Kevin Scott: Microsoft CTO

[0] The following is a conversation with Kevin Scott, the CTO of Microsoft.

[1] Before that, he was the senior vice president of engineering and operations at LinkedIn, and before that, he oversaw mobile ads engineering at Google.

[2] He also has a podcast called Behind the Tech with Kevin Scott, which I'm a fan of.

[3] This was a fun and wide -ranging conversation that covered many aspects of computing.

[4] It happened over a month ago, before the announcement of my own.

[5] Microsoft's investment opening eye that a few people have asked me about.

[6] I'm sure there'll be one or two people in the future.

[7] They'll talk with me about the impact of that investment.

[8] This is the Artificial Intelligence Podcast.

[9] If you enjoy it, subscribe on YouTube, give it five stars on iTunes, support it at Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F -R -I -D -M -A -N.

[10] And I'd like to give a special thank you to Tom and Nolanti Bikhausen for their support of the podcast on Patreon.

[11] Thanks, Tom and Nalanti.

[12] Hope I didn't miss up your last name too bad.

[13] Your support means a lot and inspires me to keep this series going.

[14] And now, here's my conversation with Kevin Scott.

[15] You've described yourself as a kid in a candy store at Microsoft because of all the interesting projects that are going on.

[16] Can you try to do the impossible task and give a, uh, brief whirlwind view of all the spaces that Microsoft is working in, both research and product.

[17] If you include research, it becomes even more difficult.

[18] So I think broadly speaking, Microsoft's product portfolio includes everything from, you know, big cloud business, like a big set of SaaS services.

[19] We have sort of the original, or like some of what are among the original productivity software products that everybody use.

[20] We have an operating system business.

[21] We have a hardware business where we make everything from computer mice and headphones to high -end, high -end personal computers and laptops.

[22] We have a fairly broad -ranging research group where like we have people doing everything from economics research so like there's this really really smart young economist glim while 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 entire technical book about 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 a 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.

[23] Gaming to Xbox and so on.

[24] Yeah, gaming for sure.

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

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

[27] And like, we're, you know, doing some, uh, interesting collaboration now with, uh, the folks who made, uh, Day of the Tenticle.

[28] And I was like completely nerding out with Tim Schaefer, like the guy who wrote, uh, day of the tentacle, uh, this morning, just a complete fanboy, uh, which, uh, you know, sort of, uh, it like happens a lot, uh, like, you know, Microsoft has been doing so much stuff at such breadth for such a long period of time that, uh, you know, like being, In 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.

[29] Yeah, to reach back into the sentimental.

[30] And what's the radical markets and the economics?

[31] 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?

[32] Can the incentive structures that are built into these systems produce outcomes that are creating sort of equitably distributed benefits?

[33] benefits for every member of society, you know, and I think it's a reasonable, a reasonable set of questions to be asking.

[34] And so what Glenn, and so, like, you know, one mode of thought there, like, if you have doubts that the, 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.

[35] gets done and, you know, like where the, you know, where the investments and where the outputs of those investments get distributed.

[36] Glenn's notion is, like, lean more into, like, the market -based mechanism.

[37] So, like, for instance, you know, this is one of the more radical ideas.

[38] Like, suppose that you had a radical pricing mechanism for assets like real estate where you were, you could be bid out of your position in in your home uh 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 uh like then 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, which would make it very difficult to have the same sort of rent -seeking behaviors that you've got right now, because, like, if you did speculative bidding, like, you would very quickly, like, lose a whole lot of money.

[39] And so, like, the prices of the assets would be sort of, like, very closely indexed to, like, the value that they could produce.

[40] And, like, 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 tax appropriately.

[41] Like, you couldn't sort of sit on this thing and say, oh, like, this house is only worth $10 ,000 when, like, everything around it is worth $10 million.

[42] That's really interesting.

[43] So it's an incentive structure that, where the prices match the value much better.

[44] Yeah.

[45] And Glenn does a much better job than I do at selling it.

[46] I probably picked the world's worst example, you know, and -in -and.

