We Need An Ecosystem in AI, And Every Company Can Win A Place In It

What does it mean for a business to truly operate at the AI frontier? In a special crossover episode at Microsoft Build, Sarah Guo and Elad Gil team up with Latent Space host “swyx” to talk with Microsoft Chairman and CEO Satya Nadella about the future of AI platforms, software development, and the tech ecosystem. Satya reflects on the latest breakthroughs from Microsoft Build, the strategic shift toward multi-model harnesses, and why private evaluations (evals) are now a company’s most important intellectual property. They also discuss how autonomous AI agents are reshaping the role of software engineers, the durability of SaaS business models, and why showing communities the ROI on data centers is so critical. Plus, Satya shares his thoughts on the economic and societal impacts of the token economy, as well as the future of AI-driven education startups. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @satyanadella | @Microsoft | @latentspacepod | @swyx Chapters: 00:00 – Satya Nadella Introduction 01:48 – Reflections from Microsoft Build 03:12 – Microsoft’s AI Training Strategy 05:48 – Complexity of Real-World Deployment of AI 07:33 – Augmenting Human Capital 09:37 – Harnesses for Enterprise 11:49 – Developer Value 15:09 – Can Everybody Operate at the Frontier with Their Frontier Intelligence? 15:51 – Modern Definition of IP 17:38 – Future of Vendor vs. Enterprise Agents 21:48 – Near-Term Predictions on Model Pricing 24:02 – Durability of SaaS 25:58 – What Satya’s Building 28:18 – Future of Engineering Roles 30:54 – How Microsoft Can Be More Ambitious 34:36 – Data Centers and Community Impact 38:01 – AI’s Impact on Society 39:52 - AI and Education 42:28 – Conclusion

• 0:00Speaker 00

  The world is going to be very 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, because it's too important this time around. It's too much of the economy for it not to be the case. True ambition is about making the impossible possible. I take great inspiration from sort of the people who were managing the Azure network. We built in the last 15 months more Azure capacity than we built in the first 15 years. I mean, it's crazy. Our job is not to do Azure networking. Our job is to build the agentic system that does Azure networking. Right, the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much.

• 0:43Speaker 00

  Maybe the next big startup could be someone who builds a new university, a new pedagogy even of how to get someone to go through a curriculum and find economic opportunity that's highly valuable.

• 1:11Speaker 01

  Please welcome Swix, Saragoa, Alad Gill, and Chairman and Chief Executive Officer of Microsoft, Satya Nadella.

• 1:29Speaker 00

  Hello.

• 1:32Speaker 02

  I'm so excited to be here. Welcome to a crossover episode of No Priors in Lane Space with Satya Nadella. Congratulations on an amazing build.

• 1:41Speaker 00

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

• 1:48Speaker 02

  So you're just talking about these amazing announcements from across the Microsoft estate all morning for, I think, three hours. What is the, what's the most important reflection or takeaway you have?

• 1:59Speaker 00

  I'd say there are, perhaps the biggest one for me is...

• 2:06Speaker 00

  Let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right? I mean, yeah, whatever, at least for me, having grown up at Microsoft, having seen whatever, four major platform shifts, I sort of fall into that camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you view what's happening right now, I think this morning's keynote was, how can any company, whether it's an AI-native company or a traditional enterprise company,

• 2:47Speaker 00

  participate as a first-class participant where they can point to AI they create, right? 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 a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do. Yeah.

• 3:10Speaker 02

  Ecosystem strategy is very complicated, right? Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the training strategy for Microsoft now.

• 3:27Speaker 00

  Yeah, so, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right? Starting with pre-training, with very good data quality, doing all the ablations, making sure, because in some senses, it's becoming even harder to build a clean lineage model, yes, because there's so much stuff out there that you truly need to ablate out to be able to have a fantastic pre-trained model. In fact, that's one of the challenges of a lot of the open-rate models is, they look great on one benchmark or two, but they're not great on practice.

• 4:06Speaker 00

  So that's why, in fact, even in our FDEs are pretty, gone really excited about these MAI models, because how the heck can a small five Model hill climb. Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage, then creating that ability for companies to be able to use this. Right? Not just as a generalist, but to create their own specialist. By building this hill climbing scaffold around it, right? So it's not just the model, but you have a hill climb scaffold around it, then

• 4:50Speaker 00

  you will start building your RLE, you will start collecting the traces. Most importantly, you'll have private evals because we know all the evals out there are good, interesting, but they're not really that critical at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end to end platform story around our models is sort of uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up is I do feel there's a new frontier, like people talk about the frontier and are you operating at the frontier? Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in, in, in fact, the, the Land O' Lakes demo we showed was pretty cool.

