella Oh, yeah, that is correct. And so that’s sort of, that’s one of the reasons why I think we ask sort of, you know, when we even allocate compute, let’s allocate compute for what is that alignment challenge. And then more importantly, what is the runtime environment in which you are really going to be able to monitor these things? The observability around it. By the way, we do deal with a lot of these things today in the classical side of the things as well, like cyber, right? We just don’t like, we just don’t write software and then just let it go, right? You have software and then you monitor it, you monitor it for cyber attacks, you monitor it for, you know, you know, fault injections and what have you. And so therefore, I think we will have to build enough software engineering around the deployment side of these. And then inside the model itself, what’s the alignment? And these are all, some of them are real science problems. Some of them are real engineering problems. And then we will have to tackle it. And by the way, that also means that, like, take our own liability in all of this. So that’s why I’m more interested in deploying these things in where, you know, you can actually govern what the scope of these things is and the scale of these things is. And so you just can’t unleash something out there in the world that creates harm because the social permission for that is not going to be there. (Time 0:55:45)
  • LLMs and the Future of SaaS
    • LLMs could transform SaaS applications by enhancing workflows and enabling more effective use of tools like Excel and databases.
    • LLMs might even commoditize traditional office software by becoming the primary interface for knowledge work. Transcript: Dwarkesh Patel Control. Yeah. And separate from the safety issues, as you think about your own product suite, and you think about like, if you do have AIs as powerful, at some point, it’s not just like co-pilot in the Example you mentioned about how you’re prepping for this podcast. It’s more similar to like how you actually delegate work to your colleagues. What does it look like given your current suite to add that in? And I mean, you know, there’s one question about whether LLMs get commodified by other things. I wonder if these like databases or canvases or Excel sheets or whatever, if the LLM is your main gate point into accessing all these things, is it possible that the LLMs commodify office? Yeah. Satya Nadella I mean, it’s possible to see. So it’s an interesting one, right? I think the way I think about the first phase, at least of it would be, can the LLM help me do my knowledge work using all of these tools or canvases more effectively? Like one of the best demos that I’ve seen is doctor getting ready for a tumor board workflow, right? So she’s going in to a tumor board meeting. And so she, one of the first things she uses Copilot for is to create an agenda for the meeting because the LLM helps reason about all the cases which are in some SharePoint site and says, Hey, these cases, obviously, you know, a team of board meeting is a high stakes meeting where you want to be mindful of the differences in cases so that you can then allocate the right Time, right? So even that reasoning task of creating an agenda that knows even how to split time, super. So I use the LLM to do that. Then I go into the meeting. I’m in a team’s call with all my colleagues. Guess what? I’m focused on the actual case versus taking notes because you now have this AI co-pilot doing a full transcription of all of this. And just basically an intelligent, it’s not just a transcript, but it’s a, think of it as a database entry of what is in the meeting that is recallable for all time, right? So then she comes out of the meeting, having sort of discussed the case and not been distracted by note-taking. And she’s a teaching doctor. She wants to go and prep for her class. And so she takes and she goes into Copilot and says, hey, take my tumor board meeting and then create a PowerPoint slide deck out of it so that I can talk to my students about it. So that’s the type. So the UI and the scaffolding that I have are canvases that are now getting populated using LLNs. And the workflow itself is being reshaped. Knowledge work is getting done. Like, here’s an interesting thing, right? If somebody is like, one of the ways I think about it is if someone came to me in the late 80s and said, you’re going to have a million documents on your desk, you know, we would say, what the Heck is that? Right. I mean, I would literally sort of thought, oh, there’s going to be a literally, you know, a million physical copies of things on my desk, except we do have a million spreadsheets and a Million documents. I know, you do. And they’re all there. And so I think that’s kind of what’s going to happen with even agents. So there will be a UI layer. To me, Office is not just about the Office of today. It’s the UI layer for knowledge work. It’ll evolve as the workflows evolve. That’s what we want to build. I do think the SaaS applications that exist today, right, these CRUD applications are going to fundamentally be changed because the business logic will go more into this agentic tier. In fact, one of the other cool things today in my co-pilot experience is when I say, hey, I’m getting ready for a meeting with a customer, I just go and say, give me all the notes for it that I should know. And it pulls from my CRM database. It pulls from my Microsoft Graph, creates a composite, essentially artifact. And that means, and then it applies even logic on it, right? And that to me is going to transform the SaaS applications as we know of it today in a big way. (Time 0:59:20)
  • Maintaining Relevance in Tech
    • Satya Nadella believes longevity in the tech industry comes from continuous relevance and adaptation.