[47] But like, and it's, it's intentionally provocative, you know, so like this whole notion, like, I, you know, like, I'm not sure whether I like this notion that, like, we could have a set of market mechanisms where I could get bid out of, out of my property, you know, but, but, you know, like if you're thinking about something like Elizabeth Warren's wealth tax, for instance, like, you would have, I mean, it would be really interesting in like how you would actually set the, the price on the asset.

[48] and like you might have to have a mechanism like that if you put a tax like that in place.

[49] It's really interesting that that kind of research, at least tangentially, is touching Microsoft research.

[50] Yeah.

[51] That you're really thinking broadly.

[52] Maybe you can speak to, this connects to AI.

[53] So we have a candidate Andrew Yang who kind of talks about artificial intelligence and the concern that people have about automations impact on society.

[54] and arguably Microsoft is at the cutting edge of innovation in all these kinds of ways.

[55] And so it's pushing AI forward.

[56] How do you think about combining all our conversations together here with radical markets and socialism and innovation in AI that Microsoft is doing?

[57] And then Andrew Yang's worry that that will result in job loss for the lower and so on.

[58] How do you think about that?

[59] I think it's sort of one of the most important questions in technology, like maybe even in society right now about how is AI going to develop over the course of the next several decades and what's it going to be used for and what benefits will it produce and what negative impacts will it produce and who gets to steer this whole thing.

[60] 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.

[61] And so, you know, like Bill has this thing that he said a bunch of years ago where, you know, the measure of a successful platform is that it produces far more economic value for the people who build on top of the platform than is.

[62] creative for the platform owner or builder.

[63] And I think we have to think about AI that way.

[64] As a platform.

[65] 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.

[66] 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.

[67] So I think that's bad from a sort of economics and sort of equitable distribution of value perspective, like, you know, sort of back to this whole notion of, you know, like, do the markets work?

[68] But I think it's also bad from an innovation perspective because, like, I have infinite amounts of faith in human beings that if you, you know, give folks powerful tools, they will go do interesting things.

[69] And it's more than just a few tens of thousands of people with the interesting tools.

[70] It should be millions of people with the tools.

[71] So it's sort of like, you know, you think about the steam engine and the late 18th century.

[72] Like it was, you know, maybe the first large scale substitute for human labor that we've built like a machine.

[73] 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.

[74] And like they built factories around them and businesses and the experts who knew how to build and maintain them.

[75] But access to that technology, democratized over time.

[76] Like now, like an engine is not a, it's not like a differentiated thing.

[77] Like there isn't one engine company that builds all the engines and all of the things that use engines are made by this company and like they get all the economics from all of that.

[78] Like, no, no, like fully demarcated.

[79] Like they're probably, you know, we're sitting here in this room.

[80] And like even though they don't, they're probably things, you know, like the the MIMS gyroscope that are in both of our like they're like little engines.

[81] you know, sort of everywhere.

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

[83] Like, AI needs to get there.

[84] Yeah, so that's a really powerful way to think.

[85] 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.

[86] That's really interesting, actually.

[87] And Microsoft throughout its history has been positioned well to do that.

[88] And, you know, the tieback 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 like the sort of father of virtual reality.

[89] Like he's one of the most interesting human beings on the planet, like a sweet, sweet guy.

[90] 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.

[91] And so the idea is that if you, again, going back to this sort of industrial analogy, if you think about data as the raw material that is consumed by the machine of AI in order to do useful things, then like, we're not doing a really great job right now and having transparent marketplaces for valuing those data contributions.

[92] So like, and we all make them, like explicitly, like you go to LinkedIn, you sort of set up your profile on LinkedIn.

[93] Like, that's an explicit contribution.

[94] Like, you know exactly the information that you're putting into the system.

[95] And like, you put it there because you have some nominal notion of like what value you're going to get in return.

[96] But it's like only nominal.

[97] Like you don't know exactly what value you're getting in return.

[98] like services free, you know, like it's low amount of like perceived out.

[99] 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.

[100] 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.

[101] 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.

[102] And, like, you can sort of see it in explicit ways.

[103] 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.

[104] So, like, you're doing supervised machine learning.

[105] You need lots and lots of label training data.

