Satya Nadella 谈 AI：2026 Microsoft Build 大会 @NoPriorsPodcast x Latent Space 跨界特别节目

Hello, Satya? I'm so excited to be here. Welcome to a crossover episode of No Priors and Latent Space with Satya Nadella. Congratulations on an amazing Build.

No, thank you so much. It's great to be with both of you. I listen to both podcasts all the time. It's great to be on them.

Thank you so much. Sarah has been talking about these amazing announcements from across the Microsoft estate all morning, for I think 3 hours. What's the most important reflection or takeaway you have?

I'd say perhaps the biggest one for me is to conceptualize this more as an ecosystem play, as opposed to a single model or even a single platform. Having grown up at Microsoft and seen four major platform shifts, I fall into the camp where a platform is defined by its ability to create more value above the platform than is captured in the platform.

If you view what's happening right now, I think this morning's keynote was about how any company—whether it's an AI-native company or a traditional enterprise company—can participate as a first-class participant, where they can point to AI they created. It's not that they don't use other people's AI. Of course, they will. But to me, what's the path? What's the recipe? How do I do it? What does the stack look like? What does the tooling look like? What is valuable? How do you do that? That's our job to do.

Ecosystem strategy is very complicated, because you end up building certain components, partnering for certain components, and supporting them. You just announced this big suite of models. Tell us a little bit about the training strategy for Microsoft.

The thing that we wanted to do with the MAI models was to build, as Mustafa talked about, first of all, a great lineage: starting with pretraining with very good data quality, doing all the ablations, and making sure that we have a clean lineage model. In some sense, it's become even harder to build one because there's so much stuff out there that you truly need to ablate out to have a fantastic pretrained model.

In fact, that's one of the challenges of a lot of the open-weight models. They look great on 1 benchmark or 2, but they're not great in practice. That's why, even in the RFTs, we're pretty darn excited about these MAI models, because how the heck can a small 5B model hill-climb?

It goes back to what I think is ultimately the key thing to do, which is to pursue finding that cognitive core. To me, that means starting with a clean lineage and then creating the ability for companies to use this not just as a generalist, but to create their own specialist by building this hill-climbing scaffold around it. It's not just the model; you have a hill-climbing scaffold around it. Then you'll start building your RL. You'll start collecting the traces. Most importantly, you'll have private evals, because we know all the evals out there are good and interesting, but they're not really that critical at this point because they can all be gamed.

Each company will have its own private evals. That end-to-end platform story around our models is what I think is interesting. The other thing, Sarah, since you brought that up, is that I do feel there's a new frontier.

People talk about the frontier and whether you're operating at the frontier. Interestingly enough, if you add a little temporality to it, you can use—for example, the Land O'Lakes demo we showed was pretty cool. We used GPT-55, collected a bunch of traces, and then took a 5B reasoning model and achieved higher performance. That is another aspect of what it means to operate at the frontier.

I first of all have to congratulate you on basically building a frontier neural lab inside of Microsoft in 2 years. I'm wondering, you have all this AI strategy that you're rolling out—what do you know now that you wish you could tell yourself 2 or 3 years ago? Three years for the Jensen partnership, 2 years for MAI.

The thing that I reflect on quite a bit is that I got into all this when I got excited by the scaling laws paper. Even the OpenAI partnership came about when those folks said, “Hey, we're going to really throw a lot of compute at transformers.” They held.

The thing that I always look back on and say is, “Wow, these things do have capabilities, and they're climbing.” The crude way of saying it is that intelligence as a log of compute kind of works. What I think we underestimated, perhaps, is the real-world complexity of deploying these so that they actually deliver value in the real world.

The outcomes as measured by any benchmark are interesting and important, but the true eval is when people out there are able to do unique things that they can value. It's very measurable. I wish we had had more of that in our consciousness, because when people say, “I don't want a token max,” it's an artifact of us not having thought of ourselves as an industry that we're using tokens to create value every step of the way. That's what I wish we had gotten to, but I'm glad we are here.

What are some other use cases that you've seen create the most value for your customers? I know that people talk a lot about code, and I think it's pretty clear that it's having a very large-scale impact. Are there other areas where you find that your customers are really benefiting?

