#141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology

Erik Brynjolfsson is an economist at Stanford. Please support this podcast by checking out our sponsors: – Vincero: https://vincerowatches.com/lex to get up to 25% off + free shipping – Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off – ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free – Cash App: https://cash.app/ and use code LexPodcast to get $10 EPISODE LINKS: Erik’s Twitter: https://twitter.com/erikbryn Erik’s Website: https://www.brynjolfsson.com/ The Second Machine Age (book): https://amzn.to/33f1Pk2 Machine, Platform, Crowd (book): https://amzn.to/3miJZ76 PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/LexFridmanPage – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (08:23) – Exponential growth (12:51) – Elon Musk exponential thinking (15:08) – Moore’s law is a series of revolutions (20:31) – GPT-3 (22:09) – Autonomous vehicles (29:11) – Electricity (33:40) – Productivity (38:47) – Why is Twitter and Facebook free? (49:03) – Dismantling the nature of truth (52:24) – Nutpicking and Cancel Culture (58:39) – How will AI change our world (1:04:40) – Existential threats (1:06:33) – AI and the nature of work (1:12:39) – Thoughts on Andrew Yang and UBI (1:18:30) – Economics of innovation (1:24:37) – Effect of COVID on the economy (1:33:50) – MIT and Stanford (1:38:23) – Book recommendations (1:41:28) – Meaning of life

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Eric Brinjalson. He's an economics professor at Stanford and the director of Stanford's Digital Economy Lab. Previously, he was a long, long time professor at MIT, where he did groundbreaking work on the economics of information. He's the author of many books, including The Second Machine Age and Machine Platform Crowd, co-authored with Andrew McAfee. Quick mention of each sponsor, followed by some thoughts related to the episode. Ventura Watches, the maker of classy, well-performing watches. Four Sigmatic, the maker of delicious mushroom coffee. ExpressVPN, the VPN I've used for many years to protect my privacy on the internet.

• 0:45Lex Fridman?

  And Cash App, the app I use to send money to friends. Please check out these sponsors in the description to get a discount and to support this podcast. As a side note, let me say that the impact of artificial intelligence and automation on our economy and our world is something worth thinking deeply about. Like with many topics that are linked to predicting the future evolution of technology, it is often too easy to fall into one of two camps. The fear mongering camp or the technological utopianism camp. As always, the future will land us somewhere in between. I prefer to wear two hats in these discussions and alternate between them often.

• 1:26Lex Fridman?

  The hat of a pragmatic engineer and the hat of a futurist. This is probably a good time to mention Andrew Yang, the presidential candidate who has been one of the high-profile thinkers on this topic. And I'm sure I will speak with him on this podcast eventually. A conversation with Andrew has been on the table many times. Our schedules just haven't aligned, especially because I have a strongly held to preference for long form, two, three, four hours or more, and in person. I work hard to not compromise on this. Trust me, it's not easy, even more so in the times of COVID, which requires getting tested nonstop, staying isolated,

• 2:11Lex Fridman?

  and doing a lot of costly and uncomfortable things that minimize risk for the guest. The reason I do this is because to me, something is lost in remote conversation. That's something, that magic, I think is worth the effort, even if it ultimately leads to a failed conversation. This is how I approach life, treasuring the possibility of a rare moment of magic. I'm willing to go to the ends of the world for just such a moment. If you enjoy this thing, subscribe on YouTube, review it with five stars on Apple Podcasts, follow on Spotify, support on Patreon, connect with me on Twitter at Lex Fridman. As usual, I'll do a few minutes of ads now and no ads in the middle.

• 2:54Lex Fridman?

  I try to make these interesting, but I give you timestamps. So if you skip, please still check out the sponsors by clicking the links in the description. It's the best way to support this podcast. This episode is sponsored by Vincero Watches. They create exceptionally crafted classy watches. I personally feel best in a suit and a good watch. So I'm interested to give Vincero Watches a try. They have a ton of options that I like. For example, the Apex Rose Golden Black. To be honest, I prefer to have just one watch since it's a kind of companion through some of the more difficult things I do in life. So Vincero is now officially the number one candidate for the position.

• 3:38Lex Fridman?

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• 4:21Lex Fridman?

  Vincero, by the way, is spelled with no H, just C. Vincero.

• 4:27Lex Fridman?

  This show is sponsored by Four Sigmatic, the maker of delicious mushroom coffee and plant-based protein. I enjoy both. The coffee has lion's mane mushroom for productivity and chaga mushroom for immune support. The plant-based protein has immune support as well and tastes delicious. Supporting your immune systems is one of the things that we can actually control to improve our health in this difficult time for the human species. They have a big holiday sale for you. Not only does Four Sigmatic always have 100% money-back guarantee, but right now you can try their amazing products—I sound like a salesman—for up to 50% off.

• 5:09Lex Fridman?

  On top of up to 50% off, we've worked out an exclusive additional 10% off all sale products, but this is just for listeners of this podcast. So, go to foursigmatic.com/lex. That's foursigmatic.com/lex. This offer is only in capital letters for listeners of this podcast and is not available for their regular website. Hurry, the sale ends on the 30th of November, so stock up on their coffee now. I think from that ad read, you can tell that I have a second career, if everything fails, in reading infomercials. This episode is sponsored by ExpressVPN.

• 5:54Lex Fridman?

  You can use ExpressVPN to unlock movies and shows that are only available in other countries. ExpressVPN lets you change your online location so you can control where you want sites to think you're located. Open the app, select the location, tap the one big red button to connect, and refresh the page to access thousands of new shows and movies. I personally have used it to watch Dunkirk, the film about the Dunkirk evacuation in World War II that Churchill called a miracle in his "we shall fight on the beaches" speech. That is one of the most powerful speeches of the war. Plus Churchill is a badass. You can stream in HD, no problem, no buffering or lag.

• 6:38Lex Fridman?

  It's compatible with all of your devices, phones, laptops, smart TVs, and so on. It also encrypts your data and lets you surf the web safely and anonymously. Get it at expressvpn.com/lexpod to get extra three months free. That's expressvpn.com/lexpod. Finally, this show is presented by Cash App, the number one finance app in the App Store. When you get it, use code LEXPODCAST. Cash App lets you send money to friends, buy Bitcoin, and invest in the stock market with as little as $1. I'm thinking of doing more conversations with folks who work in and around the cryptocurrency space. Similar to artificial intelligence, there are a lot of charlatans in this space, but

• 7:23Lex Fridman?

  there are also a lot of free thinkers and technical geniuses whose ideas are worth exploring in depth and with care. If I make mistakes in guest selection and details in conversations, I'll keep trying to improve, correct where I can, and also keep following my curiosity wherever the heck it takes me. So again, if you get Cash App from the App Store or Google Play and use the code LEXPODCAST, you get $10 and Cash App will also donate $10 to FIRST, an organization that is helping to advance robotics and STEM education for young people around the world. And now here's my conversation with Eric Brungjolson.

• 8:23Lex Fridman?

  You posted a quote on Twitter by Albert Bartlett saying that "the greatest shortcoming of the human race is our inability to understand the exponential function."

• 8:35Lex Fridman?

  Why would you say the exponential growth is important to understand?

• 8:39Speaker 01

  Yeah, that quote, I remember posting that. It's actually a reprise of something Andy McAfee and I said in the second machine age, but I posted it in early March when COVID was really just beginning to take off and I was really scared. There were actually only a couple dozen cases, maybe less at that time, but they were doubling every like two or three days. And you know, I could see, oh my God, this is going to be a catastrophe and it's going to happen soon. But nobody was taking it very seriously, or not a lot of people were taking it very seriously. In fact, I remember I did my last in-person conference that week. I was flying back from Las Vegas and I was the only person on the plane wearing a mask. And the flight attendant came over to me. She looked very concerned. She kind of put her hands on my shoulder. She

• 9:22Speaker 01

  was touching me all over, which I wasn't thrilled about. And she goes, you know, do you have some kind of anxiety disorder? Are you okay? And I was like, no, you know, it's because of COVID.

• 9:30Lex Fridman?

  This is early March.

