Kevin Scott: Microsoft CTO

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. They'll talk with me about the impact of that investment. This is the Artificial Intelligence podcast.

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Give it five stars on iTunes, support it on Patreon, or simply connect with me on Twitter exfriedman spelled F R I D M A N and I'd like to give a special thank you to Tom and Alanthe Bickhousen for their support of the podcast on Patreon. Thanks Tom and Alanthe. 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 SC.

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. I think broadly speaking, Microsoft's product portfolio includes everything from big cloud business like a big set of SaaS services. We have the original or 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, really smart young economist, Glenn Weil, who like my group works with a lot, who's doing this research on these things called radical markets. Like he's written an entire technical book about this whole notion of radical markets. So like the research group sort of 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.

Gaming to Xbox and so on.

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, with the folks who made Day of the Tentacle. And I was completely nerding out with Tim Schaefer, like the guy who wrote Day of the Tentacle this morning. Just a complete fanboy, which sort of happens a lot. Microsoft has been doing so much stuff at such breadth for such a long period of time that being cto, like most of the time my job is very, very serious. And sometimes like I get to, I get caught up in like 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? I think we're having this debate right now. Does capitalism work? 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? And I think it's a reasonable set of questions to be asking. And so what Glenn, one mode of thought there have doubts that the markets are actually working. You can sort of like tip towards like, okay, let's become more socialist and have central planning and governments or some other central organization is making a bunch of decisions about how sort of work gets done and where the investments and where the outputs of those investments get distributed. Glenn's notion is lean more into the market based metrics 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, in, in your home, you know, for instance. So like if somebody came along and said, you know, like I've, I can find higher economic utility for this piece of real estate that you're running your, 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 you 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 really interesting. 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 in, 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 bid out of my product 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.

That you're really thinking broadly. Maybe you can speak to. 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 automation's impact on society. And arguably Microsoft is at the cutting edge of innovation in all these kinds of ways and so is 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 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 what's it going to be used for and what benefits will it produce and what negative impacts will it produce and who gets to steer this whole thing? 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 like Bill, Bill has this thing that he said a bunch of years ago where 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. Yeah, 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 equitable distribution of value perspective back to this whole notion of do the markets work? But I think it's also bad from an innovation perspective because I have infinite amounts of faith in human beings that if you 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 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, 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 of that fully Democrat. They're probably, we're sitting here in this room and even though they're probably things like the MEMS gyroscope that are in both of our. They're like little engines 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 tie back to this radical markets thing, my team has been working with Glenn on this And Jaron Lanier, actually. So Jaron 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 Jaron 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, the idea is that if you, 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 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 going to get in return. But it's like only nominal. Like you don't know exactly what value you're getting in return. Like service is free, you know, like it's low amount of like perceived. And then you've got all this indirect contribution that you're making just by virtue of interacting with all of the technology that in your daily life. And so like what Glenn and Jaron 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. These companies like scale AI and there are a whole bunch of them in China right now that are basically data labeling companies. So you're doing supervised machine learning. You need lots and lots of label training data. And 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, that's possible?

I don't know. It's hard. It really is because, you know, we don't have as much transparency as is. I think we need in like how the data is getting used and it's, you know, super complicated. Like, you know, we, we, you know, I think as technologists sort of appreciate like some of the subtlety there. It's like, you know, the data, the data gets created and then it gets, you know, it's not valuable like the data exhaust that you give off or the, you know, the explicit data that I am putting into the system isn't value valuable, isn't super valuable atomically. It, it's only valuable when you sort of aggregate it together into sort of large numbers. This is true even for these folks who are getting compensated for labeling things for supervised machine learning. Now 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 sort of figure out because this data is getting combined in so many ways through these combinations, like how the value is flowing.

