Kevin Scott: Microsoft CTO | Lex Fridman Podcast #30
 The following is a conversation with Kevin Scott, the CTO of Microsoft. Before that, he was the Senior Vice President of Engineering and Operations at LinkedIn, and before that, he oversaw mobile ads engineering at Google. He also has a podcast called Behind the Tech with Kevin Scott, which I'm a fan of. This was a fun and wide ranging conversation that covered many aspects of computing. It happened over a month ago before the announcement of Microsoft's investment in OpenAI that a few people have asked me about. I'm sure there'll be one or two people in the future that'll talk with me about the impact of that investment. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, give it five stars on iTunes, support it on Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D-M-A-N. And I'd like to give a special thank you to Tom and Nalanti Bickhausen for their support of the podcast on Patreon. Thanks, Tom and Nalanti. Hope I didn't mess up your last name too bad. Your support means a lot and inspires me to keep this series going. And now, here's my conversation with Kevin Scott. You've described yourself as a kid in a candy store at Microsoft because of all the interesting projects that are going on. Can you try to do the impossible task and give a brief, whirlwind view of all the spaces that Microsoft is working in, both research and product? If you include research, it becomes even more difficult. So I think, broadly speaking, Microsoft's product portfolio includes everything from big cloud business, like a big set of SaaS services. We have sort of the original, or like some of what are among the original productivity software products that everybody uses. We have an operating system business. We have a hardware business where we make everything from computer mice and headphones to high-end personal computers and laptops. We have a fairly broad-ranging research group where we have people doing everything from economics research. So there's this really smart young economist, Glen Weil, who my group works with a lot, who's doing this research on these things called radical markets. He's written an entire technical book about this whole notion of radical markets. So the research group spans from that to human-computer interaction, to artificial intelligence. And we have GitHub, we have LinkedIn, we have a search advertising and news business, and probably a bunch of stuff that I'm embarrassingly not recounting in this list. On gaming to Xbox and so on, right? Yeah, gaming for sure. I was having a super fun conversation this morning with Phil Spencer. So when I was in college, there was this game that LucasArts made called Day of the Tentacle that my friends and I played forever. And we're doing some interesting collaboration now with the folks who made Day of the Tentacle. And I was completely nerding out with Tim Schafer, the guy who wrote Day of the Tentacle this morning, just a complete fan boy, which sort of, it happens a lot. Microsoft has been doing so much stuff at such breadth for such a long period of time that being CTO, most of the time, my job is very, very serious. And sometimes I get caught up in how amazing it is to be able to have the conversations that I have with the people I get to have them with. You have to reach back into the sentimental. And what's the radical markets and the economics? So the idea with radical markets is like, can you come up with new market-based mechanisms to, you know, I think we have this, we're having this debate right now, like does capitalism work, like free markets work? Can the incentive structures that are built into these systems produce outcomes that are creating sort of equitably distributed benefits for every member of society? You know, and I think it's a reasonable set of questions to be asking. And so what Glenn, and so like, you know, one mode of thought there, like if you have doubts that the markets are actually working, you can sort of like tip towards, like, okay, let's become more socialist and, you know, like have central planning and, you know, governments or some other central organization is like making a bunch of decisions about how, you know, sort of work gets done and, you know, like where the, you know, where the investments and where the outputs of those investments get distributed. Glenn's notion is like lean more into, like the market-based mechanism. So like, for instance, you know, this is one of the more radical ideas. Like suppose that you had a radical pricing mechanism for assets like real estate where you were, you could be bid out of your position in your home, you know, for instance. So like if somebody came along and said, you know, like I can find higher economic utility for this piece of real estate that you're running your business in, like then like you either have to, you know, sort of bid to sort of stay or like the thing that's got the higher economic utility, you know, sort of takes over the asset, which would make it very difficult to have the same sort of rent seeking behaviors that you've got right now, because like if you did speculative bidding, like you would very quickly like lose a whole lot of money. And so like the prices of the assets would be sort of like very closely indexed to like the value that they could produce. And like, because like you'd have this sort of real-time mechanism that would force you to sort of mark the value of the asset to the market, then it could be taxed appropriately. Like you couldn't sort of sit on this thing and say, oh, like this house is only worth 10,000 bucks when like everything around it is worth 10 million. That's for the, so it's a incentive structure that, where the prices match the value much better. Yeah, and Glenn does a much better job than I do at selling and I probably picked the world's worst example, you know, and, and, and, but like, and it's intentionally provocative, you know, so like this whole notion, like I, you know, like I'm not sure whether I like this notion that like we could have a set of market mechanisms where I could get bit out of, out of my property, you know, but, but, you know, like if you're thinking about something like Elizabeth Warren's wealth tax, for instance, like you would have, I mean, it'd be really interesting in like how you would actually set the, the, the price on the assets. And like, you might have to have a mechanism like that if you put a tax like that in place. It's really interesting that that kind of research at least tangentially is touching Microsoft research. Yeah, you're really thinking broadly. Maybe you can speak to, if this connects to AI. So we have a candidate, Andrew Yang, who kind of talks about artificial intelligence and the concern that people have about, you know, automation's impact on society. And arguably Microsoft is at the cutting edge of innovation in all these kinds of ways. And so it's pushing AI forward. How do you think about, combining all our conversations together here with radical markets and socialism and innovation in AI that Microsoft is doing, and then Andrew Yang's worry that that will result in job loss for the lower and so on. How do you think about that? I think it's sort of one of the most important questions in technology, like maybe even in society right now about how is AI going to develop over the course of the next several decades and like, what's it gonna be used for? And like, what benefits will it produce? And what negative impacts will it produce? And, you know, who gets to steer this whole thing? You know, I'll say at the highest level, one of the real joys of getting to do what I do at Microsoft is Microsoft has this heritage as a platform company. And so, you know, like Bill has this thing that he said a bunch of years ago where, you know, the measure of a successful platform is that it produces far more economic value for the people who build on top of the platform than is created for the platform owner or builder. And I think we have to think about AI that way. Like, yeah, it has to, like, it has to be a platform that other people can use to build businesses, to fulfill their creative objectives, to be entrepreneurs, to solve problems that they have in their work and in their lives. It can't be a thing where there are a handful of companies sitting in a very small handful of cities geographically who are making all the decisions about what goes into the AI. And then on top of all this infrastructure, then build all of the commercially valuable uses for it. So I think that's bad from a sort of economics and sort of equitable distribution of value perspective, like, you know, sort of back to this whole notion of, you know, like, do the markets work? But I think it's also bad from an innovation perspective because, like, I have infinite amounts of faith in human beings that if you, you know, give folks powerful tools, they will go do interesting things. And it's more than just a few tens of thousands of people with the interesting tools. It should be millions of people with the tools. So it's sort of like, you know, you think about the steam engine in the late 18th century. Like, it was, you know, maybe the first large scale substitute for human labor that we've built like a machine. And, you know, in the beginning, when these things are getting deployed, the folks who got most of the value from the steam engines were the folks who had capital, so they could afford to build them. And like, they built factories around them and businesses, and the experts who knew how to build and maintain them. But access to that technology democratized over time. Like now, like an engine is not a, it's not like a differentiated thing. Like there isn't one engine company that builds all the engines and all of the things that use engines are made by this company. And like, they get all the economics from all of that. Like, no, no, no, like fully democratized. Like they're probably, you know, we're sitting here in this room. And like, even though they don't, they're probably things, you know, like the MEMS gyroscope that are in both of our, like they're like little engines, you know, sort of everywhere. They're just a component in how we build the modern world, like AI needs to get there. Yeah, so that's a really powerful way to think. If we think of AI as a platform versus a tool that Microsoft owns, as a platform that enables creation on top of it, that's a way to democratize it. That's really interesting, actually. And Microsoft throughout its history has been positioned well to do that. And the, you know, the tie back to this radical markets thing, like the, so my team has been working with Glenn on this and Jaren Lanier actually. So Jaren is the, like the sort of father of virtual reality. Like he's one of the most interesting human beings on the planet, like a sweet, sweet guy. And so Jaren and Glenn and folks in my team have been working on this notion of data as labor or like they call it data dignity as well. And so the idea is that if you, you know, again, going back to this sort of industrial analogy, if you think about data as the raw material that is consumed by the machine of AI in order to do useful things, then like we're not doing a really great job right now in having transparent marketplaces for valuing those data contributions. So like, and we all make them like explicitly, like you go to LinkedIn, you sort of set up your profile on LinkedIn. Like that's an explicit contribution. Like, you know exactly the information that you're putting into the system. And like, you put it there because you have some nominal notion of like what value you're gonna get in return. But it's like only nominal. Like you don't know exactly what value you're getting in return. Like services free, you know, like it's low amount of like perceived debt. And then you've got all this indirect contribution that you're making just by virtue of interacting with all of the technology that's in your daily life. And so like what Glenn and Jaren and this data dignity team are trying to do is like, can we figure out a set of mechanisms that let us value those data contributions so that you could create an economy and like a set of controls and incentives that would allow people to like maybe even in the limit, like earn part of their living through the data that they're creating. And like, you can sort of see it in explicit ways. There are these companies like Scale AI and like there are a whole bunch of them in China right now that are basically data labeling companies. So like you're doing supervised machine learning, you need lots and lots of label training data. And like those people are getting, who work for those companies are getting compensated for their data contributions into the system. And so. That's easier to put a number on their contribution because they're explicitly labeling data. Correct. But you're saying that we're all contributing data in different kinds of ways. And it's fascinating to start to explicitly try to put a number on it. Do you think that's