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 in iTunes, support it on Patreon, or simply connect with me on Twitter at Lex Fridman, spelled F R I D M A N. And I'd like to give a special thank you to Tom and Nolante Bhausen, for their support of the podcast on Patreon. Thanks, Tom and Elante. 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?
So like I think broadly speaking Microsoft's product portfolio includes everything from, you know, big cloud business, like a big set of SaaS services. We have, you know, 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 like there's this really smart young economist Glenn Weyl who 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 like probably a bunch of stuff that I'm embarrassingly not recounting in this list.
Gaming too, Xbox and so on, right?
Yeah, gaming for sure. Like 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 like we're, you know, doing some interesting collaboration now with the folks who made Day of the Tentacle and I was like completely nerding out with Tim Schafer, like the guy who wrote Day of the Tentacle this morning. Just a complete fanboy. Which, you know, sort of happens a lot. Like you know Microsoft has been doing so much stuff at such breadth for such a long period of time that, you know, like being CTO, like 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.
Yeah. To reach back into the sentimental and 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're having this debate right now like does capitalism work, do 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 tip towards like okay let's become more socialist and, you know, have central planning and, you know, governments or some other central organization is making a bunch of decisions about how work gets done and where the investments and where the outputs of those investments get distributed. Glenn's notion is like lean more into 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 could be bid out of your position in your home, you know, for instance. So like if somebody came along and said, you know, I can find higher economic utility for this piece of real estate that you're running your business in, then you either have to bid to sort of stay or the thing that's got the higher economic utility 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 you very quickly lose a whole lot of money. And so the prices of the assets would be sort of very closely indexed to the value that they could produce. And because you'd have this sort of real-time mechanism that would force you to mark the value of the asset to the market, then it could be taxed appropriately. Like you couldn't sit on this thing and say, 'Oh, like this house is only worth 10,000 bucks when everything around it is worth 10 million.'
That's really interesting. So it's an incentive structure where the prices match the value much better.
Yeah. And Glenn does a much much better job than I do at selling it. I probably picked the world's worst example, you know, but it's intentionally provocative. You know, so like this whole notion, I'm not sure whether I like this notion that we could have a set of market mechanisms where I could get bid out of my property. But, you know, like if you're thinking about something like Elizabeth Warren's wealth tax for instance, you would have, I mean it'd be really interesting how you would actually set the price on the assets and 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, right? 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 it's pushing AI forward. How do you think about, I mean combining all our conversations together here with radical markets and socialism and innovation and 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 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. 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, Bill has this thing that he said, you know, 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.
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 like I think that's bad from a economics and equitable distribution of value perspective, you know, sort of 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 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 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 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 an engine is not 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 they get all the economics from all of that. Like fully democratized, like they're probably, we're sitting here in this room and even though they don't, there are probably things, like the MEMS gyroscope that are in both of our phones, like there are 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 tieback to this radical markets thing, so my team has been working with Glenn on this and Jaron Lanier actually. So Jaron is 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 is labor or they call it data dignity as well. And so the idea is that if you, again going back to this 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 you go to LinkedIn, you sort of set up your profile on LinkedIn, that's an explicit contribution, you know exactly the information that you're putting into the system and you put it there because you have some nominal notion of what value you're going to get in return but it's only nominal, you don't know exactly what value you're getting in return, service is free, low amount of 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's in your daily life. And so what Glenn and Jaron and this data dignity team are trying to do is can we figure out a set of mechanisms that let us value those data contributions so that you could create an economy and a set of controls and incentives that would allow people to maybe even in the limit 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 there are a whole bunch of them in China right now that are basically data labeling companies.
So like if you're doing supervised machine learning, you need lots and lots of labeled training data and those people who work for those companies are getting compensated for their data contributions into the system.
