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The following is a conversation with Kevin Scott, the CTO of Microsoft.
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Before that, he was the Senior Vice President of Engineering and Operations at LinkedIn.
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And before that, he oversaw mobile ads engineering at Google.
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He also has a podcast called Behind the Tech with Kevin Scott, which I m a fan of.
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This was a fun and wide-ranging conversation that covered many aspects of computing.
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It happened over a month ago, before the announcement of Microsoft s investment in OpenAI that a few people have asked me about.
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I m sure there ll be one or two people in the future that ll talk with me about the impact of that investment.
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This is the Artificial Intelligence Podcast.
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If you enjoy it, subscribe on YouTube, give it five stars on iTunes, support it on Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D-M-A-N.
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And I d like to give a special thank you to Tom and Nolante Bickhausen for their support of the podcast on Patreon.
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Thanks Tom and Elanthi, hope I didn t mess up your last name too bad.
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Your support means a lot and inspires me to keep this series going.
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And now, here s my conversation with Kevin Scott.
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You ve described yourself as a kid in a candy store at Microsoft because of all the interesting projects that are going on.
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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?
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If you include research, it becomes even more difficult.
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So,
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So I think broadly speaking, Microsoft s product portfolio includes everything from, you know, big cloud business, like a big set of SaaS services.
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We have, you know, sort of the original or like some of what are among the original productivity software products that everybody uses.
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We have an operating system business.
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We have a hardware business where we make everything from
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computer mice and headphones to high-end personal computers and laptops.
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We have a fairly broad-ranging research group where we have people doing everything from economics research, so there s this really
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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.
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Like, he s written an entire technical book about this whole notion of radical markets.
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So, like, the research group sort of spans from that to human-computer interaction to artificial intelligence.
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And we have GitHub, we have LinkedIn, we have
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a search advertising and news business and like probably a bunch of stuff that I m embarrassingly not recounting in this list.
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Gaming to Xbox and so on.
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Yeah, gaming for sure.
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Like I was having a super fun conversation this morning with Phil Spencer.
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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
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you know, doing some interesting collaboration now with the folks who made Day of the Tentacle.
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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.
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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,
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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.
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You have to reach back into the sentimental.
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And what s the radical markets and the economics?
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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?
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Like, free markets work?
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Can
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The incentive structures that are built into these systems produce outcomes that are creating sort of equitably distributed benefits for every member of society.
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know, and I think it s a reasonable set of questions to be asking.
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And so, what Glenn – and so, like, you know, one mode of thought there, like, if you have doubts that the markets are actually working, you can sort of, like, tip towards, like, okay, let s become more socialist and, you know, like, have central planning and, you know, governments or some other central organization is, like, making a bunch of decisions about how, you know, sort of,
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work gets done and, you know, like where the, you know, where the investments and where the outputs of those investments get distributed.
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Glenn s notion is, like, lean more into, like, the market-based mechanism.
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So, like, for instance, you know, this is one of the more radical ideas.
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Like, suppose that
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you had a radical pricing mechanism for assets like real estate where you could be bid out of your position in your home, for instance.
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So if somebody came along and said, I can find higher economic utility for this piece of real estate that you re running your business in, then
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Uh, 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, uh, you know, sort of takes over the asset and which would make it very difficult to have the same sort of rent seeking, uh, behaviors that you ve got right now.
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Because, uh, like if you did speculative bidding.
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Like you would.
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You very quickly, like lose a whole lot of money.
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And so like the prices of the assets would be sort of like very closely indexed to, uh, like the value that they can produce.
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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 can be taxed appropriately.
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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.
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$10 million.
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So it s an incentive structure where the prices match the value much better.
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Yeah.
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And Glenn does a much better job than I do at selling, and I probably picked the world s worst example, and, and, and.
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And it s
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It s intentionally provocative.
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So 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.
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But if you re thinking about something like
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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 price on the assets and like you might have to have a mechanism like that if you put a tax like that in place.
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It s really interesting that that kind of research, at least tangentially, is touching Microsoft Research.
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Yeah.
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You re really thinking broadly.
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Maybe you can speak to
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this connects to AI.
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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.
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And arguably, Microsoft is at the cutting edge of innovation in all these kinds of ways.
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and so it s pushing AI forward.
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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
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that will result in job loss for the lower and so on.
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How do you think about that?
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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 gonna be used for, and what benefits will it produce, and what negative impacts will it produce, and who gets to steer this whole thing.
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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.
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And so, you know, like Bill
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Bill has this thing that he said, you know, a bunch of years ago, where, you know, the measure of a successful platform is that it produces far more economic value for the people who build on top of the platform than is created for the platform owner or builder.