To your point, obviously coding is now a given. But it's interesting, by the way, Elad, to even talk about coding. Coding has worked so well that we now have to rebuild the IDE. It's kind of nuts to see what we saw: “Oh, my God, I have these 100 agent sessions.” The cognitive load it transfers back to me as a human is so excessive that now I need a new UI.

The chat as the only artifact is also impossible, so that's why we need a canvas. It's interesting, for all the things about where software is needed or where UI is needed, you need that even for code in a fully agentic world.

One of the things we're starting to see—we started seeing it with Cowork, but even some of the work we showed with our autopilot, and what you see with Claude, is a good example. If you think about it, a lot of human capital is doing the glue work. If you can augment that with tokens / agents that are long-running and durable, then your ability to scale even what is still judgment and glue work gets amplified, like coding does.

I'm positive that 6 months from now we'll all be saying, “Oh, wow. All through the night, there was a bunch of stuff that all these autopilots I have working on my behalf, with my delegated authority, so to speak, and even with my identity, did a bunch of work.” Then, of course, I'll need my new ADE to say, “What did you do? Did I do this work?”

That's where compressing workflows and completing tasks is where I think a lot of the value gets created.

You raise a really interesting point. There's the actual agent doing the code, and then there's a harness around it. That's the environment, the context, and everything you're setting up as a developer around an actual coding agent. What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept generally?

In some sense, you want the harness to define the models, the data, and the tools, so that you have a loop across those 3. What we're trying to do, first of all, is make sure that each of our products—whether it's GitHub Copilot, Security Copilot, the stuff we showed with Microsoft 365, or even Discovery for Science—has a multimodal harness with tool access, so that you can do this progressive disclosure of tools and make them token-efficient.

You're feeding it with very rich context, because that's the other hard lesson we've learned in the last 2 years. The amount of work you need to do to prepare the context layer so that your plan can execute in the most efficient way is where the magic is.

In our case, we have the GitHub harness, which we're using across all our products. It's available in Foundry, and we're open: you can use your Llama harness, any open harness, or any harness of your own, and train it with your tools, multiple models, and your context.

That's the pitch, because right now a lot of the dialogue is, “Hey, if I train the harness, the tools, and the model together, I get evals.” What we're proving out—and the best example of that is what we did with M365—is that when it launched, it found bugs or vulnerabilities that were not found by Mithril. There is an existence proof, I would claim, that you can have a multimodal harness that can, in fact, be more performant in the real world.

The premise behind the training at the independent frontier labs is that we're going to have these models, and we'll have an API business and support enterprise and startups, but a first-party product, be it productivity, code, or search, drives the majority of revenue. That's a different value equation than you're describing.

I think with the Microsoft ecosystem, if that's the case—tell me if it's the case, because obviously you have first-party products and enablement products—what is the role of the developer? What's going to be hard, and what are the set of skills and the value capture for the developer in that world?

Yeah, I think there's always going to be a case where someone who's super successful as a platform builder can also have first-party products. It was true with Windows, and it was true with the SaaS side and the cloud side as well, with us and others. But the thing is, it should not be a limiter to other people achieving that same success. I think that's the core difference.

The network effects this time around, around intelligence, are such because they learn from data—and not really lots of data. It's just the few samples that you have to see to understand what's novel about something. That's why the game becomes how to protect. I would say every company having private evals may be the biggest IP.

Think about it: what's that private eval that you can then use even a frontier model to hill-climb on and not leak the traces? That may be one of the biggest drivers of IP. Another acid test is: you have a private eval, and you're using Model A. Can you switch it to Model B and climb up? If you can, then you're in control. If you can't, you're not in control.

That's where even the harness becomes super important, right? Therefore, having an open harness, letting all models come in, and having your evals, your contexts, and your tools help you hill-climb—I think those are the skills that an AI-native startup needs, a SaaS company needs, or every enterprise needs.

Yeah, I think in a very real way, you are Microsoft: historically an operating systems company and then becoming a cloud company. Maybe the third act is that you're a harness or evals company—whatever conglomerate of concepts you want to put together.

I think enabling every company to have frontier intelligence, or whatever the exact term that you used was, is the mission, right? That is the platform promise: you build with us, and you will get your intelligence for your data.

That's it. To me, if there was one tagline for this entire developer conference, it's: Can everybody operate at the frontier with their frontier intelligence? To me, that is so important, because otherwise I don't know how you achieve stable equilibrium.