[106] and like those people are getting competent like who work for those companies are getting compensated for their data contributions into the system and so that's easier to put a number on their contribution because they're explicitly labeling data correct but you're saying that we're all contributing data in different kinds of ways and it's fascinating to start to explicitly try to put a number on it do you think that's possible i don't know it's hard it really is because you know, we don't have as much transparency as, uh, is, uh, is I think we need, uh, in like how the data is getting used.

[107] And it's, you know, super complicated.

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

[109] It's like, you know, the data, the data gets created and then it gets, you know, it's not valuable like the, the data exhaust that you give off, uh, or the, you know, the, the, you know, explicit data that I am putting into the system isn't value valuable.

[110] It's super valuable atomically.

[111] It's only valuable when you sort of aggregate it together into, you know, sort of large numbers.

[112] It's true even for these like folks who are getting compensated for like labeling things.

[113] Like for supervised machine learning now, like you need lots of labels to train a, you know, a model that performs well.

[114] And so, you know, I think that's one of the challenges.

[115] It's like, how do you, you know, how do you sort of figure?

[116] out like because this data is getting combined in so many ways like through these combinations like how the value is flowing yeah that's that's that's tough yeah and it's fascinating that you're thinking about this and i wasn't even going into this conversation expecting the breadth of uh of research really that uh microsoft broadly is thinking about you are thinking about in Microsoft so if we go back to uh 89 when Microsoft released off office or 1990 when they released Windows 3 .0.

[117] 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?

[118] The good thing is it's started off as a platform company.

[119] Like, it's still a platform company, like the parts of the business that are like thriving and most successful or those that are building platforms, like the mission.

[120] of the company now is, the missions change.

[121] It's like change in a very interesting way.

[122] 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.

[123] 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, uh, you know, from kits like the way people build ham radios, uh, right now.

[124] And I, and I think this is sort of the interesting thing for folks who build platforms in general.

[125] Bill saw the opportunity there and what personal computers could do.

[126] And it was like a, it was sort of a reach.

[127] Like you just sort of imagine like where things were you know when they started the company versus where things are now like in success when you've democratized a platform it just sort of vanishes into the platform you don't pay attention to it anymore like operating systems aren't a thing anymore like they're super important like completely critical and like you know when you see one you know fail like you just you sort of understand but like you know it's not a thing where you're you're not like waiting for you know the next operating system thing in the same way that you were in 1995, right?

[128] Like in 1995, like, you know, we had Rolling Stones on the stage with the Windows 95 rollout.

[129] Like, it was like the biggest thing in the world.

[130] Everybody lined up for it the way that people used to line up for iPhone.

[131] But like, you know, eventually, and like this isn't necessarily a bad thing.

[132] Like, it just sort of, you know, the success is that it's sort of, it becomes ubiquitous.

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

[134] So the mission now that Satya re -articulated five plus years ago now when he took over his CEO of the company, our mission is to empower every individual and every organization in the world to be more successful.

[135] And so, you know, again, like that's a platform mission.

[136] And like the way that we do it now is different.

[137] It's like we have a hyperscale.

[138] cloud that people are building their applications on top of.

[139] We have a bunch of AI infrastructure that people are building their AI applications on top of.

[140] We have, you know, we have a productivity suite of software like Microsoft Dynamics, which, you know, some people might not think is the sexiest thing in the world, but it's like helping people figure out how to automate all of their business processes and workflows and to, you know, like help those businesses using it to like grow and be more so it's uh it's a much broader vision uh 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 uh you know that it it both exist and is going to get better and better over time or at least more and more powerful over time.

[141] So, like, you know, what you have to do as a platform player is just much bigger.

[142] Right.

[143] There's so many directions in which you can transform.

[144] You didn't mention mixed reality, too.

[145] You know, that's probably early days, or it depends how you think of it.

[146] But if we think in a scale of centuries, it's the early days of mixed reality.

[147] Oh, for sure.

[148] And so with HoloLens, Microsoft is doing some really interesting work there.

[149] Do you touch that part of the effort?

[150] What's the thinking?

[151] Do you think of mixed reality as a platform too?

[152] Oh, sure.

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

[154] 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.

[155] But like we also think of the like mixed reality and quantum is like these two interesting, you know, potentially.

[156] Quantum computing.

[157] Yeah.

