This transcript is generated with the help of AI and is lightly edited for clarity.
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SATYA:
So what I think we have not yet conceptually gotten right and a shared understanding is what is this future of work going to look like?
If you’re a tech CEO, you have to be deep inside of what’s the tech stack. AI is not a technology; it’s the future of the firm.
One of the dictums I have is don’t use frontier models for non-frontier problems.
I think in the AI age that is going to be everything. Right. I think I would be very surprised, Reid, if you were sitting here a year from now. If the world is not completely turned on, what does my AI supply chain look like?
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REID:
I couldn’t be more delighted to introduce a special episode of Possible with Satya Nadella, the chairman and CEO of Microsoft. Satya and I have known each other a long time and part of in this kind of AI revolution for humanity. I thought this would be a great episode to get out. I mean, we covered all kinds of important topics. Satya, as always, is elegant, is cohesive, is smart, is comprehensive, and above all humanist in what our AI future is. This will be an amazing episode.
REID:
Actually, one of the things that’s great, Satya, about filming this here is that it reminds me of the earliest days when we were talking about Microsoft and LinkedIn.
SATYA:
That’s right.
REID:
Because we did one of our very first conversations here in the Greylock office. So it’s just awesome to be back. I want to start with something that I don’t know as many people realize and appreciate about you, which is how many books of poetry you have in your house. Can you say a little bit about your attraction to poetry? Favorite poets? What’s the engagement there?
SATYA:
Yeah, I mean, I actually got into it in different times of my life. I remember, you know, as a middle schooler we had this standard-issue English poetry book which I’ve been trying to reclaim and get all my life. It unfortunately is out of print, but it sort of had, you know, even getting, you know, introduced to Shelley or Wordsworth and it had even sort of Indian authors like Sarojini Naidu writing in English. And it is, I don’t know, I felt maybe it was my attention span or what have you. I was naturally drawn to poetry as a thing to enjoy and love. And I’ve always compared it even to code. Right. Which is, it’s sort of compression in its best form. And so whenever I’m bored, I get to go read.
SATYA:
I’m not great actually at understanding it deeply. I’ve never studied it. I’m not like. So that’s why my reputation of knowing about poetry far exceeds my knowledge of poetry. But I still, you know, continue to use poetry as perhaps the best expression of the human experience. Right. I mean, if you, if you sort of broadly think about literature as sort of what captures more than even history, the human experience, I think poetry is the compressed form of it.
REID:
I completely agree. Is there a particular poet that you go back to?
SATYA:
Yeah. So I, the thing that I really, I would say the poetry that probably speaks to me most deeply is the Urdu poetry, because I grew up in Hyderabad, India, and Urdu is sort of in the air. And some of the Urdu poets, both modern and sort of, you know, people in the 17th, 18th century were just extraordinary. And in particular, you know, there’s this poet who goes by Ghalib and he’s just extraordinary. Or even a modern poet like Faiz. I was also very, having grown up in Hyderabad, I was very influenced by Rumi. In fact, you know, the high school I went to was fascinating.
SATYA:
In fact, we had, you know, the number of languages which were all taught was obviously there was English, there was Hindi, which was the national language. And then we had Sanskrit, like basically, you know, like Latin here, I guess. And then we had the local language, Telugu, which is my mother tongue, but we also had Urdu and Persian. And so all of them. In fact, when we would break for what was called second language, my second language was Sanskrit. But, you know, I had classmates who would go to Persian, Urdu, Telugu, Hindi, and it was fascinating.
REID:
Well, among the experiences, I’m experiencing a little bit of language envy because I. A joke that I like is, you know, what do you call a person who speaks three languages? Trilingual, two languages, bilingual, one language American. And unfortunately I resemble this joke. All right, so let’s, we’re just coming out of Build, which was amazing. Let’s kind of actually start with kind of going back in history. So, you know, Microsoft’s first product was a BASIC interpreter. And 50 years later, here at Build, framed the company’s future around what others can build with AI. So what is the new BASIC interpreter in the AI age?
SATYA:
That’s a great one. So in fact that I think is the core of the DNA of the company, right, which is what can others build? And therefore what should we build? Is sort of the two questions that we always ask ourselves. And in the AI era, to me, the answer is the BASIC interpreter is the hill-climbing machine. Because you bring all of this AI, what is it? It is basically taking an objective, an outcome, an eval that you have and learning how to achieve that by learning using data, using some reinforcement, reward. The entire conference was about not, “hey, here is a new frontier model.”
SATYA:
It was about helping every developer, every company, whether it’s AI-native startup or an enterprise building their own hill-climbing machine so that they can operate at the frontier. Right. So I think that that’s it. The idea in fact, you know, being very clear about the evals and the objectives that you care deeply about, knowing how to evaluate them. Right. That it’s in some sense is the most and keeping. That’s the new IP. Yes, because everything else is pretty mechanical. But knowing what is the set of data that you want to train a model on and how you reward it is probably where the next level of IP gets created.
REID:
Well, and one of the things, let’s dig into a couple different areas here because you and I have had a number of conversations with Microsoft strategy and one of them is kind of this question about how enterprises keep the advantage and integrity of their own data. Microsoft is the most natural company in the entire world to do this. So say a little bit about how enterprises should be thinking about like we need our own frontier intelligence, but we also need to maintain control of our data.