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And I think we have to think about AI that way.
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Like having...
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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.
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It can t be a thing where there are a handful of companies sitting in a very small handful of cities geographically who are
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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.
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So I think that s bad from a sort of
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you know, economics and sort of equitable distribution of value perspective, like, you know, sort of back to this whole notion of, you know, like, do the markets work?
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But I think it s also bad from an innovation perspective, because, like, I have infinite amounts of faith in human beings that if you, you know, give folks powerful tools, they will go do
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interesting things.
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And it s more than just a few tens of thousands of people with the interesting tools.
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It should be millions of people with the tools.
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So it s sort of like, you know, you think about the steam engine in the late 18th century, like it was, you know, maybe the first large scale substitute for human labor that we ve built like a machine.
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And
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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.
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They built factories around them and businesses, and the experts who knew how to build and maintain them.
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access to that technology democratized over time.
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Like now, like an engine, it s not like a differentiated thing.
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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.
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And like, they get all the economics from all of that.
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Like, no, no, like fully democratized.
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Like they re probably, you know, we re sitting here in this room and like, even though they don t, they re probably things, you know, like the,
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the MEMS gyroscope that are in both of our phones, like they re like little engines, you know, sort of everywhere.
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They re just a component in how we build the modern world, like AI needs to get there.
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Yeah, so that s a really powerful way to think.
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If we think of AI as a platform versus a tool that Microsoft owns,
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as a platform that enables creation on top of it.
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That s the way to democratize it.
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That s really interesting, actually.
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And Microsoft, throughout its history, has been positioned well to do that.
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And the tie back 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.
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He s one of the most interesting human beings on the planet, like a sweet, sweet guy.
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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.
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And so, the idea is that if you, you know, again, going back to this, you know, sort of industrial analogy,
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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.
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We all make them explicitly.
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You go to LinkedIn, you set up your
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profile on LinkedIn, like that s an explicit contribution, like you know exactly the information that you re putting into the system.
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And like you put it there because you have some nominal notion of like what value you re going to get in return.
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But it s like only nominal, like you don t know exactly what value you re getting in return, like services free, you know, like it s low amount of like perceived.
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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
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daily life.
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And so, like, what Glenn and Jaren and this data dignity team are trying to do is, like, can we figure out a set of mechanisms that let us value those data contributions so that you could create an economy and, like, a set of controls and incentives that would allow people to
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like, maybe even in the limit, like, earn part of their living through the data that they re creating.
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And, like, you can sort of see it in explicit ways.
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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.
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So, like, you re doing supervised machine learning, you need lots and lots of label training data.
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And those people who work for those companies are getting compensated for their data contributions into the system.
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And so... That s easier to put a number on their contribution because they re explicitly labeling data.
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Correct.
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But you re saying that we re all contributing data in different kinds of ways.
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And it s fascinating to start to explicitly try to put a number on it.
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Do you think that s possible?
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I don t know.
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It s hard.
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It really is.
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Because...
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you know, we don t have as much transparency as I think we need in, like, how the data is getting used.
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And it s, you know, super complicated.
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Like, you know, we, you know, I think as technologists sort of appreciate, like, some of the subtlety there.
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It s like, you know, the data gets created and then it gets, you know, it s not valuable.
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the data exhaust that you give off or the explicit data that I am putting into the system isn t super valuable atomically.
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It s only valuable when you aggregate it together into large numbers.
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It s true even for these folks who are getting compensated for labeling things.
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For supervised machine learning now, you need
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lots of labels to train a, you know, a model that performs well.
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And so, you know, I think that s one of the challenges.
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It s like, how do you, you know, how do you sort of figure out, like, because this data is getting combined in so many ways, like, through these combinations, like, how the value is flowing?
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Yeah, that s fascinating.
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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.
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So, if we go back to 89, when Microsoft released Office, or 1990, when they released Windows 3.0,
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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?
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The good thing is it s started off as a platform company, like
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It s still a platform company, like the parts of the business that are thriving and most successful are those that are building platforms.
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The mission of the company now is, the mission s changed.
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It s changed in a very interesting way.
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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.
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Integrated circuit microprocessors were a brand new thing and like people were building, you know, homebrew computers, uh, you know, from kits, like the way people build ham radios, uh, right now.
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And I think this is sort of the interesting thing for folks who build platforms in general.
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Bill saw the opportunity there and what personal computers could do.
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And it was sort of a reach.
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You just sort of imagine where things were when they started the company versus where things are now.
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In success, when you ve democratized a platform, it just sort of vanishes into the platform.
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You don t pay attention to it anymore.