• 9:31Speaker 01

  Early March. But, um, you know, I was worried because I knew I could see, or I suspected, I guess, that that doubling would continue and it did. And pretty soon we had thousands of times more cases. Most of the time when I use that quote, I try to, you know, it's motivated by more optimistic things like Moore's law and the wonders of having more computer power. But in either case, it can be very counterintuitive. I mean, if you, if you walk for 10 minutes, you get about 10 times as far away as if you walk for one minute. You know, that's the way our physical world works. That's the way our brains are wired. But if something doubles for 10 times as long, you don't get 10 times as much. You get 1,000 times as much. And after 20,

• 10:14Speaker 01

  it's a billion. After 30, it's a, no, sorry. After 20, it's a million. After 30, it's a billion. And pretty soon after that, it just gets to these numbers that you can barely grasp. Our world is becoming more and more exponential, mainly because of digital technologies. So more and more often, our intuitions are out of whack and, uh, and that can be good in the case of things creating wonders, but it can be dangerous in the case of viruses and other things.

• 10:42Lex Fridman?

  Do you think it generally applies? Like, is there spaces where it does apply and where it doesn't? How are we supposed to build an intuition about?

• 10:51Lex Fridman?

  In which aspects of our society does exponential growth apply?

• 10:55Speaker 01

  Well, you know, you can learn the math, but the truth is our brains, I think, tend to be learn more from experiences. So we just start seeing it more and more often. So hanging around Silicon Valley, hanging around AI and computer researchers, I see this kind of exponential growth a lot more frequently. And I'm getting used to it, but I still make mistakes. I still underestimate some of the progress in just talking to someone about GPT three and how rapidly natural language has improved. But I think that as the world becomes more exponential, we'll all start experiencing it more frequently. The danger is that we may make some mistakes in the meantime, using our old kind of caveman intuitions about how the world works.

• 11:37Lex Fridman?

  Well, the weird thing is it always kind of looks linear in the moment, like the ever, you know, it's hard to feel, it's hard to like introspect and really acknowledge how much has changed in just a couple of years or five years or 10 years with the internet. If we just look at advancements of AI or even just social media, all the various technologies that go under the digital umbrella.

• 12:03Speaker 01

  Yeah.

• 12:04Lex Fridman?

  It feels pretty calm and normal and gradual.

• 12:07Speaker 01

  A lot of stuff, you know, I think there are parts of the world, most of the world is not exponential. You know, the way humans learn, the way organizations change, the way our whole institutions adapt and evolve. Those don't improve at exponential paces. And that leads to a mismatch oftentimes between these exponentially improving technologies, or let's say changing technologies, because some of them are exponentially more dangerous, and our intuitions and our human skills and our institutions that that just don't change very fast at all. And that mismatch, I think, is at the root of a lot of the problems in our society, the growing inequality and other dysfunctions in our political and economic systems.

• 12:52Lex Fridman?

  So one guy that talks about exponential functions a lot is Elon Musk. He seems to internalize this kind of way of exponential thinking. He calls it first principles thinking, sort of the kind of.

• 13:07Lex Fridman?

  Going to the basics, asking the question like, what were the assumptions of the past? How can, how can we throw them out the window? How can we do this 10x much more efficiently? And constantly practicing that process. And also using that kind of thinking to estimate sort of when, you know, create deadlines and estimate when you'll be able to deliver on some of these technologies. Now, it, it often gets him in trouble because he overestimates like he, uh, he doesn't meet the initial estimates of the deadlines. But he seems to deliver late, but deliver.

• 13:50Speaker 01

  Right.

• 13:51Lex Fridman?

  And which is kind of interesting. Like, what are your thoughts about this?

• 13:53Speaker 01

  Well, no, I, I, I think we can all learn from Elon. I think going to first principles, I talked about two ways of, of getting more of a grip on the exponential function. And one of them just comes from first principles. You know, if you understand the math of it, you can see what's going to happen. And even if it seems counterintuitive, that a couple of dozen of COVID cases could become thousands or tens or hundreds of thousands of them in a, in a month. It makes sense once you just do the math. And I think Elon tries to do that a lot. I, you know, in fairness, I think he also benefits from hanging out in Silicon Valley. And he's experienced it in a lot of different applications. So, you know, it's not as much of a shock to him anymore. But that's, uh, that's something we can all learn from.

• 14:34Speaker 01

  I, I, in my own life, I remember one of my first experiences, um, really seeing it was when I was a grad student and my, my advisor asked me to plot the growth of computer power in the US economy in different industries. And there are all these, you know, exponentially growing curves. And I was like, holy shit, look at this. Each, in each industry, it was just taking off. And, you know, you'd have to be a rocket scientist to extend that and say, wow, this means that, this was in the late eighties and early nineties that, you know, if it goes anything like that, we're going to have orders of magnitude more computer power than we did at that time. And of course we do.

• 15:08Lex Fridman?

  So, you know, when people look at Moore's Law.

• 15:12Lex Fridman?

  They often talk about it as just, so the exponential function is actually a stack of S-curves. So basically, it's you milk or whatever, take the most advantage of a particular little revolution, and then you search for another revolution, and it's basically revolutions stacked on top of revolutions. Do you have any intuition about how the heck humans keep finding ways to revolutionize things?

• 15:39Speaker 01

  Well, first, let me just unpack that first point that I talked about exponential curves, but no exponential curve continues forever.

• 15:49Speaker 01

  It's been said that if anything can't go on forever, eventually it will stop.

• 15:54Lex Fridman?

  That's very profound.

• 15:55Speaker 01

  It's very profound, but it seems that a lot of people don't appreciate that half of it as well either. And that's why all exponential functions eventually turn into some kind of S-curve or stop in some other way, maybe catastrophically. And that's happened with COVID as well. I mean, it was, it went up and then it sort of, you know, at some point, it starts saturating the pool of people to be infected. There's a standard epidemiological model that's based on that. And it's beginning to happen with Moore's Law or different generations of computer power. It happens with all exponential curves. The remarkable thing, as you allude in the second part of your question, is that we've been able to come up with a new S-curve on top of the previous one and do that generation after generation with new materials, new processes,

• 16:39Speaker 01

  and just extend it further and further.

• 16:43Speaker 01

  I don't think anyone has a really good theory about why we've been so successful in doing that. It's great that we have been, and I hope it continues for some time. But it's, you know, one beginning of a theory is that there's huge incentives when other parts of the system are going on that clock speed of doubling every two to three years. If there's one component of it that's not keeping up, then the economic incentives become really large to improve that one part. It becomes a bottleneck, and anyone who can do improvements in that part can reap huge returns so that the resources automatically get focused on whatever part of the system is in keeping up.

• 17:24Lex Fridman?

  Do you think some version of the Moore's Law will continue?

• 17:27Speaker 01

  Some version, yes, it is. I mean, one version that has become more important is something I call Kumi's Law, which is named after John Kumi, who I should mention was also my college roommate, but he identified the fact that energy consumption has been declining by a factor of two. And for most of us, that's more important. You know, the new iPhones came out today as we're recording this. I'm not sure when you're gonna make it.

• 17:50Lex Fridman?

  Very soon after this, yeah.

• 17:52Speaker 01

  And for most of us, you know, having the iPhone be twice as fast, you know, it's nice. But having it the battery life longer, that would be much more valuable. And the fact that a lot of the progress in chips now is reducing energy consumption is probably more important for many applications than just the raw speed. Other dimensions of Moore's Law are in AI and machine learning.

• 18:19Speaker 01

  Those tend to be very parallelizable functions, especially deep neural nets. And so instead of having one chip, you can have multiple chips or you can have a GPU, a graphic processing unit that goes faster and now special chips designed for machine learning like tensor processing units. Each time you switch, there's another 10x or 100x improvement above and beyond Moore's Law. So I think that the raw silicon isn't improving as much as it used to, but these other dimensions are becoming important, more important, and we're seeing progress in them.

• 18:53Lex Fridman?

  I don't know if you've seen the work by OpenAI where they show the exponential improvement of the training of neural networks, just literally in the techniques used. So that's almost like the algorithm, the, it's fascinating to think like, can I actually continue? Us figuring out more and more tricks of how to train networks faster and faster.