Yeah, that's fascinating. Yeah. And it's fascinating that you're thinking about this. And I wasn't even going into this conversation expecting the breadth of research really that Microsoft broadly is thinking about, you are thinking about in 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 missions change. It's like change in a very interesting way. So back in 89, 90, they were still on the original mission, which was put a PC on every desk and in every home. 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 homebrew computers 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 a, it was sort of a reach. Like you just sort of imagine like where things were or 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 fail, like you, you just, you sort of understand but like, you know, it's not a thing where you're, you're not like waiting for, you know, the next operating system thing in the same way that you were in 1995, right? Like 1995, like, you know, we had Rolling ST on the stage with the Windows 95 rollout. Like, it was like the biggest thing in the world. Everybody lined up for it the way that people used to line up for iPhone. But eventually, and this isn't necessarily a bad thing, it just sort of, 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 re articulated 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 again, that's a platform mission. And the way that we do it now is different. It's like we have a hyperscale cloud that people are building their applications on top of. Like, we have a bunch of AI infrastructure that people are building their AI applications on top of. We have a productivity suite of software like Microsoft Dynamics, which 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 help those businesses using it to like, grow and be more so it's, 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 fact of life that it, you know, that it both exists and is going to get, 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. Yep, that's, 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, yeah, with HoloLens, 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 fairly obvious that AI is one. You don't have to. You sort of say it to someone and they get it. But we also think of the mixed reality and quantum. Is these two interesting? 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. It's here it is incredible stuff and.

Look and it's having impact right now. Like one of the more interesting things that's happened with mixed reality over the past last couple of years that I didn't clearly see is that it's become the computing device for folks 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 machine maintenance on factory floors so they, because they're mobile and they're out in the world and they're working with their hands and sort of servicing these very complicated things. They don't use their mobile phone and they don't carry a laptop with them and they're not tethered to a desk. And so mixed reality 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 for them like the same sort of productivity boost that an office worker had when they got their first personal computer.

Yeah, but you did mention it's fairly 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 want to do using neural networks. How do you think of AI infusing as a platform that Microsoft can provide?

Yeah, I mean I think it's, it's super interesting. It's like everywhere and like we, 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, 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 going to manifest in like an experience for something that's going to 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 is trying to help you do, like the AI just sort of makes it better. And you know, this is a gross exaggeration but like I, yeah, 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 like 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 a, 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. Increasingly, things like content moderation on our Xbox and xCloud platform.

When you mean moderation, do you mean the recommenders is showing what you want to look at next?

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, it's targeted towards a gaming audience. So it's like a very particular type of thing where, you know, the, the line between playful banter and like legitimate bullying is like a subtle one and like, you have to like, 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.

Yep.

And if we look at, 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 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 if you have a clear notion of what your social network should be used for or what you are designing a community around, then you don't have as many dimensions to your content safety problem as you do in a general purpose platform. I mean, so like on, 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 sort of professional identity to their, their network of peers and collaborators and sort of professional community. In some ways that's very, very broad, but in other ways it's sort of, it's narrow. And so you can build AI's machine learning systems that are capable with those boundaries of making better automated decisions about what is sort of inappropriate and offensive comment or dangero comment or illegal content when you have some constraints. Same thing with, same thing with the gaming social network. So for instance, it's about playing games, about having fun. And the thing that you don't want to have happen on the platform is why bullying is such an important thing. 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 sort of a tough problem in general and it's one where I think eventually we're going to have to have some sort of clarification from our policymakers about what it is that we should be doing. Like where the lines are, because it's tough in democracy, right? Like you don't want, you want some sort of democratic involvement. Like people should have a say and like where, where the lines, lines are drawn. Like you don't want a bunch of people making like unilateral decisions. And like we are in a, we're in a state right now for some of these platforms where you actually do have to make unilateral decisions where the policy making isn't going to happen fast enough in order to like prevent very bad things from happening. But like we need the policy making side of that to catch up I think is as quickly as possible because you want that whole process to be a democratic thing, not some sort of weird thing where you've got a non representative group of people making decisions that have 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.