• 5:33Speaker 00

  We used whatever G P T five, five, right? Then you collected a bunch of traces and then you took a five B reasoning model and achieved higher. Uh, so that is another aspect of what it means to appear, I mean, operate at the frontier.

• 5:48Speaker 03

  Yeah, I, I think, uh, I first of all, I have to congratulate you on basically building a frontier in your lab inside of Microsoft in two years. Um, I'm wondering, you know, you have all this A I strategy that you're rolling out. But I'm wondering, what do you know now that you wish you would tell yourself two years ago? Wait, uh, two, three years ago, three years for the Jensen partnership, two years for, uh, M A I.

• 6:07Speaker 00

  Yeah, I mean, I think the, the thing that I reflect quite a bit, right? Which is sort of obviously I got into all this when I got excited by the, the scaling laws paper. And, you know, when, you know, even the OpenAI partnership came about, when those folks said, hey, we're going to really throw a lot of computer transformers. Uh, and they've helped, right? The thing that I always look back and say, wow, these things, um, do have capability that they're climbing up. Wait, I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now, what I think we underestimated perhaps is the real world complexity of deploying these so that they actually deliver the value in the real world, right?

• 6:51Speaker 00

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

• 7:33Speaker 04

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

• 7:46Speaker 00

  Yeah, I think, yeah, to your point, obviously coding is now got. But it's interesting, by the way, to even talk about the coding, right? Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we launched is like, oh, my God, I have these hundred agent sessions. I the cognitive load it transfers back to me as a human is so excessive that now I need a new UI. Oh, by the way, like the chat as the only artifact is also impossible. So that's why we need a canvas. So it's kind of interesting for all the things about where is software needed or where is UI needed? You kind of need that even for code, right? In a fully agentic world.

• 8:29Speaker 00

  But that said, one of the things that we are starting to see, we started seeing with cowork, but even some of the work we showed with autopilot, right? On what you see with claws is a good one, because if you sort of think about a lot of human capital is doing the glue work, right? If you now can Augment that with tokens slash agents that are long running durable. Right then your ability to scale even what is still judgment and glue work. Gets amplified like coding does. Uh, so you can like, I'm positive that six months from now we'll all be saying, oh, wow.

• 9:09Speaker 00

  Like all through night, the night, there was a bunch of stuff that all these autopilots that I have working on my behalf with my. Delegated authority, so to speak, right? I can sort of given even my identity. Did a bunch of work, then of course I'll need my new ADE to say, what did you do like that? My, did I do this work and so on? So I think that that's where compressing of workflows, uh, completing of tasks. Uh, that's where I think a lot of the value gets created.

• 9:37Speaker 04

  I think you raised a really interesting point, which is there's the actual agent is doing the code and then there's the harness around it. And that's the environment, that's the context, that's everything you're setting up as a developer around actually a 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 sort of?

• 9:56Speaker 00

  That's right. So, so in some sense, you kind of want the harness to define the models, the, the data. Uh, and the tools and so that you have a loop across those three. And so what we are trying to first of all, make sure is each of our products that we build, right? Whether it's get up copilot or the security copilot, the, the stuff we showed with M dash. Or even the discovery for science, it doesn't matter. All of them are multi-model harnesses. Um, with tools access so that you can do this progressive disclosure of tools even so that they're token efficient. Uh, and then you're feeding it with very rich context because.

• 10:37Speaker 00

  That's sort of the other hard lesson we've learned in the last two years is, oh my God, the amount of work you need to do. To prep the context layer. Uh, such that your plan can execute in the most efficient way is where the magic is. So we have in our case, we have the GitHub harness, which essentially we're using. Using across all our products, it's available in Foundry. And we're open, like you can use your Llama harness, whatever. Or you can use the, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now, a lot of dialogue is, hey, if I train the harness plus tools and the model together,

• 11:20Speaker 00

  you get evals. And what we are proving out is, and the best example of that is what we did with M dash, right? Because when it launched, it found bugs or vulnerabilities that were not found by Mythos. And so there is existence proof, I would claim, that you can have a multimodal harness that can in fact be more performant in the real world.

• 11:48Speaker 02

  So a premise behind the training at the independent Frontier Labs is really, you know, we're going to have these models and we'll have an API business. We'll support enterprises and startups, but if first-party product, be it productivity or 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 you have enablement products. What is the role of the developer? Like, what's going to be hard and the set of skills and the value capture the developer has in that world?