possible? I don't know, it's hard. It really is. Because we don't have as much transparency as I think we need, in like how the data is getting used. And it's super complicated. Like we, I think as technologists sort of appreciate like some of the subtlety there. It's like the data gets created and then it gets, it's not valuable. Like the data exhaust that you give off or the explicit data that I am putting into the system isn't super valuable atomically. Like it's only valuable when you sort of aggregate it together into sort of large numbers. It's true even for these like folks who are getting compensated for like labeling things. Like for supervised machine learning now, like you need lots of labels to train a model that performs well. And so, I think that's one of the challenges. It's like, how do you, how do you sort of figure out because this data is getting combined in so many ways, like through these combinations, like how the value is flowing. Yeah, that's fascinating. Tough. Yeah, and it's fascinating that you're thinking about this. And I wasn't even going to this conversation expecting the breadth of research really that Microsoft broadly is thinking about, you are thinking about it, Microsoft. So, if we go back to 89 when Microsoft released Office or 1990 when they released Windows 3.0, how's the, in your view, I know you weren't there the entire, you know, through its history, but how has the company changed in the 30 years since as you look at it now? The good thing is it's started off as a platform company, like it's still a platform company, like the parts of the business that are like thriving and most successful are those that are building platforms. Like the mission of the company now is, the mission's changed. It's like changed in a very interesting way. So, you know, back in 89, 90, like they were still on the original mission, which was like put a PC on every desk and in every home, like, and it was basically about democratizing access to this new personal computing technology, which when Bill started the company, integrated circuit microprocessors were a brand new thing. And like people were building, you know, homebrew computers, you know, from kits, like the way people build ham radios right now. And I think this is sort of the interesting thing for folks who build platforms in general. Bill saw the opportunity there and what personal computers could do. And it was like, it was sort of a reach, like you just sort of imagine like where things were, you know, when they started the company versus where things are now. Like in success, when you've democratized a platform, it just sort of vanishes into the platform. You don't pay attention to it anymore. Like operating systems aren't a thing anymore. Like they're super important, like completely critical. And like, you know, when you see one, you know, fail, like you just, you sort of understand, but like, you know, it's not a thing where you're not like waiting for, you know, the next operating system thing in the same way that you were in 1995, right? Like in 1995, like, you know, we had Rolling Stones on the stage with the Windows 95 rollout. Like it was like the biggest thing in the world. Everybody was like lined up for it in the way that people used to line up for iPhone. But like, you know, eventually, and like this isn't necessarily a bad thing. Like it just sort of, you know, the success is that it's sort of, it becomes ubiquitous. It's like everywhere and like human beings, when their technology becomes ubiquitous, they just sort of start taking it for granted. So the mission now that Satya rearticulated five plus years ago now, when he took over as CEO of the company, our mission is to empower every individual and every organization in the world to be more successful. And so, you know, again, like that's a platform mission. And like the way that we do it now is different. It's like we have a hyperscale cloud that cloud or building our applications on top of, like we have a bunch of AI infrastructure that people are building their AI applications on top of. We have, you know, we have a productivity suite of software like Microsoft dynamics, which, you know, some people might not think is the sexiest thing in the world, but it's like helping people figure out how to automate all of their business processes and workflows and to, you know, like help those businesses using it to like grow and be more successful. So it's a much broader vision in a way now than it was back then. Like it was sort of very particular thing. And like now, like we live in this world where technology is so powerful and it's like such a basic basic fact of life that it, you know, that it both exists and is going to get better and better over time or at least more and more powerful over time. So like, you know, what you have to do as a platform player is just much bigger. Right. There's so many directions in which you can transform. You didn't mention mixed reality too. You know, that's probably early days or it depends how you think of it. But if we think on a scale of centuries, it's the early days of mixed reality. Oh, for sure. And so with how a lens, the Microsoft is doing some really interesting work there. Do you touch that part of the effort? What's the thinking? Do you think of mixed reality as a platform too? Oh, sure. When we look at what the platforms of the future could be, it's like fairly obvious that like AI is one. Like you don't have to, I mean, like that's, you know, you sort of say it to like someone and you know, like they get it. But like we also think of the like mixed reality and quantum is like these two interesting, you know, potentially. Quantum computing. Yeah. Okay, so let's get crazy then. So you're talking about some futuristic things here. Well, the mixed reality, Microsoft is really not even futuristic is here. It is. It's incredible stuff. And look, and it's having an impact right now. Like one of the more interesting things that's happened with mixed reality over the past couple of years that I didn't clearly see is that it's become the computing device for folks who, for doing their work who haven't used any computing device at all to do their work before. So technicians and service folks and people who are doing like machine maintenance on factory floors. So like they, you know, because they're mobile and like they're out in the world and they're working with their hands and you know, sort of servicing these like very complicated things, they're, they don't use their mobile phone and like they don't carry a laptop with them. And you know, they're not tethered to a desk. And so mixed reality like where it's getting traction right now where HoloLens is selling a lot of units is for these sorts of applications for these workers and it's become like, I mean, like the people love it. They're like, oh my God, like this is like for them, like the same sort of productivity boost that, you know, like an office worker had when they got their first personal computer. Yeah, but you did mention it's certainly obvious AI as a platform, but can we dig into it a little bit? Sure. How does AI begin to infuse some of the products in Microsoft? So currently providing training of for example, neural networks in the cloud or providing pre-trained models or just even providing computing resources and whatever different inference that you wanna do using neural networks. Yep. Well, how do you think of AI infusing as a platform that Microsoft can provide? Yeah, I mean, I think it's super interesting. It's like everywhere and like we run these, we run these review meetings now where it's me and Satya and like members of Satya's leadership team and like a cross-functional group of folks across the entire company who are working on like either AI infrastructure or like have some substantial part of their product work using AI in some significant way. Now the important thing to understand is like when you think about like how the AI is gonna manifest and like an experience for something that's gonna make it better, like I think you don't want the AI-ness to be the first order thing. It's like whatever the product is and like the thing that it's trying to help you do, like the AI just sort of makes it better. And this is a gross exaggeration, but like people get super excited about like where the AI is showing up in products and I'm like, do you get that excited about like where you're using a hash table in your code? Like it's just another. It's just a tool. It's a very interesting programming tool, but it's sort of like it's an engineering tool. And so like it shows up everywhere. So like we've got dozens and dozens of features now in Office that are powered by like fairly sophisticated machine learning. Our search engine wouldn't work at all if you took the machine learning out of it. The like increasingly, you know, things like content moderation on our Xbox and X cloud platform. When you mean moderation, you mean like the recommender is like showing what you wanna look at next? No, no, no, it's like anti-bullying stuff. So the usual social network stuff that you have to deal with. Yeah, correct. But it's like really it's targeted towards a gaming audience. So it's like a very particular type of thing where, you know, the lime between playful banter and like legitimate bullying is like a subtle one. And like you have to, it's sort of tough. Like I have. I'd love to, if we could dig into it, because you're also, you led the engineering efforts of LinkedIn. And if we look at LinkedIn as a social network, and if we look at the Xbox gaming as the social components, the very different kinds of, I imagine, communication going on on the two platforms, right? And the line in terms of bullying and so on is different on the two platforms. So how do you, I mean, it's such a fascinating philosophical discussion of where that line is. I don't think anyone knows the right answer. Twitter folks are under fire now, Jack, at Twitter for trying to find that line. Nobody knows what that line is. But how do you try to find the line for, you know, trying to prevent abusive behavior, and at the same time, let people be playful and joke around and that kind of thing. I think in a certain way, like, you know, if you have what I would call vertical social networks, it gets to be a little bit easier. So like, if you have a clear notion of like what your social network should be used for, or like what you are designing a community around, then you don't have as many dimensions to your sort of content safety problem as you know, as you do in a general purpose platform. I mean, so like on LinkedIn, like the whole social network is about connecting people with opportunity, whether it's helping them find a job or to, you know, sort of find mentors or to, you know, sort of help them like find their next sales lead, or to just sort of allow them to broadcast their, you know, sort of professional identity to their network of peers and collaborators and, you know, sort of professional community. Like that is, I mean, like in some ways, like that's very, very broad, but in other ways it's sort of, you know, it's narrow. And so like, you can build AI's, like machine learning systems that are, you know, capable with those boundaries of making better automated decisions about like, what is, you know, sort of inappropriate and offensive comment or dangerous comment or illegal content when you have some constraints. You know, same thing with, yeah, same thing with like the gaming social networks. So for instance, like it's about playing games, about having fun. And like the thing that you don't want to have happen on the platform is why bullying is such an important thing. Like bullying is not fun. So you want to do everything in your power to encourage that not to happen. And yeah, but I think it's a, it's sort of a tough problem in general. And it's one where I think, you know, eventually we're gonna have to have some sort of clarification from our policy makers about what it is that we should be doing, like where the lines are, because it's tough. Like you don't, like in democracy, right? Like you don't want, you want some sort of democratic involvement. Like people should have a say in like where the lines are drawn. Like you don't want a bunch of people making like unilateral decisions. And like we are in a state right now for some of these platforms where you actually do have to make unilateral decisions where the policymaking isn't gonna happen fast enough in order to like prevent very bad things from happening. But like we need the policymaking side of that to catch up I think as quickly as possible because you want that whole process to be a democratic thing, not a, you know, not some sort of weird thing where you've got a non-representative group of people making decisions that have, you know, like national and global impact. It's fascinating because the digital space is different than the physical space in which nations and governments were established. And so what policy looks like globally, what