Satya, thank you so much for coming on the podcast. In a second, we're going to get to the two breakthroughs that Microsoft has just made, and congratulations, same day in Nature: the Majorana zero chip, which we have in front of us right here, and also the world human action models. But can we just continue the conversation we were having a second ago? You're describing the ways in which the things you were seeing in the 80s and 90s, you're seeing them happen again. The thing that is exciting for me...
Dwarkesh, first of all, it's fantastic to be on your podcast. I'm a big listener, and I love the way that you do these interviews and the broad topics that you explore. The thing that is exciting for me… It reminds me a little bit of my, I'd say, first few years even in the tech industry, starting in the 90s, where there was real debate about whether it's going to be RISC or CISC, or, "Hey, are we really going to be able to build servers using x86?" When I joined Microsoft, that was the beginning of what was Windows NT. So, everything from the core silicon platform to the operating system to the app tier—that full stack approach—the entire thing is being litigated. You could say cloud did a bunch of that, and obviously distributed computing and cloud did change client-server. The web changed massively. But this does feel a little more like maybe more full-stack than even the past that at least I've been involved in.
When you think about 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 were at Sun Microsystems, they had an interesting experience with the 90s dotcom 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?
If I go back, the four big transformations that I've been part of, the client and the client-server. So that's the birth of the graphical user interface and the x86 architecture, basically allowing us to build servers. It was very clear to me. I remember going to what is PDC in '91, in fact I was at Sun at that time. In '91, I went to Moscone. That's when Microsoft first described the Win32 interface and it was pretty clear to me what was going to happen, where the server was also going to be an x86 thing. When you have the scale advantages accruing to something, that's the secular bet you have to place. What happened in the client was going to happen on the server side, and then you were able to actually build client-server applications. So, the app model 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, the Netscape browser or the Mosaic browser came out what, I think, December or November of '93, right? I think is when Andreessen and crew had that. So that was a big game-changer, 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 because the browser was a new app model. We were able to embrace it with everything we did, whether it was HTML in Word or building a new thing called the browser ourselves and competing for it, and then building a web server on our server stack and 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 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 one lesson learned for me: you have to not only get the tech trend right, you also have to get where the value is going to be created with that trend. 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. So I think there are two places where I can say with some confidence. One is the hyperscalers that do well, because the fundamental thing is if you sort of go back to even how Sam and others describe it, if intelligence is log of compute, whoever can do lots of compute is a big winner. The other interesting thing is, if you look at underneath even any AI workload, like take ChatGPT, it's not like everybody's excited about what's happening on the GPU side, it's great. In fact, I think of my fleet even as a ratio of the AI accelerator to storage, to compute. And at scale, you've got to grow it. And so, that infrastructure need for the world is just going to be exponentially growing. So in fact it's manna from heaven to have these AI workloads because guess what? They're more hungry for more compute, not just for training, but we now know, for test time. When you think of an AI agent, it turns out the AI agent is going to exponentially increase compute usage because you're not even bound by just one human invoking a program. It's one human invoking programs that invoke lots more programs. That's going to create massive, massive demand and scale for compute infrastructure. So our hyperscale business, Azure business, and other hyperscalers, I think that’s a big thing. Then after that, it becomes a little fuzzy. You could say, hey, there is a winner-take-all model—I just don't see it. This, by the way, is the other thing I’ve learned: being very good at understanding what are winner-take-all markets and what are not winner-take-all markets is, in some sense, everything. I remember even in the early days when I was getting into Azure, 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, it's winner-take-all." Having competed against Oracle and IBM in client-server, I knew that the buyers will not tolerate winner-take-all. Structurally, hyperscale will never be a winner-take-all because buyers are smart. Consumer markets sometimes can be winner-take-all, but anything where 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. That, I think, is what will happen even on the model side. 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. I think in models there is one dimension of, maybe there will be a few closed source, but there will definitely be an open source alternative, and the open-source alternative will actually make sure that the closed-source, winner-take-all is mitigated. That's 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, I don't see it as a winner-take-all. Then above 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-all network effect. After all, ChatGPT is a great example. It's an at-scale consumer property that has already got real escape velocity. I go to the App Store, and I see it's always there in the top five, and I say “wow, that's pretty unbelievable”. So they were able to use that early advantage and parlay that into an app advantage. In consumer, that could happen. In the enterprise again, I think there will be, by category, different winners. That's sort of at least how I analyze it.