• 12:23Speaker 00

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

• 13:07Speaker 00

  So that's why the game becomes how to protect. So that's why I would say every company. Having private evals may be the biggest IP. Right? I think about it like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces? Maybe one of the biggest drivers of IP like so in other words, another test is you have an eval that's private. You're using a model A. Can you switch it to model B? And you know, climb up? If you can, then you're in control. If you can't, you're not in control. And that's where even the harness decision becomes super important, right?

• 13:50Speaker 00

  So therefore, having an open harness, letting all models come in, having your evals, your context, your tools. Help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs.

• 14:07Speaker 03

  Yeah, I think in a very real way, you are, Microsoft historically as an operating systems company and then become a cloud company. Maybe like the third act is that you're a harness or evals. Company, whatever, whatever the sort of conglomerate of concepts that you want to put together. Um, I, I think like enabling every company to have like frontier intelligence or what, what, I forget the, the exact term that you use. Um, is the, is the mission, right?

• 14:32Speaker 00

  That's it.

• 14:33Speaker 03

  That is, that is the platform promise that you build with us. You will get your intelligence for your data.

• 14:39Speaker 00

  That's it. That to me, that is the like, if there was one tagline for this entire developer conferences. Can everybody operate at the frontier with their frontier intelligence, right? To me, that is so important because otherwise it, I, I don't know how you achieve stable equilibrium, right? Which is 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 what's a platform that gets better, right? So when like Windows obviously came out. Adobe built, Autodesk built, or even like take what Jensen said. We built DX and he built, you know, CUDA on top of it.

• 15:23Speaker 00

  Right? I mean, I always say to Jensen, God, I got the short end of that, right? I wish we had recognized it. But nevertheless, but that idea that you can build a platform layer that someone else can then extend out and build their own intelligence layer. In this case, I think is everything, right? Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. But that's not a developer conference.

• 15:50Speaker 03

  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 in sort of applying agents to the company. I just want you to like freshen it all a bit, Marcus.

• 16:07Speaker 00

  Yeah, it's a great way to frame it, right? Because at the end of the day, every company is going to have both the human capital that is still going to be super valuable because humans and their ability to find the gaps that exist at all times is going to be the way we all will create value. Right? I mean, so 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. Right? So let's say any corporation has lots of tokens and a lot of human capital. The question is, how do you compound the two? So if you have a, like, if you take in teams, I have a bunch of agents doing work and a bunch of humans doing work.

• 16:49Speaker 00

  And the traces between those, that is really important context of how that enterprise is creating value. Then that goes back to train, not a generalist model, but to train the company veteran agent, right? That is super valuable again, right? Which is when a company goes, it should in fact go on to the balance sheet is how I think about it. Right? That's so, in fact, there may be like, human capital was never possible to go put on a balance sheet because you then know how to capture the tacit knowledge. Whereas now I think you can with the agents that have learned through the, through time, through all the traces. So that's what at least we think will happen.

• 17:31Speaker 03

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

• 17:37Speaker 02

  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 the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. And this was like the generation of SaaS companies and Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they're differentiated against. I'm sure you have heard much and participated much 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 what agents get built in enterprises

• 18:21Speaker 02

  versus in their vendors in the future?

• 18:23Speaker 00

  Yeah, so I think what's happening there is, see, we had a particular way we captured, I would say, workflow in apps, right? Because we built a data model, right? We schematized some part of some business process. We then built a bunch of business logic.

• 18:42Speaker 02

  Yep.

• 18:43Speaker 00

  And then we put a bunch of UI on top of it, right? So that's kind of what every SaaS company...

• 18:47Speaker 02

  And then a little configuration.

• 18:48Speaker 00

  For like 20 years, that was the plan. And that was it. So interestingly enough, now you kind of get to relitigate that vertical stacking, right? So I still think, for example, that data model that you build underneath every SaaS application is super good, right? Why reinvent it? My general ledger better be a general ledger. I don't need new schema creation. In fact, that entity relationship is actually a pretty good, robust thing that I want to feed.

• 19:18Speaker 02

  And you want it to be stable.

• 19:19Speaker 00

  That's right.

• 19:20Speaker 02

  Yeah.

• 19:20Speaker 00

  Then same thing with business logic, right? If you look at, we have this product called Power BI, right? It was like dashboards galore people created.

