HIBT Lab! OpenAI: Sam Altman

Artificial Intelligence was once the realm of science fiction. But over the last several years, advances in machine learning and deep neural networks have moved us closer to a reality where computers can learn and solve problems independently, the way a human does. From art and music to medicine and politics, the potential applications of AI are nearly endless, and the technology just keeps getting better. This week on How I Built This Lab, Guy talks with one of the leaders in the field of AI development, Sam Altman. Sam talks about his journey from Stanford dropout and teenage entrepreneur to president of the legendary startup incubator Y Combinator and co-founder of the nonprofit OpenAI. Plus, Sam shares his hopes and fears for the future of AI and how his company is working to ensure it ultimately benefits all of humanity. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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• 0:00Guy Raz?

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• 5:12Guy Raz?

  Hello and welcome to How I Built This Lab. I'm Guy Raz. So, artificial intelligence is getting really good, really fast. For many years, most of us assumed AI was going to replace jobs in manufacturing and the service sector. But what's actually happening is that machine learning devices are becoming much more adept at generating things we often associate with human creativity. You might have seen that piece of digital art that recently won a competition in Colorado. Artwork completely drawn by a piece of software. AI software is getting really good at composing original music, even writing newspaper articles. And it means that, in the not-too-distant future,

• 5:56Guy Raz?

  machines might be able to diagnose diseases, or answer any questions we have, or maybe even solve some of the world's biggest problems. One of the pioneers in AI technology is a nonprofit company called OpenAI. It was co-founded by Sam Altman in 2015. Sam is the former president of the legendary tech incubator Y Combinator, the accelerator program that helped launch Airbnb, Stripe, Coinbase, DoorDash, and so many others. Today, Sam's on a mission to make artificial intelligence Work in a way that has the maximum benefit for the maximum number of people. OpenAI has made headlines with some pretty impressive AI tools, including GPT-3.

• 6:41Guy Raz?

  It's a language generator that's written op-eds and scientific research papers. And also DALL-E, which is an algorithm that can generate incredibly realistic images from any written description. But of course, there are lots of open questions about what happens if we can no longer control how this technology is used, which we will hear about. Sam Altman grew up in the St. Louis area, obsessed with computers from a pretty early age.

• 7:11Speaker 01

  I was a nerdy kid, that's true, but also like the just the power of this idea that we were all going to be connected to each other, which I would say was evident by, you know, maybe like when I was like 10.

• 7:24Guy Raz?

  Yeah.

• 7:25Speaker 01

  That like I was just like, this is like more important and more exciting and more fun than than everything else going on. And this is just, this is what I want to do.

• 7:33Guy Raz?

  From what I gather, like a big, a big kind of moment for you was getting that your first Mac 2.

• 7:39Speaker 01

  Yeah, Mac LC2. That was a really big moment.

• 7:42Guy Raz?

  And how did you use that computer? Did you play games on it?

• 7:45Speaker 01

  I played games. I used AOL, which was sort of what I thought was the internet at the time. And then after that, figured out there was an actual internet out there, which was was even cooler. I read like most of the encyclopedia on a CD-ROM.

• 7:59Guy Raz?

  Yeah.

• 8:00Speaker 01

  I learned to program.

• 8:02Guy Raz?

  What were you programming in like BASIC or in?

• 8:05Speaker 01

  In BASIC, yeah.

• 8:06Guy Raz?

  And what were you, what were you making?

• 8:08Speaker 01

  You know, like very little stuff, like print out all the prime numbers, like make a little interactive text game, stuff like that. But it didn't matter. It was just like the thrill of getting to make anything.

• 8:16Guy Raz?

  Yeah.

• 8:17Speaker 01

  The excitement of like watching somebody else use something you made.

• 8:20Guy Raz?

  Yeah.

• 8:20Speaker 01

  Was just unbelievably exciting. Actually still is, but especially as a little kid.

• 8:26Guy Raz?

  And so you describe yourself as a computer nerd in high school, but was it clear in your mind that this was what you were going to do? Or were your parents like, hey, you know, you're really good at math and science. Maybe you should go to med school or something like that.

• 8:39Speaker 01

  It was totally clear in my mind. My parents are great. They never, they never like pushed me to do something I didn't want to do. There was, there was, you know, gentle suggestions of science or whatever else, but like, it was very clear to me from a very young age, and I'm thankful for this because I know it doesn't happen to everybody that like, computers were my thing.

• 8:57Guy Raz?

  Yeah.

• 8:58Speaker 01

  Or technology more broadly was my thing.

• 9:00Guy Raz?

  What was it about, about computers as a teenager that you remember feeling so passionate about? I mean, was it, was it a community of other people that you connected with locally or even beyond where you lived in St. Louis?

• 9:14Speaker 01

  That was certainly part of it. I also was into like ham radio as a kid. And the one thing that I noticed across both of them was the fact that you could like, A, there was interesting engineering and science problems and technology to figure out. There's like the fun of using a computer at all, especially back then when like they didn't work that often, you had to like upgrade parts of them yourselves. That's just like a cool puzzle and technology is interesting. But definitely one thing that I felt across both of them is this ability to connect with anyone around the world instantaneously, which was incredibly appealing to me. The ability to sort of like learn anything, talk to anyone was, was just like, and still is like pretty magical. I think we've, we've all just now accepted that as standard, but if you rewind the clock

• 9:59Speaker 01

  like 30 years, things were very different.

• 10:02Guy Raz?

  Yeah.

• 10:03Speaker 01

  I mean, I think the computer revolution is as big of a deal as the, the other great technological revolutions of the past, as big as the agricultural industrial revolution. But we've kind of just lived through it so recently that we don't talk about it as historic as it's going to turn out to be.

• 10:21Guy Raz?

  Yeah, for sure. So when you set out to, I know you studied at Stanford and you studied computer science. So it was clear you were very focused on entering this field and presumably, when you get to college, your thought is eventually go work for one of these big companies?

• 10:39Speaker 01

  Yeah. I picked Stanford because it was the best computer science program. I mean, it turned out to be like a wonderful place in a lot of other ways, but that was what I wanted to study. And my dream job after school was to work at Google.

• 10:52Guy Raz?

  That didn't happen?

• 10:52Speaker 01

  That didn't happen.

• 10:53Guy Raz?

  While you were at Stanford, you basically had a business idea and you dropped out?

• 11:00Speaker 01

  Yeah.

• 11:01Guy Raz?