SATYA:
Yeah. See that I think is the question which is this economy is going to be shaped going forward by both human capital and let’s call it this, token capital. Right? That is true for Microsoft, that is true for a new startup, that’s going to be true for any bank that’s been in existence for 100 years. Right. Doesn’t matter. All of us will now need to sort of, in fact the interplay between human capital and token capital and compounding the returns of it is what you need to do. So if you sort of frame it that way, then one of the most important things is not just even thinking of data in its aggregate sense. Right.
SATYA:
See, what is the tacit knowledge of an enterprise or a firm? It’s the unique ways that you are able to operate, pass judgment, have taste, all that’s the tacit knowledge mostly captured today in the tacit knowledge that is there with the human capital and some artifacts that are digital. So now when it comes to AI, the model in some sense is able to extract if anything that tacit knowledge through human trajectories and encode it in a set of weights in a model. So what I claim is that every enterprise now needs to be more mindful about that interplay of humans and their digital estate working together those trajectories training essentially the models that they keep as IP versus leaking it. Because if you leak it, it’s a one-way door. You’re done in some sense. Right.
SATYA:
Which is what is unique, what you may have spent a hundred years. Right. In fact, we don’t even know how to articulate it. Right. Nobody sort of has a line item in their balance sheet called tacit knowledge. But we take it for granted that because we have human capital, we have it now I believe that can leak and in fact it is leaking. Right. If you look at even how the model companies learn, they’re essentially setting up these gyms with rewards which are employing employees who worked at your company previously. I mean that sort of should tell you everything what should not be happening.
SATYA:
So that’s one of the fundamental reasons why we want this effectively the regime to change or the paradigm to change where you welcome the models to come in. They should hill climb inside a machine that you control. Your data is your context, you feed the model, you collect in fact these traces or trajectories of how work gets done between humans and agents inside the enterprise. But you have a continuous loop of that and you’re not letting that leak. That I think is the fundamental operation.
REID:
Yep. And actually the related parallel to the tacit knowledge is, and this is one of the conversations you and I had actually at the Microsoft board, which was what is the future role of AI employees? Right. Because part of the question is, say, hey, we’re going to provision not just kind of tools to amplify human work, great kind of AI companies, but we’re also going to provision at least specialist employees. But the challenge with that is that one of the things that really matters to enterprises is your employees embody a lot of tacit knowledge. how to know what to do, do you want that in other companies and provisioned in other places? What’s your and Microsoft view of the future of work when it comes down to how to think about AI as also maybe even specialist employees?
SATYA:
Yeah, I mean I think that that’s right. So the way to think about this, there are two ways I come at this, right? Which is, let’s take one analogy and I’ll come back to it, right? If somebody in the early 80s had come to us and said, you know what? There are going to be 4 billion typists who are going to wake up every morning and start typing and we would have said, what for? It makes no sense, right? We have a typist pool, we have a slide pool. And we’re fine with that, except we invented this completely new thing called knowledge work where everybody was typing and creating artifacts and so on.
SATYA:
So what I, I think we have not yet conceptually gotten, right, a good understanding and a shared understanding is what is this future of work going to look like when you have, let’s call it, you know, let’s take Microsoft, we have 200,000 employees and we have, let’s say 2 million agents or 20 million agents all in a loop. What is happening? What is the tacit knowledge that gets created? What is, you know, the artifacts that are going between agents, between agents and humans and so on. All that’s now going to be played out in the next whatever, even multiple years. You can see early forms of this in coding, right? It’s a great place to observe in fact this sort of social change, right?
SATYA:
I mean think about, we started in a good old IDE and said, hey, AI is inside, you know, in there and it’s doing code completion. That itself was useful, easy to understand. We’ve always had spelling correction and IntelliSense was there in VS Code from 15 years ago. And it just got better. Then we said, oh, instead of going out of band and going to a browser and going to Stack Overflow and searching, you can now bring all the coding knowledge to a chat session. That was also easy to understand. You still were in the IDE, you had sort of the chat. Then we said, okay, you now have reasoning models and some preliminary agent loop and so on, and so you can assign tasks. So we had agent mode, right?
SATYA:
So you not only had chat, but you can give it small tasks and you could see it complete and then you could accept what it did and then you could insert it and what have you. Then came the big breakthrough of total autonomy and agentic loop working for long periods of time where you could literally fire and forget, right? Where you could assign a high-level intent, it would go off and do the full PR and you would accept the PR. So that, that transition is what I think is going to happen across all work. And now we’re seeing it with even in Copilot we now have chat and we have Cowork. And now in fact at Build we announced something called Scout and Autopilot. So what is happening in coding will happen even in knowledge work.
SATYA:
Interestingly enough, one of the things we launched even at Build in GitHub was a new feature called Canvas. Because what has happened is as we’ve all gotten so good at using these coding agents in fact, the biggest challenge we now have is I have 100 CLI sessions open where I’m trying to operate these 100 agents. And now the cognitive load on me managing this is so high. First of all, think about it. It’s a linear chat session in a command line and I have 100 of them. So guess what, we now are back to an IDE, right? So I have a new IDE, we give it a new fancy name. It’s called an ADE. It’s an agentic development environment. That’s what the new GitHub Copilot app is.