• 19:30Speaker 00

  The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures. And you want that, that's business logic, right? I want that to be available to me. So I think the challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right? I mean, if you look at it, what's happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, what we are realizing is, oh my God, like, if you look at it, in fact, there's a historical parallel to, right?

• 20:13Speaker 00

  We sold First Exchange and SharePoint and, you know, before Teams, we had a thing called Link Server and what have you. And we thought, oh, that's all going to move to the cloud. But literally, we realized that the number of people who will use servers in the cloud is 10x, 100x, right? Because people were not buying servers, they were just buying a subscription. The same thing is now happening with M365 because with Work IQ, we have exposed what was perhaps the most important database in a company that never got used as a database because it was only captive to our apps, right? It was all email operated on it, Teams operated on it, Word, Excel, PowerPoint, SharePoint. But now, like, this is one of the coolest things I get to do with Work IQ.

• 20:56Speaker 00

  I go to a GitHub repo and I 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? I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a codebase, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10x more. But it does require us to have, for example, there's going to be usage around M365, right? Which is going to be perhaps more than even the end users. And we have to even re-architect, like, in fact, like, what I use to serve an inbox or a mailbox

• 21:41Speaker 00

  cannot be used to serve an agent. And so that's sort of what we're doing.

• 21:47Speaker 02

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

• 21:59Speaker 02

  Enterprise bundles.

• 22:00Speaker 00

  Yeah, the way I think about this is always we've had, like, let's even take the per-user pricing. The per-user pricing is really an artifact of someone creating a budget needing certainty. Right? Because it's the most important thing. Like, somebody wants a budget, they need a per-user. And, and per-user is just a set of entitlements to usage. Right? That's kind of what it is. And so the way is, if the first bundling will be, take some usage, bundle it into per-user stacks and, you know, then sell subscriptions. So subscriptions, I think, are going to be there. Per-user is going to be there. Then the next big thing will be consumption. So people will say, I want consumption.

• 22:43Speaker 00

  And it's also possible that people will say, I don't even want to pay for any of the subscriptions or the consumption's outcome. But remember, most people love outcomes until they have an outcome. Because once you have an outcome, it's like giving away royalty. Right? I mean, like, I've, I've talked to customers who love, you know, outcome-based pricing, and I say, I'm all in. Until they, oh, my God, like, 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. Right? So I think that debate will go on. Uh, but, and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility. And quite frankly, we faced this with GitHub, right?

• 23:27Speaker 00

  We just recently announced a per-user pricing on GitHub. Because little, you know, GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents. Right? It was an interactive way for a developer to use code complete, maybe task. It was not like, oh, I launched 10,000, you know, agents that are going on all day. Right? So that is what the adjustment is about. So now that we really want, there will always be a per-user, but there will have to be a consumption meter.

• 24:02Speaker 04

  How do you think about the durability of SaaS more generally? One thing I've observed is 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. They're going to their SaaS vendors and saying, we're not going to work with you anymore. Or we're considering an internal project. And it seems like in six to nine months, maybe some of those people will come back and say, actually, we, we can't rebuild everything. How do you think about what's durable in this world and what isn't?

• 24:28Speaker 00

  I think we have to go through one full budget cycle on this to really see the

• 24:37Speaker 00

  the sort of the emergence of the equilibrium because at the end of the day there's marginal cost to even generating the app, right? In fact, there can be even a simple way to say it like if you should always acquire something if the marginal cost of building and maintaining something on your own is higher. Right? That should be like it's a quantifiable, right? A quantifiable thing and the maintenance part is important, right? Even like you gotta remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a a cycle that you've got to think through.

• 25:21Speaker 00

  And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would 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? Right? Because I think there'll be very little tolerance for anybody who's inflexible at the vendor level. But at the same time, I think that anyone who has got that flexibility, shows up, delivers the value will be back again, right? We're selling software, but with just different business models, in fact.

• 25:58Speaker 03

  Speaking about building software, one of my favorite moments from, I think, a previous build, maybe one or two years ago was they had a they there was a section of you building your own software. I'm curious if you're building anything now.

