Behind The Tech with Kevin Scott - Kevin Scott and Reprogramming the American Dream

Episode Date: April 14, 2020

Listen in for a sneak peek into Kevin's new book “Reprogramming the American Dream.” Co-author Greg Shaw interviews Kevin about the future of AI and how it can evolve to better serve us all, from rural America to Silicon Valley. Kevin Scott Visit our site for more info: https://www.microsoft.com/en-us/behind-the-tech

We're not on some sort of inexorable path where we're building these technologies and like things are going to unfold in this very particular way that's not inclusive. Like we get to choose our future and I think if we make careful choices that that future can be like very very inclusive. Hi everyone, welcome to Behind the Tech. I'm your host Kevin Scott, Chief Technology Officer for Microsoft. In this podcast we're going to get behind the tech. We'll talk with some of the people who've made our modern tech world possible and understand what motivated them to create what they did. So join me to maybe learn a little bit about the history of computing and get a few behind

the scenes insights into what's happening today. Stick around. Hello and welcome to the show. I'm Kevin Scott, and today we're going to turn the tables a bit. As some of you may know, I've just written a book called Reprogramming the American Dream. So our guest on the show today is my co-author, Greg Shaw. Greg was a journalist in his home state of Oklahoma, including editor of the Cherokee Tribal Newspaper. He went to work with Bill Gates, both at Microsoft and the Bill and Melinda Gates Foundation. He is co-author of Satya Nadella's Hip Refresh and a fellow rural native. Greg,

I'm really glad that you joined us today to talk about the book, and I'm grateful that you agreed to lend your word mastery and expertise to the creation of it. Well, thanks, Kevin. It's great to be here with you, and it's really been terrific being along for the journey. You know, I got to go back to Gladys, Virginia, and I met your mother and brother. So, before we jump into the book, I must ask how they're doing. We're talking in the midst of the COVID-19 crisis. And so I'm curious how your family in Northern Virginia and your family in rural Central Virginia are doing. Yeah, so far so good. I think that just as with all of the communities in the country and throughout the world, the virus is coming. So

just because there aren't a lot of cases at the particular point in time when we're recording this podcast doesn't mean that there won't be. So they're taking all of the precautions that they should be. Interestingly, the hardest person to get to abide by the sheltered place is my 90-year-old grandmother, who has like a very active social calendar with all of her church ladies. And so, I think two weeks ago, we got her convinced that she needed to curtail her activities and keep herself at home. So, so far, so good. Everybody's doing okay. Oh, that's great. Well, let's jump into the book. As you said in your introduction, the title is Reprogramming the American Dream. The subtitle of the book is From Rural America to Silicon Valley, Making AI Serve Us All. And the book is published by Harper Business. It's

available now for purchase wherever you buy books. One of the first things that we talked about, really, in one of our first discussions was that, you know, storytelling, as you say, is a Southern thing. Tell me the story of the origin of this book and the story that you wanted to write about. Well, I've been part of the development of technology for a really long time. And technology itself was the way that I was able to build a life for myself.

Like I think in a whole bunch of ways, like it really saved me as a kid. It was the thing that I latched onto that helped give me something productive to do with all of the energy that I had. And I just got really lucky that personal computing was emerging right at the time that as a preteen, I was just trying to figure out what to do with myself. And I got hooked and it has served as a platform for me for building a career and for doing a whole bunch of things that I think have been helpful to other people in at least some small ways. And, you know, when I look at the state of technology right now, like we have never had a more powerful platform in terms of technology. So whether it's, you know, like the set of things that we have right now, like computers that let us stay

in touch with one another and interact and collaborate and, you know, do really interesting, interesting things that wouldn't otherwise be possible. But like, I have particularly been involved with the development of AI for the past 15 years or so. So like one of the first big projects that I worked on when I left academia to go work in industry was a machine learning thing. And I've just sort of watched this technology progress in both power and accessibility. And like what I mean in particular by that is power is like what you're able to accomplish with the tools of machine learning. And accessibility is like who is able to use these tools to create things. It's just been on this incredible curve on both dimensions.

And I sat down to, you know, with you, what is it now, two and a half years ago when we started this whole process? Seems like only yesterday. Yeah, it does. But, you know, the thing that I wanted to make sure was that people understood stories of how they could choose to use this technology to help build a better world for themselves and for their communities in this very inclusive way. And I wanted to make sure that there are things about the development of machine learning and its uses that we need to be cautious about. But I want people to also feel hopeful about what it is that they can do with these tools. Yeah.