So that's easier to put a number on their contribution because they're explicitly labeling data. 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, you know, we don't have as much transparency as I think we need in how the data is getting used and it's, you know, super complicated. Like we, I think as technologists sort of appreciate some of the subtlety there. It's like the data gets created and then 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, it's only valuable when you sort of aggregate it together into large numbers. It's true even for these folks who are getting compensated for labeling things, like for supervised machine learning now you need lots of labels to train a model that performs well. And so, you know, I think that's one of the challenges, is how do you figure out because this data is getting combined in so many ways, through these combinations, how the value is flowing.
Yeah, that's fascinating. 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're thinking about it at Microsoft so if we go back to '89 when Microsoft released Office or 1990 when they released Windows 3.0, you know, how's the, in your view, I know you weren't there the entire history but how's the company changed in the 30 years since as you look at it now?
The good thing is it started off as a platform company, like it's still a platform company. Like the parts of the business that are thriving and most successful are those that are building platforms. Like the mission of the company now has changed in a very interesting way. So, you know, back in '89, '90, they were still on the original mission, which was put a PC on every desk and in every home. Like 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 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 a reach, like you just imagine where things were 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, right? They're super important, completely critical and when you see one fail you sort of understand, but it's not a thing where you're waiting for the next operating system thing in the same way that you were in 1995. Like 1995, you know, we had Rolling Stones on the stage with the Windows 95 rollout, like it was the biggest thing in the world, everybody lined up for it the way that people used to line up for iPhone. But eventually, and like this isn't necessarily a bad thing, it just sort of becomes ubiquitous. It's everywhere and 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, that's a platform mission and the way that we do it now is different. 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, you know, some people might not think is the sexiest thing in the world but it's helping people figure out how to automate all of their business processes and workflows and to help those businesses using it to grow and be more. So it's a much broader vision in a way now than it was back then, like it was a very particular thing and now we live in this world where technology is so powerful and it's such a basic fact of life 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 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. And so 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 like fairly obvious that AI is one, like you don't have to, I mean like that's, you sort of say it to someone and they get it. But we also think of mixed reality and quantum as these two interesting potentially, quantum computing. And look, it's having 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 machine maintenance on factory floors. So like 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, the people love it. They're like oh my god, like this is for them 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 certainly obvious AI as a platform but can we dig into it a little bit? 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, 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 super, it's everywhere. And we run these review meetings now where it's me and Satya and members of Satya's leadership team and a cross-functional group of folks across the entire company who are working on either AI infrastructure or 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 how the AI is going to manifest in an experience for something that's going to make it better, I think you don't want the AI-ness to be the first order thing. It's like whatever the product is and the thing that is trying to help you do, the AI just sort of makes it better. And, you know, this is a gross exaggeration, but like people get super excited about where the AI is showing up in products. And I'm like, do you get that excited about where you're using a hash map in your code? Like it's just another, it's a very interesting programming tool, but it's an engineering tool. And so it shows up everywhere. So like we've got dozens and dozens of features now in Office that are powered by 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.
Yeah. When you mean moderation, you mean like the recommenders? It's like showing what you want to 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 really targeted towards a gaming audience. So it's a very particular type of thing where the line between playful banter and legitimate bullying is a subtle one.
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 Xbox, gaming is the social component, the very different kinds of 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 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 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 sort of content safety problem as you do in a general purpose platform. I mean so like on LinkedIn the whole social network is about connecting people with opportunity whether it's helping them find a job or to find mentors or to help them find their next sales lead or to just sort of allow them to broadcast their professional identity to their network of peers and collaborators and professional community. That is, in some ways, very broad but in other ways it's narrow. And so you can build AI, machine learning systems that are capable with those boundaries of making better automated decisions about what is inappropriate and offensive comment or dangerous comment or illegal content. When you have some constraints.
Same thing with gaming, social networks for instance. 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. Bullying is not fun, so you want to do everything in your power to encourage that not to happen. And yeah, I think it's a tough problem in general is one where I think, you know, eventually we're going to 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 in a democracy, right? You don't want, you want some sort of democratic involvement. People should have a say in where the lines are drawn. You don't want a bunch of people making unilateral decisions. And 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 going to happen fast enough in order to prevent very bad things from happening. But 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 some sort of weird thing where you've got a non-representative group of people making decisions that have 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.