• 26:09Speaker 00

  Yeah, so I I think the, you know, first of all, let's face it, right? Building software has made it possible for even the incompetence of a CEO of a company like ours. You can build, so thank God. But that said, I I I do feel that You know, something like GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before, right? So for me, as a CEO even, to go to a codebase, to be able to learn about it. Like, I remember joining Microsoft long back, you know, first, and then you say,

• 26:53Speaker 00

  man, everybody had to go in and look at, you know, whatever, Cutler's, Malacca, or what have you, to learn how to write good C++ code. So 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, see things? I think it's just so much more. And so to me, what I'm building a lot of is these long-running foundry agents, right? So there's autopilots. So the easiest thing is, to me, I think I just built one even last week, where the idea was, hey, can I have an agent that is continuously monitoring,

• 27:38Speaker 00

  essentially, my own chief of staff autopilot, right? 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. I said, take work IQ, go and build a foundry long-running agent, store all the memory in, using Raffin, right? Basically, at my backend as a service. And lo and behold, it built it. And not only built it, I could say, published to Teams, and it published the damn thing to Teams. So the ability to have, you know, some end-to-end project like this complete is just pretty miraculous.

• 28:17Speaker 04

  How do you think that impacts the different types of engineering roles that exist in the future? Because right now, I think there's, you know, a dozen different types of engineers that you can be, from QA, front-end, etc. You know, there's a big swath. I've heard some people argue that in four or five years, we'll basically end up with four engineering roles. It'll be people who are managing agents. It'll be four deployed engineers, or FDEs. It'll be security engineers. And then people working on large-scale infrastructure for a small number of services. And then everything else just collapses into the agentic world.

• 28:47Speaker 00

  Yeah.

• 28:47Speaker 04

  Do you think that's a correct view of the world?

• 28:49Speaker 00

  Yeah, I mean, I think... I think we'll have to experiment our way through it. But what you said is what, there are some very at-scale things. At LinkedIn, they did structurally change. And, you know, basically built up a new discipline called Full Stack Builder, right? So they went and said, hey, let's bring people from design and product management, front-end engineering, all, put them together. But also have an edge, right? It's not like the design person still doesn't have the design edge, or the front-end person doesn't have the front-end edge. But you can give yourself bigger scope in roles, so that you're not confined to one role. And then, equally, infrastructure has become very critical, right?

• 29:31Speaker 00

  So in other words, like, I mean, RLEs, I mean, one thing we have realized is, even for the Excel team, for example, building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems. And so you kind of need even new talent, right? Distributor 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. So I think we'll see how these evolve, right? Where's the real, I mean, always the world will have a bunch of specialists. You know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist

• 30:17Speaker 00

  is where we are going to see the maximum returns, right? When you said, hey, I'm coding, I'm now a gen, like, what? I basically translated knowledge work, right? Which I did, where I created a Word document, or a spreadsheet, or even, and now I can build an app, right? It's in the same sentence, right? That idea that, oh, wow, my generalist skills have gotten a higher leverage, I think is what we're going to see across the board.

• 30:46Speaker 02

  Music to the ears of CEOs and VCs that are like, a little dangerous and a lot of things.

• 30:51Speaker 03

  Golden age for idea people.

• 30:52Speaker 02

  Idea people, man, with a lot of agency. If you take that idea of personal agency and you just zoom it out to the organizational context. My partner Mike Rinaldi, who actually started his career at Microsoft, just wrote an essay where one of the big takeaways is 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, actually, users and companies are open to adopting new technologies. How do you think about, 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?

• 31:28Speaker 00

  It's a great question. I think the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right? In fact, Kevin Scott has this nice line, which is, when you can make the impossible, like when you're making hard things easier, that's sort of one point of leverage. But true ambition is about making the impossible possible.

• 32:08Speaker 00

  So now, the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build? What was impossible and what can we build? And I'll give you one example of this, which is, I take great inspiration from the people who were managing the Azure network. And they came to the, this was from even last year. You know, 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. I mean, it's crazy, right? It's pretty wild. And it's the same team. So they saw that and they said, Bob, this just ain't gonna work if we don't reconceptualize our work.

• 32:50Speaker 00

  So they built, 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 van, right? All over. And fiber operations ultimately is a physical operation. Things get caught, things get, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, 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, right? They started sort of screaming for more tokens and so on. And so they were saying, look, we don't need headcount.

• 33:34Speaker 00

  We need tokens in order to be able to manage our operation. That reconceptualization of what their work is, right? They basically took their work and made it meta, right? That meta work is now their new work, right? In the eighties, if somebody had come to us and said, four billion people are going to get up in the morning and start typing. My model would have been, we need four billion typists? But we're not doing typing, we're doing knowledge work. So that, to me, I think is it, right? Which is, whether it's Microsoft or whether it's any organization, is to give ourselves permission to do new types of meta cognition, meta work, using these new tools to change the outputs that matter

• 34:19Speaker 00

  and then really make the impossible possible. So completing that dot or the connective tissue across those, I think is where a lot of the enterprise value will get created.