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Operating systems aren t a thing anymore.
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They re super important.
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completely critical.
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And when you see one fail, you sort of understand.
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But it s not a thing where you re not waiting for the next operating system thing in the same way that you were in 1995, right?
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In 1995, we had Rolling Stones on the stage with the Windows 95 rollout.
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It was the biggest thing in the world.
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Everybody lined up for it the way that people used to line up for iPhone.
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But eventually,
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And like, this isn t necessarily a bad thing.
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Like it just sort of, you know, it, the success is that it s sort of, it becomes ubiquitous.
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It s like everywhere.
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And like human beings, when their technology becomes ubiquitous, they just sort of start taking it for granted.
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So the mission now that Satya rearticulated five plus years ago now, when he took over as CEO of the company.
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Our mission is to empower every individual and every organization in the world to be more successful.
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And so, again, that s a platform mission.
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And the way that we do it now is different.
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It s like we have a hyperscale cloud that people are building their applications on top of.
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We have a bunch of AI infrastructure that people are building their AI applications on top of.
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productivity suite of software like Microsoft Dynamics, which, you know, some people might not think is the sexiest thing in the world, but it s like helping people figure out how to automate all of their business processes and workflows and, you know, like help those businesses using it to like grow and be more so.
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It s a much broader vision in a way now than it was back then.
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It was a very particular thing.
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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.
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So what you have to do as a platform player is just much bigger.
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There s so many directions in which you can transform.
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You didn t mention mixed reality, too.
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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.
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Oh, for sure.
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With HoloLens, Microsoft is doing some really interesting work there.
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Do you touch that part of the effort?
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What s the thinking?
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Do you think of mixed reality as a platform, too?
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Oh, sure.
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When we look at what the platforms of the future could be, it s fairly obvious that AI is one.
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You don t have to... I mean, that s...
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you sort of say it to someone and they get it.
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But we also think of the mixed reality and quantum as these two interesting, potentially.
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Quantum computing?
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Yeah.
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Okay, so let s get crazy then.
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So you re talking about some futuristic things here.
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Well, the mixed reality, Microsoft, is really not even futuristic.
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It s here.
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It is incredible stuff.
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And look, it s having an impact right now.
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One of the more interesting things that s happened with mixed reality over the past
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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.
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So technicians and service folks and people who are doing like machine maintenance on factory floors.
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So like they
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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.
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And so mixed reality, like, where it s
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getting traction right now where HoloLens is selling a lot of a lot of units is for these sorts of applications for these workers.
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And it s become like I mean, like the people love it.
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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.
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Yeah, but you did mention it s certainly obvious AI as a platform, but can we dig into it a little bit?
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Sure.
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How does AI begin to infuse some of the products in Microsoft?
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So currently providing training of, for example, neural networks in the cloud, or providing
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pre-trained models or just even providing computing resources, whatever different inference that you want to do using neural networks.
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How do you think of AI infusing as a platform that Microsoft can provide?
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Yeah, I mean, I think it s it s super interesting.
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It s like everywhere.
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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
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like either AI infrastructure or like have some substantial part of their of their product work using AI in some significant way.
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Now the important thing to understand is like when you think about like how the AI is going to manifest in like
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an experience for something that s going to make it better.
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I think you don t want the AI-ness to be the first order thing.
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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.
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This is a gross exaggeration, but I
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Yeah, people get super excited about, like, where the AI is showing up in products.
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And I m like, do you get that excited about, like, where you re using a hash table, like, in your code?
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Like, it s just another.
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It s just a tool.
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It s a very interesting programming tool, but it s sort of a, like, it s an engineering tool.
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And so, like, it shows up everywhere.
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So, like, we ve got dozens and dozens of features now in Office that are powered by, like, fairly sophisticated machine learning.
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Our search engine wouldn t work at all if you took the machine learning out of it.
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like increasingly things like content moderation on our Xbox and xCloud platform.
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When you mean moderation, do you mean like the recommenders showing what you want to look at next?
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No, no, no.
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It s like anti-bullying stuff.
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So the usual social network stuff that you have to deal with.
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Yeah, correct.
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But it s like really it s targeted
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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
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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.
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And if we look at LinkedIn as a social network, and if we look at the Xbox gaming as the social components, the very different kinds of, I imagine, communication going on on the two platforms, right?
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And the line, in terms of bullying and so on, is different on the two platforms.
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So how do you, I mean it s such a fascinating philosophical discussion of where that line is.
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I don t think anyone knows the right answer.
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Twitter folks are under fire now, Jack at Twitter, for trying to find that line.
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Nobody knows what that line is.