  Presumably with the intention of coming back?

• 11:04Speaker 01

  Absolutely with the intention of going back. One thing that's very cool about Stanford is they let you stop out and return, sort of no questions asked, very easily.

• 11:12Guy Raz?

  Yeah. So you drop out to pursue this idea. It's your business called Looped. This is like 2005. So this is pre-iPhone, pre-apps. But the idea was, hey, on your cellphone, because people had cellphones at that point, you could see where your friends were all at the same time on a map?

• 11:31Speaker 01

  Yeah. And also, you know, meet new people near you, find like interesting places to go or events to go to.

• 11:37Guy Raz?

  And so the idea was that like, because I'm trying to go back to 2005, and I know I had, I definitely had a Nokia at that time.

• 11:46Speaker 01

  A Palm Trio was like the height of coolness in 2005?

• 11:48Guy Raz?

  The Palm Trio, yeah, right, right, right.

• 11:50Speaker 01

  But most, you know, the great, great majority of the world had like a Nokia something. But there were all of these like little mobile app stores. There's something called J2ME at the time where you could like make a little mobile app on a, on a flip phone or whatever. It wasn't a very compelling experience, but it was sort of the idea was like, as people lived more mobile lives, knowing everything in the context of location would be super powerful.

• 12:13Guy Raz?

  Yeah.

• 12:14Speaker 01

  We turned out to be mostly wrong about that, by the way. I think it didn't really work for a bunch of reasons. But one of them that is still somewhat surprising to me is even as the world has gotten very mobile, most people are sort of still at home or work all the time. And there's just like far less spontaneity than it felt like to like a, you know, 20 year old or whatever I was at the time.

• 12:34Guy Raz?

  Yeah, I mean, it's interesting because on the one hand, I think most people wouldn't want to do that with other people. You can do that on an iPhone. But what they do do is they post on social media all the time. So we do know where people are all the time.

• 12:45Speaker 01

  Exactly.

• 12:45Guy Raz?

  So it's a different version of that. I'm not, I'm not giving away the plot here by saying that Loop ultimately did not sort of become what you envisioned, but it did, it did bring you into this world that would become a really big part of your life, Y Combinator.

• 13:01Speaker 01

  Yes.

• 13:02Guy Raz?

  You were in the sort of inaugural class of Y Combinator. Paul Graham, famously one of the main co-founders, I guess you met him and he liked the idea. But what's the story?

• 13:14Speaker 01

  Yeah. I, so I was like, you know, interested in this idea, playing around with it. A guy that lived in my freshman dorm named Blake Ross posted something, I believe on Facebook, but memory could be wrong there now, that there was this new thing and it was called the Summer Founders Program at the time and, you know, people should check it out. The deadline was like the next day or something. I had followed Paul Graham online, was like a big fanboy. So I immediately applied with my co-founders and they invited us out to interview. That was the first, like, meeting the Y, the four YC founders, Paul, Jessica, Trevor, and Robert. That was like the first time I felt like, all right, I have like finally found the kind of people I want to be around. I didn't really know

• 13:57Speaker 01

  about this startup thing, you know, you, I heard about it as like a kid during the dot com boom, but I wasn't really paying attention. I had met a few VCs on campus, but I could like tell I didn't like them. The world felt impenetrable. But I met the, I was like, wow, these are my people and everything they say resonates. And even the other founders I met they're interviewing. And it was this immediate click for me of like, this is awesome. This is what I want to do. These are the people I want to be around. This is so cool.

• 14:22Guy Raz?

  What were you doing at Loop? Like, were you actually writing the software?

• 14:29Speaker 01

  I wrote the first version of it. Then I got pretty busy with other things. I think a lot of people assume they're going to keep coding, but

• 14:37Guy Raz?

  Yeah.

• 14:37Speaker 01

  you know, hiring, managing the team. My job ended up being like a huge amount of business development. I spent a lot of the time on airplanes, flying around to the mobile carriers that were our partners. But I was not able to code for very long.

• 14:50Guy Raz?

  Yeah.

• 14:52Guy Raz?

  So you're basically, this is a 19 year old kid, right? It's a crash course.

• 14:56Speaker 01

  Yeah.

• 14:57Guy Raz?

  And at one point, you raised money at a valuation of $175 million. You raised almost $40 million. First of all, how did you learn how to become that person you had to become? To, you know, to be in charge of a company with $40 million in, in capital that was entrusted to you?

• 15:14Speaker 01

  I had really great advisors. Like, I think this is something that is still underappreciated about Y Combinator. People are capable of learning at a very fast rate. And one of the things that PG says that has always really resonated with me is you can get tough really fast. And that's sort of like one of the skills that is important for young founders. But if you jump into the deep end and you are like surrounded by supportive, thoughtful advisors.

• 15:46Speaker 01

  It was like interesting to me at the time, how quickly I could learn. And one of the most fun things about running YC later was helping other people learn very quickly.

• 15:55Guy Raz?

  And just to clarify, when you say PG, you mean, of course, Paul Graham, one of the founders of Y Combinator?

• 16:00Speaker 01

  Yes. And I think this is one of the things that YC still, just like, people on the outside don't see it. There's this assumption that like, oh, if you get into YC, you're gold plated. You have this like, you know, stamp of approval. That's why the companies do better, like all the VCs are into it. But YC does so much that no other program, as far as I know, has figured out about how to get people up the learning curve really quickly. That it's, it's a very big deal and it was super important to me personally. And I think to many of YC's biggest successes, it's been super important. And the difference that you can make there is huge.

• 16:36Guy Raz?

  Yeah. So, so this was your life, looped was your life?

• 16:40Speaker 01

  Absolutely.

• 16:40Guy Raz?

  With support from, from Y Combinator for several years, but ultimately it just didn't, it didn't take off. It was acquired for, I think for about $43 million.

• 16:51Speaker 01

  Yeah, it was acquired for like about what we raised.

• 16:53Guy Raz?

  So the later investors didn't make much money. You, you earned a little bit, actually more than a little bit. You know, I think about $5 million from that sale.

• 17:00Speaker 01

  I earned some, but you know, like this is another thing that is just sort of, I'm trying to think about. I made like orders of magnitude more money from angel investments that I made.

• 17:09Guy Raz?

  Right.

• 17:09Speaker 01

  That I spent no time on, the thing I poured my life into.

• 17:12Guy Raz?

  Right, right.

• 17:15Guy Raz?