[158] Okay.

[159] So let's get crazy then.

[160] So you're talking about some futuristic things here.

[161] Well, the mixed reality Microsoft is really not even futuristic is here.

[162] It is incredible stuff.

[163] And look, and it's having an impact right now.

[164] Like one of the more interesting things that's happened with mixed reality over the past couple of years that I didn't clearly see is that it's become the computing device for folks who, for doing their work who haven't used any computing device at all to do their work before.

[165] So technicians and service folks and people who are doing like machine maintenance on factory floors.

[166] So like they, you know, because they're mobile and like they're out in the world and they're working with their hands and, you know, sort of servicing these like very complicated things, they're, they don't use their mobile phone and like they don't carry a laptop with them.

[167] and, you know, they're not tethered to a desk.

[168] 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.

[169] 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.

[170] Yeah, but you did mention it's a fairly obvious AI.

[171] as a platform, but can we dig into it a little bit?

[172] Sure.

[173] How does AI begin to infuse some of the products in Microsoft?

[174] So currently providing training of, for example, neural networks in the cloud or providing pre -trained models or just even providing computing resources and whatever different inference that you want to do using neural networks?

[175] How do you think of AI infusing as a platform that Microsoft can provide?

[176] Yeah, I mean, I think it's super interesting.

[177] It's like everywhere.

[178] 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.

[179] like either AI infrastructure or like have some substantial part of their of their product work using AI in some significant way.

[180] Now the important thing to understand is like when you think about like how the AI is going to manifest and like an experience for something that's going to make it better, like I think you don't want the AIness to be the first order thing.

[181] like whatever the product is and like the thing that is trying to help you do like the AI just sort of makes it better and you know this is a gross exaggeration but like I yeah people get super excited about like where the AI is showing up in products and I'm like do you get that excited about like where you're using a hash table like in your code like it's just another it's just a tool it's a very interesting programming tool but it's sort of a like it's an engineering tool and so like it shows up everywhere.

[182] So like we've got dozens and dozens of features now in office that are powered by like fairly sophisticated machine learning.

[183] Our search engine wouldn't work at all if you took the machine learning out of it.

[184] The like increasingly, you know, things like content moderation on our Xbox and X cloud platform.

[185] When you mean moderation, you mean like the recommenders, It's like showing what you want to look at next.

[186] No, no, no, it's like anti -bullying stuff.

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

[188] Yeah, correct.

[189] But it's like really, it's targeted, it's targeted towards a gaming audience.

[190] So it's like a very particular type of thing where, you know, the line between playful banter and like legitimate bullying is like a subtle one.

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

[192] Like I have, I'd love to if we could dig into it.

[193] you're also, you led the engineering efforts of LinkedIn.

[194] Yep.

[195] And if we look at, if we look at LinkedIn as a social network.

[196] Yep.

[197] 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?

[198] And the line in terms of bullying and so on is different on the two platforms.

[199] So how do you, I mean, it's such a fascinating philosophical discussion of where that line is.

[200] I don't think anyone knows the right answer.

[201] Twitter folks are under fire now, Jack at Twitter, for trying to find that line.

[202] 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?

[203] 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.

[204] So if you have a clear notion of what your social network should be used for or what you are designing a community around, then you don't have as many dimensions to your sort of content safety problem as you do in a general purpose platform.

[205] I mean, so like on LinkedIn, like the whole social network.

[206] is about connecting people with opportunity, whether it's helping them find a job or to, you know, sort of find mentors or to, you know, sort of help them, like, find their next sales lead or to just sort of allow them to broadcast their, you know, sort of professional identity to their network of peers and collaborators and, you know, sort of professional community.

[207] like that is i mean like in some ways like that's very very broad uh but in other ways it's sort of you know it's narrow and so like you can build ai's uh like machine learning systems that are you know capable with those boundaries of making better automated decisions about like what is uh you know sort of inappropriate an offensive comment or dangerous comment or illegal content uh when you have some constraints uh you know Same thing with the gaming social networks.

[208] For instance, it's like, it's about playing games, about having fun.

[209] And like the thing that you don't want to have happen on the platform is why bullying is such an important thing.

[210] Like, bullying is not fun.