• 19:14Speaker 01

  Well, the progress has been staggering. You know, if you look at image recognition, as you mentioned, I think it's a function of at least three things that are coming together. One, we just talked about faster chips, not just Moore's Law, but GPUs, TPUs, and other technologies. The second is just a lot more data. I mean, we are awash in digital data today in a way we weren't 20 years ago. Photography. I'm old enough to remember it used to be chemical. And now everything is digital. I took, you know, probably fifty digital photos yesterday. I wouldn't have done that if it was chemical and and we have the Internet of Things and all sorts of other types of data. And we walk around with our phone. It's just broadcasting a huge amounts of digital data that can be used as training sets.

• 19:56Speaker 01

  And then last but not least, as they mentioned at Open and I, as they mentioned at OpenAI, there have been significant improvements in the techniques. You know, the core idea of deep neural nets has been around for a few decades. But the advances in making it work more efficiently have also improved a couple of orders of magnitude or more. So you multiply together, you know, a hundredfold improvement in computer power, a hundredfold or more improvement in data, a hundredfold improvement in in techniques of software and algorithms, and soon you're getting into millionfold improvements.

• 20:31Lex Fridman?

  You know, somebody brought this up, this idea with GPT three that

• 20:36Lex Fridman?

  it's so it's training in a self-supervised way on basically Internet data. And that's one of the, I've seen arguments made and that seem to be pretty convincing that the bottleneck there is going to be how much data there is on the Internet, which is a fascinating idea that it literally will just run out of human generated data to train on.

• 21:00Speaker 01

  It's I know we make it to the point where it's consumed basically all of human knowledge or all digitized human knowledge. Yeah.

• 21:06Lex Fridman?

  And that will be the bottleneck. I mean, but the the interesting thing with bottlenecks is people often use bottlenecks as a way to argue against exponential growth. They say, well, there's no way you can overcome this bottleneck, but we seem to somehow keep coming up in new ways to like overcome whatever bottlenecks the the critics come up with, which is fascinating. I don't know how you overcome the data bottleneck, but probably more efficient training algorithms.

• 21:34Speaker 01

  Yeah, well, you already mentioned that, that that these training algorithms are getting much better at using smaller amounts of data. We also are just capturing a lot more data than we used to, especially in China, but but all around us. So those are both important, you know, in in some applications, you can simulate the data, you know, video games. Some of the the self-driving car systems are, you know, simulating driving and Of course, that has some risks and weaknesses, but you can also, if you want to exhaust all the different ways you could beat a video game, you could just simulate all the options.

• 22:09Lex Fridman?

  Can we take a step in that direction of autonomous vehicles? I'm actually talking to the CTO of Waymo tomorrow. And obviously, I'm talking to Elon again in a couple of weeks.

• 22:21Lex Fridman?

  What's your thoughts on autonomous vehicles? Like, where do we stand?

• 22:27Lex Fridman?

  As a problem that has the potential of revolutionizing the world?

• 22:32Speaker 01

  Well, you know, I'm really excited about that. But it's become much clearer that the original way that I thought about it, most people thought about like, you know, will we have a self-driving car or not, is way too simple. The better way to think about it is that there's a whole continuum of how much driving and assisting the car can do. I noticed that you're right next here, next door to Toyota Research Institute.

• 22:53Lex Fridman?

  That's a total accident. I love the TRI folks, but yeah.

• 22:56Speaker 01

  Have you talked to Gil Pratt?

• 22:58Lex Fridman?

  Yeah, we're going to, we're supposed to talk. It's kind of hilarious.

• 23:02Speaker 01

  So there's kind of the, I think it's a good counterpart to say what Elon is doing. And hopefully, they can be frank in how, what they think about each other, because I've heard both of them talk about it. But they're much more, you know, this is an assistant, a guardian angel that watches over you as opposed to try to do everything. I think there are some things like driving on a highway, you know, from LA to Phoenix, where it's mostly good weather, straight roads. That's close to a solved problem, let's face it. In other situations, you know, driving through the snow in Boston, where the roads are kind of crazy. And most importantly, you have to make a lot of judgments about what the other driver's going to do at these intersections that aren't really right angles and aren't very well described. It's more like game theory.

• 23:42Speaker 01

  That's a much harder problem and requires understanding human motivations. So there's a continuum there of some places where the cars will work very well and others where it could probably take decades.

• 23:58Lex Fridman?

  What do you think about the Waymo? So you mentioned two companies that actually have cars on the road. There's the Waymo approach that it's more like, we're not going to release anything until it's perfect and we're going to be very strict about the streets that we travel on, but it better be perfect.

• 24:16Speaker 01

  Yeah.

• 24:19Speaker 01

  I'm smart enough to be humble and not try to get between. I know there's very bright people on both sides of the argument. I've talked to them and they make convincing arguments to me about how careful they need to be and the social acceptance. Some people thought that when the first few people died from self-driving cars, that would shut down the industry, but it was more of a blip, actually. And, you know, so that was interesting. Of course, there's still a concern that if there could be setbacks if we do this wrong. You know, your listeners may be familiar with the different levels of self-driving, you know, level one, two, three, four, five. I think Andrew Ng has convinced me that this idea of really focusing on level four, where you only go in areas that are well mapped, rather than just going out in the wild,

• 25:05Speaker 01

  is the way things are going to evolve. But you could just keep expanding those areas where you've mapped things really well, where you really understand them, and eventually all become kind of interconnected. And that could be a kind of another way of progressing to make it more feasible over time.

• 25:22Lex Fridman?

  I mean, that's kind of like the Waymo approach, which is they just now released, I think just like a day or two ago, a public, like anyone from the public in the,

• 25:35Lex Fridman?

  in the Phoenix, Arizona, to, you know, you can get a ride in a Waymo car with no person, no driver.

• 25:43Speaker 01

  Oh, they've taken away the safety driver?

• 25:45Lex Fridman?

  Oh, yeah. There's for a while now, there's been no safety driver.

• 25:48Speaker 01

  Okay, because, I mean, I've been following that one in particular, but I thought it was kind of funny about a year ago when they had the safety driver, and then they added a second safety driver because the first safety driver would fall asleep.

• 25:59Speaker 01

  I'm not sure they're going in the right direction with that.

• 26:00Lex Fridman?

  No, they've Waymo in particular done a really good job of that. They actually have a.

• 26:09Lex Fridman?

  Very interesting infrastructure of remote like observation. So they're not, they're not controlling the vehicles remotely, but they're able to, it's like a customer service. They can anytime tune into the car. I bet they can probably remotely control it as well, but that's officially not the function that they.

• 26:28Speaker 01

  Yeah, I can see that being really, because I think the thing that's proven harder than maybe some of the early people expected was there's a long tail of weird exceptions. So you can deal with 90, 99, 99.99% of the cases, but then there's something that's just never been seen before in the training data. And humans, you know, more or less can work around that, although let me be clear and note, there are about 30,000 human fatalities just in the United States and maybe a million worldwide. So they're far from perfect, but I think people have higher expectations of machines. They don't, wouldn't tolerate that level of death and damage from a machine. And so we have to do a lot better at dealing with those edge cases.

• 27:11Lex Fridman?

  And also the, the tricky thing that if I have a criticism for the Waymo folks, there's such a huge focus on safety. Where people don't talk enough about creating products that people, that customers love, that human beings love using. You know, it's very easy to create a thing that's safe at the extremes, but then nobody wants to get into it.

• 27:34Speaker 01

  Yeah. Well, back to Elon, I think one of, part of his genius was with the electric cars. Before he came along, electric cars were all kind of underpowered, really light, and they were sort of wimpy cars that, you know, weren't fun. And the first thing he did was, you know, he made a roadster that went zero to 60 faster than just about any other car. And went the other end, and I think that was a really wise marketing move as well as a wise technology move.

• 28:00Lex Fridman?

  Yeah, it's difficult to figure out what the right marketing move is for AI systems. That's always been a. I think it requires guts and risk taking, which is what Elon practices. I mean, to the chagrin of perhaps investors or whatever.