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but how do you try to find the line for trying to prevent abusive behavior and at the same time let people be playful and joke around and that kind of thing?
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I think in a certain way, if you have
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what I would call vertical social networks, it gets to be a little bit easier.
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SPEAKER_01
So, like, if you have a clear notion of, like, what your social network should be used for or, like, what you are designing a community around, then you don t have as many dimensions to your sort of content safety problem as, you know, as you do in a general purpose platform.
1
SPEAKER_01
I mean, so, like, on LinkedIn,
1
SPEAKER_01
Like, the whole social network is about connecting people with opportunity, whether it s helping them find a job or to, you know, sort of find mentors or to, you know, sort of help them, like, find their next sales lead or to just sort of allow them to broadcast their, you know, sort of professional identity to their
1
SPEAKER_01
their network of peers and collaborators and, you know, sort of professional community.
1
SPEAKER_01
Like, that is, I mean, like, in some ways, like, that s very, very broad.
1
SPEAKER_01
But in other ways, it s sort of, you know, it s narrow.
1
SPEAKER_01
And so, like, you can build AIs, like machine learning systems that are
1
SPEAKER_01
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.
1
SPEAKER_01
You know, same thing with, you know, same thing with, like, the gaming social network.
1
SPEAKER_01
So, for instance, like, it s about playing games, about having fun.
1
SPEAKER_01
And, like, the thing that you don t want to have happen on the platform is why
1
SPEAKER_01
bullying is such an important thing, like bullying is not fun.
1
SPEAKER_01
So you want to do everything in your power to encourage that not to happen.
1
SPEAKER_01
And yeah, but I think it s a it s sort of a tough problem in general.
1
SPEAKER_01
And it s one where I think, you know, eventually we re going to have to have
1
SPEAKER_01
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.
1
SPEAKER_01
Like you don t, like in democracy, right, like you don t want, you want some sort of democratic involvement, like people should have a say in like where the lines are drawn.
1
SPEAKER_01
Like, you don t want a bunch of people making, like, unilateral decisions.
1
SPEAKER_01
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.
1
SPEAKER_01
But, like, we need the policymaking side of that to catch up, I think, as quickly as possible, because you want that whole process to be a democratic thing, not a
1
SPEAKER_01
you know, not some sort of weird thing where you ve got a non-representative group of people making decisions that have, you know, like national and global impact.
1
SPEAKER_02
And it s fascinating because the digital space is different than the physical space in which nations and governments were established.
1
SPEAKER_02
And so what policy looks like globally, what bullying looks like globally, what healthy communication looks like globally is an open question.
1
SPEAKER_02
And we re all figuring it out together, which is fascinating.
1
SPEAKER_02
Yeah, I mean, with, you know, sort of fake news, for instance, and... Deep fakes and fake news generated by humans?
1
SPEAKER_01
Yeah.
1
SPEAKER_01
So, I mean, we can talk about deepfakes.
1
SPEAKER_01
Like, I think that is another, like, you know, sort of very interesting level of complexity.
1
SPEAKER_01
But, like, if you think about just the written word, right?
1
SPEAKER_01
Like, we have, you know, we invented papyrus, what, 3,000 years ago, where we, you know, you could sort of put word on paper.
1
SPEAKER_01
And then 500 years ago, we get the printing press, where the word gets a little bit more ubiquitous.
1
SPEAKER_01
And then you really, really didn t get ubiquitous printed word until
1
SPEAKER_01
the end of the 19th century when the offset press was invented.
1
SPEAKER_01
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.
1
SPEAKER_01
But like we had
1
SPEAKER_01
three thousand 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 of the
1
SPEAKER_01
noise that the technology made possible to like, you know, sort of getting to something that society could cope with.
1
SPEAKER_01
And like, if you think about just the piece, the PC didn t exist 50 years ago.
1
SPEAKER_01
And so in like this span of, you know, like half a century, like we ve gone from no digital, you know, 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,
1
SPEAKER_01
What did Mary have?
1
SPEAKER_01
Mary Meeker just released her new slide deck last week.
1
SPEAKER_01
We ve got 50% penetration of the internet to the global population.
1
SPEAKER_01
There are 3.5 billion people who are connected now.
1
SPEAKER_01
So it s like, it s crazy, crazy, like, inconceivable, like, how fast all of this happens.
1
SPEAKER_01
So, you know, it s not surprising that we haven t figured out what to do yet.
1
SPEAKER_01
But, like, we gotta, like, we gotta really, like, lean into this set of problems because, like, we basically have three millennia worth of work to do about how to deal with all of this and, like, probably what
1
SPEAKER_01
amounts to the next decade worth of time.