bullying looks like globally, what healthy communication looks like globally is an open question. And we're all figuring it out together. Which is fascinating. Yeah, I mean with, you know, sort of fake news for instance and... Deep fakes and fake news generated by humans. Yeah, so and we can talk about deep fakes. Like I think that is another like, you know, sort of very interesting level of complexity. But like if you think about just the written word, right? Like we have, you know, we invented Papyrus, what, 3000 years ago where we, you know, you could sort of put word on paper. And then 500 years ago, like we get the printing press, like where the word gets a little bit more ubiquitous. And then like you really, really didn't get ubiquitous printed word until the end of the 19th century when the offset press was invented. And then, you know, just sort of explodes and like, you know, the cross product of that and the industrial revolution's need for educated citizens resulted in like this rapid expansion of literacy and the rapid expansion of the word. But like we had 3000 years up to that point to figure out like how to, you know, like what's journalism, what's editorial integrity, like what's, you know, what's scientific peer review. And so like you built all of this mechanism to like try to filter through all of the noise that the technology made possible to like, you know, sort of getting to something that society could cope with. And like, if you think about just the piece, the PC didn't exist 50 years ago. And so in like this span of, you know, like half a century, like we've gone from no digital, you know, no ubiquitous digital technology to like having a device that sits in your pocket where you can sort of say whatever is on your mind to like, what did Mary Haven or Mary Meeker just released her new like slide deck last week. You know, we've got 50% penetration of the internet to the global population. Like there are like three and a half billion people who are connected now. So it's like, it's crazy, crazy, like inconceivable, like how fast all of this happens. So, you know, it's not surprising that we haven't figured out what to do yet, but like we gotta like, we gotta really like lean into this set of problems because like we basically have three millennia worth of work to do about how to deal with all of this and like probably what, you know, amounts to the next decade worth of time. So since we're on the topic of tough, you know, tough, challenging problems, let's look at more on the tooling side in AI that Microsoft is looking at is face recognition software. So there's a lot of powerful positive use cases for face recognition, but there's some negative ones and we're seeing those in different governments in the world. So how do you, how does Microsoft think about the use of face recognition software as a platform in governments and companies? So yeah, how do we strike an ethical balance here? Yeah, I think we've articulated a clear point of view. So Brad Smith wrote a blog post last fall, I believe, that sort of like outline like very specifically what, you know, what our point of view is there. And, you know, I think we believe that there are certain uses to which face recognition should not be put. And we believe again, that there's a need for regulation there, like the government should like really come in and say that, you know, this is where the lines are. And like we very much wanted to like figuring out where the lines are should be a democratic process. But in the short term, like we've drawn some lines where, you know, we push back against uses of face recognition technology, you know, like the city of San Francisco, for instance, I think has completely outlawed any government agency from using face recognition tech. And like that may prove to be a little bit overly broad, but for like certain law enforcement things, like you really, I would personally rather be overly sort of cautious in terms of restricting use of it until like, we have, you know, sort of defined a reasonable, you know, democratically determined regulatory framework for like where we could and should use it. And, you know, the other thing there is like, we've got a bunch of research that we're doing and a bunch of progress that we've made on bias there. And like, there are all sorts of like weird biases that these models can have, like all the way from like the most noteworthy one where, you know, you may have underrepresented minorities who are like underrepresented in the training data and then you start learning like strange things, but like they're even, you know, other weird things like we've, I think we've seen in the public research, like models can learn strange things like all doctors or men, for instance. Yeah, I mean, and so like, it really is a thing where it's very important for everybody who is working on these things before they push publish, they launch the experiment, they, you know, push the code to, you know, online or they even publish the paper that they are at least starting to think about what some of the potential negative consequences are, some of this stuff. I mean, this is where, you know, like the deep fake stuff I find very worrisome just because there are gonna be some very good beneficial uses of like GAN generated imagery. And like, and funny enough, like one of the places where it's actually useful is we're using the technology right now to generate synthetic visual data for training some of the face recognition models to get rid of the bias. So like, that's one like super good use of the tech, but like, you know, it's getting good enough now where, you know, it's gonna sort of challenge a normal human being's ability to like, now you're just sort of say like, it's very expensive for someone to fabricate a photorealistic fake video. And like GANs are gonna make it fantastically cheap to fabricate a photorealistic fake video. And so like what you assume you can sort of trust is true versus like be skeptical about is about to change. And like, we're not ready for it, I don't think. The nature of truth, right. That's, it's also exciting because I think both you and I probably would agree that the way to solve, to take on that challenge is with technology. Yeah. Right, there's probably going to be ideas of ways to verify which kind of video is legitimate, which kind is not. So to me, that's an exciting possibility, most likely for just the comedic genius that the internet usually creates with these kinds of videos. And hopefully will not result in any serious harm. Yeah, and it could be, like I think we will have technology to that may be able to detect whether or not something's fake or real, although the fakes are pretty convincing even like when you subject them to machine scrutiny. But we also have these increasingly interesting social networks that are under fire right now for some of the bad things that they do. Like one of the things you could choose to do with a social network is like you could use crypto and the networks to like have content signed where you could have a like full chain of custody that accompanied every piece of content. So like when you're viewing something and like you want to ask yourself like, how much can I trust this? Like you can click something and like have a verified chain of custody that shows like, oh, this is coming from this source and it's like signed by like someone whose identity I trust. Yeah, I think having that chain of custody like being able to like say, oh, here's this video, like it may or may not have been produced using some of this deep fake technology. But if you've got a verified chain of custody where you can sort of trace it all the way back to an identity and you can decide whether or not like I trust this identity, like, oh no, this is really from the White House or like this is really from the office of this particular presidential candidate or it's really from Jeff Weiner, CEO of LinkedIn or Satya Nadella, CEO of Microsoft. Like that might be like one way that you can solve some of the problems. So like that's not the super high tech, like we've had all of this technology forever. And but I think you're right, like it has to be some sort of technological thing because the underlying tech that is used to create this is not gonna do anything but get better over time and the genie is sort of out of the bottle. There's no stuffing it back in. And there's a social component, which I think is really healthy for a democracy where people will be skeptical about the thing they watch in general. So, you know, which is good. Skepticism in general is good. It's good, I think. So deepfakes in that sense are creating global skepticism about can they trust what they read? It encourages further research. I come from the Soviet Union where basically nobody trusted the media because you knew it was propaganda and that kind of skepticism encouraged further research about ideas as opposed to just trusting any one source. Well, look, I think it's one of the reasons why the scientific method and our apparatus of modern science is so good. Because you don't have to trust anything. Like the whole notion of modern science beyond the fact that this is a hypothesis and this is an experiment to test the hypothesis and this is a peer review process for scrutinizing published results. But stuff's also supposed to be reproducible. So you know it's been vetted by this process, but you also are expected to publish enough detail where if you are sufficiently skeptical of the thing, you can go try to reproduce it yourself. I don't know what it is. I think a lot of engineers are like this where your brain is sort of wired for skepticism. You don't just first order trust everything that you see and encounter. And you're sort of curious to understand the next thing. But I think it's an entirely healthy thing. And we need a little bit more of that right now. So I'm not a large business owner. So I'm just a huge fan of many of Microsoft products. I mean I still, actually in terms of I generate a lot of graphics and images and I still use PowerPoint to do that. It beats Illustrator for me. Even professional sort of, it's fascinating. So I wonder what is the future of let's say Windows and Office look like? Is do you see it, I mean I remember looking forward to XP. Was it exciting when XP was released? Just like you said, I don't remember when 95 was released. But XP for me was a big celebration. And when 10 came out I was like okay, well it's nice. It's a nice improvement. So what do you see is the future of these products? I think there's a bunch of exciting, I mean on the Office front there's gonna be this increasing productivity wins that are coming out of some of these AI powered features that are coming. Like the products are sort of getting smarter and smarter in a very subtle way. There's not gonna be this big bang moment where Clippy is gonna re-emerge and it's gonna be. Wait a minute, okay well I have to, wait, wait, wait, is Clippy coming back? But quite seriously, so injection of AI, there's not much, or at least I'm not familiar, sort of assistive type of stuff going on inside the Office products. Like a Clippy style assistant, personal assistant. Do you think that, there's a possibility of that in the future? So I think there are a bunch of very small ways in which machine learning powered assistive things are in the product right now. So there are a bunch of interesting things like the auto response stuff's getting better and better and it's getting to the point where it can auto respond with like okay, this person's clearly trying to schedule a meeting so it looks at your calendar and it automatically tries to find a time and a space that's mutually interesting. Like we have this notion of Microsoft search where it's like not just web search, but it's like search across like all of your information that's sitting inside of like your Office 365 tenant and like potentially in other products. And like we have this thing called the Microsoft Graph that is basically an API federator that sort of like gets you hooked up across the entire breadth of like all of the, like what were information silos before they got woven together with the graph. Like that is like getting increasing, with increasing effectiveness sort of plumbed into some of these auto response things where you're gonna be able to see the system like automatically retrieve information for you. Like if, like I frequently send out emails to folks where like I can't find a paper or document or whatnot, there's no reason why the system won't be able to do that for you. And like, I think the, it's building towards like having things that look more like a fully integrated assistant, but like you'll have a bunch of steps that you will see before you, like it will not be this like big bang thing where like Clippy comes back and you've got this like, manifestation of like a fully powered assistant. So I think that's definitely coming out, like all of the collaboration co-authoring stuff's getting better, it's like really