• 28:18Speaker 01

  It requires guts and risk taking. It also requires rethinking what you're doing. I think way too many people are unimaginative, intellectually lazy. And when they take AI, they basically say, what are we doing now? How can we make a machine do the same thing? Maybe we'll save some costs, we'll have less labor. And yeah, it's not necessarily the worst thing in the world to do, but it's really not leading to a quantum change in the way you do things. You know, when Jeff Bezos said, hey, we're going to use the internet to change how bookstores work and we're going to use technology. He didn't go and say, okay, let's, let's put a robot cashier where the human cashier is and leave everything else alone. That would have been a very lame way to automate a bookstore. He's like went from soup to nuts, said, let's just rethink it. We get rid of the physical bookstore.

• 29:00Speaker 01

  We have a warehouse, we have delivery, we have people order on a screen. And everything was reinvented. And that's been the story of these general purpose technologies all through history. And in my books, I write about like electricity and how for 30 years, there was almost no productivity gain from the electrification of factories a century ago. Now, it's not because electricity is a wimpy, useless technology. We all know how awesome electricity is. It's because at first they really didn't rethink the factories. It was only after they reinvented them, and we describe how in the book. Then you suddenly got a doubling and tripling of productivity growth. But it's the combination of the technology with the new business models, new business organization. That just takes a long time and it takes more creativity than most people have.

• 29:44Lex Fridman?

  Can you maybe linger on electricity because that's a fun one?

• 29:47Speaker 01

  Yeah, well, sure, I'll tell you what happened. Before electricity, there were basically steam engines or sometimes water wheels. And to power the machinery, you had to have pulleys and crank shafts. And you really can't make them too long because they'll, they'll break the torsion. So all the equipment was kind of clustered around this one giant steam engine. You can't make small steam engines either because of thermodynamics. So if you have one giant steam engine, all the equipment clustered around it, multi-story, they have it vertical to minimize the distance as well as horizontal. And then when they did electricity, they took out the steam engine. They got the biggest electric motor they could buy from General Electric or someone like that. And nothing much else changed.

• 30:25Speaker 01

  It took until a generation of managers retired or died thirty years later that people started thinking, "Wait, we don't have to do it that way. You can make electric motors, you know, big, small, medium. You can put one with each piece of equipment." There's this big debate, if you read the management literature, between what they call group drive versus unit drive, where every machine would have its own motor. Well, once they did that, once they went to unit drive, those guys won the debate. Then you started having a new kind of factory, which is sometimes spread out over acres, single-story, and each piece of equipment had its own motor. And most importantly, they weren't laid out based on who needed the most power. They were laid out based on what is the workflow of materials, you know? Assembly line, let's have it go from this machine to that machine to that machine.

• 31:10Speaker 01

  Once they rethought the factory that way, huge increases in productivity was just staggering. People like Paul David have documented this in their research papers. And, you know, I think that there's a, that is a lesson you see over and over. It happened when the steam engine changed manual production. It's happened with the computerization, you know? People like Michael Hammer said, "Don't automate, obliterate." In each case, the big gains only came once smart entrepreneurs and managers basically reinvented their industries. I mean, one other interesting point about all that is that during that reinvention period, you often actually, not only you don't see productivity growth, you can actually see a slipping back. Measured productivity actually falls.

• 31:54Speaker 01

  I just wrote a paper with Chad Sieverson and Daniel Rock called "The Productivity J-Curve", which basically shows that in a lot of these cases, you have a downward dip before it goes up. And that downward dip is when everyone's trying to like reinvent things. And you could say that they're creating knowledge and intangible assets, but that doesn't show up on anyone's balance sheet. It doesn't show up in GDP. So it's as if they're doing nothing. Like, take self-driving cars, we were just talking about it. There have been hundreds of billions of dollars spent developing self-driving cars. And basically, no chauffeur has lost his job, no taxi driver. I guess I gotta check out the ones- It's a big J curve. Yeah, so there's a bunch of spending and no real consumer benefit.

• 32:35Speaker 01

  Now, they're doing that in the belief, I think the justified belief, that they will get the upward part of the J curve and there will be some big returns. But in the short run, you're not seeing it. That's happening with a lot of other AI technologies, just as it happened with earlier general purpose technologies. And it's one of the reasons we're having relatively low productivity growth lately. You know, as an economist, one of the things that disappoints me is that as eye popping as these technologies are, you and I are both excited about some of the things they can do. The economic productivity statistics are kind of dismal. We actually, believe it or not, have had lower productivity growth in the past about 15 years than we did in the previous 15 years, in the 90s and early 2000s.

• 33:19Speaker 01

  And so that's not what you would have expected if if these technologies were that much better. But I think we're in kind of a long J curve there. Personally, I'm optimistic we'll start seeing the upward tick, maybe maybe as soon as next year. But the past decade has been a bit disappointing if you thought there's a one to one relationship between cool technology and higher productivity.

• 33:40Lex Fridman?

  What would you place your biggest hope for productivity increases on? Because you kind of said at a high level AI, but if I were to think about what has been so revolutionary in the last 10 years, I would, 15 years, and thinking about the internet, I would say things like, I hope I'm not saying anything ridiculous, but everything from Wikipedia to Twitter. So like these kind of websites, not so much AI, but like I would expect to see some kind of big productivity increases from just the connectivity between people and the access to more information.

• 34:25Speaker 01

  Yeah, well, so that's another area I've done quite a bit of research on, actually, is these free goods like Wikipedia, Facebook, Twitter, Zoom. We're actually doing this in person, but almost everything else I do these days is is online. The interesting thing about all those is most of them have a price of zero, you know? What do you pay for Wikipedia? Maybe like a little bit for the electrons to come to your house. Basically zero, right?

• 34:52Lex Fridman?

  I take a small pause and say I donate to Wikipedia often. You should too.

• 34:56Speaker 01

  Good for you. Yeah. So but what does that do mean for GDP? GDP is based on the price and quantity of all the goods, things bought and sold. If something has zero price, you know how much it contributes to GDP? To a first approximation, zero. So these digital goods that we're getting more and more, we're spending more and more hours a day consuming stuff off of screens, little screens, big screens. That doesn't get priced into GDP, it's like they don't exist. That doesn't mean they don't create value. I get a lot of value from watching cat videos and reading Wikipedia articles and listening to podcasts, even if I don't pay for them. So we've got a mismatch there.

• 35:38Speaker 01

  Now, in fairness, economists, since Simon Kuznets invented GDP and productivity, all those statistics back in the 1930s, he recognized, he in fact said, this is not a measure of well-being, this is not a measure of welfare, it's a measure of production. But almost everybody has kind of forgotten that he said that, and they just use it. It's like, how well off are we? What was GDP last year? It was 2.3% growth or whatever. That is how much physical production, but it's not the value we're getting. We need a new set of statistics, and I'm working with some colleagues, Avi Kalas and others, to develop something we call GDP-B. GDP-B measures the benefits you get, not the cost.

• 36:23Speaker 01

  If you get benefit from Zoom or Wikipedia or Facebook, then that gets counted in GDP-B, even if you pay zero for it. So, you know, back to your original point, I think there is a lot of gain over the past decade in these digital goods that doesn't show up in GDP, doesn't show up in productivity. By the way, productivity is just defined as GDP divided by hours worked. So if you mismeasure GDP, you mismeasure productivity by the exact same amount. That's something we need to fix. I'm working with the statistical agencies to come up with a new set of metrics, and you know, over the coming years, I think we'll see, we're not going to do away with GDP, it's very useful, but we'll see a parallel set of accounts that measure the benefits. How difficult is it to get that B in the GDP?

• 37:08Speaker 01

  It's pretty hard. I mean, the thing, one of the reasons it hasn't been done before is that, you know, you can measure the cash register, what people pay for stuff, but how do you measure what they would have paid, like what the value? That's a lot harder. You know, how much is Wikipedia worth to you? That's what we have to answer. And to do that, what we do is we can use online experiments. We do massive online choice experiments. We ask hundreds of thousands now, millions of people to do lots of sort of A B tests. How much would I have to pay you to give up Wikipedia for a month? How much would I have to pay you to stop using your phone? And in some cases, it's hypothetical. In other cases, we actually enforce it, which is kind of expensive. Like, we we pay somebody thirty dollars to stop using Facebook and we see if they'll do it. And and some people will give it up for ten dollars.