I have so many follow-up questions. We have to get to quantum in just a second, but on the idea that maybe the models get commoditized: maybe somebody could have made a similar argument a couple of decades ago about the cloud – that fundamentally, it's just a chip and a box. But in the end, of course, you and many others figured out how to get amazing profit margins in the cloud. You figured out ways to get economies of scale and add other value. Fundamentally, even forgetting the jargon, if you've got AGI and it's helping you make better AIs – right now, it's synthetic data and RL; maybe in the future, it's an automated AI researcher – that seems like a good way to entrench your advantage there. I'm curious what you make of that, just the idea that it really matters to be ahead there.
At scale, nothing is commodity. To your point about cloud, everybody would say, "Oh, cloud's a commodity." Except, when you scale... That's why the know-how of running a hyperscaler... You could say, "Oh, what the heck? I can just rack and stack servers." In fact, in the early days of hyperscale, most people thought “there are all these hosters, and those are not great businesses. Will there be anything? Is there a business even in hyperscale?” And it turns out there is a real business, just because of the know-how of running, in the case of Azure, the world's computing of 60-plus regions with all the compute. It's just a tough thing to duplicate. So I was more making the point, is it one winner? Is it a winner-take-all or not? Because that you've got to get right. I like to enter categories which are big TAMs, where you don't have to have the risk of it all being winner-take-all. The best news to be in is a big market that can accommodate a couple of winners, and you're one of them. That's what I meant by the hyperscale layer. In the model layer, one is models need ultimately to run on some hyperscale compute. So that nexus, I feel, is going to be there forever. It's not just the model; the model needs state, that means it needs storage, and it needs regular compute for running these agents and the agent environments. And so that's how I think about why the limit of one person running away with one model and building it all may not happen.
On the hyperscaler side, and by the way, it's also interesting the advantage you as a hyperscaler would have in the sense that, especially with inference time scaling and if that's involved in training future models, you can amortize your data centers and GPUs, not only for the training, but then use them again for inference. I'm curious what kind of hyperscaler you consider Microsoft and Azure to be. Is it on the pre-training side? Is it on providing the O3-type inference? Or are you just, we’re going to host and deploy any single model that's out there in the market, and we are sort of agnostic about that?
It’s a good point. The way we want to build out the fleet is to, in some sense, ride Moore's law. I think this will be like what we've done with everything else in the past: every year keep refreshing the fleet, you depreciate it over whatever the lifetime value of these things are, and then get very very good at the placement of the fleet such that you can run different jobs at it with high utilization. Sometimes there are very big training jobs that need to have highly concentrated peak flops that are provisioned to it that also need to cohere. That's great. We should have enough data center footprint to be able to give that. But at the end of the day, these are all becoming so big, even in terms of if you take pre-training scale, if it needs to keep going, even pre-training scale at some point has to cross data center boundaries. It's all more or less there. So, great, once you start crossing pre-training data center boundaries, is it that different than anything else? The way I think about it is hey, distributed computing will remain distributed, so go build out your fleet such that it's ready for large training jobs, it's ready for test-time compute, it’s ready—in fact, if this RL thing that might happens, you build one large model, and then after that, there’s tons of RL going on. To me, it's kind of like more training flops, because you want to create these highly specialized, distilled models for different tasks. So you want that fleet, and then the serving needs. At the end of the day, speed of light is speed of light, so you can't have one data center in Texas and say, "I'm going to serve the world from there." You've got to serve the world based on having an inference fleet everywhere in the world. That's how I think of our build-out of a true hyperscale fleet. Oh, and by the way, I want my storage and compute also close to all of these things, because it's not just AI accelerators that are stateless. My training data itself needs storage, and then I want to be able to multiplex multiple training jobs, I want to be able to then have memory, I want to be able to have these environments in which these agents can go execute programs. That's kind of how I think about it.
You recently reported that your yearly revenue from AI is $13 billion. But if you look at your year-on-year 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 doing with all that intelligence, this industrial scale use? Is it going to be through Office? Is it going to be you deploying it for others to host? You've got to have the AGIs to have $130 billion in revenue? What does it look like?
