My guest today is a dear colleague of mine sitting with me on the Microsoft Senior Leadership team. He is someone who thinks deeply about how technology will affect people, communities and society overall. He is Executive Vice President and CTO of Microsoft, Kevin Scott. Kevin plays a key role in shaping Microsoft’s strategy. He is deeply, deeply passionate about creating technology that benefits everyone. It's his job, actually, to ask where we need to fill the gaps or build something that does not exist yet. Kevin grew up in a rural town of Gladys, Virginia, a place that's been and will continue to be changed by emerging technologies. And I know from working closely with him for the last couple of years and talking to him over the years, that he thinks deeply about the potential impact of AI on the people and workplaces in towns like Gladys and many others. So he's also the host of the brilliant Behind the Tech podcast, which invites listeners to geek out with an amazing line-up of tech heroes, inventors, innovators. He's also the author of a great book, Reprogramming the American Dream, which I highly recommend, by the way. So, Kevin, a very warm welcome to the Positive Leadership Podcast.
Thank you for having me and for the very kind and generous introduction.
So, Kevin, one of the big themes running through your book that I was mentioning is the power of storytelling. It's foundational to our existence, and that's something that I like to pick up in our conversations going on. In the Positive Leadership Podcast, one of the first things that we get people to do, actually, is to write down or share the story of their life a little bit, at least, to reflect on the moments that have shaped them into the person and leader that they are today. So I'd like to start at the very beginning with your story back in Gladys, Virginia. What was it like for you as a kid growing up? Who or what was the largest influence on you and your core beliefs, Kevin?
The biggest influence on my beliefs and – maybe the better way to say it is just sort of my ethos and values – were my family. We certainly weren't well off. We had, at times, a lot of financial difficulty and at best my family was always paycheck to paycheck. But I don't think we really paid much attention to that. Or at least that's not the thing that we were always talking about. My dad was a construction worker, and my mom hustled with a bunch of little side jobs that she did. She was a tax preparer. She ran a nursery school for the kids at church for a brief period. She did a whole bunch of work, but her primary job was taking care of the family. I just had the luxury of being around a bunch of people who were hardworking, very curious, who always worked with their hands, who were always tinkering with something and who felt like they had an obligation to their community to do something valuable, that our job was to take care of each other. People who cared for their community and who were serving others in their day to day lives as well, all the time.
So something that's important to you, Kevin, and is also referenced in the title of your book is The American Dream. This idea that anyone, regardless of where they were born and what class they were born into, can attain their own version of success. And it's an idea that has served as a powerful motivation for many people in the US for many years and around the world as well, inspiring people to strive again for a better life. In your book, you paint a vivid picture again of your parents and you just discussed some of that. So I'd like to understand did the American Dream seem like something that was within their grasp, or yours as their child?
I've thought a lot about this idea of the American dream over the past handful of years. And I think one of the things that a lot of people may be getting confused about is confusing the idea of a dream and a promise. The thing that both my parents and I believed when I was growing up – and I still believe this and my family back in Virginia still believes this – is that there is this motivating idea that if you go figure out some way to be valuable to your fellow human beings, you try to understand what they want and need, and then you go do work and work as hard as you possibly can to try to meet those needs of your fellow human beings, that good things will happen to you. I tell this to my kids all the time… CTO of Microsoft or Head of Global Business for Microsoft or whatever these big titles are like, there's so much luck involved in that. That's a crazy thing to try to fixate on and plan for. But I would have been okay no matter what my outcome was by just applying this set of ideas that I can work hard, try to discover what's valuable to do, and then going to do that thing, and it really isn't a promise. And it was kind of implicit in the way that my parents talked to me about this and in how they acted. It's like, you just have to go do the work and you’ve got to go grind through it. And, you know, sometimes it works and sometimes it doesn't. And it's maybe even more valuable to do something and fail and learn something from the failure and then get up and go again. Sometimes success teaches you nothing.
Yeah, absolutely. So in a way, very much a hard work ethic and also almost like an entrepreneurship mindset the way you described it actually, Kevin.