1
SPEAKER_02
So since we re on the topic of tough challenging problems, let s look at more on the tooling side in AI that Microsoft s looking at as face recognition software.
1
SPEAKER_02
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.
1
SPEAKER_02
So how do you, how does Microsoft think about the use of face recognition software as a platform in governments and companies?
1
SPEAKER_02
How do we strike an ethical balance here?
1
SPEAKER_01
Yeah, I think we ve articulated a clear point of view.
1
SPEAKER_01
So Brad Smith wrote a blog post last fall, I believe, that sort of outlined very specifically what our point of view is there.
1
SPEAKER_01
And I think we believe that there are certain uses to which face recognition should not be put.
1
SPEAKER_01
And we believe, again, that there s a need for regulation there, like the government should
1
SPEAKER_01
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 uh you know we push back against uses of
1
SPEAKER_01
face-recognition technology.
1
SPEAKER_01
You know, like the city of San Francisco, for instance, I think has completely outlawed any government agency from using face-recognition tech.
1
SPEAKER_01
And like that may prove to be a little bit overly broad, but for like certain law enforcement things, like you
1
SPEAKER_01
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
1
SPEAKER_01
you know, democratically determined regulatory framework for, like, where we could and should use it.
1
SPEAKER_01
And, you know, the other thing there is, like, we ve got a bunch of research that we re doing and a bunch of progress that we ve made on bias there.
1
SPEAKER_01
And, like, there are all sorts of, like, weird biases that these models can have, like, all the way from, like, the most noteworthy one where, you know, you may have,
1
SPEAKER_01
underrepresented minorities who are, like, underrepresented in the training data, and then you start learning, like, strange things.
1
SPEAKER_01
But, like, there are even, you know, other weird things.
1
SPEAKER_01
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.
1
SPEAKER_01
Yeah, I mean, so, like,
1
SPEAKER_01
It really is a thing where it s very important for everybody who is working on these things before they push publish.
1
SPEAKER_01
They launch the experiment, they push the code to online, or they even publish the paper that they are
1
SPEAKER_01
at least starting to think about what some of the potential negative consequences are of some of this stuff.
1
SPEAKER_01
I mean, this is where, you know, like the deep fake stuff, I find very worrisome just because.
1
SPEAKER_01
They re going to be some very good, beneficial uses of like GAN generated imagery.
1
SPEAKER_01
And funny enough, one of the places where it s actually useful is we re using the technology right now to generate synthetic visual data for training some of the face recognition models to get rid of the bias.
1
SPEAKER_01
So that s one super good use of the tech.
1
SPEAKER_01
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 re just sort of say, like, it s very expensive for someone to fabricate a photorealistic fake video.
1
SPEAKER_01
And, like, GANs are going to make it fantastically cheap to fabricate a photorealistic fake video.
1
SPEAKER_01
And so, like,
2
SPEAKER_00
Like what you assume you can sort of trust is true versus like be skeptical about is about to change.
2
SPEAKER_00
And like we re not ready for it, I don t think.
2
SPEAKER_01
The nature of truth, right.
2
SPEAKER_01
That s, it s also exciting because I think both you and I probably would agree that the way to solve, to take on that challenge is with technology.
2
SPEAKER_01
Yeah.
2
SPEAKER_01
Right.
2
SPEAKER_01
There s probably going to be ideas of ways to verify which kind of video is legitimate, which kind is not.
2
SPEAKER_01
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.
2
SPEAKER_01
And hopefully will not result in any serious harm.
2
SPEAKER_00
Yeah, and it could be, you know, like I think we will have technology to
2
SPEAKER_00
that may be able to detect whether or not something s fake or real, although the fakes are pretty convincing, even like when you subject them to machine scrutiny.
2
SPEAKER_00
But, you know, we also have these increasingly interesting social networks, you know, that are under fire right now for
2
SPEAKER_00
some of the bad things that they do like one of the things you could choose to do with a social network is like you could you could use crypto and the networks to like have content signed where you could have a like full chain of custody that accompanied every piece of content so like when you re viewing something
2
SPEAKER_00
And you want to ask yourself, how much can I trust this?
2
SPEAKER_00
You can click something and have a verified chain of custody that shows, oh, this is coming from this source, and it s signed by someone whose identity I trust.
2
SPEAKER_00
Yeah, I think having that, you know, having that chain of custody, like being able to like say, oh, here s this video, like it may or may not have been produced using some of this deep fake technology.