[211] So you want to do everything in your power to encourage that not to happen.

[212] And yeah, but I think it's sort of a tough problem in general.

[213] It's one where I think, you know, eventually we're going to have to have.

[214] some sort of clarification from our policy makers about what it is that we should be doing, like where the lines are, because it's tough.

[215] 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.

[216] Like, you don't want a bunch of people making, like, unilateral decisions and like we are in a we're in a state right now for some of these platforms where you actually do have to make unilateral decisions where the policy making isn't going to happen fast enough in order to like prevent very bad things from happening but like we we need the policy making side of that to catch up i think is as quickly as possible because you want that whole process to be a democratic thing not a you know not 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.

[217] And it's fascinating because the digital space is different than the physical space in which nations and governments were established.

[218] And so what policy looks like globally, what bullying looks like globally, what healthy communication looks like globally is an open question.

[219] And we're all figuring it out together, which is fascinating.

[220] Yeah.

[221] I mean, with, you know, sort of fake news.

[222] 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 like you know sort of very interesting level of complexity but like if you think about just the written word right like we have you know we invented papyrus what 3 000 years ago where we you know you could sort of put put word on on paper and then uh 500 years ago like we we we get the printing press, like where the word gets a little bit more ubiquitous.

[223] And then, like, you really, really didn't get ubiquitous printed word until the end of the 19th century when the offset press was invented.

[224] 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.

[225] But like, we had 3 ,000 years up to that point to figure out, like, how to, you know, like, what's, what's journalism, what's editorial integrity, like, what's, you know, what's scientific peer review.

[226] And so, like, you built all of this mechanism to, like, try to filter through all of the noise that the technology made possible to, like, you know, sort of getting to something that society could cope with.

[227] 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 mary have and or mary meeker just released her new like slide deck last week you know we've got 50.

[228] 50 % penetration of the internet to the global population.

[229] Like there are like three and a half billion people who are connected now.

[230] So it's like, it's crazy.

[231] Crazy.

[232] Like inconceivable.

[233] Like how fast all of this happens.

[234] So, you know, it's not surprising that we haven't figured out what to do yet.

[235] But like we got to like we got to 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.

[236] and like probably what amounts to the next decade worth of time.

[237] So since we're on the topic of tough, you know, tough challenging problems, let's look at more on the tooling side in AI that Microsoft is looking at face recognition software.

[238] So there's a lot of powerful positive use cases.

[239] Yep.

[240] For face recognition, but there's some negative ones and we're seeing those in different governments in the world.

[241] So how do you, how does Microsoft think about the use of face recognition, software as a platform in governments and companies.

[242] Yeah, how do we strike an ethical balance here?

[243] Yeah, I think we've articulated a clear point of view.

[244] So Brad Smith wrote a blog post last fall, I believe, that's sort of like outline, like very specifically what, you know, what our point of view is there.

[245] And, you know, I think we believe that there are certain uses.

[246] to which face recognition should not be put.

[247] And we believe, again, that there's a need for regulation there.

[248] Like, the government should, like, really come in and say that, you know, this is, this is where the lines are.

[249] And, like, we very much wanted to, like, figuring out where the lines are should be a democratic process.

[250] But in the short term, like, we've drawn some lines where, you know, we push back against uses of face recognition technology.

[251] you know like this city of San Francisco for instance I think has completely outlawed any government agency from using face recognition tech and like that may prove to be a little bit overly broad but for like certain law enforcement things like you really I would personally rather be overly cautious in terms of restricting use of it until like we have you know sort of defined a reasonable, you know, democratically determined regulatory framework for, like, where we could and should use it.

[252] 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.

[253] 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, um, um, underrepresented minorities who are, like, underrepresented in the training data, and then you start learning, uh, like, strange things, but like there, they're, they're even, you know, other weird things.

[254] Like, we've, uh, I think we've seen in the public research, like models can learn, uh, strange things, uh, like, uh, all doctors or men, uh, for instance.

[255] Uh, just, yeah, I mean, and so, like, it really is a thing where, It's very important for everybody who is working on these things before they push, publish, they launch the experiment, they push the code to, you know, online, or they even publish the paper that they are at least starting to think about what some of the potential negative consequences are, some of this stuff.