  But after that happened, you were in 2014, you were named president of Y Combinator. Already by that point, Y Combinator had kind of become this sort of legendary incubator.

• 17:28Speaker 01

  Yeah.

• 17:29Guy Raz?

  Tell me a little bit about sort of taking on that role. Did it feel scary to be? Because again, you were still pretty young, leading this thing that had gained this kind of massive reputation.

• 17:40Speaker 01

  You know, it, this may be some sort of like weird flaw of mine. It didn't feel scary at all. It felt a little sad. Like, it took PG a while to convince me because I really wanted to do another startup. I wanted to like prove myself. I was 28 or something at the time. I didn't feel ready to like go retire and do a career of venture, which is really how I thought of it. I didn't like have a ton of respect for the career path. And so there was like some sense in which it was an admission of defeat about, you know, I just, I can't run a company, so I'm going to go do the easy job, or I'm going to go do the retirement job or something. And it took me a long time to get comfortable with

• 18:23Speaker 01

  the idea of doing it. But I can't overstate how much at the time and how much now still I love YC, feel incredible gratitude towards it personally, also think it's just this incredibly good force in the world. And as I, I thought about the things that I wanted to do, I had this list from when I was like a college student of what I wanted to work on. It was like a long list. I realized I couldn't start all of those companies. I also really was interested in pushing more investment in hard tech, deep tech, whatever you want to call it, which I didn't think there was much of happening at the time.

• 18:57Guy Raz?

  Right. Because at the time, YC was focused mainly on like software, right?

• 19:01Speaker 01

  Yes.

• 19:02Guy Raz?

  And you wanted, you wanted them to focus more on things like nuclear energy, self-driving cars, like, like actual physical things that you use.

• 19:11Speaker 01

  Not necessarily physical things, but I would just say like hard technology. Like, there's a thing about Silicon Valley, which is it's, it had not been that well geared towards the time and capital intensive science projects.

• 19:23Speaker 01

  Um, so I wanted to do that and I kind of was just like thinking about, you know, I had like basically taken a year off. It had been really fun. I had like learned a lot about everything. I had met a ton of smart people. I had read like many, many dozens of textbooks and there was like all of this stuff that I wanted to do. And I was like, you know what? In some sense, yes, being a VC is like an admission of failure, but in some other sense, what an incredible platform. You know, I could like do whatever I wanted here. And, and I love Y C so much.

• 19:54Guy Raz?

  All right. So you became president of Y Combinator and lots of Y C companies have been on how I built this in the past, as I think you know, and including Stripe and Airbnb and Reddit and Coinbase and lots of others. And I think maybe like a year in to that job, you co-founded this nonprofit called OpenAI, which is mainly what you're focused on now. Tell me, what was the thinking behind it? What was the idea behind OpenAI?

• 20:24Speaker 01

  So, so I studied AIs in undergrad, which was like dark times. Nothing was working. And then in 2012,

• 20:32Speaker 01

  deep neural networks started to work. And not only did they start to work, it appeared that the more compute you threw at them, the better they got.

• 20:40Guy Raz?

  Are you talking about like Watson and these, these kinds of neural networks?

• 20:44Speaker 01

  The one that really kicked it off, I think for the field, certainly for me personally, was the ImageNet result.

• 20:49Guy Raz?

  This is the identifying images based on descriptions?

• 20:53Speaker 01

  Yes.

• 20:54Guy Raz?

  So you would type in the computer horse eating an apple or whatever, and it could find them very quickly.

• 21:00Speaker 01

  It was more like you'd showed an image and it would correctly say, this is a horse eating an apple.

• 21:04Guy Raz?

  That's right.

• 21:05Speaker 01

  Or at the time, it would just be more like, this is a horse.

• 21:07Guy Raz?

  And that was 2012?

• 21:08Speaker 01

  Yeah, it, it came out in my kind of like year off when I was just learning about things and I looked at it with like great interest. You know, but kind of like it was one of many things that I spent time on that year, but I kept watching it and kept paying attention. And certainly, like of all of the hard tech dreams, AGI is the top of the list.

• 21:28Guy Raz?

  And just to clarify, Sam, when you talk about AGI, artificial general intelligence, you're distinguishing that from very narrow, specific types of artificial intelligence?

• 21:39Speaker 01

  Correct. So, you know, we already have made good progress with narrow AI that can categorize images or some one task like that.

• 21:47Guy Raz?

  Yeah.

• 21:48Speaker 01

  But for me, AGI is a system that can learn and self-improve and create and reason about new information like a human does. This amazing general ability. And I think if we're able to accomplish that, it will be perhaps the most important invention in human history. It will be the culmination of this phenomenal amount of effort all the way down the stack. And like the collective knowledge, accomplishment, moral progress, creativity of humanity into this one system that I think will be an even bigger revolution than the computer revolution we talked about earlier.

• 22:25Guy Raz?

  We're gonna take a quick break, but we'll be back with more from Sam Altman, co-founder and CEO of OpenAI. Stay with us. You're listening to How I Built This Lab.

• 22:39Guy Raz?

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• 26:16Guy Raz?

  Welcome back to How I Built This Lab. I'm Guy Raz, and my guest is Sam Altman, founder of the startup Loop, former president of Y Combinator, and now co-founder and CEO of the artificial intelligence company OpenAI. All right, so Sam, in 2015, you and some other pretty well-known people, including Elon Musk and Reid Hoffman and others, formed this thing called OpenAI, which is a nonprofit designed to do research around artificial intelligence. But beyond that, what was the purpose of OpenAI? What problem was it designed to address?

• 26:53Speaker 01

  It was to figure out how to, and still is, to figure out how to build, deploy, and share the benefits of safe, general artificial intelligence. And it didn't seem like there was enough effort being put into this, given the stakes. And so the purpose was looking at this thing saying, this is as important as anything we can imagine. And the upside is sort of unimaginable. The downside is catastrophic. And we look around the world, and we don't see people taking this seriously. We don't see people caring. We don't see really any good safety research. And so the thought was really like, can we help nudge us in a good direction?

• 27:37Speaker 01

  Now, AGI, or at least powerful AI, is like the hottest thing in the world. Everyone's interested in it. But what I can't overstate enough is at the time, we really got started in January of 2016. So it's like, you know, six and a half years ago. At the time, people thought we were crazy. Like, good researchers say you've totally discredited yourself by talking about AGI. Most other people were like, this is 100 years away or more. There's no idea about how to do this. And we just kind of like got cracking on it and had a couple of huge discoveries, some good engineering, and now here we are.

