Y Combinator Startup Podcast - Satya Nadella: Microsoft's AI Bets, Hyperscaling, Quantum Computing Breakthroughs

Episode Date: June 25, 2025

A fireside with Satya Nadella on June 17, 2025 at AI Startup School in San Francisco.Satya Nadella started at Microsoft in 1992 as an engineer. Three decades later, he’s now Chairman & CEO, navigating the company through one of the most profound technological shifts yet: the rise of AI.In this conversation, he shares how Microsoft is thinking about this moment— from the infrastructure needed to train frontier models, to the social permission required to use that compute. He draws parallels to the early PC and internet eras, breaks down what makes a great team, and reflects on what he’d build if he were starting his career today.

What are the tools that we can put in the hands of people that will give them that sense of empowerment? That's what I would love to work on. I'm not into this anthropomorphizing AI at all. I come at it as it's a tool. There is going to be a job called a software engineer. It's going to be different. But I look at it, you are really taking a software engineer and saying you're now a software architect. It's my pleasure to welcome the chairman and CEO of Microsoft.

Satcha Nadella. Thank you. This is the home crowd. All right. Man, San Francisco, you should move to Seattle. I started my career in Seattle. There you go. Very fantastic place.

Anyone who's successful starts at Microsoft. That's right. So, Sacha, you've emphasized before that AI is going to shape all that we do. What does this look like in practice? you know, at Microsoft, how does this actually drive your strategy and particularly thinking about how AI will influence ideas beyond, you know, the immediate incredible products suite, you know, like the broader economy? At Microsoft, I feel we are a platform company, a product company, and a partner company.

So I think of those three dimensions. And I've kind of in my 35 years, I've lived through clients. client server, web internet, mobile cloud, this is the fourth. So that's just at least how I pattern match. So the first thing that I think about is the platform opportunity. When I sort of look at all the folks here, the interesting thing is the compounding effects of all these platforms, right? So there's AI piece.

The reason why I think the rate of diffusion is so fast, so well, you know, and so wide is because it builds on the previous generation. I think about it. Like, if the cloud was not there, we wouldn't have been able to build the AI supercomputers, which then led to the models, which then led to the products, right? So that compounding effect is the interesting thing to me. So that's why you always sort of take the previous platform and build the next platform, and you want to be able to get that right. And then you've got to build the next generation products on top of it. With each one of these platforms shift, there's a new workload. When I first remember looking at the large-scale training job, I mean, it's kind of a very

different workload to what we built, for example, the cloud with, right? It's a data-parallel, synchronous workload, which is so different than, let's say, a Hadoop job or what have you. And so the platform itself then completely gets re-chained, you know, completely relitigated and changed. So to me, that's, I think, the exciting thing on the platform, It's golden age of system software. Quite frankly, today, if I had to think about anybody who's building at the infrastructure layer, not just the hyperscalers, but even the startups, I think that's a tremendous opportunity.

Obviously, there's a tremendous opportunity in the model side, and then the products on top of it. So, yeah, we think of these, and then ultimately, what's it for? It's for one thing and one thing alone, which is to drive ultimately economic growth and GDP growth. So if I had to ask me, my benchmark for AI is, is it creating surplus in the world around us, one community, one country, one industry, one company at a time? I mean, it seems like the app level, you know, you guys have built, you know, sort of the defining apps at the app layer for so many decades. It feels like we're at this weird, lumpy moment where, you know, maybe

the models have popped up and we're sort of astonished by what's happening. But, but but then sort of the compute and the apps need to actually catch up. And the hope here is actually the people in this room will be the people who build those apps. Yeah, it's a good question, right? One of the questions is, is the model like SQL? Or is it the SaaS app itself and the model? I mean, I think the place where does the model end and where does the product begin? because if you sort of say model with some scaffolding and tool calling in some infinite loop

is the product, if that is what it is, then I think that that's where it gets a little confusing. But that's like saying a bunch of SQL business logic is with SQL is what is an app. So I think it's still possible for anyone to build an app tier on top of a model. and you have to sort of abstract yourself and say, yeah, the model is just like SQL was to me. And so I think that, I mean, I always dreamt of a moment when AI slash machine learning will have a SQL moment. Because if you think about it, we never had a stable platform layer in the past, because everything was vertically built and integrated. For the first time in this model layer now, we have something like a SQL engine that we can then use to build pretty sophisticated products and these techniques also, right?