Pretty much. I didn't even know the word entrepreneur when I was growing up. But I think that’s what I was surrounded by, a bunch of entrepreneurs.
So early on as a child, at one point you'd been saving up little money and you bought your first computer, Radio Shack Color Computer Two, if I'm not mistaken. And I think it was a huge deal, a big deal for you personally, because as basic as it was, you were able to create your first programs, I think, including one for the fantasy game Dungeons and Dragons, which caught the attention, I think, of your teachers and then the rest, I would say, is almost history. So I'd love to go back to that, because when I listen to some of your, again, life stories, you talked about the fact that actually as an undergrad you studied English, and you kept it as your minor for college and computer science as your major. You actually almost went – I think people don't know that – for an English PhD. So what happened? What made you decide between mastering programming versus mastering your native language English?
Part of it is pragmatic. I was constantly trying to figure out how I was going to support myself and support my family, because I got a scholarship that partially covered the cost of going to school, but didn't completely, so I had student loans. I was on my own financially, and part of that was my decision and part of it was my parents’ financial circumstances. I just had to take care of myself and eventually I had to take care of my entire family. And so part of it was a pragmatic thing. I enjoyed both equally and I think that's a tremendous luxury because sometimes it's hard to even find the one thing that you really enjoy. And so I picked computer science. And the story that I told myself about picking computer science is that there's a way to do computer science that is artful, that is grounded in the humanities, and that I wasn't really forsaking anything by making this choice. And, truth be told, I'm interested in a lot of things. I drive my wife nuts sometimes with the amount of stuff that I'm interested in. So I think it's fine having a vocation that you go and focus on and try to make yourself as good at it as you possibly can and also like it's great for you, the person, and maybe even for the vocation to have these other things that you're interested in as well.
We'll discuss that later Kevin, as well, as I ask you to reflect, of course, on AI in a few questions, a couple of questions, to think about the way those historical routes between English as a language and technology and AI are kind of coming together now with the so-called LLMs and other language models. So more to come, because I think it'd be great to understand, actually, in your mind, in your brain, what prepared you for that new era. So I think you wrote your first line of code back in 1983. Interestingly enough that was the year I joined a French software start-up company. I was doing modest – because I was not good at that – basic programming on an Apple Two. It was more than 40 years ago. So today, now shifting really to today actually… What would be your advice to a young 18-year-old Kevin, who’d love to learn how to develop a piece of software without being able to go through a computer science degree? Which studies, if any, should he consider? How would you go about that today?
It sort of depends on what stage of your career and your life that you're in. I don't know whether I personally would have done anything different because I like to understand things as deeply as humanly possible. I've always been fascinated with tools. Young Kevin would have looked at these AI systems and would have wanted to understand what's behind the scenes of them and how they worked. But I think the advice that I would give people more generally is like, find whatever it is that you really, really, really have a passion about, that is valuable to other people and try to use these tools that you now have available to you to go do that thing. I have a 13-year-old and a 15-year-old who are like thinking about what it is that they want to be. My 15-year-old daughter has wanted to be a cardiothoracic surgeon for the past year. It’s sort of a weird thing that she got fixated on when she was really young, because she watched too much Grey's Anatomy and she had a couple of doctor parents of her friends in school. She had decided that she's not a tech person, but her mind has been changed over the past few years. The way that she's using tools like ChatGPT and GitHub Copilot to help her solve these medical problems and healthcare problems that she's really motivated by is quite interesting. I think that that's the mindset. It's like all of these problems we need to go solve, all of these things that matter and that are important, and we have increasingly powerful tools to go do them. You don't have to be a programmer anymore to get the computer to do unbelievably powerful things for you in service of what it is you're trying to accomplish.
I think it's a great story, and it reminds me of one of the episodes I had with Sal Khan. Of course, he's been developing the Khanmigo, an AI agent who is becoming increasingly – when you ask him questions about the future – kind of your personal tutor, beyond actually just the course you are taking on, and he could even portray a day where he would say he could become actually a lifelong learning agent for all of us. We could actually all of us keep learning a bunch of things differently. We all learn differently, and we all learn at different rates.