Yeah, I mean, with sort of fake news, for instance, and deep fakes and.

Fake news generated by humans.

Yeah. So we can talk about deep fakes. I think that is another sort of very interesting level of complexity. But if you think about just the written word, right, like we have, you know, we invented papyrus, what, 3,000 years ago where we, you know, you could sort of put, put word on, on paper. And then 500 years ago, like we, 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 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 3,000 years up to that point to figure out like how to, you know, like, what's, what's journalism, what's editorial integrity? Like, what's, you know, what scientific peer review. And so like, you built all of this mechanism to like, try to filter through all the noise that the technology made possible to sort of getting to something that society could cope with. And if you think about just the PC didn't exist 50 years ago. And so in this span of half a century, we've gone from no ubiquitous digital technology to having a device that sits in your pocket where you can sort of say whatever is on your mind to like, what did Mary have? And her, 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, they're 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, we gotta really lean into this set of problems because we basically have three millennia worth of work to do about how to deal with all of this and probably what amounts to the next decade worth of time.

So since we're on the topic of 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 are 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 and governments and companies. 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, 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, should like really come in and say that, you know, this is, 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 this 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, democratically determined regulatory framework for like where we could and should use it. And the other thing there is, we've got a bunch of research that we're doing and a bunch of progress that we've made on bias there. And there are all sorts of weird biases that these models can have, all the way from the most noteworthy one where you may have underrepresented minorities who are 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 are men, for instance. Yeah. I mean, 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 push the code to online or they even publish the paper that they are at least starting to think about what some of the potential negative consequences are of some of this stuff. I mean, this is where the deep fake stuff I find very worrisome just because there are going to be some very good beneficial uses of like gan generated imagery. And funny enough, like one of the places where it's actually useful is we're using the technology right now to generate synthetic, synthetic visual data for training some of the face recognition models to get rid of the bias. So, like, that's one super good use of the tech. But, like, you know, it's getting good enough now where, you know, it's going to sort of challenge a normal human being's ability to, like, now you just sort of say, like, it's, it's very expensive for someone to fabricate a photorealistic fake video. And like, Gans are going to 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. 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 of is not legit. 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. I think we will have technology that may be able to detect whether or not something's fake or real, although the fakes are pretty convincing even 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. One of the things you could choose to do with a social network is you could. You could use crypto and the networks to have content signed where you could have a full chain of custody that accompanied every piece of content. So when you're viewing something and you want to ask yourself, how much can I trust this? You can click something and have a verified chain of custody that shows, oh, this is coming from this source. And it's like, like signed by someone whose identity I trust. Yeah. I think having that chain of custody, like, being able to say, oh, here's this video. 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 I trust this identity. Like, oh, no, this is really from the White House, or 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. That might be one way that you can solve some of the problems. So that's not the super high tech. We've had all of this technology forever. But I think you're right, it has to be some sort of technological thing because the underlying tech that is used to create this is, is not going to 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 for content. 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. 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 wired for skepticism. You don't just first order trust everything that you see and encounter and you're curious to understand the next thing. But I think it's an entirely healthy, 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 an 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 ten came out I was like, okay, what's next?

Nice.

It's a nice improvement. But so what do you see the future of these products?

I think there's a bunch of exciting, I mean on the office front there's going to be this like increasing productivity wins that are coming out of some of these AI powered features that are coming. Like the products will sort of get smarter and smarter in like a very subtle way. Like there's not going to be this, this big bang moment where you know like Clippy is going to reemerge and it's going to be.