I mean, just the inference time compute plus tool calling is giving us, I think, a pretty robust harness to be able to build pretty sophisticated products. It's kind of wild how much it's the integration piece that is also the app layer now. Yeah. It's just, you know, the model sitting on their own are incredibly smart, but right now they feel, you know, sort of there's just giant gulf between that and the data that really matters to you for business users? I think that's a good observation because I think at least my read of the situation is

the model is an important piece, the model scaffolding and all this tool calling. So there's a real app server that you kind of need in order to be able to build sophisticated applications. But the interesting thing is the feedback loop, the data path inside the product that then is used in order to post-trained, in order to be able to do the right tool selection, that seems to be the place where product creation is all going to happen. AI scaling laws are continuing to hold, and the demand for intelligence appears to be potentially infinite. Yesterday, Elon was mentioning that there will be 99 hyper-intelligent

beings to one human, which is kind of a wild prognostication, but seems possible, given this, where does the building for the future of AI truly demand for global compute infrastructure? How do you anticipate these demands evolving as models don't just become larger, but more intelligent and capable of complex multi-agent interactions? Yeah, I mean, look, if you sort of really step back and say, you know, first, if you sort of go with the, you know, compute or intelligence is whatever, a log of compute. And then you ask the question, how much energy does compute consume? Let's take in the United States, maybe 2% today, 3% tops.

Let's say doubles. It's 6%. That's, like, massive because then the amount of extra energy that needs to be produced in order for AI to use it is pretty high. I think that's why we all have to sort of keep in mind that if there's one lesson history has thought us, is that if you're going to use energy, you better have social permission to use energy. So that means you've got to make sure that the output of this AI is socially useful.

In other words, if we really are not creating social surplus, economic surplus, as measured by countries and communities, then we just can't consume energy. And so that, to me, is the bigger thing. Like, everybody is today hot and bothered about, okay, what do I do about energy production? I think the real question in the next five years is we've got to produce enough products that are creating great value, which I'm very confident of, by the way, in health care, and education, in productivity. So there's many, many domains.

But that's the real challenge for us as a tech industry, is to prove, unequivocally that what we have created is showing up in real stats that is not just an AGI or AI benchmark. I mean, the hope is that this will show up in sort of the real things that you sort of interact with on a daily basis. 100%. You know, you go use, you get a mortgage loan and instead of, you know, three months or two months of waiting around and you don't know if you're going to get approved or.

there's just so many things that are important parts of your life that, you know, get drowned in paperwork or bureaucracy, that those things could potentially go away. 100%. So I think, yeah, if you take some of the public services, right? I mean, if you take any country, you know, it's GDP, or take healthcare. Like in the United States, what is it, 18, 19% of our cost is healthcare. And a lot of it, like everybody talks about the magical drug, blah, blah, blah, except. all of the cost is in workflow. And so if you really take something like a simple thing like discharge,

the amount, like you take the back end of an EMR system with just an LLM and a prompt, that itself is going to save so much time and money and energy that it would sort of pay for itself. I mean, it's kind of very direct, right? You spend an incredible amount of GDP on health care and rightly so, but every dollar that's spent on clerical work could have been spent towards some sort of treatment that would have saved someone's life. Or the simple time allocation of a physician

away from paperwork to the patient is right there to be had. What do you see as being the biggest rate limiter for AI deployment today? See, here's the interesting thing, right? This audience is so young that none of my metaphors would sort of work. But nevertheless, if you sort of work, It came in in the early part of, let's say you were a multinational company, pre-PCs.