How do I then go and say, "Wow, my company is going to have a terminal value because I now know how to continuously compound on top of a platform that gets better"? When Windows came out, Adobe built on it, Autodesk built on it, or even—take what Jensen said: "We built DX." He built CUDA on top of it. I always say to Jensen, "God, I got the short end of that," but I wish we had recognized it.

Nevertheless, the idea that you can build a platform layer that someone else can then extend and build their own intelligence layer on top of, I think, is everything. Without it, why have a developer conference? I can just come and have you all worship at the altar of one model. But that's not a developer conference.

Backstage, we had a discussion about what is IP, or what is the value in a company. It used to be the length of human experience at a company, and now it's this other thing, which is the evals—the experience of applying agents to the company. I just want you to flesh that out a little bit more.

Yeah, it's a way to frame it, right? At the end of the day, every company is going to have both human capital, which is still going to be super valuable, because humans and their ability to find the gaps that exist at all times are going to be the way we all create value.

I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition, even as token capital goes up. Let's say any corporation has lots of tokens and a lot of human capital. The question is: how do you compound the two?

If you take teams, I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those are really important context for how that enterprise is creating value. That goes back to training not a generalist model, but training the company-veteran agent. That is super valuable, again, right?

That's why I think a company should, in fact, put it on the balance sheet. Human capital was never possible to put on a balance sheet because you didn't know how to capture the tacit knowledge, whereas now I think you can, with the agents that have learned through time, through all the traces. That's what, at least, we think will happen.

I think the SEC is going to have to have accounting standards for token expertise.

You're talking about the equilibrium state and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.

Another challenge to that considered equilibrium is that there are applications and workflows that are common to a vertical or a horizontal. This was the generation of SaaS companies, and Microsoft has lots of SaaS properties as well. Then there are things that are very specific to every enterprise, which they're differentiated against.

I'm sure you have heard much of and participated in the debate about the end of software, because all these workflows are cheap to generate now. Do you think the equilibrium looks different between the agents that get built in enterprises versus the agents built by their vendors in the future?

Yeah, so I think what's happening there is that we had a particular way we captured workflow in apps, right? We built a data model. We schematized some part of some business process. We then built a bunch of business logic, and then we put a bunch of UI on top of it. That's what every SaaS company did.

Configuration, right?

For 20 years, that was it.

Right.

And that was it. Interestingly enough, now you get to relitigate that vertical stacking. I still think, for example, that the data model you built underneath every SaaS application is super good. Why reinvent it? My general ledger better be a general ledger. I don't need new schema creation.

Yeah.

That entity-relationship model is actually a pretty good, robust thing that I want to feed.

And you want it to be stable.

That's right.

Yeah.

Then the same thing with business logic. If you look at it, we have this product called Power BI. It is dashboards galore; people have created so many of them. The beauty underneath those dashboards is a very rich semantic model. Someone took the pain to create a dashboard and do all the measures, and you want that business logic to be available to you.

I think the challenge of the SaaS business model is that we packaged things one way. We now have to learn how to unbundle them and rebundle them in new ways, and discover new business models.

If you look at what's happening today with Microsoft 365, it's a great example. We have this thing called Work IQ. What we're realizing is, oh my God, if you look at it, there's a historical parallel, too.

We sold Exchange and SharePoint first, and before Teams, we had a thing called Lync Server and what have you. We thought all that was going to move to the cloud, but little did we realize that the number of people who would use servers in the cloud would be 10x or 100x, because people were not buying servers; they were just buying a subscription.

The same thing is now happening with Microsoft 365, because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps. Email operated on it, Teams operated on it, and so did Word, Excel, PowerPoint, and SharePoint.

Now, one of the coolest things I get to do with Work IQ is go to a GitHub repo and say, "Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?"

Think about that. It can literally look at all those transcripts and come back with a plan to change a codebase. Previously, you could never have thought of using Microsoft 365 for something like that.

The value-creation opportunity now in the agent world is, in fact, 10x more. But it does require us to have usage around Microsoft 365, which is going to be perhaps more than even the end users. We even have to rearchitect. What I used to serve an inbox or a mailbox cannot be used to serve an agent.