SATYA:
The GitHub app looks like an inbox, except it’s an inbox of agents that are working across all the repos, allowing me to do the micro steering of the macro delegation I gave them. But it’s a complete UI for the agents to deal with me and for me to deal with agents. And so I feel. So we introduced this new feature called Canvas in GitHub where it can, like for example, you know, sometimes it’s easy to have a Kanban board visualization as a way to run down your PRs. Very useful both for agents and me to interact versus some chat session. So I think that that type of innovation is how work will change.
SATYA:
In fact, one of the other fascinating things for me is a line of AI research would be the models will also get much more tuned learning how to stay the course and understand human preferences in steerability. Right? Because that’s what I want. In fact, I want models that not only do instruction following, but are also really steerable. And that when you have that, that’s when you have confidence in it. So I think the work of the future is about tacit knowledge that gets created by this interplay. Just like knowledge work got created because of digital artifacts and human capital, the new one would be this AI capital and the human capital working together, creating in some sense the digital artifacts.
REID:
And what do you think are the things that people miss about how important it is to build out additional kind of structure for enabling enterprises. So one of them is obviously the enabling humans, canvases, et cetera. But what are the, for example, notions of what is security in an agentic environment.
SATYA:
That’s a great point. It’s a great point. So in fact to your point, one is the experience layer, clearly that’s important. And another one which we referenced earlier was hey, we need this hill-climbing machine as a concept that needs to be instantiated. But the third is the manageability of it and the security of it. Starting with observability, right? One of the first things we did even and we talked about it even, yeah, is something called Agent 365. I need to know, I need to have an inventory. I said, oh, there may be 20 million agents at Microsoft. I first need to know what are these agents, what are they doing, what are their reasoning traces. They need to be fully inspectable, fully auditable.
SATYA:
And by the way, when the agents also have this other attribute they are running, they can generate code and execute. So you need the environment in which they are executing code with maybe even file system access, network access, to be governed by policy. So therefore you now need to give them identities, you need to give them sandboxes, then you need to set policies to govern them. And so we built this entire thing called Agent 365 which really has, you know, we have extended Entra so that you have an identity, we extended Defender so that you have security, we extended Purview so that you can even label the data that is getting created automatically so that you can have data protection. So I think security containment, manageability, observability is the way we will have confidence, confidence around these agents.
SATYA:
The one other thing I’d say Reid also was salient in our developer conference was how important it is when you’re building these long-running agents, right. Like if you think about, right, we have always had in programming languages, you know, programming models for understanding the verifiability of a program, right. And it’s executed at runtime. So one of the attributes of these long-running agents that we added to Foundry was something called asserts. So this allows us to assert what are the boundaries, right? So instead of talking about guardrails as a thing that is sort of just a classifier of some form, you really need to now have the ability during execution to really have that execution path not go off the rails.
SATYA:
And so there’s a lot of engineering sophistication now that’s emerging as we build out the platform, the runtime, the security layer, the management layer, the observability layer.
REID:
So let’s go to a little bit of the role of CEO. And I think Fortune recently described you as acting like a startup CEO inside of Microsoft’s AI teams. But the more general question, because I think a lot of CEOs seek technology strategy advice about what’s going on in the world from you. What, what do you think the CEOs should be doing around AI? AI brings a refounding moment to lots of companies because of the nature. And so what’s your advice to them for how they should engage?
SATYA:
Great question, because I’ve been thinking about this because in some sense it clearly, if you said, if you’re a tech CEO, you have to be deep inside of what’s the tech stack, there’s no way you can be a tech CEO and not essentially have a fundamental worldview on where the future is going. And then to be long before it’s conventional wisdom, you have to pass judgment on where the company’s going. So that’s sort of our industry’s sort of pretty binary transition. So I think there’s no hope if you don’t have the CEO leading from the front and taking the shot on goal, knowing that these are fairly harsh transitions.
SATYA:
But the interesting thing that now I’m up to now, if you’re not a tech CEO, you needed to be a great CEO, which means you need to be great at banking or healthcare or whatever it is, and you could always have great partners and technology advisors and what have you. But now I’m changing my prior on that, right? Because I think, because I’m noticing even what’s happening and I don’t think the rest of the industry and the CEO community has broadly woken up to this. And I’m like perplexed at it because they’re still pretty happy doing a press release with a tech company and saying, yeah, I’ve got my AI strategy and pointing to eight agents they built and some outcome.
SATYA:
But AI is no longer like a sort of technology, it’s the future of the firm, right. It’ll be like saying, oh, I don’t know about the human capital in my firm. Right. So I think that this is where my thing would be that you have to now get a deep understanding of what’s your token capital. Can you answer that question right, when you concretely say, oh, last night, or based on all the work that happened across all of our operations, we were able to translate that into a set of knowledge that is somehow now part of my token capital. It can be some context, it can be some skill, it can be some weights in a model.
SATYA:
It doesn’t need to be any one thing, but you need to be able to specifically identify it clearly as something that you own, you control, you created and you put in place a system to have that compound. So I think that that is going to be probably the toughest change. So this change is like, unlike, oh, this is like going to a mobile phone era or a PC era or a cloud era where all I needed to do is to have an IT department that knew how to deal with a bunch of vendors and did some smart things to reduce cost or improve my efficiency. It should start there.
SATYA:
I’m not saying that that’s not the place, but this is about what happens structurally in your industry when AI basically knows everything that it needs to know about being in your industry. And if you start there, then I think you will start understanding that this is not just tech, it is really about a fundamental change to the firm.