How the heck did we do forecasts? Like a simple sales forecast. The way one would do sales forecast was you would send faxes. People would then take those faxes and send inter-office memos. And those inter-office memos would be annotated and a forecast would come hopefully before the quarter-end. And then suddenly people said with email and PCs and Excel, they said, let's print, like let me just send an Excel spreadsheet in email, people enter a number and you have a forecast. So what happened was the work, the work artifact and the workflow changed.

That is what needs to happen with AI. When someone says, I'm going to now do my job, but with whatever, 99 agents that I am directing on my behalf, the workflow is not going to be constant, right? I mean, you now are really going to have to change even the scope of your job is going to change. So that change management is a real rate remitter, because you're now taking the means of production in an insurance company, in a financial services company,

in a healthcare company, in a software company, and saying, we are going to change everything in the way we work. In fact, we're going to change what jobs they are. Like, you know, at LinkedIn, I think they took multiple of these functions, the design function, the front-end engineer function, the product function, put them all together and said, we're going to have full-stack builders. That's a change in scope of even a job. And so how do you then rebuild the product team with new roles, new scopes, and what have you? That's, to me, more the social rate limiter, not that there's lots of other things that are in deployment of this technology, getting it out to the world, power,

is one, there are other issues. But I would say change management. When I look at even a lot of the AI startups, when I talk to them, everyone has now, you know, you worked at Palantir, so you know this, everyone has forward deployment engineers. That's like the exciting thing is the Palantir model, which I think is a fantastic model. And why is that? That is because of that change management. Because I think you really need to help customers, partners really understand the benefits of any product you're creating, but not just the technology, but even how to use the technology in a workflow. At YC, we have this funny saying that we tell a lot of people here to do,

which is, you know, these are some of the smartest AI researchers, computer scientists who are just starting out in their careers. We tell them, go undercover. So go work as a medical biller and see to what degree, how many, you know, quote-unquote knowledge work jobs are actually copying paste from a browser into a spreadsheet, into an email, and then clicking send, and do that for a while and realize, like, actually, these are not necessarily,

you know, using your prefrontal cortex and your highest mind kind of jobs. Like, these are not, you know, can you imagine so many people, like, their lives are basically, like, you know, we used to, you know, coming up at our age, we would call it paper pushing. But, you know, they're not paper pushing anymore, but they're sending emails.

You know, they're not sending faxes anymore. but they're trying to get business done by, like, attaching files to things. You know, that seems like a pretty big shift, actually. Yeah, I mean, I mean, I think one of the most understated things as an opportunity for anyone creating products or fundamental breakthroughs, even at the model layer, is the amount of drudgery there is in knowledge work, right? I mean, I mean, in software engineering, we saw that. I mean, the amount of, you know, we're taking the joy out of software engineering

because, you know, you were out of your flow, to be able to stay in the flow, to be able to complete a task. That itself is a great example of what I think is going to happen to all knowledge work. You're absolutely right. The amount of cycles you spend out of band collecting information, because if you think about the prefrontal cortex and the synthesis part, the amount of time you spend there is pretty low. Now, like, having a sophisticated reasoning model and your prefrontal cortex work together,

whereas a lot of the mundane stuff is getting done by even some coo agent or what have you. That I think is definitely the frontier. So beyond simply adopting AI tools, what are the biggest transformational shifts you're seeing in the field today? I think to me even like, I mean, look, this field is changing so rapidly, right?

I had not even imagined last year, even this time, that we would get this far with RL and with basically test time compute. And it seems pretty limitless. So the way I think about it is pre-training worked. All the post-training techniques on top of it were fantastic. Then this inference time compute seems to have really added in another massive scaling law.