Wait a minute. Okay, we'll 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 autoresponse stuff's getting better and better and it's getting to the point where it can autorespond with like okay, let's. That 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. We have this notion of Microsoft Search where it's not just web search but it's search across all of your information that's sitting inside of your office 365 tenant and potentially in other products. And we have this thing called the Microsoft Graph that is basically an API Federator that gets you hooked up across the entire breadth of all of the, what were information silos before they got woven together with the graph that is getting increasing, with increasing effectiveness sort of plumbed into some of these autoresponde things where you're going to be able to see the system automatically retrieve information for you. I frequently send out emails to folks where I can't find a paper or a document or whatnot. There's no reason why the system won't be able to do that for you. I think it's building towards having things that look more like a fully integrated assistant. But you'll have a bunch of steps that you will see before you like. It will not be this big bang thing where like Clippy comes back and you've got this like, you know, manifestation of, you know, like A fully, fully powered assistant. So I think that's, that's definitely coming in like all of the, you know, collaboration, co authoring stuff's getting better. You know, 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 it's this thing where you've got collaboration is at the center of the product and we built some really cool stuff, which is about to be open source that are sort of framework level things for doing co authoring.

That's awesome. So is there a cloud component to that 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 with our view.

We're already a little bit better than that and like, you know, so the fact that you're unaware of it means we've got a better job to do like helping you discover, discover this stuff. But yeah, I mean it's already like got a huge, huge cloud component and like part of this framework stuff, I think we're calling it 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 at Bill, it's called the Fluid framework. 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 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 the, 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 an engineer, I know what it's like to sort of, 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. So it's more than 10 or 15. 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 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 very, very important for big engineering teams. Like, one is like having some sort of forethought about what it is that you're going to be building over large periods of time. Like, not exactly, like, you don't need to know that. Like, you know, I'm putting all my chips on this one product and like this is going to 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, like, 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 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. 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, of technical debt and culture debt that is excruciatingly painful to clean up. So that's one bundle of things. Another bundle of things is it's just really, really important to have a clear mission. That's not just some cute crap you say because you think you should have a mission, but something that Clarif clarifies for people where it is that you're headed together. I know it's probably a little bit too popular right now, but Yuval Harari's book Sapiens, one of the central ideas in his book is that storytelling is the quintessential thing for coordinating the activities of large groups of people. Once you get past Dunbar's number. And I've really, really seen that just managing engineering teams, you can just brute force things when you're less than 120150 folks where you can sort of know and trust and understand what the dynamics are between all the people. But past that, 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 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 know, you sort of really think about like a 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 have technology that is progressing at the fastest rate that it ever has. And you've got, you got some really big social problems, like society scale problems that we have to tackle. And so I think we're going to rise to the challenge and figure out how to intersect 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 the biggest remainder of the population boom is in Africa for the next next 50 years or so. And global warming is going to make it increasingly difficult to feed the global population in particular in this place where you're going to have the biggest population boom. I think AI is going to, if we push it in the right direction, it can do incredible things to empower all of us to achieve our full potential and to, you know, like, 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, you know, like our quality and cost of, and sort of ubiquity of health coverage is, is better and better over Time like that's more and more important. Every day is like in the, in the United States and like the rest of the industri industrialized world. So Western Europe, China, Japan, Korea, you've got this population bubble of aging working age folks who are at some point over the next 20, 30 years they're going to be largely retired and you're going to have more retired people than working age people. And then you've got natural questions about who's going to take care of all the old folks and who's going to do all the work. The answers to all of these sorts of questions where you're running into constraints of the, you know, the, the, the world and of society has always been like, what tech is going to like help us get around this? You know, like when I was, 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, 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, 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 the idea overpopulation anymore because like we can more or less we sort of figured out how to feed the world. Like that's a, that's a technology story. And so like I'm, 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, I've like one of the things that I, I'm trying to spend my time doing right now is trying to get everybody else to be hopeful as well. Because back to Harari, we are the stories that we tell. If we get overly pessimistic right now about the potential future of technology, we may fail to get all of the things in place that we need to 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 future, which is super exciting. And I agree with everything you said, except I do hope Clippy comes back. We, we miss him. I speak for the people. So Kellen thank you so much for talking today.

Oh, thank you so much for having me. It was pleasant, Sam.