I don't believe in permanent business models for any of these domains, but in the near term, do you have a prediction between outcomes-based pricing, token-based pricing, and enterprise bundles?

Yeah, the way I think about this is, let's even take per-user pricing.

Mhm.

The per-user pricing is really an artifact of someone creating a budget and needing certainty. Right, because that's the most important thing: somebody wants a budget. They need a per-user price, and per-user is just a set of entitlements to usage. That's kind of what it is. So, the first bundling will be to take some usage, bundle it into per-user stacks, and then sell subscriptions.

Subscriptions, I think, are going to be there. Per-user is going to be there. Then the next big thing will be consumption. People will say, “I want consumption.” It's also possible that people will say, “I don't even want to pay for any of the subscriptions or the consumption; I want to pay for the outcome.”

But remember, most people love outcomes until they have an outcome. Because once you have an outcome, it's like giving away a royalty. I've talked to customers who love outcome-based pricing, and I say, “I'm all in,” until they say, “Oh my God, what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption-price me.”

I think that debate will go on, and all of these business models have a particular time and a place, versus one to rule them all. If you're a SaaS vendor or a platform vendor, having that flexibility is important. Quite frankly, we face this with GitHub. We recently announced per-user pricing on GitHub because GitHub Copilot was constructed at a per-user level before we even understood the intensity of agent usage.

It was an interactive way for a developer to use code completion, maybe task completion. It was not like, “Oh, I launched 10,000 agents that are going on all day.” So, now we know there really will always be a per-user model, but there will have to be a consumption meter.

How do you think about the durability of SaaS more generally? One thing I've observed is that in a lot of enterprises, internally, there'll be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications, or going to other SaaS vendors and saying, “We're not going to work with you anymore,” or, “We're considering an internal project.”

It seems like in 6 to 9 months, maybe some of those people will come back and say, “Actually, we can't rebuild everything.” How do you think about what's durable in this world and what isn't?

I think we have to go through one full budget cycle on this to really see the emergence of the equilibrium. At the end of the day, there's marginal cost to even generating the app. In fact, there can be a simple way to say it: You should always acquire something if the marginal cost of building and maintaining something on your own is higher. That's quantifiable.

The maintenance part is important. You've got to remember, all the security stuff that AI will find, you'd better fix it too, fast. Of course, there's a coding agent to help you with that, but then that burns tokens. Whose responsibility is it? It's kind of a cycle that you've got to think through.

I think we've gone through the excitement of “I can generate a lot of software.” The next thing will be, “What software do I really want to generate? What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over?”

I think there'll be very little tolerance for anybody who's inflexible at the vendor level. But at the same time, I think anyone who has that flexibility, shows up, and delivers the value will be back at it again. We're selling software, but with just different business models, in fact.

Speaking about building software, one of my favorite moments from, I think, a previous Build, maybe 1 or 2 years ago, was a section where you were building your own software. I'm curious if you're building anything now.

Let's face it: building software has made it possible that even the incompetent CEO of a company like ours can build. [laughter] So, thank God.

That said, I do feel that something like GitHub Copilot, and especially the new Sessions app, has made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before. For me, as a CEO, to go to a codebase and be able to learn about it is a big deal.

I remember joining Microsoft a long time ago. Everybody had to go in and look at Cutler's Mallet, or what have you, to learn how to write good C/C++ code. Now, that ability to be more full-stack, up and down, is so good. But that doesn't mean every one of us should be doing the same thing. The question is, how do you then have the ability to inspect things, learn things, and see things? I think it's just so much more.

To me, what I'm building a lot of are these long-running Foundry agents. There are autopilots. The easiest thing is—I think I just built one even last week—where the idea was, “Can I have an agent that is continuously monitoring, essentially, my own chief-of-staff autopilot?”

We're going to have that, obviously, in Scout. That's what we showed, but it is so easy and trivial to build. I took Work IQ and said, “Take Work IQ, go and build a Foundry long-running agent, and store all the memory using Raven”—basically, my backend as a service. Lo and behold, it built it. Not only did it build it, I could say, “Publish to Teams,” and it published the damn thing to Teams. The ability to have an end-to-end project like this complete is just pretty miraculous.

How do you think that impacts the different types of engineering roles that exist in the future? Right now, I think there's a dozen different types of engineers that you can be, from QA to front-end and so on. There's a big swath.