interesting. Like if you look at how we use the office product portfolio at Microsoft, like more and more of it is happening inside of like Teams as a canvas. And like it's this thing where you've got collaboration is like at the center of the product and like we built some like really cool stuff that's some of which is about to be open source that are sort of framework level things for doing co-authoring. That's awesome. So in, is there a cloud component to that? So on the web or is it, and forgive me if I don't already know this, but with Office 365, we still, the collaboration we do if we're doing Word, we still send the file around. Now whatever. So this is. We're already a little bit better than that. And like, you know, so like the fact that you're unaware of it means we've got a better job to do, like helping you discover this stuff. But yeah, I mean, it's already like got a huge cloud component and like part of this framework stuff, I think we're calling it, like we've been working on it for a couple of years. So like, I know the internal code name for it, but I think when we launched it, a bill is called the Fluid Framework. And, but like what Fluid lets you do is like, you can go into a conversation that you're having in Teams and like reference like part of a spreadsheet that you're working on where somebody's like sitting in the Excel canvas, like working on the spreadsheet with a, you know, chart or whatnot. And like, you can sort of embed like part of the spreadsheet in the Teams conversation where like you can dynamically update it and like all of the changes that you're making to this object are like, you know, coordinated and everything is sort of updating in real time. So like you can be in whatever canvas is most convenient for you to get your work done. So out of my own sort of curiosity as engineer, I know what it's like to sort of lead a team of 10, 15 engineers, Microsoft has, I don't know what the numbers are, maybe 50, maybe 60,000 engineers, maybe 40. I don't know exactly what the number is, it's a lot. It's tens of thousands. Right, this is more than 10 or 15. What, I mean, you've led different sizes, mostly large size of engineers. What does it take to lead such a large group into continue innovation, continue being highly productive and yet develop all kinds of new ideas and yet maintain, like what does it take to lead such a large group of brilliant people? I think the thing that you learn as you manage larger and larger scale is that there are three things that are like very, very important for big engineering teams. Like one is like having some sort of forethought about what it is that you're gonna be building over large periods of time. Like not exactly, like you don't need to know that, like I'm putting all my chips on this one product and like this is gonna be the thing. But like it's useful to know like what sort of capabilities you think you're going to need to have to build the products of the future. And then like invest in that infrastructure, like whether, and I'm not just talking about storage systems or cloud APIs, it's also like, what does your development process look like? What tools do you want? Like what culture do you want to build around like how you're sort of collaborating together to like make complicated technical things? And so like having an opinion and investing in that is like, it just gets more and more important. And like the sooner you can get a concrete set of opinions, like the better you're going to be. Like you can wing it for a while at small scales. Like, you know, when you start a company, like you don't have to be like super specific about it. But like the biggest miseries that I've ever seen as an engineering leader are in places where you didn't have a clear enough opinion about those things soon enough. And then you just sort of go create a bunch of technical debt and like culture debt that is excruciatingly painful to clean up. So like that's one bundle of things. Like the other, you know, another bundle of things is like it's just really, really important to like have a clear mission. That's not just some cute crap you say because like you think you should have a mission, but like something that clarifies for people like where it is that you're headed together. Like I know it's like probably like a little bit too popular right now, but Yuval Harari's book, Sapiens, one of the central ideas in his book is that like storytelling is like the quintessential thing for coordinating the activities of large groups of people. Like once you get past Dunbar's number and like, I've really, really seen that just managing engineering teams, like you can just brute force things when you're less than 120, 150 folks where you can sort of know and trust and understand what the dynamics are between all the people, but like past that, like things just sort of start to catastrophically fail if you don't have some sort of set of shared goals that you're marching towards. And so like, even though it sounds touchy feely and like a bunch of technical people will sort of balk at the idea that like you need to like have a clear, like the missions, like very, very, very important. Yuval's right, right? Stories, that's how our society, that's the fabric that connects us all of us is these powerful stories. And that works for companies too, right? It works for everything. Like I mean, even down to like, you sort of really think about like our currency, for instance, is a story. Our constitution is a story, our laws are, I mean like we believe very, very, very strongly in them and thank God we do. But like they are, they're just abstract things, like they're just words. Like if we don't believe in them, they're nothing. And in some sense, those stories are platforms and the kinds, some of which Microsoft is creating, right? They have platforms on which we define the future. So last question, what do you, let's get philosophical maybe, bigger than even Microsoft, what do you think the next 20, 30 plus years looks like for computing, for technology, for devices? Do you have crazy ideas about the future of the world? Yeah, look, I think we're entering this time where we've got, we have technology that is progressing at the fastest rate that it ever has and you've got some really big social problems like society scale problems that we have to tackle. And so I think we're gonna rise to the challenge and like figure out how to intersect like all of the power of this technology with all of the big challenges that are facing us, whether it's global warming, whether it's like the biggest remainder of the population boom is in Africa for the next 50 years or so. And like global warming is gonna make it increasingly difficult to feed the global population in particular, like in this place where you're gonna have like the biggest population boom. I think we, like AI is gonna, like if we push it in the right direction, like it can do like incredible things to empower all of us to achieve our full potential and to live better lives. But like that also means focus on like some super important things, like how can you apply it to healthcare to make sure that like our quality and cost and sort of ubiquity of health coverage is better and better over time. Like that's more and more important every day is like in the United States and like the rest of the industrialized world. So Western Europe, China, Japan, Korea, like you've got this population bubble of like aging working age folks who are, you know, at some point over the next 20, 30 years, they're gonna be largely retired and like you're gonna have more retired people than working age people. And then like you've got, you know, sort of natural questions about who's gonna take care of all the old folks and who's gonna do all the work. And the answers to like all of these sorts of questions, like where you're sort of running into, you know, like constraints of the world and of society has always been like what tech is gonna like help us get around this. You know, like when I was a kid in the 70s and 80s, like we talked all the time about like, oh, like population boom, population boom, like we're gonna, like we're not gonna be able to like feed the planet. And like we were like right in the middle of the green revolution where like this massive technology driven increase in crop productivity, like worldwide. And like some of that was like taking some of the things that we knew in the West and like getting them distributed to the, you know, to the developing world. And like part of it were things like, you know, just smarter biology, like helping us increase. And like we don't talk about like, yeah, overpopulation anymore because like we can more or less, we sort of figured out how to feed the world. Like that's a technology story. And so like I'm super, super hopeful about the future and in the ways where we will be able to apply technology to solve some of these super challenging problems. Like I've, like one of the things that I'm trying to spend my time doing right now is trying to get everybody else to be hopeful as well, because, you know, back to Harare, like we are the stories that we tell. Like if we, you know, if we get overly pessimistic right now about like the potential future of technology, like we, you know, like we may fail to get all the things in place that we need to like have our best possible future. And that kind of hopeful optimism. I'm glad that you have it because you're leading large groups of engineers that are actually defining, that are writing that story, that are helping build that future, which is super exciting. And I agree with everything you said, except I do hope Clippy comes back. We miss him. I speak for the people. So, Kellen, thank you so much for talking today. Thank you so much for having me. It was a pleasure.
 The following is a conversation with Kevin Scott,
 the CTO of Microsoft.
 Before that, he was the Senior Vice President
 of Engineering and Operations at LinkedIn,
 and before that, he oversaw mobile ads engineering at Google.
 He also has a podcast called Behind the Tech
 with Kevin Scott, which I'm a fan of.
 This was a fun and wide ranging conversation
 that covered many aspects of computing.
 It happened over a month ago
 before the announcement of Microsoft's investment
 in OpenAI that a few people have asked me about.
 I'm sure there'll be one or two people in the future
 that'll talk with me about the impact of that investment.
 This is the Artificial Intelligence Podcast.
 If you enjoy it, subscribe on YouTube,
 give it five stars on iTunes, support it on Patreon,
 or simply connect with me on Twitter at Lex Friedman,
 spelled F-R-I-D-M-A-N.
 And I'd like to give a special thank you
 to Tom and Nalanti Bickhausen
 for their support of the podcast on Patreon.
 Thanks, Tom and Nalanti.
 Hope I didn't mess up your last name too bad.
 Your support means a lot
 and inspires me to keep this series going.
 And now, here's my conversation with Kevin Scott.
 You've described yourself as a kid
 in a candy store at Microsoft
 because of all the interesting projects that are going on.
 Can you try to do the impossible task
 and give a brief, whirlwind view
 of all the spaces that Microsoft is working in,
 both research and product?
 If you include research, it becomes even more difficult.
 So I think, broadly speaking,
 Microsoft's product portfolio
 includes everything from big cloud business,
 like a big set of SaaS services.
 We have sort of the original,
 or like some of what are among the original
 productivity software products that everybody uses.
 We have an operating system business.
 We have a hardware business where we make everything
 from computer mice and headphones
 to high-end personal computers and laptops.
 We have a fairly broad-ranging research group
 where we have people doing everything
 from economics research.
 So there's this really smart young economist, Glen Weil,
 who my group works with a lot,
 who's doing this research
 on these things called radical markets.
 He's written an entire technical book
 about this whole notion of radical markets.
 So the research group spans from that
 to human-computer interaction, to artificial intelligence.
 And we have GitHub, we have LinkedIn,
 we have a search advertising and news business,
 and probably a bunch of stuff
 that I'm embarrassingly not recounting in this list.
 On gaming to Xbox and so on, right?
 Yeah, gaming for sure.
 I was having a super fun conversation this morning
 with Phil Spencer.
 So when I was in college,
 there was this game that LucasArts made
 called Day of the Tentacle
 that my friends and I played forever.
 And we're doing some interesting collaboration now
 with the folks who made Day of the Tentacle.
 And I was completely nerding out with Tim Schafer,
 the guy who wrote Day of the Tentacle this morning,
 just a complete fan boy,
 which sort of, it happens a lot.
 Microsoft has been doing so much stuff
 at such breadth for such a long period of time
 that being CTO, most of the time,
 my job is very, very serious.