So now I'm interested in whether there is some new algorithmic breakthrough because I always say this entire regime could be changed by one person here who comes up and says, I have a more efficient thing to do or a way to do this stuff. So you have to be open-minded that the last big breakthrough algorithmically has not yet been found. So that's one. I'm always sort of interested in that. The other one is what is the next step up, right? because what is the pre-training to RL,

the end-to-end training loop that's the next, you know, big sample? That I think is also what I think will happen in the next year. So I would say if that is another scaling law breakthrough, because we will be, like if you sort of take any lab now, all of us, I think, will be working on saying, what's a more integrated response reasoning model that we can build? And that, I think, is going to be the interesting fleet. there's something very interesting here, I think, in that if you think about an LLM instance as a consciousness, which I think some people are starting to say, you know, it's sort of instantiated, you do a bunch of work with it, and then it sort of goes away and you open a new chat box and it's, you know, I guess I'm curious, like, do you think that that might be one of the things that needs, the loop needs to be completed, right?

that like yeah i mean so i'm not sort of i don't to me this artificial intelligence is unfortunately the worst name we could have ever picked and so i'm not into this anthropomorphizing i i mean i think of it more i come at it as it's a tool it's not trying to replicate how we think uh it is it's definitely showing signs of intelligence but it's not uh intelligence that i have and i think of human agency still will matter will be there and we will sort of use these as tools. So that's kind of my position. That said, let's just say, oh, yeah, a memory system is a good thing. These things do need. If I look at the next frontier, I would say there are three things, right? One is memory. The other one is tools use. And then the third, which I think is perhaps

the most important thing, is entitlements, which is basically, if I'm going to take action, what entitlements do I have to take action? So these three systems, have to be built as first class around the model in order for us to build more sophisticated applications. One of the arguments people are starting to make around the future of software is, well, we have the database, and then you're going to have basically middleware that is, you know, I think, you know, what you call entitlements, it's kind of like access control list. That's it. Like, you know, what's the business logic?

Who gets to do what? And then, you know, you basically put the agent on top of there. Is that sort of the future? That's right. That's why I think about, like, people say when you think about the scaffolding layer, right, you have a model plus scaffolding. The scaffolding now really gets first class by thinking of these three things. Tools use is one, memory is one, and then entitlements.

And you put that stuff together. Then you can create an agent. An agent has an ID. Agent has management and provision control on it. It has, you know, so that's the way I think to think about it. Do you worry with CodeGen? And do you think users at some point will just prefer to make software just in time instead of using package software? I mean, that's something that we're having lots of conversations in the hallway about that.

Because a lot of us in this room, YC will actually fund a ton of SaaS and will continue to do so. But in the background, we're starting to have that worry. There's some venture capitalist friends of mine who are in the audience. They're actually like, I actually don't know if I can keep. continue to fund B2B SaaS. How do you think about that? Yeah, it's a great question. I mean, you know, it's interesting. At the same time, I look at the number of people who are forking via code and I say, man, we must have done something right. And so therefore, there is something to be said about building a great IDE. In fact, when I think about Excel, I think of it as an

IDE. So the fact that there's a great canvas, you can then bring, let's call it, the best analyst model to this IDE, and then create a loop between the canvas and the model. So I think, yes, you can generate applications just in time. You could have a prefabbed application that is really helping with the feedback loop to the model, and I think both of these things will exist together. Do you think there's a role for design in all of this? I mean, basically, you know, a human being sitting in front of VS code is sort of like the translator between, you know, the software and what the end user really wants. And then I think some of this idea that software goes away presupposes that just, you know,

normal people walking around are going to want to create software. And, you know, I don't know if that's going to work. I think that's a good point. So I think the way I sort of say, because one of the, the basic question you're asking is what happens to software engineering, right? I mean, that's the, let's take the following thought experiment, right? If you sort of said some Martian intelligence came in the 1980s and watched how we all work, they'd say, oh, wow, these humans kind of work in the offices, and they have a typist pool, they have a slide pool, and people then work with paper. And then if they came back today, they'll say, God, man, all 8 billion people are typists now. Right.