I was reflecting on this actually this morning… I'm learning how to do ceramics right now. And the way that I learn is like, I can go watch a bunch of videos and read a bunch of books and I can absorb it all, but when it comes to the practice of the thing that I'm trying to learn, I have to actually go do it because I don't know how to prioritize all of this information I've stuffed in my head and I think the way that I learn is like, other people do it, but it is idiosyncratic there are other ways that people learn that are more effective for them than how I learn. And so this idea that you could have an infinitely patient tutor that could adapt to your particular learning needs very individually, I think it’s just a really interesting, powerful idea.
I think it's a wonderful analogy. Again, another very recent podcast that I had with a French chef, in terms of cooking called Thierry Marx, who is least well known in the country and beyond. He's doing some amazing work as well socially. He is someone who loves about talking about this philosophy of basically learning by doing, putting his hands into, what he called the katas of cooking, the fundamentals of cooking. And this is the way he grew up as a kid, actually, in the kitchens of big chefs. Anyway, so a very nice analogy, which I love when it comes to AI. Let's go back to the roots of AI with Alan Turing, a pioneering figure in computer science and AI. In a seminal 1950 paper called Computing Machinery and Intelligence, he asked the following questions. Can machines think? Can machines do what we as thinking entities can do? And he said, it is possible to do a machine which will learn by experience. And obviously we know that his work laid the foundational concept that continues to guide our research and development today. A lot has happened since Alan Turing obviously, and even his first test, over the past 70 years – I can't believe it. What did you personally experience as the most stunning breakthrough over the past years, and what has been the most defining moment in a way, if I can ask a question in your own tech life, when you realized that we are really entering into a very different new era?
Maybe I could just pull back for a second and talk about the history of AI. We’ve had an extraordinary, difficult time across the entire history of the discipline of AI. So when Turing wrote that paper, the term artificial intelligence hadn't been coined yet. It was five years later at a workshop at Dartmouth, when bunch of these mathematicians and computer scientists and information theorists sat down and said we want to build a machine that in many ways can replicate what a human brain does and we’re going to call this study, this new field, artificial intelligence. And we have had a really hard time over the entire history of the discipline even defining what artificial intelligence is. We’ve got this track record of we think that we understand what an intelligence is like, what our own intelligence is, and then we go build machines to replicate some aspect of it. And as soon as we accomplish the thing, then we redefine what the measure of intelligence is. We used to think that the apotheosis of human cognition are these very challenging games like chess and go. In 1997, IBM built a system called Deep Blue that beat Garry Kasparov, who was the world champion chess player at the time. And it was stunning, nobody thought it was possible. As soon as it happened, we figured out that this really isn't the breakthrough that is going to tip us over into this idea of artificial general intelligence like this solves a very narrow thing. Funnily enough, I think chess is now a more popular human pastime and hobby. You could even think about it as a sport, given how competitive it is and how much joy people derive from competing and watching the competition, even though since 1997 there has never been a human chess player as good as a computer at playing chess. We just don't care anymore. I think that's a really important thing to keep in mind. And to answer your exact question, the most stunning breakthrough in my career I think probably happened recently. It was when GPT-4 finished training, and this idea that we had that you could follow a particular path of developing artificial intelligence was going to result in an AI system that was very generally useful for doing a bunch of cognitive tasks. That was a big a-ha moment. I had a strong sense that it was coming, but I didn't know it was coming this fast. And the thing doesn't think. Going back to your Turing quote, he said two distinct things. One is he's talking about thinking and one he's talking about, can we write a piece of software that emulates aspects of what our brains do? Absolutely, we can do the latter. But the former is like almost a philosophical thing. When I say, JP, what are you thinking about? How do you think? That’s a very human question. It’s very different from what these software systems are doing.