 And sometimes I get caught up in how amazing it is
 to be able to have the conversations that I have
 with the people I get to have them with.
 You have to reach back into the sentimental.
 And what's the radical markets and the economics?
 So the idea with radical markets is like,
 can you come up with new market-based mechanisms to,
 you know, I think we have this,
 we're having this debate right now,
 like does capitalism work, like free markets work?
 Can the incentive structures
 that are built into these systems produce outcomes
 that are creating sort of equitably distributed benefits
 for every member of society?
 You know, and I think it's a reasonable set of questions
 to be asking.
 And so what Glenn, and so like, you know,
 one mode of thought there,
 like if you have doubts that the markets
 are actually working, you can sort of like tip towards,
 like, okay, let's become more socialist
 and, you know, like have central planning and, you know,
 governments or some other central organization
 is like making a bunch of decisions about how, you know,
 sort of work gets done and, you know, like where the,
 you know, where the investments
 and where the outputs of those investments get distributed.
 Glenn's notion is like lean more into,
 like the market-based mechanism.
 So like, for instance, you know,
 this is one of the more radical ideas.
 Like suppose that you had a radical pricing mechanism
 for assets like real estate where you were,
 you could be bid out of your position in your home,
 you know, for instance.
 So like if somebody came along and said, you know,
 like I can find higher economic utility
 for this piece of real estate
 that you're running your business in,
 like then like you either have to, you know,
 sort of bid to sort of stay
 or like the thing that's got the higher economic utility,
 you know, sort of takes over the asset,
 which would make it very difficult
 to have the same sort of rent seeking behaviors
 that you've got right now,
 because like if you did speculative bidding,
 like you would very quickly like lose a whole lot of money.
 And so like the prices of the assets would be sort of
 like very closely indexed to like the value
 that they could produce.
 And like, because like you'd have this sort
 of real-time mechanism that would force you
 to sort of mark the value of the asset to the market,
 then it could be taxed appropriately.
 Like you couldn't sort of sit on this thing and say,
 oh, like this house is only worth 10,000 bucks
 when like everything around it is worth 10 million.
 That's for the, so it's a incentive structure that,
 where the prices match the value much better.
 Yeah, and Glenn does a much better job than I do
 at selling and I probably picked the world's worst example,
 you know, and, and, and, but like,
 and it's intentionally provocative, you know,
 so like this whole notion, like I, you know,
 like I'm not sure whether I like this notion
 that like we could have a set of market mechanisms
 where I could get bit out of, out of my property, you know,
 but, but, you know, like if you're thinking
 about something like Elizabeth Warren's wealth tax,
 for instance, like you would have, I mean,
 it'd be really interesting in like how you would actually
 set the, the, the price on the assets.
 And like, you might have to have a mechanism like that
 if you put a tax like that in place.
 It's really interesting that that kind of research
 at least tangentially is touching Microsoft research.
 Yeah, you're really thinking broadly.
 Maybe you can speak to,
 if this connects to AI.
 So we have a candidate, Andrew Yang,
 who kind of talks about artificial intelligence
 and the concern that people have about, you know,
 automation's impact on society.
 And arguably Microsoft is at the cutting edge of innovation
 in all these kinds of ways.
 And so it's pushing AI forward.
 How do you think about,
 combining all our conversations together here
 with radical markets and socialism and innovation in AI
 that Microsoft is doing, and then Andrew Yang's worry
 that that will result in job loss for the lower and so on.
 How do you think about that?
 I think it's sort of one of the most important questions
 in technology, like maybe even in society right now
 about how is AI going to develop over the course
 of the next several decades and like,
 what's it gonna be used for?
 And like, what benefits will it produce?
 And what negative impacts will it produce?
 And, you know, who gets to steer this whole thing?
 You know, I'll say at the highest level,
 one of the real joys of getting to do what I do at Microsoft
 is Microsoft has this heritage as a platform company.
 And so, you know, like Bill has this thing
 that he said a bunch of years ago where, you know,
 the measure of a successful platform is that it produces
 far more economic value for the people who build
 on top of the platform than is created
 for the platform owner or builder.
 And I think we have to think about AI that way.
 Like, yeah, it has to, like, it has to be a platform
 that other people can use to build businesses,
 to fulfill their creative objectives,
 to be entrepreneurs, to solve problems that they have
 in their work and in their lives.
 It can't be a thing where there are a handful of companies
 sitting in a very small handful of cities geographically
 who are making all the decisions
 about what goes into the AI.
 And then on top of all this infrastructure,
 then build all of the commercially valuable uses for it.
 So I think that's bad from a sort of economics
 and sort of equitable distribution of value perspective,
 like, you know, sort of back to this whole notion of,
 you know, like, do the markets work?
 But I think it's also bad from an innovation perspective
 because, like, I have infinite amounts of faith
 in human beings that if you, you know,
 give folks powerful tools,
 they will go do interesting things.
 And it's more than just a few tens of thousands of people
 with the interesting tools.
 It should be millions of people with the tools.
 So it's sort of like, you know,
 you think about the steam engine in the late 18th century.
 Like, it was, you know, maybe the first large scale substitute
 for human labor that we've built like a machine.
 And, you know, in the beginning,
 when these things are getting deployed,
 the folks who got most of the value from the steam engines
 were the folks who had capital,
 so they could afford to build them.
 And like, they built factories around them and businesses,
 and the experts who knew how to build and maintain them.
 But access to that technology democratized over time.
 Like now, like an engine is not a,
 it's not like a differentiated thing.
 Like there isn't one engine company
 that builds all the engines and all of the things
 that use engines are made by this company.
 And like, they get all the economics from all of that.
 Like, no, no, no, like fully democratized.
 Like they're probably, you know,
 we're sitting here in this room.
 And like, even though they don't,
 they're probably things, you know,
 like the MEMS gyroscope that are in both of our,
 like they're like little engines, you know, sort of everywhere.
 They're just a component in how we build the modern world,
 like AI needs to get there.
 Yeah, so that's a really powerful way to think.
 If we think of AI as a platform versus a tool
 that Microsoft owns, as a platform that enables creation
 on top of it, that's a way to democratize it.
 That's really interesting, actually.
 And Microsoft throughout its history
 has been positioned well to do that.
 And the, you know, the tie back to this radical markets thing,
 like the, so my team has been working with Glenn
 on this and Jaren Lanier actually.
 So Jaren is the, like the sort of father of virtual reality.
 Like he's one of the most interesting human beings
 on the planet, like a sweet, sweet guy.
 And so Jaren and Glenn and folks in my team
 have been working on this notion of data as labor
 or like they call it data dignity as well.
 And so the idea is that if you, you know, again,
 going back to this sort of industrial analogy,
 if you think about data as the raw material
 that is consumed by the machine of AI
 in order to do useful things,
 then like we're not doing a really great job right now
 in having transparent marketplaces for valuing
 those data contributions.
 So like, and we all make them like explicitly,
 like you go to LinkedIn,
 you sort of set up your profile on LinkedIn.
 Like that's an explicit contribution.
 Like, you know exactly the information
 that you're putting into the system.
 And like, you put it there because you have
 some nominal notion of like what value
 you're gonna get in return.
 But it's like only nominal.
 Like you don't know exactly what value
 you're getting in return.
 Like services free, you know,
 like it's low amount of like perceived debt.
 And then you've got all this indirect contribution
 that you're making just by virtue of interacting
 with all of the technology that's in your daily life.
 And so like what Glenn and Jaren
 and this data dignity team are trying to do is like,
 can we figure out a set of mechanisms
 that let us value those data contributions
 so that you could create an economy
 and like a set of controls and incentives
 that would allow people to like maybe even in the limit,
 like earn part of their living
 through the data that they're creating.
 And like, you can sort of see it in explicit ways.
 There are these companies like Scale AI
 and like there are a whole bunch of them
 in China right now that are basically data labeling companies.
 So like you're doing supervised machine learning,
 you need lots and lots of label training data.
 And like those people are getting,
 who work for those companies are getting compensated
 for their data contributions into the system.
 And so.
 That's easier to put a number on their contribution
 because they're explicitly labeling data.
 Correct.
 But you're saying that we're all contributing data
 in different kinds of ways.
 And it's fascinating to start to explicitly try
 to put a number on it.
 Do you think that's possible?
 I don't know, it's hard.
 It really is.
 Because we don't have as much transparency as I think we need,
 in like how the data is getting used.
 And it's super complicated.
 Like we, I think as technologists sort of appreciate
 like some of the subtlety there.
 It's like the data gets created and then it gets,
 it's not valuable.
 Like the data exhaust that you give off
 or the explicit data that I am putting into the system
 isn't super valuable atomically.
 Like it's only valuable when you sort of aggregate it together
 into sort of large numbers.
 It's true even for these like folks who are getting
 compensated for like labeling things.
 Like for supervised machine learning now,
 like you need lots of labels to train a model
 that performs well.
 And so, I think that's one of the challenges.
 It's like, how do you, how do you sort of figure out
 because this data is getting combined in so many ways,
 like through these combinations,
 like how the value is flowing.
 Yeah, that's fascinating.
 Tough.
 Yeah, and it's fascinating that you're thinking about this.
 And I wasn't even going to this conversation
 expecting the breadth of research really
 that Microsoft broadly is thinking about,
 you are thinking about it, Microsoft.
 So, if we go back to 89 when Microsoft released Office
 or 1990 when they released Windows 3.0,
 how's the, in your view, I know you weren't there
 the entire, you know, through its history,
 but how has the company changed in the 30 years since
 as you look at it now?