Well, the podcast is Behind the Tech, and one of the things that I really enjoyed is learning your story from Gladys, Virginia, to places like Illinois and Europe and into Silicon Valley. So it's a really terrific story. I want to jump into the introduction here and a topic on AI. In the introduction, you address the question that is on everyone's mind, which is, when will artificial general intelligence, or AGI, when will it be available? When will computers be as smart as humans? So, I'd like to ask you to read a passage from the introduction in which you address that

question. Okay. Yeah, I'd be happy to. So even though I have neither the expertise nor the crystal ball to predict exactly when AGI might arrive, I've been involved with modern technology long enough and read enough history to know that we've often underestimated the speed with which futuristic technology suddenly arrives. AI has historically been limited in what it has been able to accomplish by the amount of compute power we can throw at AI problems and how much time it takes for humans to encode logic and knowledge into AI algorithms. We now have enormous amounts of compute power in the cloud, and we have enormous databases of digitized human knowledge like YouTube and the Kindle bookstore that can be used to train AI systems. As our modern AI algorithms absorb that human intelligence to

accomplish new tasks we imagine for AI-powered systems, we may achieve what Thomas Kuhn defined as a paradigm shift, one in which humans will either be in the loop or out. Which one of these options we reach depends on our actions today, the story we craft, and the principles we assert about what kind of world we want our children to live in tomorrow. I feel this profound sense of cognitive dissonance. The same thing that can advance humanity can also cause people distress and even harm. This book arises out of a powerful urge, I feel, to reconcile the two. It is an engineer's tale, not the musings of a philosopher, economist,

or screenwriter. As Microsoft's chief technology, do I have skin in the game? Of course I do, but I'm also the product of rural America, one of the places most vulnerable to the dystopian story of AI. My values and many of my earliest experiences as a budding engineer occurred in a part of America, rural America, that is most at risk. I left the rural South over two decades ago, first for academia and then for Silicon Valley and the tech industry. But at my core, I am those people, rural people, and I care about creating a future that values them and their resourcefulness. That's great. Thank you, Kevin. You know, I want to ask you to talk a little bit about AI and how you define it.

You have a very savvy tech audience for this podcast, but you also wrote the book for people in your community back in Virginia and for others. My father read an early proof of the book. He's an oil and gas guy from Oklahoma, and he said he felt like for the first time he had a grasp of AI and what it might mean. How should we define for ourselves? How should we think of what AI is? So AI, on the one hand, is an incredibly complicated assembly of technologies. It's not just one thing. But maybe the simplest way to understand what it does or like how to think about it is that AI is a tool that we built for automating tasks and doing work that would otherwise require a human being to do. And like a lot of the work that AI does is sort of cognitive sorts of things. So the, you know, people talk

about this notion of artificial general intelligence, and like this was the holy grail for AI back in the mid 1950s, when a group of mathematicians and engineers and computer scientists coined the term artificial intelligence for the first time. They wanted to build a set of software and digital systems that were functionally equivalent to human intelligence in the most general terms. So, you know, could you build a thing that was sort of indiscernible from human intelligence? And what we found over the years is doing that is incredibly hard. You know, one of the things that I write about in the book is that human intelligence itself is sort of ill understood. And so, you know, in the early days of AI, the things that the founders of the field

thought were going to be the problems that once solved would be, okay, now we're over the hump, like we're, you know, we've got this whole thing nailed. We're things that we believe are high cognitive watermarks for human intelligence, like being able to master chess. And so it turns out that AI systems are pretty good at doing things like game playing. And so we built AI systems that could beat the best human chess players years ago. And like, even over the past couple of years, you know, some of the most remarkable stunts that we've done with AI, like with the most modern AI tools are like conquering a set of games where, you know, like we had set them up, you know, one after one is like, oh, like,

once we get here, we will really have cracked the nut, so to speak. The flip side of that is that AI still isn't able to solve very basic problems, like things that, you know, a human toddler can do are at the moment outside of the grasp of AI systems. And so, you know, one of the things that I would love to encourage people to think about is like, it's sort of very difficult to draw parallels between artificial intelligence and human intelligence, because just because something is hard and challenging for a human being doesn't mean that it's going to be hard and challenging for a machine. And vice versa, just because something is easy for a human being doesn't mean that it's going to be easy for a machine. So in my opinion, the best way for us to think about AI