The way I come at it, Dwarkesh, it's a great question because at some level, if you're going to have this explosion, abundance, whatever, commodity of intelligence available, the first thing we have to observe is GDP growth. Before I get to what Microsoft's revenue will look like, there's only one governor in all of this. This is where we get a little bit ahead of ourselves with all this AGI hype. Remember the developed world, which 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 we have a real growth challenge. So, the first thing that we all have to do is, when we say this is like the Industrial Revolution, 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. It can't just be supply-side. In fact that’s the thing, a lot of people are writing about it, and 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. 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. Us self-claiming some AGI milestone, that's just nonsensical benchmark hacking to me. The real benchmark is: the world growing at 10%.
Okay, so if the world grew at 10%, the world economy is $100 trillion or something, if the world grew at 10%, that's like an extra $10 trillion in value produced every single year. If that is the case, you as a hyperscaler... It seems like $80 billion is a lot of money. Shouldn't you be doing like $800 billion? If you really think in a couple of years, we could be really growing the world economy at this rate, and the key bottleneck would be: do you have the compute necessary to deploy these AIs to do all this work?
That is correct. But by the way, the classic supply side is, "Hey, let me build it and they’ll come." That's an argument, and after all we've done that, we've taken enough risk to go do it. But at some point, the supply and demand have to map. That's why I'm tracking both sides of it. You can go off the rails completely when you are hyping yourself with the supply-side, versus really understanding how to translate that into real value to customers. That's why I look at my inference revenue. That's one of the reasons why even the disclosure on the inference revenue... It's interesting that not many people are talking about their real revenue, but to me, that is important as a governor for how you think about it. You're not going to say they have to symmetrically meet at any given point in time, but you need to have existence proof that you are able to parlay yesterday's, let’s call it capital, into today's demand, so that then you can again invest, maybe exponentially even, knowing that you're not going to be completely rate mismatched.
I wonder if there's a contradiction in these two different viewpoints, because one of the things you've done wonderfully is make these early bets. You invested in OpenAI in 2019, even before there was Copilot and any applications. If you look at the Industrial Revolution, these 6%, 10% build-outs of railways and whatever things, many of those were not like, "We've got revenue from the tickets, and now we're going to..." There was a lot of money lost. So, if you really think there's some potential here to 10x or 5x the growth rate of the world, and then you're like, "Well, what is the revenue from GPT-4?" If you really think that's the possibility from the next level up, shouldn't you just, "Let's go crazy, let's do the hundreds of billions of dollars of compute?" I mean, there's some chance, right?
Here’s the interesting thing, right? That's why even that balanced approach to the fleet, at least, is very important to me. It's not about building compute. It's about building compute that can actually help me not only train the next big model but also serve the next big model. Until you do those two things, you're not going to be able to really be in a position to take advantage of even your investment. So, that's kind of where it's not a race to just building a model, it's a race to creating a commodity that is getting used in the world to drive... You have to have a complete thought, not just one thing that you’re thinking about. And by the way, one of the things is that there will be overbuild. To your point about what happened in the dotcom era, the memo has gone out that, hey, you know, you need more energy, and you need more compute. Thank God for it. So, everybody's going to race. In fact, it's not just companies deploying, countries are going to deploy capital, and there will be clearly... I'm so excited to be a leaser, because, by the way; I build a lot, I lease a lot. I am thrilled that I'm going to be leasing a lot of capacity in '27, '28 because I look at the builds, and I'm saying, "This is fantastic." The only thing that's going to happen with all the compute builds is the prices are going to come down.
Speaking of prices coming down, you recently tweeted after the DeepSeek model came out about Jevons’ Paradox. I'm curious if you can flesh that out. Jevons’ Paradox occurs when the demand for something is highly elastic. Is intelligence that bottlenecked on prices going down? Because when I think about, at least my use cases as a consumer, intelligence is already so cheap. It's like two cents per million tokens. Do I really need it to go down to 0.02 cents? I'm just really bottlenecked on it becoming smarter. If you need to charge me 100x, do a 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 it to get to 0.002 cents per million tokens?
I think the real thing is the utility of the tokens. Both need to happen: One is intelligence needs to get better and cheaper. And anytime there's a breakthrough, like even what DeepSeek did, with the efficient frontier of performance per token changes, the curve gets bent, and the frontier moves. That just brings more demand. That's what happened with cloud. 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 started putting servers in the cloud, suddenly people started consuming more because they could buy it cheaper, and it was elastic, and they could buy it as a meter versus a license, and it completely expanded. I remember going, let’s say, to a country like India and talking about “here is SQL Server”. We sold a little, but man, the cloud in India is so much bigger than anything that we were able to do in the server era. I think that's 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 would be the biggest change ever.