REID:
And one of the things that I think thinking of the nature of the firm is that you’ve done a great job in leading Microsoft. One of the many different genius moments and skills is going through acquisitions and partnerships. So it’s whether it’s LinkedIn, obviously something close to our hearts, GitHub, OpenAI, et cetera. And part of what I think Microsoft leads the entire world on is how do you build trust in these other ecosystems and in the world of AI? That’s going to be extremely important, especially as the firm changes. So what are some of the lessons you’ve learned, the things that you are advocating for in order to build and maintain that trust?
SATYA:
Yeah, it’s so key because in some sense I’ve always sort of thought about what’s long-term stable. Right. Even from a Microsoft perspective, what is long-term stable is for us to be a tools and a platform company where we fundamentally are defined by the amount of value that gets created on top of the platform which should far exceed anything that is captured in the platform. Right. That’s the only way to have stability. If you do that then you have trust. Right. Because then the customer, the partner knows that it’s not a zero-sum, especially it’s not a game-theoretic zero-sum with these games where, oh, I’m going to subsidize this until two years or three years only to then eat your lunch type of games that the tech people are really good at.
SATYA:
And so I’ve always felt like, hey look, that is all not the way. The way to think about it is to be very principled that you are a platform company, that you will really live and die by your ability to create success on top of the platform. And when they are successful, you are going to be successful. And that is the equation that builds trust and long-term stability for both sides. I think in the AI age that is going to be everything. Right.
SATYA:
I think I would be very surprised, Reid, if you were sitting here a year from now, if the world is not completely turned on, what is my AI supply chain look like where it is helping me as a firm compound the returns of AI that I can uniquely point to as my value value. In a world where we know that these learning systems by definition don’t have boundaries. They have to have boundaries because of mostly structures of markets and society and other mechanisms.
REID:
Yep. So we’ve talked a lot about chatbots and code, which have just seen amazing explosive progress. What are some of the next frontiers for LLMs over the next few years?
SATYA:
Well, I mean, talking about that, in fact, you know, one of the things that I know you have gotten very, very excited about, which I’m quite frankly, you know, excited for you, but it also has caused us to you to examine where you spend your time and I’ll let you speak to it. But it’s sort of unbelievable to see what’s happening in science. And in fact, the company that you started with, Sid and Manas, you know, it’s just a great example, I would say, of what has happened in, let’s call it coding and knowledge work if it start happening in science. In fact, I would even claim that I wish, in fact, we did it in the reverse order. Right. Because the social permission for AI would be so much higher.
SATYA:
If we had started with some nice discoveries in science that were having great societal benefits, then people would have really thought of this as a thing that is really going to be more helpful. So I’m really excited for you, Reid, with what you’re doing. Obviously you’ve spent a lot of time, but maybe you want to talk about that.
REID:
Well, yeah, so, I mean, it was. One of the things that I realized over the last month was that we’re seeing such progress with math in actually, in fact, we’ve got an internal description of move 37 for chemistry, because we’re seeing chemistry that might actually, in fact, take shots at really interesting cancers and other things. And it’s very early, and so these things take a while with all the inds, but the fact that we are already beginning to see, we’ve got some of the best computational chemists in the world. And they looked and said, that’s very interesting, we’ve never seen it before and that might work and so that kind of thing.
REID:
And so Sid and Ujjwal and I were talking about this and I said, look, I think I need to get back to founder mode in terms of how to do this. I need to be able to focus on this. Then part of the conversation, you And I had this week is to say, okay, it’s been 10 years on the Microsoft board. It’s been a huge honor and pleasure with not just LinkedIn, but obviously OpenAI, GitHub, a whole bunch of things. But, you know, at the end of the year, I should really be transitioning right now to being founder mode. So like, you know, it’s like. And you know, we’ll always be working together, so it’s like. But, you know, it’s time to kind of dig back into the company.
SATYA:
No, first of all, it’s been such a privilege to have you on the Microsoft board, work with you, obviously through all the partnerships and LinkedIn and we will definitely be missed on the market Microsoft Board, but I know we’ll be very connected. But I’m also excited for you. When you say you’re going back into founder mode, that means I’m even more curious about what you’re going to be building because as you, I think you rightfully captured, this is the moment where I think the impact of these technologies more broadly felt in the society is the need of the hour.
REID:
Yeah, no, exactly. Let’s come back to kind of questions around the patterns of thinking that happen with AI because one of the things that I think I kind of roughly think about these things is you’ve got an alien intelligence, a different version of AI that has been built to very much heavily mimic human intelligence, which creates a whole bunch of utility for us. But its patterns of reasoning are not the same as ours. Right. And people frequently encounter that and they go, oh, that’s scary. You’re like, well, no, actually that’s like you have to pay attention to make sure there’s alignment. But it’s also wonderful because it enables new things. And so that’s part of the thing, like the kinds of chemistry we’re doing. Like, the humans hadn’t discovered this chemistry at Manas. Right.
REID:
What are the other kinds of thinking about this pattern of reasoning, Right. That kind of amplify human capabilities.