is imagine it as a tool. It is a tool that can help us to automate tasks and to assist human beings in doing the work that they think is important. And when you look at it through that lens, it becomes an incredibly powerful problem solving tool. And again, one that I think is increasingly accessible to everyone for going and tackling some really, really important issues that we as human beings need to tackle and where most of the benefit is going to be creating this abundance that doesn't exist before because we simply don't have enough human horsepower to solve the problems in a way that creates benefits for everyone, if that makes sense. No, it's great. You know, one of the things we really wrestled with in writing the book, you know, the first several chapters of the book tell some great stories and, you know,

kind of develop the narrative. The latter part of the book, you know, presents some promising stories and case studies. But where we really wrestled is, you know, in the middle of the book, you wrote a chapter called How Models Learn. This is chapter seven of the book, and you talk about things like supervised learning and deep neural networks. Do you want to maybe geek out for a minute on why those things are important in thinking about AI? Yeah, so I think it is very useful for folks to have at least some high-level concepts in their head about how AI works, you know, and I'm going to go super fast. And this is one of the challenges with writing the book. Like that chapter might have been the

most difficult thing to write because I was trying to make it accessible for folks like your dad and my mom and still, you know, hew closely up to like the deep technical complexity and nuance of what actually is going on on the front lines of the field as these technologies are being developed. But so like, roughly speaking, you can sort of think about the first epoch of AI as one where we thought we were going to be able to encode human intelligence as a set of like logical rules and sort of describing knowledge in these very structured ways. And that like we were going to be able to sort of build up intelligence by like this very sort of rational, logical sort of, I think I call them systems of reasoning in the book. And progress was

interesting, but relatively slow. Like we were not moving as fast as some people would have liked in the field. And like we had these periods of, you know, very high excitement about AI that led to these booms of activity that were followed by busts where the hype sort of exceeded what was realizable by the technology. And there's even a term for this called the AI winter. And I've actually seen one AI winter in my lifetime, and there was one that occurred before I even became a computer scientist. And so the thing that's happened over the past, let's say, 15, 20 years is we have gone from these systems of reasoning to systems of learning. So things where instead of us trying to discover the logical rules of intelligence and the structured way to map the knowledge of the world, what happens in systems of learning with machine learning?

You are able to train a system to recognize patterns in data. And so if you have a large amount of data and like enough compute, you can run a learning algorithm across all of this data to get to build a model. And this model built from this data is able to do a set of things called inference that let you make judgments and predictions and classifications about things in the real world. And like, it's a really powerful

pattern. It's like the thing that we use to do a bunch of perceptual things that have seemed extraordinary over the past handful of years. So just since 2012, like we have really made super fast progress using a set of techniques called supervised deep learning to be able to accurately transcribe spoken words, speech to text. So this is speech recognition. We have made huge progresses in computer vision, like where computers can identify the objects in still images and like even in video streams with accuracy similar to a human being, like we're able to do machine translation where you can sort of take a snippet of text in one language and translate it to another. And so like, they're just all of these extraordinary things that we've been able to accomplish with this set of techniques

called supervised learning. And the reason that we call these techniques supervised learning is that you have to have human beings labeling the data. So like, you want to build a computer vision system that's able to discern the difference between buckets and kittens. You would have to go assemble like a huge collection of images of buckets and kittens. And you would have to have these images labeled. So somebody would have to sit down and say, hey, this picture has a kitten in it. And this picture has a kitten in it and this picture has a bucket in it. And you would have to have kittens of all shapes, sizes, breeds, colors, fur texture in different positions and poses under different lighting conditions.

And the same thing for buckets. And so you would feed all of this, you know, sort of labeled data into your learning system, and it would produce a model that can accurately identify, you know, buckets and kittens. And so it's fairly expensive in terms of the effort required to do all of this labeling. And then you end up with maybe a picture of a bucket with a kitten in it and really confuse the system. Yes, you can really confuse the system. But, you know, one of the interesting things that's really happened over the past couple of years and that's going to be like one of the driving forces for the next few years is we have really figured out in a bunch of areas how to do this thing called unsupervised learning, where you can bypass most or all of this labeling step. And you can just sort of point the learning system at a whole bunch of data, and you can have it sort of figure out a bunch of very complicated structure about the data that you're then able to use to build very, very powerful