• 34:30Speaker 03

  Should we talk about data centers?

• 34:31Speaker 00

  Yeah, please ask.

• 34:32Speaker 03

  Oh, okay. Well, we, this leads nicely into the data center build out. I always think, I'm just impressed with the sheer scale of the build out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler. And I just feel like that is unprecedented scale on finances, on the way you run the company, but also the communities that are impacted. Can I just talk a bit more about what you're seeing on the ground? Like when you visit your...

• 34:59Speaker 00

  Yeah, I think there are two aspects of it. Obviously, the build out is extraordinary. You know, nothing like this has happened and it's great to be one of the participants in it. But you brought up the other part, right? I think at this point, it's clear that unless we as an industry are very...

• 35:21Speaker 00

  Principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways at the community level, right? Because this is not just a campaign, right? 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 is going to be more energy. Water consumption is in fact not sort of, in fact, water is being replenished, right? You gotta really, you know, educate folks on truly what's happening, the closed-loop systems we are building. We have to invest in the training, the jobs, the tax base.

• 36:04Speaker 00

  In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction, what's the tax base that's there in the community? And all this has to be real. And 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, right? Which is, I think we have to take it as an industry pretty seriously. I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that. But at the end of the day, if this, if we can really be the producer, 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.

• 36:50Speaker 00

  If you don't do that, it's not been that great. And 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 broad spread, you know, participation, better health outcomes, then I think we will be in a great place. And that's at least what we all have to be focused on.

• 37:13Speaker 03

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

• 37:21Speaker 00

  I mean, I think both sides. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants in the real economy, right? That's, I think, the question is like if we, if the broad economy is doing well and the communities are doing well, the dots get connected is sort of the market forces are such that we will connect the dots. And that, I think, is it like you ought to be able to see the evidence. You can't be about any one company, but it has to be broad economic growth and broad, you know, community permission.

• 37:57Speaker 04

  What have you most updated your personal models on regarding societal impact of AI?

• 38:05Speaker 00

  So you're saying what's the?

• 38:07Speaker 03

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

• 38:10Speaker 00

  Yeah, I think the the the most critical thing is the first question we even 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. But that's kind of, I think, in the next 12 months, 18 months, we need a way for people to say, oh, wow, I get it. Right? There's going to be tremendous capability, tremendous amount of infrastructure. But I can see what is going to happen, whether it's the benefits like health outcomes

• 38:55Speaker 00

  or my ability to create a startup or my ability to run my local sort of store more efficiently. It's just happening and I see that benefit myself, right? That to me, you know, earning that permission in a path dependent way, we can't wait. See, the one thing you like that I've now learned is I think the world is going to be very 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.

• 39:35Speaker 00

  And quite frankly, politicians winning elections because they have advocated for that. That will be at least my adjustment because without it, 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.

• 39:53Speaker 02

  So one very simple framework I have for, you know, what are, what is going to be the broad benefit of AI beyond the communities just working in technology are sort of wealth creation. It's 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 Open Evidence. 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?

• 40:25Speaker 00

  Yeah, I mean, I think this is where again, how we think about education, how, you know, recently I met with the founders of Alpha School and learnt a lot about what they were going and going about. And it's fascinating to listen to how to even rethink what does education really look like? Because I think it's actually very important and I'm not saying anything traditionally being done is less important, right? I was even looking at the, it's fascinating to see, I forget which Stanford class it was, the Asian Guidelines for CS something, because you still need people to learn. Like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately

• 41:06Speaker 00

  versus saying, hey, fix my training run. So I think learning concepts is important. It's going to be critical, but the way we create the incentives, what are the credentials? How we value those credentials? What is the employment opportunity for those credentials? So I think that there is a complete change that has to happen, given the way to get to information, way to educate yourself, way to continue to keep yourself updated as change so much. So I think, interestingly enough, maybe the next big startup and success story could be

• 41:43Speaker 00

  someone who builds a new university or a new pedagogy even of how to get someone to go through a curriculum and find economic opportunity that's highly valuable.

• 41:57Speaker 02

  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.

• 42:03Speaker 00

  It's not possible.

• 42:04Speaker 02

  Thank you, Sanya.

• 42:05Speaker 00

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

• 42:09Speaker 02

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