 The good thing is it's started off as a platform company,
 like it's still a platform company,
 like the parts of the business that are like thriving
 and most successful are those that are building platforms.
 Like the mission of the company now is,
 the mission's changed.
 It's like changed in a very interesting way.
 So, you know, back in 89, 90,
 like they were still on the original mission,
 which was like put a PC on every desk and in every home,
 like, and it was basically about democratizing access
 to this new personal computing technology,
 which when Bill started the company,
 integrated circuit microprocessors were a brand new thing.
 And like people were building, you know, homebrew computers,
 you know, from kits,
 like the way people build ham radios right now.
 And I think this is sort of the interesting thing
 for folks who build platforms in general.
 Bill saw the opportunity there
 and what personal computers could do.
 And it was like, it was sort of a reach,
 like you just sort of imagine like where things were,
 you know, when they started the company
 versus where things are now.
 Like in success, when you've democratized a platform,
 it just sort of vanishes into the platform.
 You don't pay attention to it anymore.
 Like operating systems aren't a thing anymore.
 Like they're super important, like completely critical.
 And like, you know, when you see one, you know, fail,
 like you just, you sort of understand,
 but like, you know, it's not a thing where you're not
 like waiting for, you know, the next operating system thing
 in the same way that you were in 1995, right?
 Like in 1995, like, you know, we had Rolling Stones
 on the stage with the Windows 95 rollout.
 Like it was like the biggest thing in the world.
 Everybody was like lined up for it
 in the way that people used to line up for iPhone.
 But like, you know, eventually,
 and like this isn't necessarily a bad thing.
 Like it just sort of, you know, the success is
 that it's sort of, it becomes ubiquitous.
 It's like everywhere and like human beings,
 when their technology becomes ubiquitous,
 they just sort of start taking it for granted.
 So the mission now that Satya rearticulated
 five plus years ago now,
 when he took over as CEO of the company,
 our mission is to empower every individual
 and every organization in the world to be more successful.
 And so, you know, again, like that's a platform mission.
 And like the way that we do it now is different.
 It's like we have a hyperscale cloud
 that cloud or building our applications on top of,
 like we have a bunch of AI infrastructure
 that people are building their AI applications on top of.
 We have, you know, we have a productivity suite of software
 like Microsoft dynamics, which, you know,
 some people might not think is the sexiest thing in the world,
 but it's like helping people figure out how to automate
 all of their business processes and workflows and to,
 you know, like help those businesses using it to like grow
 and be more successful.
 So it's a much broader vision in a way now
 than it was back then.
 Like it was sort of very particular thing.
 And like now, like we live in this world
 where technology is so powerful and it's like such a basic
 basic fact of life that it, you know,
 that it both exists and is going to get better
 and better over time or at least more
 and more powerful over time.
 So like, you know, what you have to do as a platform player
 is just much bigger.
 Right.
 There's so many directions in which you can transform.
 You didn't mention mixed reality too.
 You know, that's probably early days
 or it depends how you think of it.
 But if we think on a scale of centuries,
 it's the early days of mixed reality.
 Oh, for sure.
 And so with how a lens,
 the Microsoft is doing some really interesting work there.
 Do you touch that part of the effort?
 What's the thinking?
 Do you think of mixed reality as a platform too?
 Oh, sure.
 When we look at what the platforms of the future could be,
 it's like fairly obvious that like AI is one.
 Like you don't have to, I mean, like that's, you know,
 you sort of say it to like someone and you know,
 like they get it.
 But like we also think of the like mixed reality
 and quantum is like these two interesting,
 you know, potentially.
 Quantum computing.
 Yeah.
 Okay, so let's get crazy then.
 So you're talking about some futuristic things here.
 Well, the mixed reality,
 Microsoft is really not even futuristic is here.
 It is.
 It's incredible stuff.
 And look, and it's having an impact right now.
 Like one of the more interesting things
 that's happened with mixed reality over the past couple of years
 that I didn't clearly see is that it's become
 the computing device for folks who,
 for doing their work who haven't used any computing device
 at all to do their work before.
 So technicians and service folks and people who are doing
 like machine maintenance on factory floors.
 So like they, you know, because they're mobile
 and like they're out in the world and they're working
 with their hands and you know,
 sort of servicing these like very complicated things,
 they're, they don't use their mobile phone
 and like they don't carry a laptop with them.
 And you know, they're not tethered to a desk.
 And so mixed reality like where it's getting traction
 right now where HoloLens is selling a lot of units
 is for these sorts of applications for these workers
 and it's become like, I mean, like the people love it.
 They're like, oh my God, like this is like for them,
 like the same sort of productivity boost that, you know,
 like an office worker had when they got their first
 personal computer.
 Yeah, but you did mention it's certainly obvious AI
 as a platform, but can we dig into it a little bit?
 Sure.
 How does AI begin to infuse some of the products
 in Microsoft?
 So currently providing training
 of for example, neural networks in the cloud
 or providing pre-trained models
 or just even providing computing resources
 and whatever different inference that you wanna do
 using neural networks.
 Yep.
 Well, how do you think of AI infusing as a platform
 that Microsoft can provide?
 Yeah, I mean, I think it's super interesting.
 It's like everywhere and like we run these,
 we run these review meetings now where it's me and Satya
 and like members of Satya's leadership team
 and like a cross-functional group of folks
 across the entire company who are working on
 like either AI infrastructure
 or like have some substantial part of their product work
 using AI in some significant way.
 Now the important thing to understand is like
 when you think about like how the AI is gonna manifest
 and like an experience for something
 that's gonna make it better,
 like I think you don't want the AI-ness
 to be the first order thing.
 It's like whatever the product is
 and like the thing that it's trying to help you do,
 like the AI just sort of makes it better.
 And this is a gross exaggeration,
 but like people get super excited about
 like where the AI is showing up in products
 and I'm like, do you get that excited about
 like where you're using a hash table in your code?
 Like it's just another.
 It's just a tool.
 It's a very interesting programming tool,
 but it's sort of like it's an engineering tool.
 And so like it shows up everywhere.
 So like we've got dozens and dozens of features now
 in Office that are powered by like
 fairly sophisticated machine learning.
 Our search engine wouldn't work at all
 if you took the machine learning out of it.
 The like increasingly, you know,
 things like content moderation on our Xbox
 and X cloud platform.
 When you mean moderation,
 you mean like the recommender is like showing
 what you wanna look at next?
 No, no, no, it's like anti-bullying stuff.
 So the usual social network stuff that you have to deal with.
 Yeah, correct.
 But it's like really it's targeted
 towards a gaming audience.
 So it's like a very particular type of thing
 where, you know, the lime between playful banter
 and like legitimate bullying is like a subtle one.
 And like you have to, it's sort of tough.
 Like I have.
 I'd love to, if we could dig into it,
 because you're also, you led the engineering efforts
 of LinkedIn.
 And if we look at LinkedIn as a social network,
 and if we look at the Xbox gaming
 as the social components,
 the very different kinds of, I imagine,
 communication going on on the two platforms, right?
 And the line in terms of bullying and so on
 is different on the two platforms.
 So how do you, I mean, it's such a fascinating
 philosophical discussion of where that line is.
 I don't think anyone knows the right answer.
 Twitter folks are under fire now, Jack,
 at Twitter for trying to find that line.
 Nobody knows what that line is.
 But how do you try to find the line for, you know,
 trying to prevent abusive behavior,
 and at the same time, let people be playful
 and joke around and that kind of thing.
 I think in a certain way, like, you know,
 if you have what I would call vertical social networks,
 it gets to be a little bit easier.
 So like, if you have a clear notion
 of like what your social network should be used for,
 or like what you are designing a community around,
 then you don't have as many dimensions
 to your sort of content safety problem
 as you know, as you do in a general purpose platform.
 I mean, so like on LinkedIn,
 like the whole social network is about connecting people
 with opportunity, whether it's helping them find a job
 or to, you know, sort of find mentors
 or to, you know, sort of help them
 like find their next sales lead,
 or to just sort of allow them to broadcast
 their, you know, sort of professional identity
 to their network of peers and collaborators
 and, you know, sort of professional community.
 Like that is, I mean, like in some ways,
 like that's very, very broad,
 but in other ways it's sort of, you know, it's narrow.
 And so like, you can build AI's,
 like machine learning systems that are, you know,
 capable with those boundaries
 of making better automated decisions about like,
 what is, you know, sort of inappropriate
 and offensive comment or dangerous comment
 or illegal content when you have some constraints.
 You know, same thing with, yeah, same thing with like
 the gaming social networks.
 So for instance, like it's about playing games,
 about having fun.
 And like the thing that you don't want to have happen
 on the platform is why bullying is such an important thing.
 Like bullying is not fun.
 So you want to do everything in your power
 to encourage that not to happen.
 And yeah, but I think it's a,
 it's sort of a tough problem in general.
 And it's one where I think, you know,
 eventually we're gonna have to have
 some sort of clarification from our policy makers
 about what it is that we should be doing,
 like where the lines are, because it's tough.
 Like you don't, like in democracy, right?
 Like you don't want, you want some sort
 of democratic involvement.
 Like people should have a say in like where the lines
 are drawn.
 Like you don't want a bunch of people making
 like unilateral decisions.
 And like we are in a state right now
 for some of these platforms where you actually do have
 to make unilateral decisions where the policymaking
 isn't gonna happen fast enough in order to like
 prevent very bad things from happening.
 But like we need the policymaking side of that
 to catch up I think as quickly as possible
 because you want that whole process to be a democratic thing,
 not a, you know, not some sort of weird thing
 where you've got a non-representative group
 of people making decisions that have, you know,
 like national and global impact.