I've heard some people argue that in 4 or 5 years, we'll basically end up with 4 engineering roles. It'll be people who are managing agents; forward deployment engineers or FTEs; security engineers; and then people working on large-scale infrastructure for a small number of services. Everything else just collapses into the agentic world. Do you think that's a correct view of the world?

I think we'll have to experiment our way through it. But what you said is that there are some very at-scale things. At LinkedIn, they did structurally change and basically built up a new discipline called full-stack builder. They went and said, “Hey, let's bring people from design and product management, front-end engineering, and put them all together, but also have an edge.”

It's not like the design person doesn't still have the design edge or the front-end person doesn't have the front-end edge, but you can give yourself a bigger scope and role so that you're not confined to one role. Equally, infrastructure has become very critical.

For example, one thing we've seen is that even for the Excel team, building the RL environment in which a reward can be learned is actually one of the hardest infrastructure problems. You need even new talent—distributed-systems people—even in what was considered an end-user app team, because it's a different skill set. So, yes, infrastructure science is the other one, obviously.

I mean, the world will always have a bunch of specialists. I think the generalist role is going to be the most exciting because the leverage of a generalist is where we're going to see the maximum returns. When you said, “Hey, I'm coding. I'm now a generalist,” what I've basically translated is knowledge work, right? I created a Word document or a spreadsheet, and now I can build an app. It's in the same sentence.

That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” is what I think we're going to see across the board.

Music to the ears of CEOs and VCs who are a little dangerous and a lot of fun.

Golden age for idea people.

Idea people with a lot of agency. If you take that idea of personal agency and just zoom it out to the organizational context, my partner Mike Vernal, who actually started his career at Microsoft, just wrote an essay where one of the big takeaways is that it's an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly users and companies are actually open to adopting new technologies.

I feel silly asking this of somebody running a trillion-dollar-plus company already, but how do you think about how Microsoft can be more ambitious now?

It's a great question. I think the thing in these types of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously. Kevin Scott has this nice line: When you're making hard things easier, that's sort of one point of leverage, but true ambition is about making the impossible possible.

The thing that's missing a little bit in all of our organizations is: What is that new conceptual model of what we can build? What was impossible, and what can we build? I'll give you one example of this, which is that I take great inspiration from the people who were managing the Azure network.

And they came to me. This was from even last year. We were scaling. You saw that I talked about how we built, in the last 15 months, more Azure capacity than we built in the first 15 years. It's crazy. It's pretty wild. And it's the same team. So they saw that, and they said, “Bob, this just ain't going to work if we don't reconceptualize our work.”

Essentially, they said, “Our job is not to do Azure networking. Our job is to build the agentic system that does Azure networking.” These are the folks managing the 500-plus fiber operators managing the WAN all over. Fiber operations ultimately are a physical operation. Things get cut. Things have to be repaired. We have fancy words called DevOps and so on. Basically, emails are coming in, and you have to respond to them and take care of it.

So they built this agentic system. They even have a character for it. It's called Miles, and it sort of does all this stuff. They started screaming for more tokens and so on. They were saying, “Look, we don't need headcount. We need tokens in order to be able to manage our operation.” That reconceptualization of what their work is—they basically took their work and made it meta. That meta-work is now their new work.

In the ’80s, if somebody had come to us and said, “4 billion people are going to get up in the morning and start typing,” my model would have been, “We need 4 billion typists.” But we're not doing typing. We're doing knowledge work.

So that, to me, I think, is it: whether it's Microsoft or any organization, we have to give ourselves permission to do new types of metacognition, meta-work, using these new tools to change the outputs that matter, and then really make the impossible possible. So completing that connective tissue across those, I think, is where a lot of the enterprise value will get created.

Alessio Fanelli

Can we talk about the data centers?

Yeah, please ask.

Alessio Fanelli

Oh, okay. Well, this leads nicely into the data center buildout. I'm just impressed with the sheer scale of the buildout from Microsoft, but also everyone else. This is redefining what it means to be a hyperscaler. I just feel like that is unprecedented scale in terms of finances, in the way you run the company, but also in the communities that are impacted.

Then just talk a bit more about what you're seeing on the ground.