SATYA:
It’s a great one. Like, you know, I’ve not thought that deeply about sort of how it improves the. I mean, if you sort of, sort of say there is, you know, induction and deduction that we do being in this cognitive loop with an AI, that is fascinating, right? Because my exploration space is changing. Right. Like even in coding, one of the most fascinating things that one of my colleagues introduced me to is he wrote a new skill which I have in GitHub Copilot. It’s called cognitive coverage. Right. It’s fascinating. What he’s done is whenever an agent does some work for me, he says, just like how we had test coverage, we now have this new concept called cognitive coverage where we as humans are going to learn from what it did.
SATYA:
And so it just creates a quiz. So because I literally think of learning from it is matching my ability to essentially form a deductive understanding in some sense of what an agent did. Right. So to your point, it is, it’s basically mimicking. It’s sort of an imitation game on one side. But that imitation game does create. So then my ability to then deductively understand it, I think is probably one of the more important human skills we have to develop. So this cognitive coverage is. I’m like fascinated by it, right?
SATYA:
Which is, oh, I think we’re going to have something like that, which is the agents are working, they have to be aligned on one side, but the other side of it is what is new will be us knowing, oh, we have cognitively covered what AI did.
REID:
Yes, well, and I think that point intersected with your earlier one about kind of token capital and AI capital is that part of this skill set for the modern kind of human knowledge worker is to say, okay, how do I strategize on the use of AI canvas orchestration, but also within kind of capital allocation balance. Because part of what we’ve seen is people can go crazy on spending lots of tokens in ineffective ways. It’s like buying, you know, go back to bad Pentagon days, toilet seats for a million dollars. And so putting your thinking together with the cognition, together with token management. And what do you think is part of the question around this skill of blending the cognitive coverage with also token intelligence in terms of how do you amplify the outcomes of that?
SATYA:
It’s a great one. In fact, I’ve been thinking about one of the things that I think one has to study deeply is what are all the things that become more valuable in the age of, let’s call it token abundance? One is what you reference, which is how to use tokens becomes very valuable. Whoever figures out that, oh, I can use tokens more efficiently for an outcome that matters in the world is going to get ahead. Right. By definition. So then how does one build the intuition for it? Right? Which is, oh, this is where: what is that outcome? How do I measure it? What’s the rubric? In fact, this is where I think the evaluation, the, you know, if it’s.
SATYA:
It’s fascinating how much time needs to be spent, especially for an RL regime. I think it’s been the clearest where if you really want, you want to set up the rubric and the eval dimensions or the rubric-scoring dimensions such that they’re really capturing the high taste that you only can define. Because if you did that then you could be token efficient. Right. In fact, the other side of the token efficiency is in some of the examples we even showed at Build was let’s say you have, I don’t know, you’re a retailer or you’re a packaged goods company and one of the things you do is handle sort of transactions around trade promotions. Right? Now you get all these claims from all the retailers and you’re sort of processing them and the humans are doing it.
SATYA:
You can easily automate that using an agentic workflow and you could say, oh, I use a frontier model for it. But then one of the dictums I have is don’t use frontier models for non-frontier problems. Right? So this is not a frontier problem. You’re not trying to discover some new materials science. This is a repeatable deterministic workflow, but that can benefit from all the mistakes humans can make and claims and so on. So therefore you do need intelligence in it, but you can take a model like an MAI-5B and use the traces to hill climb in your RLE to perform even outperform even a frontier-prompted. So that to me is token efficiency.
SATYA:
So that type of human understanding of both the limits of the system, the characteristics of the system, I think become a high premium at this stage.
REID:
Yeah, I agree. One of the other things that I think is important for people to grok as you know, because we’ve been in a lot of these conversations, there’s a lot of concern in other countries about kind of sovereign AI. And I think that it may be useful here given we’ve just kind of gone into depth about companies and the sovereignty like the, the companies, their information or employees. What are the parallels between what we’re doing for companies and how countries should think about kind of also engaging in trust in the AI future?
SATYA:
Yeah, I mean, this is interesting because one of the reasons why I have really pivoted to companies is because companies exist all over the world. Right? That’s the good news here, which is because in some sense even countries thinking abstract, abstractly about sovereignty, they can even make big mistakes where they ultimately could erode their comparative advantage that is naturally there today embodied in the commercial activity happening in the country through its company formation and thriving. So therefore preserving that is the best form of sovereignty. Sometimes I think people think, think oh if only I had a firewall or all the data was resident or what have you. You know, I’m not saying those are not considerations, but this is not about even any of that, right.
SATYA:
Which is this is about making sure that you have an economy that means you need to have companies, that means that those companies have to thrive in a token economy. That means they need to be able to build that IP. And so therefore, in fact having partnerships even with companies outside that give you the ability I think are more important than sort of suddenly falling behind some frontier. Right. And so I think that, therefore I think that this is one of those places where again even the policymakers have to think about infrastructure. Right. After all, tokens, the electrons on one end and tokens on the other end. So you want to be the cheapest, best environmentally good producers of those electrons to token conversion machines called data centers. Right.
SATYA:
So that one I think is going to be very important for every country to prioritize. Of course the country should even prioritize whether that’s our own semiconductor production and what have you. Right. Those are all very valid things to do. But beyond that, I think the most important thing is to take what, what Ricardo was always right about, which is countries have by definition their own comparative advantage and now they need to amplify that using AI that means best thing is that whether it’s the small business or whether it’s the large multinational or even the public sector efficiency in the country is getting better and is operating at the frontier. The worst thing for any country to in the name of sovereignty, if they’re off frontier, then that makes no sense because you’re falling behind, you have to.