• 28:13Guy Raz?

  Why did you found it as a nonprofit? I mean, initially, I know that you have a for-profit subsidiary now, but why did you originally make it a nonprofit?

• 28:22Speaker 01

  I think this technology really does deserve to belong to the world as a whole. It's going to have such a profound impact on all of us that I think we deserve, like, we globally, all the people, all of humanity, deserve a say over how it's used, what happens, what the rules are. We deserve to share in the benefits. We deserve to all have access to it. And that was why. I'm very pro-capitalism, obviously. I think capitalism is great. But I think AGI is sort of an exception to that. In a well-run society, I think even if we rewind the US 50-plus years, this would absolutely happen by the government.

• 29:00Guy Raz?

  Right, because DARPA would have done it, as they did with the Internet and so much of the other technology we use every day.

• 29:06Speaker 01

  Yeah.

• 29:07Guy Raz?

  I think most people, most of us, have different views of what artificial intelligence means, right? And I guess ultimately, it's whatever our imaginations can conjure up. But for some people, it could be a version of like, you know, humanoid robots from Westworld or machines that can detect disease in humans based on hundreds of thousands of data points. It can mean a machine that can beat the best gamers or chess masters. I mean, all of these things. But of course, that's how we see it now. When you talk about artificial general intelligence, what do you imagine in your mind? What do you think it actually could do?

• 29:51Speaker 01

  The first thing that I think it'll be and where people will use something like, wow, that is real intelligence of some sort. What I imagine in my head is a gigantic skyscraper full of computers. And we all text it like we would text a coworker or a friend or whatever. And increasingly powerfully, for whatever you want, it can answer it. So you can say like, I need life advice, or I need like, great medical advice. And it'll help you with that. Or, or if you're just trying to like, search for something to find information, it can, it can help you with that. It can look at your email and say, you know, here are all the important emails that came in overnight. I've drafted a response to all of them. If these look good, hit send and you'll be happy with all of them.

• 30:36Speaker 01

  You'll want to hit send. Already today, you can say, I'd like this art, please. Can you generate an image that looks like this for me? Can you write this code for me? Eventually, now this will require more compute, but if we, society, allocate the compute to that, you can say like, could you go off and find a cure for cancer? And it can spend a lot of its compute cycles doing amazing scientific progress like that. But, but I imagine that we get to a world where each of us has sort of like a, what feels like an AGI companion that we are talking to all day, that is helping us be the best version of ourselves, learn, be efficient, be happier. And we'll all experience that way.

• 31:17Speaker 01

  And then globally, we will say, okay, this is like very expensive, but we're going to dedicate it to a few huge projects. You know, maybe we'll have it help us figure out how to identify like better diplomacy between countries. Or the cancer example I mentioned, or figure out how to do carbon capture. But when you have this like very superhuman intelligence, I think the harder challenge is not talking about limits of what it can do. Cause that's sort of like beyond our imagination, but how to conceptualize it. And how to like, think about what it'll look like at the beginning.

• 31:52Guy Raz?

  I know that you went out originally and attracted a lot of sort of donors, right? A lot of capital to begin research. And one of the first really public things you essentially worked on was to train. An AI to play the game, the video game Dota. And it's a really complex game, right? And not to bury the lead here, but you're bot one. I mean, beat the reigning world championship team.

• 32:18Speaker 01

  Yeah.

• 32:19Guy Raz?

  Why was that? Why is it the sort of the ideal place to start in AI development?

• 32:24Speaker 01

  You need a really good environment. At the time, we kind of had a lot of people on the founding team and early employees that were good at reinforcement learning.

• 32:35Guy Raz?

  And reinforcement learning, we should explain. This is when you feed a lot of information to the AI and then.

• 32:40Speaker 01

  The AI is basically taking random actions.

• 32:43Guy Raz?

  Yeah.

• 32:43Speaker 01

  And then you're giving it a signal for, okay, that worked or that didn't.

• 32:48Guy Raz?

  Yeah.

• 32:49Speaker 01

  But the thing we learned with Dota is you can do it over a very long time horizon. So there's like, you know, hundreds of action steps a minute. There's thousands of things you could choose for each action step. And games can last, you know, the better part of an hour. No one thought that was within the realm of RL. That's just sort of too long to go without a reward signal. And we did experiment with some intermediate ones. But what it turned out is by playing gigantic amounts of time, hundreds of thousands of hours of the game per day and having the agent sort of play against itself. So it has to get better and better to keep winning. Just by trying random actions and reinforcing the ones that have a positive impact,

• 33:31Speaker 01

  the AI system can learn to beat the best humans at this game, which was not obvious to us, quite remarkable, given the size of the action space.

• 33:39Guy Raz?

  How is it different than like Deep Blue beating Garry Kasparov in chess?

• 33:46Speaker 01

  There's 16 pieces in chess. The search space is just not nearly as wide at any given mood, even if you do have to think pretty deeply about each one.

• 33:56Speaker 01

  And so chess, people were able to beat even without neural networks. That just turned out to not be as hard as we thought. But, but again, this is like the complexity and the action space of this game is just beyond what I myself did not think we were likely to succeed at the project all the way when we started.

• 34:17Guy Raz?

  Sam, given that you guys are taking a big swing here, right? You are looking to become the preeminent groundbreaking AI research organization in the world. I'm curious how you go about like the sort of the series of steps you take in deciding what next to focus on. I know there are multiple things you're focusing on, but I'm talking about the things that we hear about.

• 34:40Speaker 01

  Yeah.

• 34:40Guy Raz?

  So for example, the, you know, Dactyl was something that came out next. And this was basically, and you could, people listening can see it. It's a, it's a video of it. It's a robotic hand or arm and it solves a Rubik's Cube with one hand. I mean, it's cool. It's very cool, but I'm wondering why that was the next big challenge you wanted to solve. How does that move the, the needle just slightly forward on AI technology?

• 35:07Speaker 01

  Well, it turned out to be a mistake.

• 35:09Guy Raz?

  Okay.

• 35:10Speaker 01

  I mentioned that Dota was a great environment. It turns out that robotics are a very hard environment. Robotics is hard and it's not hard because the machine learning is hard. It's hard because the simulator is bad, the robot breaks. You can't run it that fast like you, you know, you don't have like hundreds of thousands of robots. Like you have hundreds of thousands of CPUs to run a game on. And so we thought it would be interesting because robotic control seemed very interesting to us at the time and still does. But what happened is it was too hard of an environment and we ended up pausing our robotics work. Someday we'll come back to it when the robots and the simulators get better.