I mean, that's what they'll sort of, you know, surmise. And so I think what a thing will happen is all of us are going to be creating software. But there is going to be a job called a software engineer. It's going to be different. But I look at it, right, you are really taking a software engineer and saying you're now a software architect. See, I still still the metacognition of your, I mean, one of my biggest things is, man, wipe coding is fantastic until it does stuff that I don't know what the heck happened. So that means I have to have the meta model of my repo and exactly what happened.

And I'm looking at the change logs, right? So when I look at my favorite feature of GitHub now is to really look at the complete change logs of all the agents that are working on my repo. And I think that is where a lot of the software engineering will be, like a good dev manager, right? I don't know which dev manager who worked at Microsoft, but I really looked. And a dev manager's job was to make sure bills don't break, and the code has got good quality.

And so to me, that is still a thing. And so there will be a level of abstraction uplift, even in a world of all of AI agents. Because one thing that we don't talk about is the legal liability, by the way, until some real laws change are going to be with humans and institutions humans built. And as long that is true, we're going to have to really make sure the human is in the loop at a fundamental level.

And that means we will need a lot of tools for humans to be in the loop in order to figure out what these things are doing. In AI development, you see so much, what do you think is underestimated and what is overhyped by the broader tech industry from where you're sitting? It's not short of overhyping. Let me just put it that way, right? We're at the, everything is AI all the time. So it's good. You know, for us all in this industry, we live and die by our ability to get into a frenzy about something new, right? What is the Steve Jobs thing or the Bob Dylan thing, which is you're either busy being born or busy dying?

It's better to be busy being born. So that's good. I think the thing that we have to most worry about and most work on as a tech community, I would say, is that, How do we earn that social permission? If there is one thing that I feel, to me, one of the demos I saw, which completely really blew me away, was I think in the beginning of 23, when I was in India and I saw a local developer,

Daisy Chain essentially at that time either GPD 3 or 3-5 with one of these India's stack, speech-to-text-to-speech, open-source things, and then showed a local Indian farmer who was able to sort of use a chatbot that was built in WhatsApp to be able to get some agricultural subsidy, right, by going to a government website. That, to me, was unbelievable. I felt like, man, how could something that was built in the West Coast of the United States get to a real use case that fast, thanks to sort of the diffusion rate?

and basically people everywhere, that is the story that needs to be told, right, at scale. That is the under-hyped story, I think. Because right now, the over-hyped thing is the model capability, and the model capability is fantastic, but, man, if we can somehow get the world to recognize that this is making a real difference in the lives of people everywhere, we are in good shape.

If that doesn't happen, this is all about some, valuations of us, our companies and our industry and it's the same repeat, then that is not going to end well. I love that example. I mean, you can, I don't know, it feels like Microsoft is sort of full of examples of things that lower the floor so that, you know, a lot more people can get access to technology. I mean, you could argue GitHub co-pilot is one of the biggest. Yeah.

By the way, one of the other ones, since you brought it up, there was a World Bank study. they did. I think in Nigeria, and now they've taken it to Peru or Chile, one of those in South America. It's, you know, we've been working at Microsoft forever on can there be an intervention in education, right? That's been the dream, man, we've been at it, added, added for decade after decade. It's made a difference. But this study said by access to something like a co-pilot is probably the best tech intervention in education in Africa or in Latin. in America. And that's been the dream, I think, that we all had in tech, and it's right there within our grasp. I guess, are there any interesting observations? I'm curious because, you know, your co-pilot in Windows is, you know, often, you know, here in tech, like maybe people are

really obsessed with the latest frontier models, but it's easy to forget. Like, you know, Windows and the integration with Windows is actually the first interaction people have with, you know, pre-AGI sort of AI today. Are there any observations from like people using that? Yeah, no, we are very excited about Clippy being back as co-pilot. But seriously, I mean, like, to me, the thing that I find is even in the form factor that we know and love and work in, which is a good old computer with a mouse and a keyboard, right? The dream has always been. In fact, the first research group Bill built in Microsoft research was speech in 1995. And so since then we've been saying, God, like, when will speech be first class on PCs?