SATYA:
But at the same time being dependent on one frontier model also makes no sense because then you’re not sovereign. So what’s the way to solve it is to be able to say no, we will use models to hill climb on our own one firm at a time and at an economy in aggregate. That’s I think the equation, the gesture.
REID:
And silicon actually I think is good to also bring up both for companies we are building Maia and Cobalt, but also of course partnering well with NVIDIA and AMD. So first, what’s the strategic job of Microsoft’s own silicon? What might companies or countries also learn from that?
SATYA:
Yeah, so it’s interesting if you think about it, right. We want, it’s a great way to observe it even because I want, I mean if you look at what NVIDIA has done or what AMD has done, or what Intel has done, they’ve built general-purpose technology which, you know, like. In fact, when I look at what we are doing with GPUs today, we’re of course using GPUs to do model training, model inference, but we’re also accelerating other workloads. In fact, one of the exciting announcements at Build was using NVIDIA GPUs to accelerate our Microsoft Fabric data warehouse. Right. In fact, one of the things that’s happening with the agentic workloads is we need more performance on everything. And so that’s a great. That’s because of the general-purpose nature, right?
SATYA:
GPUs have, you know, NVIDIA has CUDA. CUDA can be used as a programming model on top of those to accelerate a variety of workloads. In fact, we’re using the older chips of NVIDIA to accelerate and this also works economically, which is smart for us to take a fleet and keep using that fleet over a lifetime where we are not only using it for some cutting-edge AI, but we are also using it to in fact make an old workload even better performing. That’s great for NVIDIA, great for us. But that also speaks to what’s happened in terms of the new workloads. The new workloads of AI are these data-parallel synchronous workloads, training, inference, as well as these new agent runtime workloads which have very different call patterns, very different, and they’re highly constant.
SATYA:
This didn’t exist three years ago at any scale, and now they exist at scale. So it behooves us to start thinking whether it’s not even before we even get to the semiconductors. I’m building my data center. My civil engineering is influenced, my cooling system, my mechanical systems. In fact, the DC to AC that they. We’re trying to make sure that the electrons are coming in in kilowatts, right? Hundreds of kilowatts straight to the silicon without any losses, right. So we’re trying to minimize even the power distribution in a data center so we can optimize to the nth degree for these workloads because they’re at such scale. And that’s what we’re doing with Maia. Right?
SATYA:
Maia, for example, is being co-designed with our MAI models and the OpenAI models to get the best, best performance out of them. We’re designing Cobalt, which is our Arm-based core for compute and we are designing it for example, using all the agentic traces of GitHub. Right. Coding. You know, the call pattern of a coding agent is pretty different than human apps or even asynchronous human apps. And so therefore we are optimizing and getting massive latency gains, performance gains and what have you. And so we are going to be a systems company that continuously optimizes for the new at-scale workloads that we have while using general-purpose technology from our partners to maximize the utility of those. And that flexibility, by the way, I think is what really is good.
SATYA:
And that’s where I think your question was so good, which is countries should think of that, right? Which is if you really think, oh, there’s one thing that answers, right? If I said, oh, everything is Maia and everything is Cobalt, that’s probably not the right thing for Microsoft. But at the same time, if we said we are open to innovation from the outside, we will innovate inside, we will in fact benchmark everything, we’ll be principled about it ultimately for better economics.
REID:
Yes, because creating the efficient capital token factories that create human prosperity is the goal and enabling many companies to do it. So different threads. One of the things that I know from various conversations with you that you’re very thoughtful on is the topic of children in the modern age. Because obviously we’re creating products for tutors, coworkers, et cetera, et cetera. And one of the things we’re all exploring together is what should we be doing as an industry to navigate kind of helping children elevate the right way and also keeping them safe. What are some of the principles that Microsoft’s thinking about? You’re thinking about as ways to kind of be good as regards the next generation of humanity through children.
SATYA:
Yeah, it’s a great question. There’s obviously important things that we have to do around child safety. So when any digital technology comes out, I think we have to now think of as first-class, right. There are AI safety issues around cyber or bioweapons alignment, but it’s also child safety. So I think that that one thing that we want to make sure is some of the challenges of current sort of set of, let’s call it chatbots. Right. And their conversations with children in particular is something that we need to be very mindful of and make sure that children have the agency they need in order to interact with this on their terms versus be persuaded, for example. Right. So those are very important things.
SATYA:
But the other thing that you’re pulling on, which I think is super important, is what does it mean to even be a child in a world like this where there is all this abundance of tokens. And that I think is the question, right? How should, what should, how should learning happen? How should we inspire children? What’s their sort of ability to go have a new pedagogical system even that they can enter? Because I think even like if you take the traditional ways, right, the anxiety that let’s say I had growing up around learning, interestingly enough, that’s a sort of an artifact of the scarcity of opportunity, scarcity of good learning, a variety of things which may or may not be true going forward.
SATYA:
So one of the first things I think is creating a learning environment where students and children in particular from the earliest of ages don’t develop these phobias for math or science or what have you, in fact, because everyone’s by definition a lot more curious. It’s just that contact with the world is what, you know, erodes that curiosity and confidence that children have innately. How do we develop that even further? By giving them the ability to explore, knowing that there’s no anxiety. Because after all the expertise is always going to be there in abundance. It’s really your cognitive coverage of that expertise that’s more at a premium, right. That if I had sort of, somebody had said that to me as a five-year-old, I think I may have approached life very differently.