• 35:52Speaker 01

  It's clearly, you know, it would be great to have, but it turned out to be hard in the wrong kind of ways. The way we pick projects to work on is like not as exciting as everybody would hope. You know, I think there's this belief that you have a bunch of people like sitting in a room, picking this like brilliant secret strategy. We just sort of like. Run our own reinforcement learning algorithm. We do more of what works. We follow the technology. We realize that it's like very hard to sit in a vacuum and predict where everything is going to go. Most people who have tried that have been catastrophically wrong. And it's very smart people trying it. The technology just kind of like surprises all of us.

• 36:34Speaker 01

  And so we pay close attention to what's working. We put more effort into what's working. We then put effort into figure out how we can deploy it safely and beneficially. And then we go do the next thing on top of that. But it's very much like driving on a country road at night with headlights on. You can always like see before you have to make a turn. But you'd have no idea what the next few turns look like.

• 36:57Guy Raz?

  Right. And that's, that's a question, right? Because if you are building a product, right? If you're building an autonomous vehicle, you know what you're building towards. But you're really not building towards anything in particular. You're building towards something that we humans can't even imagine, right? And that's the thing, like it's, I guess it's a matter of just figuring out how to develop technology that can think like a human beyond how a human thinks can process information in highly complex ways.

• 37:33Speaker 01

  Yeah. You, you, you bring up a great point. The other company I'm involved with is this nuclear fusion company called Helion. And there are a lot of similarities between the two companies in terms of like a very hard scientific and engineering problem. But the biggest difference is like what to do, what success looks like, what's going to happen on the other side of this? What problem to go solve next? You can look at it backwards or forwards. It's very clear. It's all hard, but it's very clear. There's no uncertainty about what to go after, what's going to happen, what the company needs to do. And another nice thing is like, it's basically all upside. There's, there's none of the harder issues to contend with. At OpenAI, we, we just don't have any of that certainty.

• 38:16Speaker 01

  And one of the things that has been like a little bit surprising to me is how difficult it is to get advice about how to run a project or a company in the face of such uncertainty. It, it hasn't happened a lot of times. And I am like wistfully envious of the, the Helion world because it's, you know, it's still super important and super great, but it's so much clearer. And we just have to like turn over one card as we go.

• 38:43Guy Raz?

  Yeah. Sam, one of the, one of the things that you did in 2019 controversially was to form a for-profit wing of OpenAI. And originally, it was kind of this heralded nonprofit, you know, hey, we're going to make this technology available to everybody. We're not going to compete, we're going to collaborate, all of our research will be available. We want to be a responsible, steward of this technology and to encourage responsible use. 2019, you form a for-profit version. Plenty of people, including some people under, you know, involved in your own board, who weren't, who were like, wait a minute, what, you know, why?

• 39:26Speaker 01

  A hundred percent. Totally get why people don't like that kind of change. What happened is really quite simple, which is when we started OpenAI, we did not expect massive scale to be as important as it was turned out to be. And by 2019, we realized that, and that the amount of money we were going to need to succeed at the mission was beyond what we could raise as a nonprofit.

• 39:51Guy Raz?

  Yeah.

• 39:51Speaker 01

  You know, way more than 90% of that for compute, but also buying expensive data sets, compensating people in competition with Google who can, you know, pay very huge salaries.

• 40:02Guy Raz?

  Yeah.

• 40:02Speaker 01

  And we realized that like, either we had some way to like dip into the power of capitalism, and the ability to get the resources we needed, or we were just going to be irrelevant. We did, you mentioned the government earlier, I had forgotten about this until right now. We did also just see if the government wanted to fund us. They definitely did not.

• 40:23Guy Raz?

  Yeah.

• 40:24Speaker 01

  And there was no other source of capital that we could figure out. And so, we wanted to preserve as much as we could of the specialness of the nonprofit approach, the benefit sharing, the governance, what I consider maybe to be most important of all, which is the safety features and incentives. So like, for example, we have this one thing called a profit cap, where our employees and investors can only make a certain fixed amount of money with their equity. And then beyond that, all other profit is distributed as fairly as possible with the world. And I think, you know, at the time, that was like another thing that seemed crazy to people. But I think that's going to be really important because I think, and you already see some people thinking this way.

• 41:05Speaker 01

  It's going to look at some point like, wow, you can generate close to infinite wealth with AGI. And people who have equity, even if they start out not wanting that are like, hmm. You know, maybe that sounds better than I originally thought. We also have something in our documents, which says if we need to for safety, the board can just totally wipe out everyone's equity value to nothing. And we have something called the merge and assist clause, which says similar to that. If another effort's ahead and we want to avoid a race condition, we can just shut down and merge with some other effort. Like, these are things a normal company wouldn't be able to agree to, while still being able to access the capital we needed, which it was clear by that point would just be far, far greater than we ever thought.

• 41:46Guy Raz?

  Yeah. You did bring in a lot of money from Microsoft. And as a result, they get an exclusive license to use some of your technology in their software, also criticized by some, but you know, I mean, again, you got to bring in the money. So was that, in your view, a compromise you had to make? And if it was, how does it impact the openness of OpenAI? I mean, if Microsoft gets exclusive use of some of the technology you're developing, does that in some ways undermine the original vision?

• 42:19Speaker 01

  You will see us open more technology over time. We just like to be pretty sure on the safety front.

• 42:27Speaker 01

  First of all, the most powerful model in the world, as far as we know, of an available language model, is still the one we created two and a half years ago that is available in our API.

• 42:37Guy Raz?

  And that, just tell me what that is again?

• 42:39Speaker 01

  Oh, it's called GPT-3. It's like a powerful language model that people develop on top of.

• 42:44Guy Raz?

  Right, and that's available, that's open source, anybody can use that.

• 42:48Speaker 01

  It's not open source, but anyone can use it.

• 42:50Guy Raz?

  I got you, okay.

• 42:51Speaker 01

  We could open source it someday in the future. It's certainly something we'll consider. But there is a thing about the API access which we like, which is when we know less than we'd like, when we're in the fog of war, if we just publish the weights of a model on the internet and then we realize like, hmm, there's actually a safety issue here, we can't take that back. It's done. It's like a one-way door. We also cannot put any usage restrictions on it after we open source it.