• 37:53Speaker 01

  Some people won't give it up, even if you give them a hundred dollars.

• 37:55Lex Fridman?

  That's awesome.

• 37:56Speaker 01

  And then you get a whole demand curve. You get to see what all the different prices are and what how much value different people get. And not surprisingly, different people have different values. We find that women tend to value Facebook more than men. Old people tend to value it a little bit more than young people. It was interesting. I think young people maybe know about other networks that I don't know the name of that are better than Facebook. And and so you get to see these like these patterns, but you know, every person's individual. And then if you add up all those numbers, you start getting a an estimate of the value.

• 38:27Lex Fridman?

  Okay, first of all, that's brilliant. Is this work that will soon eventually be published?

• 38:33Speaker 01

  Yeah, well, there's a version of it in the Proceedings of the National Academy of Sciences about, I think we call it Massive Online Choice Experiments. I should have should remember the title, but it's on my website. So, yeah, we have some more papers coming out on it, but the first one is already out.

• 38:47Lex Fridman?

  You know, it's kind of a fascinating mystery that Twitter, Facebook, like all these social networks are free. And it seems like almost none of them, except for YouTube, have experimented with removing ads for money. Can you like, do you understand that from a both economics and the product perspective?

• 39:07Speaker 01

  Yeah, it's something that, you know, so I teach a course on digital business models. So I used to at MIT, at Stanford, I'm not quite sure. I'm not teaching until next spring. I'm still thinking what my course is going to be. But there are a lot of different business models. And when you have something that has zero marginal cost, there's a lot of forces, especially if there's any kind of competition, that push prices down to zero. But you can have ad-supported systems. You can bundle things together. You can have volunteer, you mentioned Wikipedia, there's donations. And I think economists underestimate the power of volunteerism and donations.

• 39:42Speaker 01

  Your National Public Radio. Actually, how do you, this podcast, how is this, what's the revenue model?

• 39:47Lex Fridman?

  There's sponsors at the beginning and then, and people, the funny thing is, I tell people, they can, it's very, I tell them the timestamp. So if you want to skip the sponsors, you're free. But the, it's funny that a bunch of people, so I read the advertisement and a bunch of people enjoy reading it.

• 40:06Speaker 01

  Well, they may learn something from it. And also, from the advertiser's perspective, those are people who are actually interested, you know? Like, I mean, the example I sometimes give, like, I bought a car recently and all of a sudden, all the car ads were like, interesting.

• 40:20Lex Fridman?

  Exactly.

• 40:20Speaker 01

  And then like, now that I have the car, like, I'd sort of zone out on, but that's fine. The car companies, they don't really want to be advertising to me if I'm not going to buy their product. So there are a lot of these different revenue models and, you know, it's a little complicated, but the economic theory has to do with what the shape of the demand curve is, when it's better to monetize it with charging people versus when you're better off doing advertising. I mean, in short, when the, when the demand curve is relatively flat and wide, like generic news and things like that, then you tend to do better with advertising. If it's a good that's only useful to a small number of people, but they're willing to pay a lot, they have a very high value for it, then you, advertising isn't going to work as well. You're better off charging for it.

• 41:03Speaker 01

  Both of them have some inefficiencies. And then when you get into targeting and you get these other revenue models, it gets more complicated. But there's some economic theory on it. I also think, to be frank, there's just a lot of experimentation that's needed because sometimes things are a little counterintuitive, especially when you get into what are called two-sided networks or platform effects, where you may grow the market on one side and harvest the revenue on the other side. You know, Facebook tries to get more and more users and then they harvest the revenue from advertising. So that's another way of kind of thinking about it.

• 41:39Lex Fridman?

  Is it strange to you that they haven't experimented?

• 41:42Speaker 01

  Well, they are experimenting. So I, you know, they are doing some experiments about what the willingness is for people to pay. I, I think that when they do the math, it's going to. Work out that they still are better off with an advertising driven model. But.

• 41:57Lex Fridman?

  What about a mix like this is what YouTube is, right?

• 41:59Speaker 01

  Yeah.

• 42:00Lex Fridman?

  You you allow the person to decide, the customer to decide exactly which model they prefer.

• 42:06Speaker 01

  Yeah, no, that can work really well, you know, and newspapers, of course, have known this for a long time. The Wall Street Journal, the New York Times, they have subscription revenue. They also have advertising revenue and that can that can definitely work. The online is a lot easier to have a dial that's much more personalized and everybody can kind of roll their own mix. And I could imagine, you know, having a little slider about how much advertising you want or are willing to take. And if it's done right, it's incentive compatible, it could be a win win where where both the content provider and the consumer are better off than they would have been before.

• 42:42Lex Fridman?

  Yeah, you know, the done right part is is really good point like with the Jeff Bezos and the single click purchase on Amazon, the frictionless.

• 42:50Speaker 01

  Yeah.

• 42:50Lex Fridman?

  Effort there, if I could just rant for a second about the Wall Street Journal, all the newspapers you mentioned is I have to click so many times.

• 43:00Speaker 01

  Yeah.

• 43:00Lex Fridman?

  To subscribe to them that I'm I literally don't subscribe just because of the number of times I have to click.

• 43:07Speaker 01

  I'm totally with you. I don't understand why so many companies make it so hard. I mean, another example is when you buy a new iPhone or a new computer, whatever. I feel like, OK, I'm gonna like lose an afternoon just like loading up and getting all my stuff back. And and for a lot of us, that's more of a deterrent than the price. And if they could, you know, make it painless, we'd give them a lot more money. So I'm hoping somebody listening is working on on making it more painless for us to buy your products.

• 43:37Lex Fridman?

  If we could just like linger a little bit on the social network thing, because, you know, there's this Netflix social dilemma.

• 43:45Speaker 01

  Yeah, no, I saw that.

• 43:46Lex Fridman?

  And.

• 43:48Speaker 01

  Tristan Harris and company, yeah.

• 43:51Lex Fridman?

  And, you know, people's data.

• 43:56Lex Fridman?

  People, it's really sensitive and, and social networks are at the core arguably of many of societal like tension and some of the most important things happening in society. So it feels like it's important to get this right.

• 44:09Speaker 01

  It is.

• 44:09Lex Fridman?

  Both from a business model perspective and just like a trust perspective. I, I still gotta, I mean, it just still feels like, I, I know there's experimentation going on. It still feels like everyone is afraid to try different business models, like really try.

• 44:25Speaker 01

  Well, I'm worried that people are afraid to try different business models. I'm also worried that some of the business models may lead them to bad choices. And, um, you know, Danny Kahneman talks about system one and system two, sort of like our reptilian brain that reacts quickly to what we see. See something interesting, we click on it, we retweet it versus our system two, you know, our frontal cortex that's supposed to be more careful and rational, that really doesn't make as many decisions as it should. I think there's a tendency for a lot of these social networks to really exploit system one, our quick, instant reaction, make it so we just click on stuff and pass it on

• 45:08Speaker 01

  and not really think carefully about it. And, and that system, it tends to be driven by, you know, sex, violence, disgust, anger, fear, you know, these relatively primitive kinds of emotions. Maybe they're important for a lot of purposes, but they're not a great way to organize a society. And, and most importantly, when you think about this huge, amazing information infrastructure we've had that's connected, you know, billions of brains across the globe, not just we can all access information, but we can all contribute to it and share it. Arguably, the most important thing that that network should do is favor truth over falsehoods. And the way it's been designed, not necessarily intentionally,

• 45:50Speaker 01

  is exactly the opposite. My, uh, my MIT colleagues, Aral and Deb Roy and, and others at, at MIT did a terrific paper in the cover of Science, and then they document what we all feared, which is that lies spread faster than truth.