SATYA:
And so how do we as a society create the necessary conditions for that? I think it’ll be probably very important.
REID:
And I think it’s one of the things, you know, I know from conversations with you intensely is like, this is one of our responsibilities as a tech industry, right? This is like it’s a. No, no, we can’t abrogate it. This is like given the ubiquity of the AI technology, we have to be responsible for the next.
SATYA:
Exactly what both sort of the unintended consequences is something we think about from day one and really building in safety guardrails and what have you then the great advantages of new technology have to be democratized and then I think there needs to be structural change, right? I don’t think you can say education remains exactly the same and we value the same credentials. I don’t know. I don’t think so. Something’s got to change.
REID:
So one of the things we haven’t had a chance to talk about yet, which is very natural in this context, is Pope Leo’s encyclical, which I thought was actually in fact a magnificent part of leadership on the behalf of the Church. I mean Pope Francis had actually gotten me engaged in helping them talk about AI 10, 11 years ago. Right. So like the Church has been amazingly front-footed on this. And actually part of the thing that didn’t surprise me at all in the encyclical was the humanism of it, right? Because people thought oh, it’s about religion, it’s about how, how you pray. And it’s like actually in fact at least the parts of the Church I’ve interfaced with have been humanists. Right. And do you have any reflections?
SATYA:
On the Pope’s encyclical? To see the Pope weigh in, and from what I understand, there’s historical precedent on this. Right. I believe the Pope at the time of the Industrial Revolution also had weighed in on the condition of labor and what should it do and what have you. And so to have the Pope sort of come out in defense of what I think all of us could deeply care about, which is human dignity and human agency in the age of AI, I think is so important. And I’m glad that he has advocated what he thinks is important. The other side of this to me is what is the society like?
SATYA:
If I look at what is at least describing what, when I think about some of the greatest ways technological advances were harnessed to create great prosperity. The story of the west is pretty unbelievable, right? There’s a beautiful book in fact that Joel Mokyr and a couple of other authors have written called, I think, Two Paths to Prosperity, which describes the thousand-year history of China and the west essentially. And you know, I’m paraphrasing here but fundamentally some of the constructs, cultural, societal constructs in the west on how to use even the scientific revolution, the industrial revolution, this all necessitated a real change in how the society was organized so that it could fundamentally take advantage of this new is sort of really massive, right. That in fact defined the modern world.
SATYA:
And so one of the things I feel we now need is a similar coming together from sort of both the moral philosophy. I think the Pope basically sort of said this needs to be the moral philosophy that guides us going forward. You combine that with even what is the market, what is democracy and then what is the scientific technological revolution. So if we can somehow get into a virtuous cycle where the morality and the scientific sort of breakthroughs, the political system and the markets are all reinforcing each other, then we will have abundance and we will have many stakeholders all benefiting. And if we don’t, we are going to lose social permission. Right.
SATYA:
So I just don’t think, I think this is what the narrow understanding of the success of the west as just a technological breakthrough I think is sort of not the case. Right. It’s an unbelievable coming together of a multitude of sort of forces in a virtuous cycle. It also had really bad parts to it. Right, we know that. And so you have to avoid that. Right. Which is, and so that I think was also some things that the Pope himself wrote in the encyclical, which I thought was great, which is even to think about sort of what are the bad parts? How do we not repeat it? What is the way to be, you know, advocating for this, this positive cycle I think is beautifully captured.
REID:
And I thought it was great that it was kind of a focus on how do you keep humans at the center, how do you elevate human dignity, how do you address needs not just of the wealthy countries, but the entire world, is, I think a great beacon in terms of how we should be thinking about it.
SATYA:
I think deeply about that. For example, I’ve always been someone who cared deeply about the Global South in some sense finally having their moment where there can be real catch-up growth. And so I think that there’s a real danger now in the age of AI for even that convergence growth to slow and in fact go the other way. And so what are the again, the global structures? Because by the way, someone sitting in the United States, in, you know, in Palo Alto, may think that somehow it doesn’t impact them, but there’s no such thing. Right? You know, we share this planet and our destinies are a lot more tied than we think we are just because of, you know, being far away from what’s happening.
REID:
Well, last question before we get to rapid fire. As you and I both know, you know, in the US and in Europe, there’s a lot of AI backlash. Now part of how, as you know, I’ve, I’ve been trying to address this. It is like with books like Superagency trying to say no, this is an opportunity to gain agency. The transition will be difficult, but embracing the agency and the transformation, ultimately you have to. But if you do it with forethought and kind of like leaning into it, it can be greatly helpful. What do you think we should be trying to help people in the US and the west understand about why it is that actually embracing AI is more important.
REID:
And it doesn’t mean that there aren’t genuine issues in the backlash and obviously work transition, all the rest will be real issues. But how do we help people see this could be an important part of their future?
SATYA:
Yeah, I think, Reid, at least now I’ve come to the conclusion that there needs to be real, tangible, practical, well understood outcomes that speak for themselves. Because I think what has happened, and this is one of those places where quite frankly, our industry even, you know, the way we have talked about it, I mean, when you go out and say, hey, you know, all economic opportunity will go away for knowledge workers or, you know, white-collar jobs are gone, or, you know, and, and you, and then you’re saying, I’m excited about building that technology. Right. Why would anyone want you to be successful? Right? I mean, I don’t want you to be successful. I mean, this just makes no, you know, no social sense.