• 43:19Guy Raz?

  Right.

• 43:19Speaker 01

  When we deploy it via the API, we can take it back if we find out there's a real safety issue. But more importantly than that, we can enforce usage restrictions. We can make changes if unsafe things are happening.

• 43:31Guy Raz?

  So let's kind of break down GPT-3 for people who don't know this. Because obviously, as you know how I built this, is a generalist show. We talk about chocolate chip cookies and artificial intelligence on the same show. And from what I understand, I mean, GPT-3 can do a lot of things, but there's a famous example from 2020 in the Guardian newspaper. They published an op-ed. They essentially asked GPT-3 to write an op-ed called "Why We Shouldn't Be Afraid of AI" and published it. And it's pretty interesting. I wasn't convinced by the argument the AI made, but that was pretty cool. So this is one, because when you talk about safety, right?

• 44:13Guy Raz?

  You're talking about people misusing this technology to, for example, make, you know, have AI create fake information or

• 44:22Speaker 01

  Yeah.

• 44:22Guy Raz?

  manipulative content.

• 44:24Speaker 01

  You can imagine a lot of things, but the disinformation manipulative content, I think, is high on the list. GPT-3, at this point, we're more comfortable is relatively safe, although I think there are risks related to misinformation in some cases. But we'd rather be too conservative than not. Like, at some point, there will come a time, one, two, three orders, magnitude, who knows what, how much more powerful than GPT-3, where I think a language model can really have a huge disinformation effect on the world. And again, kind of the traditional stance of the tech industry is just like, yeah, you know, launch the product and think about it later and deal with the problem later. But like, we don't want to push a button through a one-way door.

• 45:08Speaker 01

  Or at least if we do that, we want to be incredibly confident and careful about it.

• 45:13Guy Raz?

  Yeah, we're talking about five, 10 years away, I think, more or less. Is that fair to say?

• 45:20Speaker 01

  That, yeah, I would say, you know, sometimes these things take longer than you think. But let's say like in 10 years, I think there will be powerful digital intelligence in the world.

• 45:28Guy Raz?

  We're already seeing this with music and art. I think a piece of art just won an award. And it was very controversial, but we're already seeing relatively high quality content produced.

• 45:41Speaker 01

  Yeah.

• 45:42Guy Raz?

  I should say, we did put in, we asked GPT-3 to write us an intro, an overview of Sam Altman's early life for how I built this.

• 45:51Speaker 01

  How was it?

• 45:51Guy Raz?

  It gave us two versions. The first one says, Sam Altman is the president of Y Combinator and co-chairman of OpenAI. He was born in 1985 in Cambridge, Massachusetts. He attended high school at the Phillips Academy Andover, where he was a National Merit Scholar. The second one said Altman was born in 1985 in Berkeley, California, raised in the town of Los Altos, attended Los Altos High School, where he was president of the Science Olympia Team. Both these are wrong. He grew up in St. Louis and did not go to either of those high schools. But, but it does have other information in there about OpenAI. So, I mean, you know, these are coherent sentences. Like you can, you can imagine that a human could have written them, just got the facts wrong.

• 46:30Speaker 01

  Yeah, that's a big problem with the current version of language models is we haven't trained them yet to try to like, be helpful and to verify that what they're saying is accurate. They're just trying to sort of sound like coherent text.

• 46:44Guy Raz?

  Yeah.

• 46:44Speaker 01

  We have a lot of ideas about how to fix that. And my guess is in the next couple of years, we'll make significant progress there. But that, I think that is the current biggest single problem with language models is, as someone famously said to me once, they're very convincing bullshitters.

• 47:00Guy Raz?

  Yeah.

• 47:01Speaker 01

  And it makes it like very hard to use for a case like this. Now, as you mentioned, an easier problem that has worked very well, we have a system called DALL-E that will take text and generate images.

• 47:12Guy Raz?

  Really cool. You could put in like a giraffe dancing ballet on top of a hypersonic jet and it will generate that image.

• 47:20Speaker 01

  Yeah, and people love it. Like, people who'd never cared about AI before that I went to high school with or whatever are like now totally obsessed. And, you know, I've talked about it and it's very fun. But it's like, we talked about this idea of the technology gradient and kind of doing what works. Like, we still have more work to do on on language models, and that's hard. But in a world where we don't need perfect accuracy and we want creativity, man, the image generation thing is so cool.

• 47:46Guy Raz?

  Yeah. We're gonna take another quick break, but when we come back, more from Sam Altman, co-founder and CEO of OpenAI. You're listening to How I Built This Lab.

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• 49:36Guy Raz?

  Welcome back to How I Built This Lab. I'm Guy Raz and I'm talking with Sam Altman, former president of Y Combinator and co-founder of OpenAI. Sam, I wonder, I mean, we're talking about all these cool things, right? And we've talked about a lot of cool things. Now, here's the thing. I remember back in, it must have been 2010, I interviewed Mark Zuckerberg at a public forum at the Computer History Museum. And the optimism about the future of social media was just so unbelievably clear in his mind, that it was only for the benefits of the world, that it was going to be this amazing thing that will bring people together, that

• 50:18Guy Raz?

  we're all going to be part of this single global community. There's only going to be one version of who you are, the same person at home as you are online, and that it's going to, you know, open up all these channels of communication and cooperation. And of course, that vision was not realized. I know you're a really smart guy and you think about these things. And so you know that all of these wonderful things, it can answer questions for us, it can solve cancer, maybe, it could give us a diagnosis, it could identify the best doctor. That will happen. And then there's going to be other things, too. So let's talk about some of the other things and what keeps you up at night about those things.

• 50:58Speaker 01

  You know, I've had three recurring bad dreams over the course of my life. When I was in high school and college, I had this one about like, missing a test. It sort of evolved when I started my startup to like, thinking in the dream that I was like, still in school and I was missing stuff because I was running the startup. After the startup, I had this like, recurring bad dream for a while, just about like, failing, which was just incredibly shameful and very, very tough. You know, startups are great when they're working, but when you're failing, it's like, quite brutal. And the current one is about like, what if we're wrong about all of this? I think there will be like, good and bad with any new technology. And I can accept that as long as the good is order of magnitudes

• 51:39Speaker 01

  more than the bad. And that is truly what I believe will be the case. I think the world is going to be incredibly different, but there's no stopping that now or ever. And I think we will be able to responsibly deploy current technologies. And I think we, you know, one of the advantages of our nonprofit structure is we do a lot of things differently than a normal company would. But for me, the real question is, can we solve the existential issues? And I am more optimistic about that than I've ever been before. I think a lot of the, the work we've done around alignment and, and safety is like promising. And we have, and we have like a plan that I, I believe in. But you know, technology is, is impossible to predict.