systems without having to bear the expense of this supervised, you know, labeling process. And that's the, you know, the set of things that have been really driving progress in natural language processing over the past couple of years, where we've gotten some really sort of extraordinary results with question answering with systems that can generate very plausible links of texts that sound like they've been written by a human being. And so like, you know, we're making really, really fast progress there. And the interesting thing about it is, you know, when you're able to do unsupervised learning, the only thing that is bounding your progress, at least right now, and like we may run into boundaries sometime over the next handful of years, is the amount of compute that you can throw at the problem and the amount of data that you have to train. But, you know, the interesting

thing is like we've got more compute now than we ever have, and you have the whole internet full of data to train on. Yeah. Well, let's switch from the tech to society. You know, you dedicate the book to your father. In the book, you write a letter to your grandfather, Shorty explaining to him, he was obviously a craftsman and someone who would have been fascinated by AI. I mention this because the book is titled Reprogramming the American Dream, and you had your family and other families in mind. What's involved in reprogramming the American dream, and what do you mean by the American dream? So I think that we have an opportunity with better investment in advanced technology and like making those investments in a way where they're,

you know, accessible to as many people as humanly possible to have people in rural and middle America have the opportunity to create really very interesting new businesses that create jobs and economic opportunity and that help them realize their creative vision and that, you know, serves as a platform in the same way that industrial technology has served as a platform for these communities to build their economies in the early, mid-20th century, that AI can have a similar sort of effect in these communities today. You offer a number of different suggestions related to education and skilling and that sort of thing. I'm curious, what would you say is your advice to young people who might be growing up in rural central Virginia or Oklahoma, where I'm from, you know, how should

they prepare for jobs of the future? Yeah, I've chatted with a bunch of people about this over the past few weeks. And, you know, when I get this question about what we need to do to make AI accessible to those kids in rural and middle America, yeah, some of the things what we need to do to make AI accessible to those kids in rural and middle America, yeah, some of the things that we need to do are just very prosaic, I think. So the tools themselves have never been more powerful. Like the really interesting thing to me is that first machine learning project that I did 16 years ago now required me to sit down with a couple of graduate level statistical machine learning textbooks and a whole stack full of fairly complicated research papers. And then I spent six months writing a bunch of code from scratch to use machine learning to solve the

particular problem I was trying to solve at the time. If I look at the state of open source software and cloud platforms and just the online training materials that are available for free to everyone, a motivated high school student could do that same project that I did 16 years ago, probably in a weekend using modern tools. And so, you know, I think the thing that we really need to be doing is figuring out how to take these tools that are now very accessible and like we shouldn't feel intimidated by them in any shape, form or fashion and figure out how to get those into high school curricula so that we are teaching kids in a project-oriented way, like how to use these tools to solve real-world problems.

I think getting kids those skills is super important. Like, the other thing that we need to think about is just how we're connecting people to the digital infrastructure that is going to increasingly be running our future. And so there are things like the availability of broadband that are a huge, huge deal. You know, I think we write about in the book, my visit to our data center in Boynton, which is in Mecklenburg County, about an hour and a half, two hours away from where I grew up. And this is one of the most of compute power that is just in this sort of acres of data center infrastructure that we have there is just staggering. And we have a bunch of high skill technology workers who are building and operating this infrastructure on behalf of all of Microsoft's cloud customers. And some of those people who are living in that community struggle to get access from their local telecommunications

providers to the high-speed broadband that they expect. Like, they're information workers. Like, they expect in their homes to, like good broadband connectivity for students. Like it's even more critical. Like if you don't have a good broadband connection that's available to you somewhere as a student, like you're never going to be able to go find these open source tools to use these free or cheap cloud platforms to like go learn all of this, like very accessible knowledge that is on YouTube.

And so, sometimes I think it's the, you know, the prosaic things that, like, we're making more complicated than the complicated things. Yeah. Well, it was interesting, you mentioned Boydton and, you know, some of the places where Microsoft has data centers we had a chance to visit. And in those communities, we encountered this in Iowa as well when we were reporting there. You know, a lot of these high schools have been preparing students for kind of legacy jobs. In Wyoming, a lot of kids who can, you know, go get jobs in oil and gas. In Virginia, you know, it used to be, as you write about, tobacco and furniture and Textiles.