• 46:05Speaker 01

  Social networks. They looked at a bunch of tweets and retweets. And they found that false information was more likely to spread further, faster to more people. And why was that? It's not because people like lies. It's because people like things that are shocking, amazing, can you believe this? Something that is not mundane, not that something everybody else already knew. And what are the most unbelievable things? Well, lies. And so you, if you want to find something unbelievable, it's a lot easier to do that if you're not constrained by the truth. So they found that the emotional valence of false information was just much higher. It was more likely to be shocking and therefore more likely to be spread.

• 46:50Speaker 01

  Another interesting thing was that that wasn't necessarily driven by the algorithms. I know that there is some evidence, you know, Zenep Tofeki and others have pointed out in YouTube. Some of the algorithms unintentionally were tuned to amplify more extremist content. But in the study of Twitter that Sinan and Deb and others did. They found that even if you took out all the bots and all the automated tweets. You still had lies spreading significantly faster. It's just the problem is with ourselves that we just can't resist passing on this salacious content. The, but I also blame the platforms because you know, there's different ways you can design a platform.

• 47:30Speaker 01

  You can design a platform in a way that makes it easy to spread lies and to retweet and spread things on. Or you can kind of put some friction on that and try to favor truth. I had dinner with Jimmy Wales once, you know, the guy who helped found Wikipedia. And, and he convinced me that, look, you know, you can make some design choices. Whether it's at Facebook, at Twitter, at Wikipedia or Reddit, whatever. And depending on how you make those choices. You're more likely or less likely to have false news.

• 48:02Lex Fridman?

  Create a little bit of friction, like you said.

• 48:04Speaker 01

  Yeah.

• 48:05Lex Fridman?

  You know, that's the.

• 48:06Speaker 01

  It could be friction, it could be speeding the truth, you know, either way, but and I don't totally understand.

• 48:11Lex Fridman?

  Speeding the truth, I love it.

• 48:13Speaker 01

  Yeah, yeah, you know, amplifying it and giving it more credit and, you know, like in academia, which is far, far from perfect. But, you know, when someone has an important discovery, it tends to get more cited and people kind of look to it more and sort of. It tends to get amplified a little bit. So you could try to do that, too. I don't know what the silver bullet is, but I the meta point is that if we spend time thinking about it. We can amplify truth over falsehoods and I'm disappointed in in the heads of these social networks. That they haven't been as successful or maybe haven't tried as hard to amplify truth. And part of it, going back to what we said earlier, is, you know, these revenue models may push them more towards.

• 48:54Speaker 01

  Growing fast, spreading information rapidly, getting lots of users, which isn't the same thing as finding truth.

• 49:03Lex Fridman?

  Yeah, I mean, implicit in what you're saying now is a hopeful message. That with platforms, we can.

• 49:12Lex Fridman?

  Take a step towards greater and greater. Popularity of truth, but the more cynical view is that what the last few years have revealed. Is that there's a lot of money to be made in dismantling even the idea of truth. That nothing is true and ever as a thought experiment, I've been, you know, thinking about if it's possible that our future will have. Like the idea of truth is something we won't even have. Do you think it's possible, like in the future, that everything is on the table in terms of truth? And we're just swimming in this kind of digital economy where ideas are just little.

• 49:56Lex Fridman?

  Toys that are not at all connected to reality.

• 50:00Speaker 01

  Yeah, I think that's definitely possible. I'm not a technological determinist, so I don't think that's inevitable. I don't think it's inevitable that it doesn't happen. I mean, the thing that I've come away with every time I do these studies and I emphasize it in my books and elsewhere is that technology doesn't shape our destiny, we shape our destiny. So just by us having this conversation, I hope that your audience is going to take it upon themselves as they design their products and they think about their use products, as they manage companies. How can they make conscious decisions to favor truth over falsehoods? Favor the better kinds of societies and not abdicate and say, "Well, we just build the tools."

• 50:41Speaker 01

  I think there was a saying that, was it the German scientists when they were working on the missiles in late World War II, you know, they said, "Well, our job is to make the missiles go up. Where they come down, that's someone else's department." And, you know, that's obviously a, I think it's obvious, that's not the right attitude that technologists should have, that engineers should have. They should be very conscious about what the implications are. And if we think carefully about it, we can avoid the kind of world that you just described, where truth is all relative. There are going to be people who benefit from a world of where people don't check facts

• 51:18Speaker 01

  and where truth is relative and popularity or fame or money is orthogonal to truth. But one of the reasons I suspect that we've had so much progress over the past few hundred years is the invention of the scientific method, which is a really powerful tool or meta-tool for finding truth and favoring things that are true versus things that are false. If they don't pass the scientific method, they're less likely to be true. And that has, the societies and the people and the organizations that embrace that have done a lot better than the ones who haven't. And so I'm hoping that people keep that in mind and continue to try to embrace

• 52:02Speaker 01

  not just the truth, but methods that lead to the truth.

• 52:05Lex Fridman?

  So maybe on a more personal question. If one were to try to build a competitor to Twitter, what would you advise?

• 52:16Lex Fridman?

  Is there, I mean, the bigger, the meta question, is that the right way to improve systems?

• 52:23Speaker 01

  Yeah. No, I think that the underlying premise behind Twitter and all these networks is amazing that we can communicate with each other. And I use it a lot. There's a sub-part of Twitter called Econ Twitter where, you know, we economists tweet to each other and talk about new papers. Something came out in the NBER, the National Bureau of Economic Research, and we share about it. People critique it. I think it's been a godsend because it's really sped up the scientific process, if you can call economic scientific.

• 52:49Lex Fridman?

  Does it get divisive in that little?

• 52:51Speaker 01

  Sometimes, yeah, sure. Sometimes it does. It can also be done in nasty ways and, you know, there's the bad parts. But the good parts are great because you just speed up that clock speed of learning about things. You know, instead of like in the old, old days, you know, waiting to read it in a journal or the not so old days when you'd see it posted on a website and you'd read it. Now on Twitter, like people will distill it down and there's a real art to getting to the essence of things. So that's been great. But it certainly, we all know that Twitter can be a cesspool of misinformation. And like I just said, unfortunately, misinformation tends to spread faster on Twitter than truth. And there are a lot of people who are very vulnerable to it. I'm sure I've been fooled at times.

• 53:33Speaker 01

  There are agents, whether from Russia or from political groups or others, that explicitly create efforts at misinformation and efforts at getting people to hate each other. Or even more important lately, I've discovered is nutpicking. You know, the idea of nutpicking?

• 53:49Lex Fridman?

  No, what's that?

• 53:50Speaker 01

  It's a good term. Nutpicking is when you find like an extreme nutcase on the other side and then you amplify them and make it seem like that's typical of the other side.

• 54:01Lex Fridman?

  Yes.

• 54:02Speaker 01

  So you're not literally lying. You're taking some idiot, you know, ranting on the subway or just, you know, whether they're in the KKK or Antifa or whatever, they're just, and you, normally, nobody would pay attention to this guy. Like 12 people would see him and it'd be the end. Instead, with video or whatever, you get tens of millions of people say it. And I've seen this, you know, I look at it, I get angry. I'm like, I can't believe that person did such things. It's so terrible. Let me tell all my friends about this terrible person. And it's a great way to generate division. I talked to a friend who studied Russian misinformation campaigns. And they're very clever about literally being on both sides of some of these debates. They would have some people pretend to be part of BLM.

• 54:46Speaker 01

  Some people pretend to be white nationalists. And they would be throwing epithets at each other, saying crazy things at each other. And they're literally playing both sides of it. But their goal wasn't for one or the other to win. It was for everybody to be hating and distrusting everyone else. So these tools can definitely be used for that. And they are being used for that. It's been super destructive for our democracy and our society. And the people who run these platforms, I think, have a social responsibility, a moral and ethical, personal responsibility to do a better job and to shut that stuff down. Well, I don't know if you can shut it down, but to design them in a way that, that, you know, as I said earlier, favors truth over falsehoods and favors positive types of

• 55:30Speaker 01

  communication versus destructive ones.

• 55:33Lex Fridman?

  And just like you said, it's also on us. I try to be all about love and compassion, empathy on Twitter. One of the things, nut picking is a fascinating term. One of the things that people do that's, I think, even more dangerous is nut picking applied to individual statements of good people. So basically, worst case analysis in computer science is taking, sometimes out of context, but sometimes in context, a statement, one statement by a person. Like I've been, because I've been reading The Rise and Fall of the Third Reich. I often talk about Hitler on this podcast with folks. It is so easy.