But right now with co-pilot, the two things that are just pretty surreal to me, it's kind of like a new browser moment, right? There is both vision and speech. I leave it on all the time. It can see what I see and I can speak to it. That seems like a precision mouse movement, right, to me. So that is where, I think, even on existing form factors, there is a way to change the complete computer use.

And then there will be new form factors, right? So I think it's an exciting time to be building both hardware and modifying existing hardware for what is, I think, possible in terms of computer use. Yeah, computer use is fascinating in that, I mean, you have the intelligence, and then computer use is actually the super set of all the data, like your personal data, your work data, all your office docs, like everything is accessible right there. Was the movie Her correct in that literally the operating system is going to embed itself with your most trusted agent?

Yeah, I mean, I think that has been the dream, which is can these agents become your computers? and they do the computer use for you. And that absolutely, I think, is the direction of travel. And I think you mentioned the most operative thing, which is trust, which is, can I trust this to delegate what I want? And that means it's about precision. It is about sort of the privacy. It's about a lot of these considerations.

And I think that these all will in time will have to work out. I mean, in that respect, you know, when you look at, you know, both your company and you could argue Apple, they sort of have to be on the front lines of protecting privacy for all computer users in the world, actually. Yeah. I mean, so to us, you know, there are many, it's not even sort of, there's privacy, there is security, there's sovereignty. These are three big, big considerations, right? Privacy, every user cares about it. Security is what every tenant or every customer will care about it on top of privacy.

And then every country will care about sovereignty, security, and privacy. So that's the way to think about it. So you really need to build any product or any system. You need to be able to answer the questions on for the people and for organizations and for countries, how you cross all those three boundaries. Satchi, you've had an absolutely extraordinary journey at Microsoft, starting as an engineer all the way up to CEO. What lessons from that path would you share for the next generation of builders? It's not like you start any journey with sort of a specific goal of where you want to end up, but you do start with this goal of taking the first spot.

and sort of having the highest ambition for yourself on what you want to get done. I always say it's not like I was waiting to become CEO to do my best work. The first job I had I felt was the greatest job I could ever have. When I joined the company in 92, I felt like, wow, if I retired in that job, that would be fantastic. And that was a great mental model when I look back at it, right, which is not, I was not waiting for my next promotion to do something. but using the opportunity I was given to do everything I could. And I think that that's what people who are starting out

or who are founders or who are researchers or students today have. And so I would say keep that alive. Don't wait for the next big thing. Take the thing that you have as the biggest thing and then make it expansive. And then the other thing that I would say is big things are achieved by having a team around you, learn how to work in teams, making teams great.

One of the things that I feel at Microsoft I learned was what it means to be in a project, what it means to work. In fact, that's kind of the big difference between school and work is that, right, which is you join a team and you've got to figure out how to make the team successful. The incentives are actually pretty clear, except I think the thing that is least thought is how do you really make sure you can compose as a team and what's your role in it? Every one of us sort of looks and say somebody else's job is to align the team. It's your job to align the team.

So I would say if you get those two things, high ambition for your own impact, how to work in a team and make a team effective, that's magical. Here's a fun story. I actually did learn how to do product management and project management as a PM on Windows Mobile. And when I was employee number 10 at Palantir, I taught them actually how to run a project, zero bug bounds. And, you know, all of the sort of, you know, my PM training at Microsoft turned into the thing that created, you know, how even Palantir, you know, runs their product org today, which is pretty wild. So, you know, thank you to Microsoft for that. I'm curious, you know, what are the qualities that you look for in, you know, sort of people and teams just because AI is becoming a really key piece of, you know, creative work and engineering work.

It's sort of changing the way even you might interview someone and evaluate them for technical or, you know, broader skills. Yeah, I mean, look, I'm always looking for three qualities in people. One is, in fact, Bill turned me on to this, which he was describing at one point, who are good architects and who are bad architects. And he had this, you know, a nice way to summarize it, which is good architects bring clarity and bad architects bring confusion, right? Even if they're equally smart. So I sort of always go to people who innately can drop into an ambiguous, uncertain situation and bring clarity. It's an understated quality, right?