[256] I mean, this is where, you know, like the deep fake stuff, I find very worrisome just because there are going to be some very good beneficial uses of, like, GAN generated imagery.

[257] And funny enough, like, one of the places where it's actually useful is we're using the technology right now to generate synthetic, synthetic, visual data for training some of the face recognition models to get rid of the bias.

[258] So, like, that's one, like, super good use of the tech.

[259] But, like, you know, it's getting good enough now where, you know, it's going to sort of challenge a normal human being's ability to, like, now you're just sort of say, like, it's, it's very expensive for someone to fabricate a photorealistic, fake video.

[260] and like Gans are going to make it fantastically cheap to fabricate a photorealistic fake video and so like what you assume you can sort of trust is true versus like be skeptical about is about to change and like we're not ready for it I don't think.

[261] The nature of truth, right?

[262] That'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.

[263] Yeah.

[264] Right.

[265] There's probably going to be ideas of ways to verify, which kind of video is legitimate, which kind of is not.

[266] 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.

[267] Yeah.

[268] And hopefully will not result in any serious harm.

[269] Yeah, and it could be, you know, like I think we will have technology to, that may be able to, that may be able.

[270] 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.

[271] But, you know, we also have these increasingly interesting social networks, you know, that are under fire right now for some of the bad things that they do.

[272] 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 and like you want to ask yourself like how you know how much can I trust this like you can click something and like have a verified chain of custody that shows like oh this is coming from uh you know from this source and it's like signed by like someone who's identity i trust yeah 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 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, you know, the office of this particular presidential candidate, or it's really from, you know, Jeff Wiener, CEO of LinkedIn or Satya Nadella, CEO of Microsoft.

[273] Like, that might, that might be, like, one way that you can solve some of the problem.

[274] And so, like, that's not the super high tech.

[275] Like, we've had all of this technology forever.

[276] Right.

[277] And I, but I think you're right.

[278] Like, it has to be some sort of technological thing because the, the, the, the, the, the, 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 there's no stuffing it back in and there's a social component which i think is really healthy for a democracy where people will be skeptical about the thing they watch yeah in general uh so you know which is good skepticism in general is good for it is good content so deep fakes in that sense are creating uh global skepticism about can they trust what they read.

[279] It encourages further research.

[280] I come from the Soviet Union where basically nobody trusted the media because you knew it was propaganda.

[281] And that encouraged, that kind of skepticism encouraged further research about ideas, as opposed to just trusting anyone's source.

[282] Well, look, I think it's one of the reason why the, you know, the scientific method and our apparatus of modern science is so good.

[283] Like, because you don't have.

[284] to trust 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 published results but like 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 don't know what it is 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 for skepticism like you don't just first order trust everything that you see and encounter and like you're sort of curious to understand you know the next thing but I think it's an entirely healthy healthy thing and like we need a little bit more of that.

[285] right now.

[286] So I'm not a large business owner.

[287] So I'm just a huge fan of many of Microsoft products.

[288] I mean, I still actually in terms of I generate a lot of graphics and images and I still use PowerPoint to do that.

[289] It beats Illustrator for me. Even professional sort of, it's fascinating.

[290] So I wonder what is the future of, let's say, Windows and Office look like?

[291] Do you see it?

[292] I mean, I remember looking forward to XP was an exciting when XP was released.

[293] Just like you said, I don't remember when 95 was released, but XP for me, it was a big celebration.

[294] And when 10 came out, I was like, okay, well, it's nice.

[295] It's a nice improvement.

[296] So what do you see the future of these products?

[297] I think there's a bunch of exciting.

[298] I mean, on the office front, there's going to be this like increasing productivity winds 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 big bang moment where you know like clippy is going to reemerge and it's going to wait a minute okay well I have to wait wait wait wait it's clipy coming back but quite seriously um so injection of AI I, 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 clipy style assistant, personal assistant.

[299] Do you think that's, there's a possibility of that in the future?

[300] Yeah, so I think there are a bunch of, like, very small ways in which, like, machine learning powered assistive things are in the product right now.

[301] So there are a bunch of interesting things, like, the auto response stuff's getting better and better.