Back just a couple of years ago, you just alluded to that, you were really an inception point of the partnership between Microsoft and OpenAI. And because back in the spring of 2018, I think when you first went down to meet them, obviously OpenAI already had a relationship with Microsoft using Azure, and you met the team including Sam Altman and also Ilya Sutskever. What were your first impressions meeting those guys and the work they were doing at the time before GPT-4, 4.5, Turbo and more coming up now?
Well, I will tell you, I was sceptical going into the meeting. And like my scepticism in general is just a personality disorder, like not anything specific to the team. I'm always slightly sceptical of things that I haven't fully understood. And sometimes, like, I will go fully understand them and I'm still sceptical afterwards. But this was one of those rare instances where I went in and… I'd been building machine learning systems at that point since 2004, like very large-scale things, doing very valuable commercial applications of machine learning, and I was not prepared to go into that meeting to have my mind set about what was possible in the next handful of years. And I walked out of it, like, absolutely convinced that we were on the cusp of something very interesting. We were still years away, but they had a framework and a way of thinking about the problem that they were tackling that was very scientific. There was a methodology like, here's the experiment we're going to run. This is what we will learn from it and this is what it will tell us about the experiment we will go run after that. I thought we had the foundations for a partnership where we could do some valuable things for them to support this very disciplined development of a very useful AI system. And the way that it was going to be useful and interesting for us was that it wasn't trying to solve a narrow problem. A lot of the AI systems before then were built to solve a narrow problem, like play chess, play go, predict which ads someone is going to click on. But this was a, we think we're going to be able to build this system and that as a function of scale. It's going to become more broadly useful and you're going to be able to build like dozens and hundreds and thousands and millions of applications on top of it. As a platform company, that's exactly the mission Microsoft has been on for almost five decades now.
Great clarity about that trigger in the history of AI and what you experienced yourself. I'd like to come back to your book and the role you have at Microsoft, because I think you've always been quite sceptical, yes, but actually quite optimistic as well. Someone who's driving the positive side of innovation. I would say that's also my bias, I must admit, as you know. But you also experienced early on in your childhood, as you said, poverty and loss of industry in the region where you grew up. And so you understand well the need to invest in rural infrastructure. So let's imagine that today, Kevin, you are now in charge of shaping the social and economic development of Virginia that you know so well. What would you do to transform the lives of the people in those rural communities, to reshape the traditional sectors like construction, textile, furniture, agriculture, and more? In other words, how would you reprogram the American Dream in your home city? What would you do if you were in charge with AI?
I think the thing that you should do, and this is a set of things that we ought to be thinking about doing broadly actually, not just in the rural parts of the United States, but in the developing world and in all parts of the industrialized world as well. I think job one is you actually have to have the infrastructure in place to support people using AI tools to solve the problems that they want to solve. So part of it is like, do you have a platform available? Is it open? Is it getting cheaper and more powerful over time? Can you operate it freely across global boundaries? It means some very basic things, like one of the struggles that folks in my community have is the internet just doesn't work well. My mom and brother live near a local telephone building, like the exchange for Gladys and they have very good internet. And my uncle, who lives three miles away, has 300 kilobit per second internet, which was great in 1990, and is pretty horrendous now. So you can't leverage AI if you can't connect to AI. I think a bunch of the stuff you need to go do is like really educate people to be entrepreneurs in a sense. So how do you take young people and expose them to this full palette of tools that they have to use, and like help them think critically about what the interesting problems there are in the world that they could put those tools to use solving. It’s a very different educational paradigm than the one that we've had since the beginning of the Industrial Revolution, where it was like, you human being need to learn these basic skills. You need to be literate. You need to know a little bit of math. You need to have structure in your life where you can get up in the morning and go do something for this number of hours and work in teams and understand hierarchies and like all of this stuff. That was very industrial revolution sort of learning. And that's not really what we need right now.
Not anymore, no. So it's both infrastructure, it's skilling, it's actually lowering the bar of accessing as well, not just freely, but also I think the level of confidence too. Because there's a lot of fear in many countries. I travelled the world as well for a company, and you can still see a lot of confusion, fear and anxiety in just getting access to AI. So I think there's a lot to be done there as well in terms of education.