Textiles, yeah, and textiles. You know, what we discovered, and I'd love to hear your thoughts on this, is, you know, the high schools needed to begin to introduce some digital skills, and then the community colleges, where many of those students would end up going for post-secondary, also needed to, you know, create a sort of ladder toward the skills needed in those data centers. Yeah, and so, like, it's absolutely true. And then I think, you know, the thing that both of us saw, and, like, this was sort of a really, really striking thing to me, and I don't know why I was so surprised by it, because in retrospect, it's really obvious. Like, I think one of the things that you really, really have to have

in these communities, if you want kids to choose to study these concepts, to acquire these skills, to like graduate from high school and to, you know, sort of pursue further training either on their own or community colleges or going to a, you know, a four-year school to get a technical degree is like they have to have role models. Like I think this is one of the very luckiest, quirkiest breaks that I had is my great-grandfather, my grandfather, and my dad were all in construction. And the very easiest thing for me to do, and I got to say, even though I was in love with computers, there was still a part of me that was tempted. I asked myself, why wouldn't I go into the family business? And that would have been okay. I don't want to make it sound as if

going into construction is problematic in any shape, form, or fashion. There is an enormous dignity and satisfaction in doing jobs where you're working with your hands and the world needs those things. But in these communities, I think you need a balance. Like you, you want some people to choose to go do those things. And you want some kids to choose to go pursue careers in IT or security or like learn how to use the machine learning tools and like become developers or machine teachers or be prepared for the careers that don't even exist right now that are going to emerge over the next couple of decades. And in order for them to do that, like, we have to inspire them. Like, they have to see people around them that they admire or, you know, people who are

online or wherever. Like, they just have to have role models that where they can say, okay, like, I am like this person. They are telling me that, like, I can do this thing. And they're showing me why it's interesting. And, you know, I think we have to have that as well as the education in order to get kids to want to choose these careers. Well, and you speak with quite a bit of knowledge of this. I was very pleased to meet your wife, Shannon, during the process of working with you on this book. You and Shannon have a foundation that focuses on education. Do you want to say a little bit about that?

Yeah, I mean, the foundation very broadly looks at how it is that we can knock down systemic obstacles to children reaching their full potential. And like a lot of that's about education and educational equity. And like some of it is about, again, you know, if you do full system thinking about these things, it can even be about access to food and nutrition. Like a lot of the early childhood developmental things that we see is, you know, like if you have a kid who comes to school hungry, they can manifest a bunch of behaviors that look like attention deficit, hyperactivity disorder, you know, and have behavioral issues that distract them from being able to learn what they need to learn in their classrooms. And so, like, we just sort of

have to think about the full end-to-end set of problems that we need to solve to let every kid unlock their potential. And, like, one of the things that we, you know, in the local organizations that we work with that are trying to get kids educated is, you know, even here in Silicon Valley, it's like not just about the skills. It's about creating the support networks for people where they can get encouragement and positive reinforcement. Like it is so hard. And like both of my wife and I saw this because we were the first, like our parents didn't go to college. And so we were trying to figure out like all of this stuff early in our lives about how it is that we were going to go get a college degree when we didn't have as much support as we could have had just because our parents were trying their hardest, but they didn't know how to guide us necessarily.

And so we see there's this great organization here in Silicon Valley that's actually a franchise of a nationwide organization called Breakthrough. So Breakthrough Silicon Valley works with kids to make sure that they get into college and they graduate from college and they start working with them when they enter middle school. And they just look at the full problem of like, how do you, you know, what are the role models for these kids? Like, what are the patterns of success? Like, can you show them like a bunch of people who walked similar paths that they did and like they graduated and are having great careers? Like, how do you get the people who are graduating and have great careers to give back to their community.

Like, it's a really fantastic organization and a really fantastic way of looking at these problems. And I think, you know, again, when we go back to rural communities and middle America, where we want our kids not just to acquire these, you know, digital skills, but we want them to choose digital careers. And we want them to be able to stay in their communities to practice these digital skills so that you're building a foundation and an infrastructure in these communities where the community can fully participate in the economic engine that's going to drive a whole bunch of how the future unfolds for all of us.

