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 1 or 2 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 5 stars on iTunes, support it on Patreon, or simply connect with me on twitter at Lex Friedman spelled Fri DMAM and I'd like to give a special Thank you to Tom and Alanti Bighausen for their support of the podcast on Patreon.

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.

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 use.

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.

There's this 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 like probably a bunch of stuff that I'm embarrassingly not recounting in this list.

Like I was having a super fun conversation this morning with Phil Spencer.

So when I was in college, there was this game that Lucas Arts made called Day of the Tentacle that my friends and I played forever.

And like, we're 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, it like 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 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 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.

And I think it's a reasonable set of questions to be asking.

And so what Glenn, and so 1 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 1 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 and 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 and 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 can 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.

So it's a incentive structure that, where the prices match the value, much better.

and Glenn does a much, 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, it's intentionally provocative, you know, so like this whole notion, like I, you know, like I, 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 property.

But if you're thinking about something like Elizabeth Warren's wealth tax, for instance, you would have, I mean, it'd be really interesting in 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.

I think it's sort of 1 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 like what what benefits will it produce and what negative impacts will it produce and you know how who who gets to steer this whole thing.

You know I'll say it at the highest level 1 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 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, 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 equitable distribution of value perspective, back to this whole notion of, you know, like, do the markets work?

Also bad from an innovation perspective because like 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 like 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 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.

Now, an engine is not a differentiated thing.

There isn't 1 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 even though they're probably things like the MEMS gyroscope that are in both of our phones, like they're like little engines sort of everywhere.

They're just a component in how we build the modern world.

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.

And Microsoft, throughout its history, has been positioned well to do that.

And the tie back to this radical markets thing, like the, 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 1 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 idea is that if you, you know, again, going back to this, you know, 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's 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.

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, like who work for those companies are getting compensated for their data contributions into the system.

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.

Because we don't have as much transparency as I think we need in how the data is getting used.

Like, you know, we, you know, I think as technologists sort of appreciate like some of the subtlety there.

It's like, you know, the data gets created and then it gets, you know, it's not valuable.

Like the, 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, 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.

It's like how do you figure out, because this data is getting combined in so many ways, through these combinations, how the value is flowing.

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 at 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

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.

You just sort of imagine where things were when they started the company versus where things are now.

In success, when you've democratized a platform, it just sort of vanishes into the platform.

Like operating systems aren't a thing anymore.

Like they're super important, like completely critical.

And like, you know, when you see 1, you know, fail, like 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 in 1995, we had Rolling Stones on the stage with the Windows 95 rollout.

Everybody lined up for it 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, it, the success is that it's sort of, it becomes ubiquitous.

It's like everywhere, like human beings, when their technology becomes ubiquitous, they just sort of start taking it for granted.

So the mission now, that Satya rearticulated 5 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, you know, 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 grow and be more.

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 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 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 think of mixed reality as a platform too?

When we look at what the platforms of the future could be, it's like fairly obvious that like AI is 1.

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 the mixed reality and quantum as these 2 interesting, potentially.

So you're talking about some futuristic things here.

Well, the mixed reality, Microsoft is really, it's not even futuristic, it's here.

And look, it's having an impact right now.

Like 1 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 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 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 boosts that, you know, like 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?

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 interesting.

And like 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 gonna manifest in 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 the thing that is trying to help you do, the AI just sort of makes it better.

This is a gross exaggeration, but like I, you know, 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?

It's a very interesting programming tool, but it's an engineering tool.

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 X cloud platform.

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

So the usual social network stuff that you have to deal with.

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 the line between playful banter and like legitimate bullying is like a subtle 1.

And like you have to, it's sort of tough.

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 2 platforms, right?

And the line in terms of bullying and so on is different on the 2 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 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 allow them to broadcast their professional identity to their network of peers and collaborators and 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, it's narrow.

And so like you can build AIs, 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, same thing with like the gaming, gaming social networks, 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.

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.

It's 1 where I think, you know, 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.

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, 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 going to 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 some sort of weird thing where you've got a non-representative group of people making decisions that have national and global impact.

And 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

With sort of fake news, for instance, and...

Deep fakes and fake news generated by humans?

Yeah, so we can talk about deep fakes, like I think that is another sort of very interesting level of complexity.

But if you think about just the written word, we invented papyrus, what, 3,000 years ago, where you could put word on paper.

And then 500 years ago, we get the printing press, like where the word gets a little bit more ubiquitous.

Then 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 we had 3,000 years up to that point to figure out what's journalism, what's editorial integrity, what's scientific peer review.

And so you built all of this mechanism to try to filter through all of the noise that the technology made possible to 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 would it Mary have in 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 there are 3 and a half billion people who are connected now.

Crazy, like inconceivable how fast all of this happens.

It's not surprising that we haven't figured out what to do yet, but we got to really lean into this set of problems because we basically have 3 millennia worth of work to do about how to deal with all of this and like probably what amounts to

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 as 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.

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Kevin Scott: Microsoft CTO | Lex Fridman Podcast #30