• 52:25Speaker 01

  We can make high confidence guesses, but a, as you said, a lot of smart, well intentioned people thought that social media was going to be like an unequivocal good for the world.

• 52:36Guy Raz?

  Yeah.

• 52:37Speaker 01

  And it is good in some ways, but it's, it's come with some, some real negatives. We try to learn every lesson we can about what's gone, gone wrong there. And I am obsessed with this idea of incentives being a superpower. And if you can get the incentives right in an organization, then I think you avoid a lot of the sort of things that cause well meaning people to do things that maybe later they'll wish they hadn't.

• 53:00Guy Raz?

  Yeah.

• 53:01Speaker 01

  But my fear is that there's just an unknown unknown that we totally missed. And technology or social pressure or, or something else is going to push us in a different way. Again, I think we're getting a lot of things right, right now. You know, we deploy our technology slowly and cautiously, like we're, we're willing to piss users off to go slowly. But as the systems get much more powerful, the, the challenges become more and more unprecedented.

• 53:29Guy Raz?

  Yeah. There are a lot of intelligent people on planet earth and, and some of those intelligent people are bad actors. I mean, you look at how bad actors have deployed social media or digital technology to steal money from people's bank accounts or crypto accounts or you know, just, these are not dummies. These are intelligent criminal networks, right? That have figured out how to use a variety of tools that are now available to them. To do real harm. And that's going to happen with this technology, right? There will be really intelligent people who figure out how to get an AI to create a new virus and deploy it very effectively. Like, that could happen. Or how to, you know, build your own very powerful explosive.

• 54:14Guy Raz?

  Now, that being said, do we just accept that that is part and parcel of being human? That there are humans with bad intentions and there are humans with good intentions? And that it doesn't matter what we do with technology? Because some people will use it for good and some people will use it for evil? And you just cannot prevent that?

• 54:35Speaker 01

  I mean, what you said is true. Some people will use technology for good and some people will use it for bad. But it doesn't mean we don't do anything. It means we work as hard as we can to have the technology be as useful for good as possible and as difficult to use for evil as possible. And it is, in some sense, like somewhat one dimensional, like more powerful tools let bad actors do worse stuff. But I think it's like very defeatist to just say, and so there's nothing that can be done. Like, our strategy, and it's, you touched on, been controversial, has been to deploy the technology in a way that maximizes the good and minimizes the bad.

• 55:20Speaker 01

  And, you know, there will be lots of technology where it's turned out we're too conservative and actually we can just make it more open to the world. But I'm confident there will come a day where the world will say like, it's good that OpenAI is on the conservative side with new stuff.

• 55:35Guy Raz?

  Most of us interact with AI already multiple times on a daily basis. Like, when I type an email on Gmail, it makes suggestions, which are often very helpful.

• 55:44Speaker 01

  Yeah.

• 55:45Guy Raz?

  And so there is some of that already that we use. You know, Siri is a version of this. But let's say in five years from now, based on what you are seeing and what you guys are working on, right? How do you think the average kind of person might use the technology in five years from now?

• 56:05Speaker 01

  I think it is just gonna kind of be everywhere. You know, when the app store on the iPhone launched, it would be a big deal for companies to talk about being mobile companies or their mobile strategy at that point. And at this point now, it's just like every company is mobile. And you wouldn't think about it. It would just be like ridiculous not to have a mobile app for most companies. But no one goes around talking about being a mobile company. And I think the same thing will happen for AI. There will be a layer of intelligence provided by us and others. It's just everywhere. And as the systems continually get smarter, everything you use will get smarter and smarter as well. So there will be like, you know, there'll be the direct usage where we talked earlier about this idea that you'll you'll chat back and forth with this like super intelligent assistant

• 56:50Speaker 01

  that'll help you with everything. But also in everything else you use, you'll just have a very high expectation that it's really smart. So you mentioned that you kind of like the Google suggestions in your email. Those will look quite trivial and you will expect every morning to wake up and for Google to have detailed thoughtful replies that you yourself would be proud to come up with to every email in your inbox. Which, by the way, I'm thrilled for because if I never spent another minute in my inbox again, I'd be really delighted.

• 57:20Guy Raz?

  Yeah.

• 57:22Guy Raz?

  You know, it's interesting because I think for a long time, there was this this feeling that AI and automation would replace like menial work or, you know, factory work, which is still can. I mean, you know, you could make the argument that there's no need for a barista because a machine could do everything that that somebody making a cup of coffee, not as beautifully.

• 57:44Speaker 01

  Doesn't say hi to you, doesn't smile to you, doesn't give you that minute of human connection.

• 57:48Guy Raz?

  Or or isn't nasty to you either, right? Depending on which coffee shop you go to. But but now it seems like something different is is happening, which is AI looks like it could replace.

• 58:02Guy Raz?

  More and more creative types of jobs, jobs that are quote-unquote white collar jobs, maybe even before it replaces so-called blue collar jobs.

• 58:12Speaker 01

  Absolutely. You know, the the strong consensus five, 10 years ago was that first of all, the AI was going to come for the blue collar jobs. Second of all, would be the less sophisticated white collar jobs. Third of all, would be the very high cognitive load, white collar jobs, like a computer programmer, a mathematician, or whatever. And then last of all, and maybe never, because maybe this was like special and human only, would be the creative jobs. And if we look at it now, it appears relatively clear that it's going to go in exactly the opposite direction. And there's a lot of things one can take away from that. But one of the most important ones is predictions about technology

• 58:52Speaker 01

  roadmaps are hard, and a lot of very confident experts get them wrong. So my my observation is just that, like, this was hard to predict. It seems fairly clear now. With the benefit of hindsight, it also seems fairly obvious now. But everybody was confident and wrong in the other direction. Robotics are really hard, as we talked about earlier.

• 59:17Guy Raz?

  Yeah. So, I mean, what does that mean in practical terms? Right now, we're talking here in 2022. There is a labor shortage in the United States, right? There's just are not enough humans to fill all the jobs available. And there's a big crisis in many companies across the board, not just in the service sector, the blue collar jobs, but also in technology companies. So so on the one hand, you can you can imagine, oh, this is great, because, you know, maybe some of these jobs will be taken over by, you know, artificial intelligence, you know, devices or or software, whatever it might be. But this labor situation won't be around forever. There will be higher unemployment rates.