[302] And it's like getting to the point where, you know, it can auto respond with like, okay, you know, this person's clearly trying to schedule a meeting.

[303] So it looks at your calendar and it automatically like tries to find like a time and a space that's mutually interesting.

[304] Um, like we, we have, um, this notion of Microsoft search where it's like not just web search, but it's like search across like, all of your information that's sitting inside of like your Office 365 tenant and like, you know, potentially in other products.

[305] And like we have this thing called the Microsoft Graph that is basically an API Federator that, you know, sort of like gets you 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.

[306] like that is like getting increasing with increasing effectiveness sort of plumbed into the into some of these auto response things where you're going to be able to see the system like automatically retrieve information for you like if you know like I frequently send out you know emails to folks where like I can't find a paper or a document or whatnot there's no reason why the system won't be able to do that for you and like I think the it's building towards like having things that look more like a fully integrated, you know, assistant.

[307] But like, you'll have a bunch of steps that you will see before you, like, it will not be this like big bang thing where like clipy comes back and you've got this like, you know, manifestation of, you know, like a fully, fully powered assistant.

[308] So I think that's, that's definitely coming in.

[309] Like all of the, you know, collaboration, co -authoring stuff's getting better.

[310] You know, it's like really interesting.

[311] Like, If you look at how we use the office product portfolio at Microsoft, like more and more of it is happening inside of like teams as a canvas.

[312] And like it's this thing where, you know, you've got collaboration is like at the center of the product.

[313] And like we built some like really cool stuff that's some of which is about to be open source that are sort of framework level things for doing.

[314] for doing co -authoring.

[315] That's awesome.

[316] So is there a cloud component to that?

[317] So on the web, or is it, and forgive me if I don't already know this, but with Office 365, we still, the collaboration we do, if we're doing Word, we're still send the file around with our views.

[318] So this is.

[319] We're already a little bit better than that.

[320] And like, you know, so the fact that you're unaware of it means we've got a better job to do, like helping you discover, discover this stuff.

[321] But yeah, I mean, it's already, like, got a huge, huge cloud component.

[322] And, like, part of, you know, part of this framework stuff, I think we're calling it, like, I, like, we've been working on it for a couple of years.

[323] So, like, I know the internal code name for it.

[324] But I think when we launch it a bill, it's called the fluid framework.

[325] And, but, like, what fluid lets you do is, like, you can go into a conversation that you're having in teams and, like, reference, like, part of a spreadsheet that you're working on.

[326] where somebody's, like, sitting in the Excel canvas, like, working on the spreadsheet with a, you know, chart or whatnot.

[327] And, like, you can sort of embed, like, part of the spreadsheet in the team's conversation where, like, you can dynamically update it.

[328] And, like, all of the changes that you're making to the, to this object are, like, you know, cord.

[329] And everything is sort of updating in real time.

[330] So, like, you can be in whatever canvas is most convenient for you to get your work done.

[331] So out of my own sort of curiosity as engineer, I know what it's like to sort of lead a team of 10, 15 engineers.

[332] Microsoft has, I don't know what the numbers are, maybe 50, maybe 60 ,000 engineers, maybe 40s.

[333] I don't know exactly what the number is.

[334] It's a lot.

[335] It's tens of thousands.

[336] Right.

[337] This is more than 10 or 15.

[338] I mean, you've led different sizes, mostly large sizes.

[339] of engineers.

[340] What does it take to lead such a large group into continue innovation, continue being highly productive and yet develop all kinds of new ideas and yet maintain?

[341] What does it take to lead such a large group of brilliant people?

[342] 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.

[343] for big engineering teams.

[344] Like one is like having some sort of forethought about what it is that you're going to be building over large periods of time.

[345] Like not exactly.

[346] 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.

[347] 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.

[348] And then like invest in that infrastructure, like whether and I like, I'm not.

[349] not just talking about storage systems or cloud APIs.

[350] It's also like, what does your development process look like?

[351] What tools do you want?

[352] Like, what culture do you want to build around like how you're, you know, sort of collaborating together to like make complicated technical things?

[353] And so, like, having an opinion and investing in that is like, it just gets more and more important.