2
SPEAKER_00
But if you ve got a verified chain of custody where you can sort of trace it all the way back to an identity and you can decide whether or not like I trust this identity, like, oh, no, this is really from the White House or like this is really from the
2
SPEAKER_00
you know, the office of this particular presidential candidate, or it s really from, you know, Jeff Weiner, CEO of LinkedIn, or Satya Nadella, CEO of Microsoft.
2
SPEAKER_00
Like, that might be, like, one way that you can solve some of the problems.
2
SPEAKER_00
So, like, that s not the super high tech.
2
SPEAKER_00
Like, we ve had all of this technology forever.
2
SPEAKER_00
But I think you re right.
2
SPEAKER_00
It has to be some sort of technological thing because the underlying tech that is used to create this is not going to do anything but get better over time and the genie is sort of out of the bottle.
2
SPEAKER_00
There s no stuffing it back in.
2
SPEAKER_01
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, which is good.
2
SPEAKER_01
Skepticism in general is good for your content.
2
SPEAKER_01
So deepfakes in that sense are creating global skepticism about can they trust what they read.
2
SPEAKER_01
It encourages further research.
2
SPEAKER_01
I come from the Soviet Union.
2
SPEAKER_01
where basically nobody trusted the media because you knew it was propaganda.
2
SPEAKER_01
And that kind of skepticism encouraged further research about ideas as opposed to just trusting any one source.
2
SPEAKER_00
Well, I think it s one of the reasons why the scientific method and our apparatus of modern science is so good.
2
SPEAKER_00
Because you don t have to trust.
2
SPEAKER_00
anything like you like the whole notion of you know like modern science beyond the fact that you know this is a hypothesis and this is an experiment to test the hypothesis and you know like this is a peer review process for scrutinizing uh published results but like
2
SPEAKER_00
stuff s also supposed to be reproducible so like you know it s been vetted by this process but like you also are expected to publish enough detail where you know if you are sufficiently skeptical of the thing you can go try to like reproduce it yourself and like i
2
SPEAKER_00
I don t know what it is.
2
SPEAKER_00
Like, I think a lot of engineers are like this, where, like, you know, sort of this, like, your brain is sort of wired for skepticism.
2
SPEAKER_00
Like, you don t just first order trust everything that you see and encounter.
2
SPEAKER_00
And, like, you re sort of curious to understand, you know, the next thing.
2
SPEAKER_00
But, like, I think it s an entirely healthy thing.
2
SPEAKER_00
And, like, we need a little bit more of that right now.
2
SPEAKER_01
So I m not a large business owner.
2
SPEAKER_01
So I m just a huge fan of many of Microsoft products.
2
SPEAKER_01
I mean, I still, actually in terms of I generate a lot of graphics and images and I still use PowerPoint to do that.
2
SPEAKER_01
It beats Illustrator for me, even professional sort of, it s fascinating.
2
SPEAKER_01
So I wonder what is the future of
2
SPEAKER_01
let s say Windows and Office look like?
2
SPEAKER_01
Do you see it?
2
SPEAKER_01
I mean, I remember looking forward to XP.
2
SPEAKER_01
Was it exciting when XP was released?
2
SPEAKER_01
Just like you said, I don t remember when 95 was released, but XP for me was a big celebration.
2
SPEAKER_01
And when 10 came out, I was like, okay, well, it s nice.
2
SPEAKER_01
It s a nice improvement.
2
SPEAKER_01
So what do you see the future of these products?
2
SPEAKER_00
Yeah, 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 are sort of get smarter and smarter in like a very subtle way, like there s not going to be this
2
SPEAKER_00
big bang moment where, you know, like Clippy is gonna reemerge and it s gonna be- Wait a minute.
2
SPEAKER_01
Okay, we ll have to, wait, wait, wait.
2
SPEAKER_01
Is Clippy coming back?
2
SPEAKER_01
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.
2
SPEAKER_01
Do you think that there s a possibility of that in the future?
2
SPEAKER_00
So I think there are a bunch of very small ways in which machine learning powered assistive things are in the product right now.
2
SPEAKER_00
So there are a bunch of interesting things.
2
SPEAKER_00
the auto response stuff s getting better and better.
2
SPEAKER_00
And it s like getting to the point where, you know, it can auto respond with like, OK, look, you know, this person s clearly trying to schedule a meeting.
2
SPEAKER_00
So it looks at your calendar and it automatically like tries to find like a time and a space that s mutually interesting.
2
SPEAKER_00
Like we we have
2
SPEAKER_00
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.
2
SPEAKER_00
We have this thing called the Microsoft Graph that is basically an API federator that gets you
2
SPEAKER_00
hooked up across the entire breadth of like all of the you know like what were information silos before they got woven together with the graph like that is like getting increasing with increasing effectiveness sort of plumbed into the into some of these auto response things where
2
SPEAKER_00
you re going to be able to see the system automatically retrieve information for you.