• 59:57Guy Raz?

  And and so what happens? I mean, do you do you I mean, if this technology is is going to become so good, which it will be, what happens to the to the humans?

• 1:00:08Speaker 01

  You know, there's a huge amount of pearl clutching about what what's going to happen when AI replaces all of the jobs and what we're going to do. And I have a few thoughts about that. One, I actually think we're in a humongous labor shortage, like much more than it seems. There are many people who love their jobs and derive great meaning from it, you know, but I think those people don't recognize it's a privilege that most people don't have, and most people would love to work less or not, or do something completely different, but we have sort of this like pay to play world right now where you have to pay to live. And I I get why some people think that's important, sort of in theory. But I personally really don't think that's the society we should strive for.

• 1:00:51Speaker 01

  I think like traditional work in the way we think of it should be optional. And you should be able to do less of it. I really love work. It's my hobby. It's my passion. I I think it's great. But I know a lot of people don't, and a lot of people would choose to spend their time in a very different way if they didn't have to. I also think a lot of people who like work would clearly like to work less. We're we're seeing this in this post pandemic world where you know, a lot of people are like, oh, I go into the office two or three days. I'd really rather only work two or three days too. I have a lot of other things I like. No, I really don't want to go back. And so I think the labor shortage is actually like much bigger than it seems. I also think like a lot of things that we do were understaffed even before the pandemic.

• 1:01:32Speaker 01

  But certainly like going through an airport now or going to get medical care now, you like really feel, you know, this is not what full employment would look like, where I just like walk in, everything's ready to go all of the time. Or where like every student has like a one to one teacher to student. That could be amazing too. Who knows what? So huge labor shortage now. Also, we've seen this with every other technological revolution too. I don't know what the jobs of the future will look like, but I am confident that human creativity, desire for status, desire to like do new things and and to like accomplish, that's not going to go anywhere. The economy and society will look super different, that's for sure. But we have always worried about this. We have always found something new to do.

• 1:02:16Speaker 01

  I do accept that this time it may be different, even though you're never supposed to say that, like, maybe if we figure out intelligence, that's just very unlike anything that's happened before. But I still would never bet against our desire and ability to find very new things to be busy with.

• 1:02:32Guy Raz?

  Sam, if that is the case, right, and we're kind of getting out of our lane here, which is fine, we should be, but our species, right, Homo sapiens, we've been around for 330,000 years, more or less, and really only, you know, kind of in the last 30,000 years have we started to widely spread around the world. And previous, you know, our relatives, the other hominid species lived a long time.

• 1:02:58Speaker 01

  Yeah.

• 1:02:59Guy Raz?

  1.2 million years, 1.3 million years, and then another species replaced it. We replaced the other ones to become the kind of the dominant hominid species on the planet. We're talking about developing something that is more advanced than humans. Are we talking about, you know, a form of evolution? Are we talking about the next species that replaces Homo sapiens?

• 1:03:29Speaker 01

  I get the analogy, but I think it's basically incomparable. I hope that human Homo sapiens are the end of the line for evolution in a survival of the fittest sense, and that from here on, even as things change quite rapidly, we are much more thoughtful and deliberative. And unlike the previous process, this time around, we can reflect, we can debate, we can build the system in a certain way. This is like very different than a random natural process. It's very important to me personally, and I assume to you and hope a lot of other, almost everyone too, that humanity is in charge of the future

• 1:04:14Speaker 01

  of humanity. And the systems that we build, how we choose to deploy them, what we all collectively want out of the future, that is a discussion that I think we need to start now and have a lot of societal coordination around. But I think the analogy, although it sounds very similar, is like different in almost all ways that matter. This is not going to be just this like chaotic, stochastic process playing out, but we get to make All of the decisions about how this is going to work.

• 1:04:46Guy Raz?

  Yeah.

• 1:04:47Speaker 01

  While we're way out of our lane and talking about the very long-term future, I think if we end up in the us versus them framework, that's like some varying degree of bad. And the most positive long-term futures that I can imagine involve some degree of a merge. It doesn't have to be the crazy sci-fi, like, you know, plug something into our brains or upload ourselves or whatever. But at a minimum, I think it needs to involve something like, you know, this like AI alter ego that we're talking to all day that observes our whole life, that understands us. Its goal is to like extend our capability and will. I think those are the easiest worlds to see it being very positive.

• 1:05:27Guy Raz?

  Yeah.

• 1:05:29Guy Raz?

  Sam, you're not yet 40 years old. What is my guarantee here that you don't become like Robert Oppenheimer one day and say, this thing that I was part of was terrible. I regret it.

• 1:05:42Speaker 01

  I have that book on my desk. I look at it every day.

• 1:05:47Speaker 01

  I mean, I can't give you a guarantee on that, right? But I can tell you, I will work as hard as I possibly can and get the best people around us to make sure we don't end up there. This is going to change almost everything. This is going to just be a seismic shift. And the way we get to the best version of the world is to have as broad input on where we want to go, what we want to happen as possible. And the way to get there is for a lot of people to take this seriously, understand it, direct some of their like precious and limited attention towards it. And we really want to drive that conversation forward.

• 1:06:22Guy Raz?

  That's Sam Altman, co-founder and CEO of OpenAI. Sam, thanks so much.

• 1:06:27Speaker 01

  Thank you. I enjoyed that.

• 1:06:32Guy Raz?

  Hey, thanks so much for listening to How I Built This Lab. Please do follow us on your podcast app so you always have the latest episode downloaded. If you want to follow us on Twitter, our account is at How I Built This and mine is at Guy Raz. And on Instagram, I'm at Guy dot Raz. If you want to contact the team, our email address is h i b t at i d dot wondery dot com. This episode was produced by Chris Messini with editing by John Isabella. Our audio engineer was Neil Rouch. Our music was composed by Rameet Arubhui. Our production team at How I Built This includes Alex Chung, Carla Estevez, Casey Herman, J.C. Howard, Liz Metzger, Sam Paulson, Carrie Thompson, Catherine Seifer, Josh Lash, and Elaine Coats.

• 1:07:17Guy Raz?

  Neva Grant is our supervising editor. Beth Donovan is our executive producer. I'm Guy Raz, and you've been listening to How I Built This.