    • He highlights the importance of a “refounder mode” mindset, challenging existing assumptions and embracing change. Transcript: Dwarkesh Patel Can I ask you some questions about your time at Microsoft? Yeah. Is being a company man underrated? So you’ve spent most of your career at Microsoft. And look, you could say like, maybe one of the reasons you’ve been able to add so much value is you’ve seen the culture and the history and the technology and have all this context by rising Up to the ranks. Should more companies be run by people who have this level of context? That’s a great question. Satya Nadella I mean, I’ve not thought about it that way. But yeah, I mean, I sort of, you know, through my whatever, 34 years now of Microsoft, it has basically been that each year I felt more excited about being at Microsoft versus thinking That, oh, I’m a company person or what have you, right? I mean, that is not like, I didn’t go in there and saying it is about, and I think that seriously, even for anybody joining Microsoft, that means it’s not like they’re joining Microsoft As long as they feel that they can use this as a platform for their both economic return, but also a sense of purpose and a sense of mission that they can accomplish by using us as a platform, Right? So therefore, that’s the contract. So I think, yes, companies can have to create a culture that allows people to come in and become company people like me. And Microsoft got it more right than wrong, at least in my case. And I hope that remains the case. Dwarkesh Patel How do you, like the sixth CEO that you’re talking about that will get to use the research you’re starting now, what are you doing to retain the future Satya Nadella so that they’re in A position to become future leaders? Satya Nadella Yeah, it’s kind of fascinating. This is our 50th year and I think a lot about it, right? And the way to think about, you know, I think longevity is not a goal. Relev is. And so I think the thing that I have to do and all 200,000 of us have to do every day is are we doing things that are useful and relevant for the world as we see it evolving, not just today, but Tomorrow. Like we have to basically, you know, and we live in an industry where there’s no franchise value, right? So that’s the other hard part, which is if you take the R&D budget that we will spend this year, it’s all speculation on what’s going to happen five years from now. And so you got to basically go in with that attitude that saying, look, we are doing things that we think are going to be relevant. And so that’s what you got to focus on. And then know that there’s a batting average and you’re not going to get, you have to have high tolerance for failure. That’s the other thing, which I think is unlike you have to be able to sort of take enough shots on goal to be able to say, okay, we will make it to the other side as a company. And that’s what makes it tricky in this industry. I (Time 1:04:59)
  • The Evolving Nature of Cognitive Labor
    • Satya Nadella argues that cognitive labor isn’t static; as AI automates existing tasks, new forms of cognitive work emerge.
    • He distinguishes between knowledge work and knowledge workers, suggesting AI will augment human capabilities rather than replace them entirely. Transcript: Dwarkesh Patel One thing I’m not sure about hearing your answers on different questions is whether you think AGI is a thing in the sense of like, will there be a thing which automates all, at least like Starting with all cognitive labor, like anything that anybody can do on a computer? Satya Nadella See, this is where my problem with the definitions of how people talk about it is cognitive labor is not a static thing, right? Like there is cognitive labor today. If I have an inbox that is managing all my agents, is that new cognitive labor? And so today’s cognitive labor may be automated. What is the new cognitive labor that gets created? Both of those things have to be thought of, right? Which is the shifting. So that’s why I think this distinction, at least in my head, I make is don’t conflate knowledge worker with knowledge work. The knowledge work of today could probably be automated. Who said my life’s goal is to triage my email, right? Let an AI agent triage my email. But after having triaged my email, give me a higher level cognitive labor task of, hey, these are the three drafts I really want you to review. That’s a different abstraction. Dwarkesh Patel But will AI ever get to the second thing? Satya Nadella May. But as soon as it gets to that second thing, there will be a third thing, right? So this is where I think, why are we sort of thinking somehow that we have dealt with tools that have changed what is cognitive labor in history? Why are we worried that all cognitive labor goes away? Dwarkesh Patel I mean, I’m sure you’ve heard these examples before, but the idea that like horses can still be good for certain things, there are certain terrains you can’t take a car on, but the idea That like you’re going to see horses around the street that are going to employ millions of horses, it’s just like, it’s not happening, right? And then the idea is, could a similar thing happen with humans? Satya Nadella But in one very narrow dimension, right, It’s only 200 years of history of humans where we have valued some narrow sort of things called cognitive labor as we understand it. Let’s just take something like chemistry, right? If this thing like quantum plus AI really helped us sort of do a lot of novel material science and so on. Yeah, that’s fantastic to have novel material science being done by it. Does that really somehow take away from sort of all the other things that humans can do? Right? So why can’t we exist in a world where there are powerful cognitive machines, that our cognitive agency is not being taken away. (Time 1:10:46)