2
SPEAKER_00
I frequently send out emails to folks where I can t find a paper or a document or whatnot.
2
SPEAKER_00
There s no reason why the system won t be able to do that for you.
2
SPEAKER_00
I think it s building towards having things that look more like a fully integrated assistant.
2
SPEAKER_00
But you ll have a bunch of steps that you will see before you
2
SPEAKER_00
Like, it will not be this, like, 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.
2
SPEAKER_00
So I think that s definitely coming out.
2
SPEAKER_00
All of the collaboration, co-authoring stuff s getting better.
2
SPEAKER_00
It s really interesting.
2
SPEAKER_00
If you look at how we use the Office product portfolio at Microsoft, more and more of it is happening inside of Teams as a canvas.
2
SPEAKER_00
And it s this thing where you ve got
2
SPEAKER_00
collaboration is like at the center of the product.
2
SPEAKER_00
And like we we built some like really cool stuff.
2
SPEAKER_00
That s some of which is about to be open source that are sort of framework level things for doing for doing co-authoring.
2
SPEAKER_00
That s awesome.
2
SPEAKER_01
So in is there a cloud component to that?
2
SPEAKER_01
So on the Web, or is it?
2
SPEAKER_01
And forgive me if I don t already know this, but with Office 365,
2
SPEAKER_01
The collaboration we do, if we re doing Word, we still send the file around.
2
SPEAKER_00
We re already a little bit better than that.
2
SPEAKER_00
The fact that you re unaware of it means we ve got a better job to do, helping you discover this stuff.
2
SPEAKER_00
But yeah, I mean, it s already got a huge cloud component.
2
SPEAKER_00
And part of this framework stuff, I think we re calling it, we ve been working on it for a couple of years, so I know the internal code name for it, but I think when we launch it at Build, it s called the Fluid Framework.
2
SPEAKER_00
But what Fluid lets you do is you can go into a conversation that you re having in Teams and reference part of a spreadsheet
2
SPEAKER_00
you re working on, where somebody s like sitting in the Excel canvas, like working on the spreadsheet with a chart or whatnot.
2
SPEAKER_00
And like you can sort of embed like part of the spreadsheet in the Teams conversation where like you can dynamically update it and like all of the changes that you re making to this object are like, you know, coordinate and everything is sort of updating in real time.
2
SPEAKER_00
So you can be in whatever canvas is most convenient for you to get your work done.
2
SPEAKER_01
So out of my own sort of curiosity as an engineer, I know what it s like to sort of lead a team of 10, 15 engineers.
2
SPEAKER_01
Microsoft has, I don t know what the numbers are, maybe 50, maybe 60,000 engineers, maybe 40.
2
SPEAKER_00
I don t know exactly what the number is, it s a lot.
2
SPEAKER_00
It s tens of thousands.
2
SPEAKER_01
Right, it s more than 10 or 15.
2
SPEAKER_01
What, I mean, you ve led different sizes, mostly large size of engineers.
2
SPEAKER_01
What does it take to lead such a large group and to continue innovation, continue being highly productive and yet develop all kinds of new ideas and yet maintain, like what does it take to lead such a large group of brilliant people?
2
SPEAKER_00
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.
2
SPEAKER_00
One is having some sort of forethought about
2
SPEAKER_00
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.
2
SPEAKER_00
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
2
SPEAKER_00
that infrastructure, like whether and I like I m not just talking about storage systems or cloud APIs.
2
SPEAKER_00
It s also like what is your development process look like?
2
SPEAKER_00
What tools do you want?
2
SPEAKER_00
Like what culture do you want to build around like how you re sort of collaborating together to like make complicated technical things?
2
SPEAKER_00
And so having an opinion and investing in that, it just gets more and more important.
2
SPEAKER_00
And the sooner you can get a concrete set of opinions, the better you re going to be.
2
SPEAKER_00
You can wing it for a while at small scales.
2
SPEAKER_00
When you start a company, you don t have to be super specific about it.
2
SPEAKER_00
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.
2
SPEAKER_00
And then you just sort of go create a bunch of technical debt and like culture debt that is excruciatingly painful to to clean up.
2
SPEAKER_00
So like that s one bundle of things like the other.
2
SPEAKER_00
Uh, the other, you know, another bundle of things is like, it s just really, really important to like, have a clear mission.
2
SPEAKER_00
That s not just some cute crap you say, because like, you think you should have a mission, but like something that clarifies for people, like where it is that you re headed together.