I mean, you just think about the number of conversations you have in a day about some tough situation, tough context, and people who can bring clarity on what to do, what to do next, what's the next step. That's at a pream. I always am looking for people who bring clarity in uncertain times. The second thing I'm looking for is people who create energy. In the other words, it's like not just they bring energy, but they're also really able to bring multiple constituents.

Anybody who comes to me as a leader at Microsoft who says, my team is great, everybody else sucks, that's not really useful. I need people who can bring people together across the company, outside the company, create energy. innately. And then the last thing is people who are good at solving over-constrained problems. That's why I think my favorite interview question always is asking someone to describe like a project they worked on which really was going nowhere and they figured out a path, right? And the way they go about it, problem solving. Because essentially, what do people do who are successful? They take an over-constrained.

problem and figure out how to unconstraintrain it. And that magical sort of three things, right, which is bringing clarity, creating energy, and driving success by solving over-constrained problems is what I think leadership is about, but leadership is not about something that you do later in life. You do it every step of the way. I want to cover quantum briefly. I mean, you guys just released your Majorana 1 in February. Is there an interaction with the future of AI?

and I think there are probably some quantum researchers in the house. So curious what the future will be. Yeah, to me, it's pretty exciting to see what's happening. I mean, we're being added, man, for, like, it's like I'm the third CEO at Microsoft who has been writing checks on quantum. And we've been added for 20 plus years. And the dream, at least the focus we always had was if we really want to build a quantum computer, which is a general purpose computer,

you got a solve for really stable cubits and error-corrected cubits. So a fault-tolerant quantum computer, and we bet on this basically a physical property which was envisioned by these Italian physicist, Majorana, and that is what we went after. And finally, we've had a physics breakthrough, and we were able to actually fabricate that particle.

and so therefore that's what has led to this chip. So we feel like one of the big things that we needed to achieve has been achieved. And the way I think about it is if you say takes, you know, if you want to understand the language of nature, which is simulation, I think the best way to do it is through a quantum computer because, after all, you know, physics and nature is quantum. And so therefore, AI is, I think of it as an emulator of that simulator, right? So that's another way to perhaps even use AI today with HPC. In fact, a lot of what we are seeing is pretty good advances in using basically HPC plus AI as a way to accelerate advances in chemistry, in physics, in material science. And so quantum would be the next step in it, but we are very excited about what

AI plus quantum and HBC in a loop can do. Very cool. We're running out of time. I feel like we could go for another hour if we had the time. So just to close, I just wanted to get your sense, you know, let's do a simulation of a sort. You're 22 years old and you're level 59 at Microsoft. You're starting your career. You just graduated.

What are you working on given, you know, in 2025? you know, if you started over knowing what you know now, what would you be working on, how would you be approaching it, you know, what would you be excited about? If you look back in the history of Microsoft, how office got built is an unbelievable story in the sense of thinking of these tools, right, a word processor, a spreadsheet, a slide-making tool,

what those tools are meant, to all of us, right? I mean, that's why I always say what's your, if somebody asks me, what's my favorite product, it's always, you know, V-S code is one and the other one is Excel. It's just your, you feel so good. When you use the tool,

it's all about the sense of empowerment you have, the number sense you have, the analytical power you have, with something simple, like a spreadsheet, like what an unbelievable scaffolding it is, right? Columns and rows with, some sort of tuning machine in the middle, is just breakthrough. And so I would want to work on what are the next set of tools?

Like when I see even co-pilot today, that's kind of where I feel. Like, you know, researcher, analyst, creator. These are like the word Excel PowerPoint. Right? Every day I go to them. So to me, that's what I would love to. What are the tools that we can put in the hands of people that will give them that sense of empowerment, that's what I would love to work on.

I have a feeling the people who make those tools are sitting in this audience right now. Please give it up for Satchanadella. Thank you so much. Thank you. Thank you. Incredible. Thank you.