I think it's quite reasonable for somebody to hear people like me in San Francisco and think “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, 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. Here's an interesting thing: one of the analogies I use is, just imagine how a multinational corporation like us did forecasts pre-PC, and email, and spreadsheets. Faxes went around. Somebody then got those faxes and did an interoffice 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 artifact and the workflow changed. That is what needs to happen with AI being introduced into knowledge work. In fact, when we think about all these agents, the fundamental thing is there's a new work and workflow. For example, even prepping for our podcast, I go to my copilot and I say, "Hey, I'm going to talk to Dwarkesh about our quantum announcement and this new model that we built for game generation. Give me a summary of all the stuff that I should read up on before going." It knew the two Nature papers, it took that. I even said, "Hey, go give it to me in a podcast format." And so, it 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. 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. 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. 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-end process in manufacturing and become more efficient. 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 work of management teams and individuals who are doing knowledge work, and that's going to take its time.
Can I ask you just briefly about that analogy? One of the things Lean did is physically transform 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 what your own workflow—how your own workflow has changed as a result of AIs. I'm curious if we can add more color to what will it be like to run a big company when you have these AI agents that are getting smarter and smarter over time?
It's interesting you ask that. I was thinking, for example, today if I look at it, we are very email heavy. I get 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 Copilot agents to automatically populate my drafts so that I can start reviewing and sending. But I already have in Copilot at least ten agents, which I query them different things for different tasks. 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. It's not just a chat interface. I need a smarter thing than a chat interface to manage all the agents and their dialogue. That's why I think of this Copilot, as the UI for AI, is a big, big deal. Each of us is going to have it. So basically, think of it as: there is knowledge work, and there's a knowledge worker. The knowledge work may be done by many, many agents, but you still have a knowledge worker who is dealing with all the knowledge workers. And that, I think, is the interface that one has to build.
You're one of the few people in the world who can say that you have access to 200,000… you have this swarm of intelligence around you in the form of Microsoft the company and all its employees. And you have to manage that, and you have to interface with that, how to make best use of that. Hopefully, more of the world will get to have that experience in the future. I'd be curious about how your inbox, if that means everybody's inbox, will look like yours in the morning. 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?
This has been another 30-year journey for us. It's unbelievable. I'm the third CEO of Microsoft who's been excited about quantum. 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. We took the path of saying, the one way for having a less noisy or more reliable qubit is to bet on a physical property that by definition is more reliable and that's what led us to the Majorana zero modes, which was theorized in the 1930s. 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 Majorana zero modes in a new phase of matter effectively. This is why 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, 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 One 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's game on. You suddenly have the ability to build a real utility-scale quantum computer, and that to me is now so much more feasible. Without something like this, you will still be able to achieve milestones, but you'll never be able to build a utility-scale computer. That's why we're excited about it.
Amazing. And by the way, I believe this is it right here.
I forget now, are we calling it Majorana?
Yes, that's right. Majorana One. I'm glad we named it after that. To think that we are able to build something like a million-qubit quantum computer in a thing of this size is just unbelievable. That's the crux of it: unless and until we could do that, you can't dream of building a utility-scale quantum computer.
And you're saying the eventual million qubits will go on a chip this size? Okay, amazing. Other companies have announced 100 physical qubits, Google's, IBM's, others. When you say you've announced one, but you're saying that yours is way more scalable in the limit.
Yeah. The one thing we’ve also done is we've taken an approach where we've separated our software and our hardware. We're building out our software stack, and we now have, with the neutral atom folks, the ion trap folks, we're also working with others who even have pretty good approaches with photonics and what have you, that means there'll be different types of quantum computers. In fact, we have what, I think that the last thing that we announced was 24 logical qubits. So we have also got some fantastic breakthroughs on error correction and that's what is allowing us, even on neutral atom and ion trap quantum computers, to build these 20 plus, and I think that'll keep going even throughout the year; you'll see us improve that yardstick. But we also then said, "Let's go to the first principles and build our own quantum computer that is betting on the topological qubit." And that's what this breakthrough is about.
Amazing. The million topological qubits, thousands of logical qubits, what is the estimated timeline to scale up to that level? What does the Moore's law here, if you've got the first transistor, look like?