• 56:18Speaker 01

  That's really dangerous.

• 56:19Lex Fridman?

  But I'm all leaning in. I'm 100% because, well, it's actually a safer place than people realize because it's history and history in long form is actually very fascinating to think about. And it's But I could see how that could be taken

• 56:36Speaker 01

  Yeah.

• 56:37Lex Fridman?

  Totally out of context and it's very worrying.

• 56:38Speaker 01

  About the, you know, these digital infrastructures, not just to send me things, but they're sort of permanent. So anything you say, at some point, someone can go back and find something you said three years ago. Perhaps jokingly, perhaps not, maybe you're just wrong and you made them, you know? And like that becomes, they can use that to define you if they have intent. And we all need to be a little more forgiving. I mean, somewhere in my twenties, I told my, I was going through all my different friends and I was like, you know, every one of them has at least like one nutty opinion.

• 57:08Speaker 01

  I was like, there's like nobody who's like completely, except me, of course.

• 57:11Lex Fridman?

  Yeah.

• 57:11Speaker 01

  But I'm sure they thought that about me too. And, uh, and so you just kind of like learn to be a little bit tolerant that like, okay, there's just, you know.

• 57:18Lex Fridman?

  Yeah, I, I wonder who the responsibility lays on there. Like, I think ultimately it's about leadership. Like, uh, the previous president Barack Obama has been, I think, quite eloquent. Walk in this very difficult line of talking about cancel culture. But it's a difficult, it takes skill.

• 57:39Speaker 01

  Yeah.

• 57:39Lex Fridman?

  Cause you say the wrong thing and you piss off a lot of people. And so you have to do it well. But then also the platform of the technology is, should slow down, create friction in spreading this kind of nut picking in all its forms.

• 57:54Speaker 01

  Absolutely. No, and, and, and your point that we have to like learn over time how to manage it. I mean, we can't put it all on the platform and say, you guys design it. And cause if we're idiots about using it, you know, nobody can design a platform that withstands that. And, and every new technology people learn, it's dangerous. You know, when someone invented fire, it's great cooking and everything, but then somebody burned himself. And then you had to like, learn how to like avoid, maybe somebody invented a fire extinguisher later and what's up. So you, you kind of like figure out ways of, of working around these technologies. Someone invented seat belts, et cetera. And, and that's certainly true with all the new digital technologies that we have to figure out, not just technologies that protect us, but, but ways of using them that emphasize that are more likely to be successful than, than dangerous.

• 58:39Lex Fridman?

  So you've written quite a bit about how artificial intelligence might change our world.

• 58:46Lex Fridman?

  How do you think if we look forward, again, it's impossible to predict the future. But if we look at trends from the past and we try to predict what's going to happen in the rest of the 21st century, how do you think AI will change our world?

• 59:00Speaker 01

  That's a big question. You know, I'm mostly a techno optimist. I'm not at the extreme, you know, the singularity is near end of the spectrum. But I do think that we are likely in for some significantly improved living standards, some really important progress, even just the technologies that are already kind of like in the can that haven't diffused. You know, when I talked earlier about the J curve, it could take 10, 20, 30 years for an existing technology to have the kind of profound effects. And when I look at whether it's, you know, vision systems, voice recognition, problem solving systems, even if nothing new got invented, we would have a few decades of progress. So I'm excited about that. And I think that's going to lead to us being wealthier, healthier.

• 59:45Speaker 01

  I mean, the healthcare is probably one of the applications that I'm most excited about.

• 59:50Speaker 01

  So that's good news. I don't think we're going to have the end of work anytime soon. There's just too many things that machines still can't do. When I look around the world and think of whether it's childcare or healthcare, cleaning the environment, interacting with people, scientific work, artistic creativity, these are things that for now, machines aren't able to do nearly as well as humans, even just something as mundane as, you know, folding laundry or whatever. And many of these, I think, are going to be years or decades before machines catch up. You know, I may be surprised on some of them, but overall, I think there's plenty of work for humans to do. There's plenty of problems in society that need the human touch. So we'll have to repurpose.

• 1:00:31Speaker 01

  We'll have to, as machines are able to do some tasks, people are going to have to rescale and move into other areas. And that's probably what's going to be going on for the next, you know, 10, 20, 30 years or more, a kind of big restructuring of society. We'll get wealthier and people will have to do new skills. Now, if you turn the doubt further, I don't know, 50 or 100 years into the future. Then, you know, maybe all bets are off. Then it's possible that that machines will be able to do most of what people do. You know, say, one or 200 years, I think it's even likely. And at that point, then we're more in the sort of abundance economy than we're in a world where there's really little for that humans can do economically better than machines

• 1:01:15Speaker 01

  other than be human. And, you know, that will take a transition as well, kind of more of a transition of how we get meaning in life and what our values are. But but shame on us if we screw that up. I mean, that should be like great, great news.

• 1:01:30Lex Fridman?

  Yeah.

• 1:01:30Speaker 01

  And it kind of saddens me that some people see that as like a big problem. You know, I think I would be, should be wonderful if if people have all the health and material things that they need and can focus on loving each other and discussing philosophy and playing and doing all the other things that don't require work.

• 1:01:47Lex Fridman?

  Do you think you'd be surprised to see what the 20, like, if we were to travel in time 100 years into the future, do you think you'll be able to, like, if I gave you a month to, like, talk to people, no, like, let's say, a week, would you be, would you be able to understand what the hell's going on?

• 1:02:05Speaker 01

  You mean, if I was there for a week?

• 1:02:06Lex Fridman?

  Yeah, if you were there for a week.

• 1:02:08Speaker 01

  A hundred years in the future?

• 1:02:09Lex Fridman?

  Yeah. So like, so I'll give you one thought experiment is like, isn't it possible that we're all living in virtual reality by then?

• 1:02:17Speaker 01

  Yeah. No, I think that's very possible. You know, I've played around with some of those VR headsets and they're not great, but I mean, the average person spends many waking hours staring at screens right now. You know, they're kind of low res compared to what they could be in 30 or 50 years. But certainly games and why not any other interactions could be done with VR and that would be a pretty different world and we'd all, you know, in some ways be as rich as we wanted, you know, we could have castles and we could be traveling anywhere we want. And it could obviously be multisensory. So that would be, that would be possible, you know, and of course, there's people, you know, you've had Elon Musk on and others, you know, there are people, Nick Bostrom, you

• 1:03:01Speaker 01

  know, makes the simulation argument that maybe we're already there.

• 1:03:04Lex Fridman?

  Already there. So but but in general, or do you not even think about in this kind of way, you're self-critically thinking, how good are you? As an economist at predicting what the future looks like.

• 1:03:17Speaker 01

  Well, it starts getting, I mean, I feel reasonably comfortable the next five, 10, 20 years in terms of that path. When you start getting truly superhuman artificial intelligence,

• 1:03:32Speaker 01

  kind of by definition, I'll be able to think of a lot of things that I couldn't have thought of and create a world that I couldn't even imagine. And so I'm not sure I can, I could predict what that world is going to be like. One thing that AI researchers, AI safety researchers worry about is what's called the alignment problem. When an AI is that powerful, then they can do all sorts of things. You really hope that their values are aligned with our values. And it's even tricky defining what our values are. I mean, first off, we all have different values. And secondly, maybe if we were smarter, we would have better values. Like, you know, I like to think that we have better values than he did in 1860.

• 1:04:14Speaker 01

  And or in, you know, the year 200 B.C. on a lot of dimensions, things that we consider barbaric today. And it may be that if I thought about it more deeply, I would also be morally evolved. Maybe I'd be a vegetarian or do other things that right now, whether my future self would consider kind of immoral. So that's a tricky problem, getting the AI to do what we want, assuming it's even a friendly AI. I mean, I should probably mention, there's a non-trivial other branch where we destroy ourselves, right? I mean, there's a lot of exponentially improving technologies that could be ferociously destructive,

• 1:04:54Speaker 01

  whether it's in nanotechnology or biotech and weaponized viruses, AI and other things that.