 It's fascinating because the digital space is different
 than the physical space in which nations
 and governments were established.
 And so what policy looks like globally,
 what bullying looks like globally,
 what healthy communication looks like globally
 is an open question.
 And we're all figuring it out together.
 Which is fascinating.
 Yeah, I mean with, you know, sort of fake news
 for instance and...
 Deep fakes and fake news generated by humans.
 Yeah, so and we can talk about deep fakes.
 Like I think that is another like, you know,
 sort of very interesting level of complexity.
 But like if you think about just the written word, right?
 Like we have, you know, we invented Papyrus,
 what, 3000 years ago where we, you know,
 you could sort of put word on paper.
 And then 500 years ago, like we get the printing press,
 like where the word gets a little bit more ubiquitous.
 And then like you really, really didn't get ubiquitous
 printed word until the end of the 19th century
 when the offset press was invented.
 And then, you know, just sort of explodes and like,
 you know, the cross product of that
 and the industrial revolution's need for educated citizens
 resulted in like this rapid expansion of literacy
 and the rapid expansion of the word.
 But like we had 3000 years up to that point
 to figure out like how to, you know,
 like what's journalism, what's editorial integrity,
 like what's, you know, what's scientific peer review.
 And so like you built all of this mechanism
 to like try to filter through all of the noise
 that the technology made possible to like, you know,
 sort of getting to something that society could cope with.
 And like, if you think about just the piece,
 the PC didn't exist 50 years ago.
 And so in like this span of, you know, like half a century,
 like we've gone from no digital, you know,
 no ubiquitous digital technology to like having a device
 that sits in your pocket where you can sort of say
 whatever is on your mind to like,
 what did Mary Haven or Mary Meeker
 just released her new like slide deck last week.
 You know, we've got 50% penetration of the internet
 to the global population.
 Like there are like three and a half billion people
 who are connected now.
 So it's like, it's crazy, crazy, like inconceivable,
 like how fast all of this happens.
 So, you know, it's not surprising
 that we haven't figured out what to do yet,
 but like we gotta like, we gotta really like lean
 into this set of problems because like we basically have
 three millennia worth of work to do about how to deal
 with all of this and like probably what, you know,
 amounts to the next decade worth of time.
 So since we're on the topic of tough, you know,
 tough, challenging problems, let's look at more
 on the tooling side in AI that Microsoft is looking
 at is face recognition software.
 So there's a lot of powerful positive use cases
 for face recognition, but there's some negative ones
 and we're seeing those in different governments in the world.
 So how do you, how does Microsoft think about the use
 of face recognition software as a platform
 in governments and companies?
 So yeah, how do we strike an ethical balance here?
 Yeah, I think we've articulated a clear point of view.
 So Brad Smith wrote a blog post last fall, I believe,
 that sort of like outline like very specifically what,
 you know, what our point of view is there.
 And, you know, I think we believe that there are certain uses
 to which face recognition should not be put.
 And we believe again, that there's a need
 for regulation there, like the government should like
 really come in and say that, you know,
 this is where the lines are.
 And like we very much wanted to like figuring out
 where the lines are should be a democratic process.
 But in the short term, like we've drawn some lines where,
 you know, we push back against uses
 of face recognition technology, you know,
 like the city of San Francisco, for instance,
 I think has completely outlawed any government agency
 from using face recognition tech.
 And like that may prove to be a little bit overly broad,
 but for like certain law enforcement things,
 like you really, I would personally rather be overly sort
 of cautious in terms of restricting use of it until like,
 we have, you know, sort of defined a reasonable,
 you know, democratically determined regulatory framework
 for like where we could and should use it.
 And, you know, the other thing there is like,
 we've got a bunch of research that we're doing
 and a bunch of progress that we've made on bias there.
 And like, there are all sorts of like weird biases
 that these models can have, like all the way from like
 the most noteworthy one where, you know,
 you may have underrepresented minorities
 who are like underrepresented in the training data
 and then you start learning like strange things,
 but like they're even, you know, other weird things
 like we've, I think we've seen in the public research,
 like models can learn strange things like all doctors
 or men, for instance.
 Yeah, I mean, and so like, it really is a thing
 where it's very important for everybody
 who is working on these things before they push publish,
 they launch the experiment, they, you know,
 push the code to, you know, online
 or they even publish the paper that they are
 at least starting to think about what some of the
 potential negative consequences are, some of this stuff.
 I mean, this is where, you know, like the deep fake stuff
 I find very worrisome just because
 there are gonna be some very good beneficial uses
 of like GAN generated imagery.
 And like, and funny enough, like one of the places
 where it's actually useful is we're using the technology
 right now to generate synthetic visual data
 for training some of the face recognition models
 to get rid of the bias.
 So like, that's one like super good use of the tech,
 but like, you know, it's getting good enough now
 where, you know, it's gonna sort of challenge
 a normal human being's ability to like,
 now you're just sort of say like, it's very expensive
 for someone to fabricate a photorealistic fake video.
 And like GANs are gonna make it fantastically cheap
 to fabricate a photorealistic fake video.
 And so like what you assume you can sort of trust
 is true versus like be skeptical about is about to change.
 And like, we're not ready for it, I don't think.
 The nature of truth, right.
 That's, it's also exciting because I think both you
 and I probably would agree that the way to solve,
 to take on that challenge is with technology.
 Yeah.
 Right, there's probably going to be ideas
 of ways to verify which kind of video is legitimate,
 which kind is not.
 So to me, that's an exciting possibility,
 most likely for just the comedic genius
 that the internet usually creates with these kinds of videos.
 And hopefully will not result in any serious harm.
 Yeah, and it could be, like I think we will have technology
 to that may be able to detect whether or not
 something's fake or real, although the fakes
 are pretty convincing even like when you subject them
 to machine scrutiny.
 But we also have these increasingly interesting
 social networks that are under fire right now
 for some of the bad things that they do.
 Like one of the things you could choose to do
 with a social network is like you could use crypto
 and the networks to like have content signed
 where you could have a like full chain of custody
 that accompanied every piece of content.
 So like when you're viewing something
 and like you want to ask yourself like,
 how much can I trust this?
 Like you can click something and like have a verified
 chain of custody that shows like,
 oh, this is coming from this source
 and it's like signed by like someone whose identity I trust.
 Yeah, I think having that chain of custody
 like being able to like say, oh, here's this video,
 like it may or may not have been produced
 using some of this deep fake technology.
 But if you've got a verified chain of custody
 where you can sort of trace it all the way back
 to an identity and you can decide whether or not
 like I trust this identity, like, oh no,
 this is really from the White House
 or like this is really from the office
 of this particular presidential candidate
 or it's really from Jeff Weiner, CEO of LinkedIn
 or Satya Nadella, CEO of Microsoft.
 Like that might be like one way
 that you can solve some of the problems.
 So like that's not the super high tech,
 like we've had all of this technology forever.
 And but I think you're right,
 like it has to be some sort of technological thing
 because the underlying tech that is used to create this
 is not gonna do anything but get better over time
 and the genie is sort of out of the bottle.
 There's no stuffing it back in.
 And there's a social component,
 which I think is really healthy for a democracy
 where people will be skeptical
 about the thing they watch in general.
 So, you know, which is good.
 Skepticism in general is good.
 It's good, I think.
 So deepfakes in that sense are creating
 global skepticism about can they trust what they read?
 It encourages further research.
 I come from the Soviet Union
 where basically nobody trusted the media
 because you knew it was propaganda
 and that kind of skepticism encouraged further research
 about ideas as opposed to just trusting any one source.
 Well, look, I think it's one of the reasons why
 the scientific method and our apparatus
 of modern science is so good.
 Because you don't have to trust anything.
 Like the whole notion of modern science
 beyond the fact that this is a hypothesis
 and this is an experiment to test the hypothesis
 and this is a peer review process
 for scrutinizing published results.
 But stuff's also supposed to be reproducible.
 So you know it's been vetted by this process,
 but you also are expected to publish enough detail
 where if you are sufficiently skeptical of the thing,
 you can go try to reproduce it yourself.
 I don't know what it is.
 I think a lot of engineers are like this
 where your brain is sort of wired for skepticism.
 You don't just first order trust everything
 that you see and encounter.
 And you're sort of curious to understand
 the next thing.
 But I think it's an entirely healthy thing.
 And we need a little bit more of that right now.
 So I'm not a large business owner.
 So I'm just a huge fan of many of Microsoft products.
 I mean I still, actually in terms of
 I generate a lot of graphics and images
 and I still use PowerPoint to do that.
 It beats Illustrator for me.
 Even professional sort of, it's fascinating.
 So I wonder what is the future of
 let's say Windows and Office look like?
 Is do you see it, I mean I remember looking forward to XP.
 Was it exciting when XP was released?
 Just like you said, I don't remember when 95 was released.
 But XP for me was a big celebration.
 And when 10 came out I was like okay, well it's nice.
 It's a nice improvement.
 So what do you see is the future of these products?
 I think there's a bunch of exciting,
 I mean on the Office front there's gonna be this
 increasing productivity wins that are coming out
 of some of these AI powered features that are coming.
 Like the products are sort of getting smarter and smarter
 in a very subtle way.
 There's not gonna be this big bang moment
 where Clippy is gonna re-emerge and it's gonna be.
 Wait a minute, okay well I have to,
 wait, wait, wait, is Clippy coming back?
 But quite seriously, so injection of AI,
 there's not much, or at least I'm not familiar,
 sort of assistive type of stuff going on
 inside the Office products.
 Like a Clippy style assistant, personal assistant.
 Do you think that, there's a possibility
 of that in the future?