2
SPEAKER_00
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.
2
SPEAKER_00
Once you get past Dunbar s number,
2
SPEAKER_00
And like, I ve really, really seen that just managing engineering teams.
2
SPEAKER_00
Like you can just brute force things when you re less than 120, 150 folks where you can sort of know and trust and understand what the dynamics are between all the people.
2
SPEAKER_00
But like past that, like things just sort of start to catastrophically fail if you don t have
2
SPEAKER_00
some sort of set of shared goals that you re marching towards.
2
SPEAKER_00
And so, like, even though it sounds touchy-feely, and, you know, 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.
2
SPEAKER_00
You re always right, right?
2
SPEAKER_01
Stories, that s how our society, that s the fabric that connects us, all of us, is these powerful stories.
2
SPEAKER_01
And that works for companies, too, right?
2
SPEAKER_00
It works for everything.
2
SPEAKER_00
Like, I mean, even down to, like, you know, you sort of really think about, like, our currency, for instance, is a story.
2
SPEAKER_00
Our constitution is a story.
2
SPEAKER_00
Our laws are a story.
2
SPEAKER_00
I mean, like, we believe very, very, very strongly in them, and thank God we do.
2
SPEAKER_00
But, like, they are, they re just abstract things.
2
SPEAKER_00
Like, they re just words.
2
SPEAKER_00
Like, we don t believe in them.
2
SPEAKER_00
They re nothing.
2
SPEAKER_01
And in some sense, those stories are platforms and the kinds, some of which Microsoft is creating, right?
2
SPEAKER_01
They have platforms on which we define the future.
2
SPEAKER_01
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?
2
SPEAKER_01
Do you have crazy ideas about the future of the world?
2
SPEAKER_00
Yeah look I think we you know we re entering this time where we ve got.
2
SPEAKER_00
We have technology that is progressing at the fastest rate that it ever has and you ve got.
2
SPEAKER_00
You get some really big social problems, like society-scale problems that we have to tackle.
2
SPEAKER_00
And so, you know, I think we re going to rise to the challenge and, like, figure out how to intersect, like, all of the power of this technology with all of the big challenges that are facing us, whether it s, you know, global warming, whether it s, like, the biggest remainder of the population boom is in Africa for the next
2
SPEAKER_00
50 years or so and like global warming is going to make it increasingly difficult to feed the global population in particular, like in this place where you re going to have like the biggest population boom.
2
SPEAKER_00
I think we, you know, like AI is going to like if we push it in the right direction, like it can do like incredible things to empower all of us to achieve our full potential and to
2
SPEAKER_00
you know, like, live better lives.
2
SPEAKER_00
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 better and better.
2
SPEAKER_00
over time.
2
SPEAKER_00
Like that s more and more important every day is like in the United States and like the rest of the industrialized world.
2
SPEAKER_00
So Western Europe, China, Japan, Korea, like you ve got this population bubble of like aging, working, you know, working age folks who are
2
SPEAKER_00
you know, at some point over the next 20, 30 years, they re going to be largely retired.
2
SPEAKER_00
And like, you re going to have more retired people than working age people.
2
SPEAKER_00
And then, like, you ve got, you know, sort of natural questions about who s going to take care of all the old folks and who s going to do all the work.
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SPEAKER_00
The answers to like all of these sorts of questions, like where you re sort of running into, you know, like constraints of the, you know, the world and of society has always been like what tech is going to like help us get around this.
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SPEAKER_00
You know, like when I was a kid in the 70s and 80s, like we talked all the time about like population boom, population boom, like we re going to
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SPEAKER_00
uh like we re not going to 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 uh 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
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SPEAKER_00
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, yep.
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SPEAKER_00
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 um 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
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SPEAKER_01
these super challenging problems.
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SPEAKER_01
One of the things that I m trying to spend my time doing right now is trying to get everybody else to be hopeful as well, because back to Harari, we are the stories that we tell.
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SPEAKER_01
If we get overly pessimistic right now about the
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SPEAKER_01
the potential future of technology.
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SPEAKER_01
We may fail to get all of the things in place that we need to have our best possible future.
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SPEAKER_03
And that kind of hopeful optimism, I m glad that you have it because you re leading large groups of engineers that are actually defining, that are writing that story, that are helping build that future, which is super exciting.
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SPEAKER_03
And I agree with everything you said, except I do hope Clippy comes back.
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SPEAKER_03
We miss them.
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SPEAKER_02
I speak for the people.
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SPEAKER_02
Thank you so much for talking to me.
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SPEAKER_02
Thank you so much for having me.
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SPEAKER_02
It was a pleasure.