[354] And like, the sooner you can get a concrete set of opinions, like, the better you're going to be.

[355] like you can wing it for a while at small scales like you know when you start a company like you don't have to be like super specific about it but like the biggest miseries that i've ever seen as an engineering leader are in places where you didn't have a clear enough opinion about those things soon enough and then you just sort of go create a bunch of technical debt and like culture debt that is excruciatingly painful to to clean up so like that's one bundle of things like the other uh the other you know another bundle of things is like it's just really really important to like have a a clear mission that's not just some cute crap you say because like you think you should have a mission but like something that clarifies for people like where it is that you're headed together.

[356] Like I know it's like probably like a little bit too popular right now, but, um, Yvall Harari's book Sapiens.

[357] One of the central ideas in, in his book is that, uh, like storytelling is like the quintessential thing for coordinating the activities of large groups of people.

[358] Like once you get past Dunbar's number.

[359] and like I've really really seen that just managing engineering teams like you you can you can just brute force things when you're less than 120 150 folks where you can sort of know and trust and understand what the dynamics are between all the people but like past that like things just sort of start to catastrophically fail if you don't have some sort of set of share goals that you're marching towards and so like even though it sounds touching feeling 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 yeah like very very very important you've all's right right stories that's how our society that's the fabric that connects us all of us is these powerful stories and that works for companies too right it works for everything like i mean even down to like you know you sort of really think about like our currency for instance is a story yeah a constitution is a story our laws are, I mean, like, we believe very, very, very strongly in them.

[360] And thank God we do.

[361] But like they are, they're just abstract things.

[362] Like, they're just words.

[363] Like, if we don't believe in them, they're nothing.

[364] And in some sense, those stories are platforms and the kinds, some of which Microsoft is creating, right?

[365] They have platforms on which we define the future.

[366] So last question, what do you, let's get philosophical maybe, bigger than you of Microsoft.

[367] What do you think the next 20, 30 plus years looks like for computing, for technology, for devices?

[368] Do you have crazy ideas about the future of the world?

[369] Yeah, look, I think we, you know, we're entering this time where we've got, we have technology that is progressing at the fastest rate that it ever has, and you've got, you've got some really big social problems, like, society scale problems that we have to tackle.

[370] 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 50 years or so.

[371] 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.

[372] 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, you know, like live better lives.

[373] But like that also means focus on like some super important things like how can you apply it to health care to make sure that you know like our quality and cost of and sort of ubiquity of health coverage is is better and better over time like that's more and more important every day is like in the in the united states and like the rest of the industrialized world so western europe china japan korea like you've got this population bubble of, like, aging, working, you know, working age folks who are, you know, at some point over the next 20, 30 years, they're going to be largely retired.

[374] And like, you're going to have more retired people than working age people.

[375] 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.

[376] And the answers to, like, all of these sorts of questions, like where you're sort of running into, you know, like constraints of the, you know, the, the world and of society has always been, like, what tech is going to, like, help us get around this?

[377] You know, like, when I was, when I was a kid in the 70s and 80s, like, we talked all the time about, like, oh, like, population boom, population boom, like, we're going to, like, we're not going to be able to, like, feed the planet.

[378] And, like, we were, like, right in the middle of the green revolution where, like, this, this massive, technology -driven increase in crop productivity like worldwide and like some of that was like taking some of the things that we knew in the west and like getting them distributed to the you know to the to the developing world and like part of it were things like you know just smarter biology like helping us increase and like we don't talk about like yeah 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 super challenging problems uh like i've i've like one of the things that i uh i'm trying to spend my time doing right now is trying to get everybody else to be hopeful as well because, you know, back to Harari, like, we, we are the stories that we tell.

[379] Like, if we, you know, if we get overly pessimistic right now about, like, the, the potential future of technology, like, we, you know, like, we may fail to fail to get all the things in place that we need to, like, have our best possible future.

[380] 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.

[381] that story that are helping build that future, which is super exciting.

[382] And I agree with everything you said except I do hope clip heat comes back.

[383] We miss them.

[384] I speak for the people.

[385] So, Galen, thank you so much for talking today.

[386] Thank you so much for having me. It was a pleasure.