Yeah, I think there are 2 aspects of it. Obviously, the buildout is extraordinary. Nothing like this has happened, and it's great to be one of the participants in it. But you brought up the other part. I think at this point, it's clear that unless we as an industry are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways at the community level—because this is not just a campaign. It has to be real, where people are saying, “Look, this is not changing the prices on energy for me.”

In fact, if anything, it's bringing down prices because, long-term, there's going to be a better grid. There's going to be more energy. In fact, water is being replenished. You have to really educate folks on truly what's happening and the closed-loop systems we're building. We have to invest in the training, the jobs, and the tax base.

In fact, the least-talked-about stuff is the number of jobs that get created during construction and after construction, and the tax base that's there in the community. All this has to be real. If that is the case, then we will have permission. If it is not, we won't have permission. It's as simple as that. We have to take it as an industry pretty seriously.

I think it's good for communities to be skeptical and ask the hard questions, for us to do the hard work, and earn that. But at the end of the day, I've always felt like, in human history, if you use a lot of energy but also create a lot of value for society, the story has been fantastic. If you don't do that, it's not been that great.

This time around, I'm a firm believer that ultimately, if you do have a token economy that drives productivity, that drives economic growth, that drives widespread participation and better health outcomes, then I think we will be in a great place. That's at least what we all have to be focused on.

Alessio Fanelli

Yeah, it makes me think, actually, that with all these initiatives that you're doing, it might be easier to see ROI in the communities first before seeing it in an enterprise.

I think both sides. In fact, it comes back together. It has to be that the people in the communities are going to be employed and are going to be participants in the real economy. That's, I think, the question: if the broad economy is doing well and the communities are doing well, the dots get connected.

It's sort of—the market forces are such that we will connect the dots. And that, I think, is it. You ought to be able to see the evidence. It can't be about any one company. But it has to be broad economic growth and broad community permission.

Shawn Wang

What's the biggest mistake you've made about AI, or what have you most updated your personal models on regarding the societal impact of AI?

So you're asking what's the—

Shawn Wang

What have you updated most on in terms of the societal impact of AI?

I think the most critical thing is the first question we started with, which is: we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.

I think in the next 12 to 18 months, we need a way for people to say, “Oh, wow, I get it.” There's going to be tremendous capability and a tremendous amount of infrastructure, but I can see what is going to happen. Whether it's the benefits, like health outcomes, or my ability to create a startup, or my ability to run my local store more efficiently, it's just happening, and I see that benefit myself.

That, to me, is earning that permission in a path-dependent way. We can't wait. The one thing I've now learned is that I think the world is going to be way more skeptical of tech and tech companies that say, “Trust us. We've got it. The future is going to be glorious.” You kind of have to deliver tangible benefits.

And frankly, politicians winning elections because they have advocated for that—that will be at least my adjustment. Without it, I would be thinking that somehow, because it's too important this time around, it's too much of the economy for it not to be the case.

Shawn Wang

So, one very simple framework I have for what is going to be the broad benefit of AI, beyond the communities just working in technology: wealth creation is going to happen in a ton of different companies, startups and large companies. Then you have healthcare. You had amazing demos today. There are companies like OpenEvidence. I think that is happening.

Education seems like another one that's an obvious good, where we haven't seen as much impact as I'd expect. Do you have a hypothesis on why that might be, or if it'll come?

Yeah, I think this is where, again, how we think about education matters. Recently, I met with the founders of Alpha School and learned a lot about how they were going about it. It is fascinating to listen to how you can even rethink what education really looks like, because I think it's actually very important.

I'm not saying anything that's traditionally being done is less important. I was even looking at the Asian guidelines for CS something. It's fascinating to see. They were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.”

You still need people to learn. Learning concepts is important. It's going to be critical. But the way we create the incentives, what the credentials are, how we value those credentials, and what the employment opportunity is for those credentials—there is a complete change that has to happen, given that the way you get to information, the way you educate yourself, and the way you continuously keep yourself updated has changed so much.

Interestingly enough, maybe the next big startup and success story could be someone who builds a new university or even a new pedagogy for how to get someone to go through a curriculum and find highly valuable economic opportunity.

Shawn Wang

Well, that has felt perhaps impossible for a long time, but it's a great note to end on and something that might be possible. Thank you, Satya.

Thank you so much. Thank you. I appreciate it. Thank you all.