Kevin, you had a number of people read the book and give you comments, people from lawmakers to people who live in rural communities to the tech digerati of Silicon Valley and Seattle and other places. What are people taking away from the book? And what do you hope they take away from the book? I think almost everyone so far who has given me feedback about the book and like, maybe this bias set of folks in that, like, I haven't had anyone come to me yet saying, oh, this is a horrible, horrible book. But, you know, the thing that people seem to be taking away from the book is like, oh, this stuff isn't really as complicated as I thought it was. And, wow, I actually do now have hope that there's a path forward for developing and employing very advanced technologies're not on some sort of inexorable path where we're building these technologies and like things are going to unfold in this very particular way that's

not inclusive. Like we get to choose our future. And I think if we make careful choices that that future can be like very, very inclusive. I want to ask you, during the course of researching and writing this book, I wonder if there's a particular moment, either when you were, you know, traveling with the Rise of the Rest Fund or going to Iowa or, you know, any of the moments along the way that, you know, particularly struck you, either concerned you or gave you hope for the future? Well, I think there's a really fantastic set of people in a bunch of these communities who are pushing very hard on a similar set of things. So there's just an increasing amount of capital that is starting to flow into these technology companies that are in places

that are outside of the coastal urban innovation centers that we all are sort of familiar with and talk about all the time, which I think is a really incredible thing because what we got to see as we were visiting these places is that they're brilliant people everywhere, just ingenious, industrious, you know, sort of incredibly inspiring people doing really good work all over the country. And us collectively choosing to invest in what they're doing is really, really important. And I've seen a bunch of these folks using technology in very interesting ways. So I know all the capability is there to employ the most advanced tools that we have possible.

And in a bunch of places, they're already on this path of being able to leverage technology to, you know, build better businesses, to create more opportunities in their communities and to solve a set of problems that they are uniquely situated in and positioned to solve. But like they're at the very beginning. And that's the thing that gives me so much hope. Yeah, I'll go back to my most off-quoted example, which is my friend Huey's company that he works at that does precision plastics machining. They built a really good business in this small town in Campbell County, Virginia, where they're using the internet to market to and communicate with their customers. They're using really advanced software to be able to program automated machining equipment

to make these very high precision parts that they're then able to deliver to customers all across the country. And because they're able to leverage really advanced technology, they're competitive. And so their problem is, you know, they would love to be able to hire more people to work there. And like, they just sort of need the people with the, you know, these sort of modern skills. And all of the tools that they're using are on this almost Moore's law for machines, where the amount of dollars that you're spending on the machine is getting less and less over time as a ratio of the value of the things that the machines can produce. So in other words, the same way that Moore's Law said you got more compute per dollar, like you're able to do a more valuable set of things with these automated machines per dollar that you spend on them.

And like, that's a really exciting thing. as I saw it, like I saw anecdotes everywhere, like all of these companies that were following the same pattern where they were businesses that were starting to like serve some very important need in the marketplace where their ability to be competitive and to like move jobs back into their communities that had been outsourced, you know, some sort of larger concern or like had been moved overseas. Like the reason they got to repatriate those jobs into their communities was because they were using technology. And like, it's a really inspiring thing to see. Like I, if we had never actually published this book, I would have been happy to have undertaken the project just to see all of that happening and to, like, just be inspired and fired up by it.

Yeah, well, that seems like a good place to end, but I want to ask you to read another passage that's along these lines, which you write very movingly about your community and about the communities in this book. I wonder, would you read one last passage for us? Sure. So, there's this disinterested, even disdainful attitude that people can sometimes have about those who choose to live in different places, who choose to pursue different paths in life. It's very easy to surround yourself with the same news sources, the same political views, the same entertainment, the same activities, and the same culture as everyone else around you. With modern technology, with more of our time

spent online and on our devices, and with more and more of our connections with one another mediated by social networks, it's hard to avoid becoming trapped in self-reinforcing filter bubbles and then not to have those bubbles exert their influence on other parts of our lives. Many of my friends and colleagues see those living in rural communities, people who live outside of the urban innovation centers where the economic engines are thrumming right now, in a very different light than I do. That's not just unfortunate. It's an impediment to making the American dream real for everyone. The folks I know in rural America are some of the hardest working, most entrepreneurial, cleverest folks around. They can do anything

they set their minds to and have the same hopes for their futures and the futures of their families and communities as those of us who live in Silicon Valley and other urban innovation centers all do. They want their careers and their families to flourish just like everyone else. Where we choose to live shouldn't become a dividing line, an impediment to a good job and a promising future. That's the American dream, and it's on all of us to make sure that it works, because in a certain, very real sense, if it doesn't work for all of us, it won't work for any of us. Kevin, that's great. Thank you very much.

And we'll wrap it there. It's been a fun conversation. Yeah, thanks, Greg, so much. It's been great to have you on the show at last.