We've obviously been working on this for 30 years. I'm glad we now have the physics breakthrough and the fabrication breakthrough. I wish we had a quantum computer because by the way, the first thing the quantum computer will allow us to do is build quantum computers, because it's going to be so much easier to simulate atom-by-atom construction of these new quantum gates. But in any case, the next real thing is, now that we have the fabrication technique, let us go build that first fault-tolerant quantum computer. And that will be the logical thing. So, I would say now I can say, "Oh, maybe '27, '28, '29, we will be able to actually build this." Now that we have this one gate, can I put the thing into an integrated circuit and then actually put these integrated circuits into a real computer? That is where the next logical step is.
And what do you see as, in '27, '28, you've got it working? Is it a thing you access through the API? Is it something you're using internally for your own research in materials and chemistry?
It’s a great question. One thing that I've been excited about is, even in today's world… we had this quantum program, and we added some APIs to it. The breakthrough we had maybe two years ago was to think of this HPC stack, and AI stack, and quantum together. In fact, if you think about it, AI is like an emulator of the simulator. Quantum is like a simulator of nature. What is quantum going to do? By the way, quantum is not going to replace classical. Quantum is great at what quantum can do, and classical will also... Quantum is going to be fantastic for anything that is not data-heavy but is exploration-heavy in terms of the state space. It should be data-light but exponential states that you want to explore. Simulation is a great one: chemical physics, what have you, biology. One of the things that we've started doing is really using AI as the emulation engine. But you can then train. So the way I think of it is, if you have AI plus quantum, maybe you'll use quantum to generate synthetic data that then gets used by AI to train better models that know how to model something like chemistry or physics or what have you. These two things will get used together. So even today, that's effectively what we're doing with the combination of HPC and AI. I hope to replace some of the HPC pieces with quantum computers.
Can you tell me a little bit about how you make these research decisions which, in 20 years time, 30 years time, will actually pay dividends, especially at a company of Microsoft's scale? Obviously, you're in great touch with the technical details in this project. Is it feasible for you to do that with all the things Microsoft Research does? How do you know the current bet you're making will pay out in 20 years? Does it just have to emerge organically through the org, or how are you keeping track of all this?
The thing that I feel was fantastic is when Bill, when he started MSR back in '95 I guess. I think in the long history of these curiosity-driven research organizations, to just do a research org that is about fundamental research and MSR, over the years, has built up that institutional strength so when I think about capital allocation or budgets, we first put the chips in and say, "Here is MSR's budget." We gotta go at it each year knowing that most of these bets are not going to pay off in any finite time frame. Maybe the sixth CEO of Microsoft will benefit from it. And in tech that is I think a given. The real thing that I think about is, when the time has come for something like quantum or a new model or what have you, can you capitalize? So as an incumbent, if you look at the history of tech, it's not that people didn't invest. It's that you need to have a culture that knows how to take an innovation and scale it. That's the hard part, quite frankly, for CEOs and management teams. Which is kind of fascinating. It's as much about good judgment as it is about good culture. Sometimes we've gotten it right; sometimes we've gotten it wrong; I can tell you the thousand projects from MSR that we should have probably led with, but we didn't. And I always ask myself why. It's because we were not able to get enough conviction and that complete thought of how to not only take the innovation but make it into a useful product with a business model that we can then go to market with. That's the job of CEOs and management teams: not to just be excited about any one thing, but to be able to actually execute on a complete thing. And that's easier said than done.
When you mentioned the possibility of three subsequent CEOs of Microsoft, if each of them increases the market cap by an order of magnitude, by the time you've got the next breakthrough, you'll be like the world economy or something.
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. 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.
We're going to call it Muse. It's going to be the model of this world action, or human action model. This is very cool. One of the things is that obviously, Dall-E and Sora have been unbelievable in what they've been able to do in terms of generative models. 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? That's what this is. They were able to work with one of our game studios, and this is the other publication in Nature. The cool thing is what I'm excited about is bringing—we're going to have a catalog of games soon that we will start using these models, or we're going to train these models to generate, and then start playing them. In fact, when Phil Spencer first showed it to me, 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. That to me is a massive moment of “wow”. It's kind of like the first time we saw ChatGPT complete sentences, or Dall-E draw, or Sora. This is one such moment.