 So I think there are a bunch of very small ways
 in which machine learning powered assistive things
 are in the product right now.
 So there are a bunch of interesting things
 like the auto response stuff's getting better and better
 and it's getting to the point where it can auto respond
 with like okay, this person's clearly trying
 to schedule a meeting so it looks at your calendar
 and it automatically tries to find a time and a space
 that's mutually interesting.
 Like we have this notion of Microsoft search
 where it's like not just web search,
 but it's like search across like all of your information
 that's sitting inside of like your Office 365 tenant
 and like potentially in other products.
 And like we have this thing called the Microsoft Graph
 that is basically an API federator
 that sort of like gets you hooked up across the entire breadth
 of like all of the, like what were information silos
 before they got woven together with the graph.
 Like that is like getting increasing,
 with increasing effectiveness sort of plumbed
 into some of these auto response things
 where you're gonna be able to see the system
 like automatically retrieve information for you.
 Like if, like I frequently send out emails to folks
 where like I can't find a paper or document or whatnot,
 there's no reason why the system won't be able
 to do that for you.
 And like, I think the, it's building towards
 like having things that look more like
 a fully integrated assistant,
 but like you'll have a bunch of steps
 that you will see before you,
 like it will not be this like big bang thing
 where like Clippy comes back and you've got this like,
 manifestation of like a fully powered assistant.
 So I think that's definitely coming out,
 like all of the collaboration co-authoring stuff's
 getting better, it's like really interesting.
 Like if you look at how we use
 the office product portfolio at Microsoft,
 like more and more of it is happening inside of like Teams
 as a canvas.
 And like it's this thing where you've got collaboration
 is like at the center of the product
 and like we built some like really cool stuff
 that's some of which is about to be open source
 that are sort of framework level things
 for doing co-authoring.
 That's awesome.
 So in, is there a cloud component to that?
 So on the web or is it,
 and forgive me if I don't already know this,
 but with Office 365, we still,
 the collaboration we do if we're doing Word,
 we still send the file around.
 Now whatever.
 So this is.
 We're already a little bit better than that.
 And like, you know, so like the fact that you're unaware
 of it means we've got a better job to do,
 like helping you discover this stuff.
 But yeah, I mean, it's already like got a huge cloud component
 and like part of this framework stuff,
 I think we're calling it,
 like we've been working on it for a couple of years.
 So like, I know the internal code name for it,
 but I think when we launched it,
 a bill is called the Fluid Framework.
 And, but like what Fluid lets you do is like,
 you can go into a conversation that you're having in Teams
 and like reference like part of a spreadsheet
 that you're working on where somebody's like sitting
 in the Excel canvas, like working on the spreadsheet
 with a, you know, chart or whatnot.
 And like, you can sort of embed like part of the spreadsheet
 in the Teams conversation where like you can dynamically
 update it and like all of the changes that you're making
 to this object are like, you know, coordinated
 and everything is sort of updating in real time.
 So like you can be in whatever canvas is most convenient
 for you to get your work done.
 So out of my own sort of curiosity as engineer,
 I know what it's like to sort of lead a team
 of 10, 15 engineers, Microsoft has,
 I don't know what the numbers are, maybe 50,
 maybe 60,000 engineers, maybe 40.
 I don't know exactly what the number is, it's a lot.
 It's tens of thousands.
 Right, this is more than 10 or 15.
 What, I mean, you've led different sizes,
 mostly large size of engineers.
 What does it take to lead such a large group
 into continue innovation, continue being highly productive
 and yet develop all kinds of new ideas and yet maintain,
 like what does it take to lead such a large group
 of brilliant people?
 I think the thing that you learn as you manage larger
 and larger scale is that there are three things
 that are like very, very important for big engineering teams.
 Like one is like having some sort of forethought
 about what it is that you're gonna be building
 over large periods of time.
 Like not exactly, like you don't need to know that,
 like I'm putting all my chips on this one product
 and like this is gonna be the thing.
 But like it's useful to know like what sort
 of capabilities you think you're going to need to have
 to build the products of the future.
 And then like invest in that infrastructure,
 like whether, and I'm not just talking about storage systems
 or cloud APIs, it's also like,
 what does your development process look like?
 What tools do you want?
 Like what culture do you want to build around
 like how you're sort of collaborating together
 to like make complicated technical things?
 And so like having an opinion and investing in that
 is like, it just gets more and more important.
 And like the sooner you can get a concrete set of opinions,
 like the better you're going to be.
 Like you can wing it for a while at small scales.
 Like, you know, when you start a company,
 like you don't have to be like super specific about it.
 But like the biggest miseries that I've ever seen
 as an engineering leader are in places
 where you didn't have a clear enough opinion
 about those things soon enough.
 And then you just sort of go create a bunch of technical debt
 and like culture debt that is excruciatingly painful
 to clean up.
 So like that's one bundle of things.
 Like the other, you know, another bundle of things is
 like it's just really, really important to
 like have a clear mission.
 That's not just some cute crap you say
 because like you think you should have a mission,
 but like something that clarifies for people
 like where it is that you're headed together.
 Like I know it's like probably like a little bit
 too popular right now, but
 Yuval Harari's book, Sapiens,
 one of the central ideas in his book is that
 like storytelling is like the quintessential thing
 for coordinating the activities of large groups of people.
 Like once you get past Dunbar's number and like,
 I've really, really seen that just managing
 engineering teams, like you can just brute force things
 when you're less than 120, 150 folks where you can sort of
 know and trust and understand what the dynamics are
 between all the people, but like past that,
 like things just sort of start to catastrophically fail
 if you don't have some sort of set of shared goals
 that you're marching towards.
 And so like, even though it sounds touchy feely
 and like a bunch of technical people will sort of balk
 at the idea that like you need to like have a clear,
 like the missions, like very, very, very important.
 Yuval's right, right?
 Stories, that's how our society,
 that's the fabric that connects us all of us
 is these powerful stories.
 And that works for companies too, right?
 It works for everything.
 Like I mean, even down to like,
 you sort of really think about like our currency,
 for instance, is a story.
 Our constitution is a story, our laws are,
 I mean like we believe very, very, very strongly in them
 and thank God we do.
 But like they are, they're just abstract things,
 like they're just words.
 Like if we don't believe in them, they're nothing.
 And in some sense, those stories are platforms
 and the kinds, some of which Microsoft is creating, right?
 They have platforms on which we define the future.
 So last question, what do you,
 let's get philosophical maybe,
 bigger than even Microsoft,
 what do you think the next 20, 30 plus years
 looks like for computing, for technology, for devices?
 Do you have crazy ideas about the future of the world?
 Yeah, look, I think we're entering this time
 where we've got, we have technology that is progressing
 at the fastest rate that it ever has
 and you've got some really big social problems
 like society scale problems that we have to tackle.
 And so I think we're gonna rise to the challenge
 and like figure out how to intersect
 like all of the power of this technology
 with all of the big challenges that are facing us,
 whether it's global warming,
 whether it's like the biggest remainder
 of the population boom is in Africa
 for the next 50 years or so.
 And like global warming is gonna make it increasingly
 difficult to feed the global population in particular,
 like in this place where you're gonna have
 like the biggest population boom.
 I think we, like AI is gonna,
 like if we push it in the right direction,
 like it can do like incredible things
 to empower all of us to achieve our full potential
 and to live better lives.
 But like that also means focus on like
 some super important things,
 like how can you apply it to healthcare
 to make sure that like our quality and cost
 and sort of ubiquity of health coverage
 is better and better over time.
 Like that's more and more important every day
 is like in the United States
 and like the rest of the industrialized world.
 So Western Europe, China, Japan, Korea,
 like you've got this population bubble
 of like aging working age folks who are,
 you know, at some point over the next 20, 30 years,
 they're gonna be largely retired
 and like you're gonna have more retired people
 than working age people.
 And then like you've got, you know,
 sort of natural questions about who's gonna take care
 of all the old folks and who's gonna do all the work.
 And the answers to like all of these sorts of questions,
 like where you're sort of running into, you know,
 like constraints of the world and of society
 has always been like what tech is gonna
 like help us get around this.
 You know, like when I was a kid in the 70s and 80s,
 like we talked all the time about like,
 oh, like population boom, population boom,
 like we're gonna, like we're not gonna be able
 to like feed the planet.
 And like we were like right in the middle of the green
 revolution where like this massive technology driven
 increase in crop productivity, like worldwide.
 And like some of that was like taking some of the things
 that we knew in the West and like getting them distributed
 to the, you know, to the developing world.
 And like part of it were things like, you know,
 just smarter biology, like helping us increase.
 And like we don't talk about like, yeah,
 overpopulation anymore because like we can more or less,
 we sort of figured out how to feed the world.
 Like that's a technology story.
 And so like I'm super, super hopeful about the future
 and in the ways where we will be able to apply technology
 to solve some of these super challenging problems.
 Like I've, like one of the things that I'm trying
 to spend my time doing right now is trying to get
 everybody else to be hopeful as well, because, you know,
 back to Harare, like we are the stories that we tell.
 Like if we, you know, if we get overly pessimistic
 right now about like the potential future of technology,
 like we, you know, like we may fail to get all the things
 in place that we need to like have our best possible future.
 And that kind of hopeful optimism.
 I'm glad that you have it because you're leading
 large groups of engineers that are actually defining,
 that are writing that story, that are helping build
 that future, which is super exciting.
 And I agree with everything you said,
 except I do hope Clippy comes back.
 We miss him.
 I speak for the people.
 So, Kellen, thank you so much for talking today.
 Thank you so much for having me.
 It was a pleasure.