SATYA:
So I feel now, right, when, you know, when you have someone in a college commencement be booed because they’re saying AI means we have now crossed over to people don’t believe us and rightfully so. So therefore, I think, what do we need to do now? It’s time to do the hard work. The hard work is if you’re building a data center, let’s make sure that that community believes that this data center is great for them. It’s for their tax base, for their community efforts, their real estate value, their school, their water use. Right. Their electricity prices. It can’t be again, like, oh, I said something. No, it has to be real. Right? That’s kind of what the way we earn social permission. Same thing I would say with employment.
SATYA:
We can’t abstractly even say, hey, lump-of-labor fallacy, always. There’s going to be new jobs. What are the new jobs? What are the wages of the new jobs that I can now go apply for, train myself for and how do I really start a new company or what have you. Right? I think unless we really get clear or the third thing, which we unpacked a lot, every firm needs to participate in the frontier ecosystem. It’s not like, oh, I’m just a feeder of data to some foundation model. Like that is like talk about sovereignty and dignity both being lost. Right. Simultaneously. Right. For countries, communities and companies. So I think now we have to go all the way and say, okay, this is a positive-sum.
SATYA:
These are in fact the challenges of some of the technology. We are going to really actively work it. We can articulate the tangible benefits. And this is where again, what you will do at Manas and others are also going to be very, very helpful because the world needs more proof points that this technology is ultimately helping human condition in our societies broadly, not narrowly.
REID:
Yeah, no AI for humanity. So you don’t have to answer rapidly. But we ask the same questions all our guests. So the first one is, is there a movie, song, or book that fills you with optimism for the future?
SATYA:
It’s this Two Paths to Prosperity. Right. Because the reason why I like that is because it’s a good call to action for the next thousand years. What’s the path to prosperity? There was a blueprint, at least at the part of the world got it right in the last thousand. Can we now, as an entire planet, get it right for the next thousand? I think that this is where some of our very best work has to be done.
REID:
Agreed. What’s a question that you wish people would ask you more often?
SATYA:
That’s a good one. I would love for people to ask me what am I not excited about? Because I’m excited about a lot of things, but I’m not excited, for example, about us losing permission on AI by saying all the wrong things or doing the wrong things even and not having a complete thought on how to truly have a positive-sum construct.
REID:
Yep, 100%. So where do you see progress or momentum outside of your industry? And given that Microsoft powers a lot of the world’s industry, but you know, it could be robotics, AI, et cetera, but where do you see progress that inspires you?
SATYA:
I mean, I mean to some degree, you know, the work in bio, I mean, if I think about it, right, one of the most complex systems that we have to have a better understanding of is human biology. So anything, any tool that can help humans take care of humans is probably the thing that will have. It’s awe-inspiring. There’s this one thing I recently came across which is for immunotherapy. I believe there’s a test that is basically a complex, costly test that figures out whether a particular immunotherapy will work on that tumor or not.
SATYA:
And so Providence and some researchers at UW and Microsoft Research came together and built this thing called GigaTIME, which is a cool model which basically is a simulation of that test and reduces the cost of what could get done only at sort of, you know, it took a lot of time and a lot of money. Now can be done by any tertiary hospital in any city. And that type of economic sort of availability of sort of medicine and medical practice I think is just breakthrough.
REID:
So last question. Can you leave us with a final thought on what you think is possible to achieve if everything breaks humanity’s way in the next 15 years? And what’s our first step?
SATYA:
I think if everything breaks our way, I’ve always gone back like, what’s the dream of the world compounding at 10% GDP growth, right? That is what can happen, right? I mean, one thought experiment is if the industrial revolution had reached all corners of the world at the same time and every country could express their comparative advantage fully. Right? That’s the maximalist positive-sum construct. Can we do it? Right, because we are kind of captive to this what we will describe as, hey, that’s not how the world works. History is about dominance and dominant powers. And I’m not saying we will defy all that.
SATYA:
But the bottom line is, since you asked me to dream, I’m dreaming that if humanity can get past their bounded rationality and only sort of say, oh, history repeats itself and we are only never going to get better than, you know, fighting these wars and what have you and say, no, no, no, let’s maybe we can change the course then the first step would be to accept that that possibility exists is what I would say is what will get us down. Otherwise we’re going to back to relitigating. Oh yeah, let me look at history and then just try to match.
REID:
Beautiful dream. Satya, always a pleasure and I’ll see you next week at the board meeting.
SATYA:
Thank you so much, Reid. Thank you so much. This is awesome. This is such a fun conversation.
REID:
Yes, exactly.
REID:
Possible is produced by Palette Media. It’s hosted by Aria Finger and me, Reid Hoffman. Our showrunner is Shaun Young. Possible is produced by Thanasi Dilos, Katie Sanders, Spencer Strasmore, Yimu Xiu, Aman Suri, Lexxi Kiven, Danny Garrison, Trent Barboza, and Tafadzwa Nemarundwe.
ARIA:
Special thanks to Surya Yalamanchili, Saida Sapieva, Ian Alas, Greg Beato, Parth Patil, Ben Relles, Caitlin McCabe, Karen Ngo, Cynthia Thomsen, and the team at Greylock Partners.