• 1:05:01Lex Fridman?

  Nuclear weapons.

• 1:05:02Speaker 01

  Nuclear weapons, of course.

• 1:05:03Lex Fridman?

  The old school technology.

• 1:05:04Speaker 01

  Yeah, good old, good old nuclear weapons that could, could be devastating or even existential and new things yet to be invented. So that's a branch that.

• 1:05:16Speaker 01

  You know, I think it's pretty significant. And there are those who think that one of the reasons we haven't been contacted by other civilizations, right, is that is that once you get to a certain level of complexity in technology, there's just too many ways to go wrong. There's a lot of ways to blow yourself up and people, or I should say species, end up falling into one of those traps. The great filter.

• 1:05:41Lex Fridman?

  The great filter. I mean, there's an optimistic view of that. If there is literally no intelligent life out there in the universe, or at least in our galaxy, that means that we've passed at least one of the great filters or some of the great filters that we survived.

• 1:05:57Speaker 01

  Yeah, no, I think these Robin Hanson has a good way of maybe others. They have a good way of thinking about this, that if there are no other intelligence creatures out there and that we've been able to detect, one possibility is that there's a filter ahead of us. And when you get a little more advanced, maybe in 100 or 1,000 or 10,000 years, things just get destroyed for some reason.

• 1:06:17Lex Fridman?

  Yeah.

• 1:06:18Speaker 01

  The other one is the great filters behind us. That will be good is that most planets don't even evolve life. Or if they don't evolve life, they don't involve intelligent life. Maybe we've gotten past that. And so now maybe we're on the good side of the of the great filter.

• 1:06:33Lex Fridman?

  So if we sort of rewind back and look at the the thing where we could say something a little bit more comfortably at five years and 10 years out. You've written about jobs and the impact on sort of our economy and the jobs in terms of artificial intelligence that might it might have. It's a fascinating question of what kind of jobs are safe, what kind of jobs are not. Can you maybe speak to your intuition about how we should think about AI changing the landscape of work?

• 1:07:07Speaker 01

  Sure, absolutely. Well, this is a really important question because I think we're very far from artificial general intelligence, which is AI that can just do the full breadth of what humans can do. But we do have. Human level or superhuman level narrow intelligence, narrow artificial intelligence. And, you know, obviously, my calculator can do math a lot better than I can. And there's a lot of other things machines can do better than I can. So which is which? We actually set out to address that question with Tom Mitchell. I wrote a paper called What Can Machine Learning Do that was in Science? And we went and interviewed a whole bunch of AI experts and kind of synthesized what they thought machine learning was good at and wasn't good at.

• 1:07:49Speaker 01

  And we came up with what we called a rubric, basically a set of questions you can ask about any task that will tell you whether it's likely to score high or low on suitability for machine learning. And then we've applied that to a bunch of tasks in the economy. In fact, there's a data set of all the tasks in the US economy, believe it or not, it's called ONET. The US government put it together, part of Bureau of Labor Statistics. They divide the economy into about 970 occupations, like bus driver, economist, primary school teacher, radiologist. And then for each one of them, they describe which tasks need to be done. Like for radiologists, there are 27 distinct tasks. So we went through all those tasks to see whether or not a machine could do them. And what we found, interestingly, was-

• 1:08:34Lex Fridman?

  Brilliant study, by the way. That's so awesome.

• 1:08:36Speaker 01

  Yeah, thank you. So what we found was that there was no occupation in our data set where machine learning just ran the table and did everything. And there was almost no occupation where machine learning didn't have like a significant ability to do things. Like, take radiology. A lot of people I hear saying, you know, it's the end of radiology. And one of the 27 tasks is read medical images, really important one, like it's kind of a core job. And machines have basically gotten as good or better than radiologists. There's just an article in Nature last week, but, you know, they've been publishing them for the past few years.

• 1:09:10Speaker 01

  Showing that machine learning can do as well as humans on many kinds of diagnostic imaging tasks. But other things radiologists do, you know, they sometimes administer conscious sedation. They sometimes do physical exams. They have to synthesize the results and explain to the other doctors or to the patients. In all those categories, machine learning isn't really up to snuff yet. So that job, we're going to see a lot of restructuring. Parts of the job they'll hand over to machines, others, humans will do more of. And that's been more or less the pattern in all of them. So, you know, to oversimplify it, but we see a lot of restructuring, reorganization of work. And it's real, going to be a great time, it is a great time for smart entrepreneurs and managers to do that reinvention of work.

• 1:09:54Speaker 01

  I'm not going to see mass unemployment. To get more specifically to your question, the kinds of tasks that machines tend to be good at are a lot of routine problem solving, mapping inputs X into outputs Y. If you have a lot of data on the X's and the Y's, the inputs and the outputs, you can do that kind of mapping and find the relationships. They tend to not be very good at, even now, fine motor control and dexterity, emotional intelligence and human interactions, and thinking outside the box, creative work. If you give it a well-structured task, machines can be very good at it. But even asking the right questions, that's hard.

• 1:10:36Speaker 01

  There's a quote that Andrew McAfee and I use in our book, Second Machine Age. Apparently Pablo Picasso was shown an early computer and he came away kind of unimpressed. He goes, "Well, I don't see all the fusses. All that does is answer questions." And, you know, to him, the interesting thing was asking the questions.

• 1:10:54Lex Fridman?

  Yeah. Try to replace me, GPT-3. I dare you. Although some people think I'm a robot. You have this cool plot that shows...

• 1:11:04Lex Fridman?

  I just remember where economists land. Where I think the X axis is the income.

• 1:11:10Speaker 01

  Yes.

• 1:11:11Lex Fridman?

  And then the Y axis is, I guess, aggregating the information of how replaceable the job is. Or I think there's an index.

• 1:11:18Speaker 01

  Suitability for machine learning index, exactly. So we have all 970 occupations on that chart. And there's scattered and all, you know, all four corners have some occupations. But there is a definite pattern, which is the lower wage occupations tend to have more tasks that are suitable for machine learning, like cashiers. I mean, anyone who's gone to a supermarket or CVS knows that, you know, they not only read barcodes, but they can recognize, you know, an apple and an orange and, and a lot of things cashiers, humans used to be needed for. At the end of this, other end of the spectrum, there are some jobs like airline pilot that are among the highest paid in our economy, but also a lot of them are suitable for machine learning, a lot of those tasks are. And then, yeah, you mentioned economists, I couldn't help peeking at those and they're

• 1:12:02Speaker 01

  paid a fair amount, maybe not as much as some of us think they should be. But, but they have some tasks that are suitable for machine learning, but for now at least, most of the tasks economists do are, didn't end up being in that category. And I should say, I didn't like create that data, we just took the, took the analysis and that's what's, it came out of it. And over time, that scatter plot will be updated as, as, as the technology improves. But it was just interesting to see the pattern there and it is a little troubling insofar as if you just take the technology as it is today, it's likely to worsen income inequality on a lot of dimensions.

• 1:12:39Lex Fridman?

  So on the, on this topic of the effect of AI on our, on the landscape of work, one of the people that have been speaking about it in the public domain, public discourse is the presidential candidate, Andrew Yang.

• 1:12:55Speaker 01

  Yeah.

• 1:12:56Lex Fridman?

  What are your thoughts about Andrew? What are your thoughts about UBI? That universal basic income that he made one of the core idea? By the way, he has like hundreds of ideas about like everything. It's kind of, it's kind of interesting.

• 1:13:11Speaker 01

  Yeah.

• 1:13:11Lex Fridman?

  But what are your thoughts about him and what are your thoughts about UBI?

• 1:13:14Speaker 01

  Let me answer, you know, the, the, the question about his broader, you know, approach first, I mean, I just love that. He's really thoughtful, analytical. I agree with his values. So that's awesome. And, and he read my book and, and mentions it sometimes, so it makes me even more exciting.

• 1:13:32Speaker 01

  And the thing that he really made the centerpiece of his campaign was UBI. And I was originally kind of a fan of it. And then, as I studied it more, I became less of a fan, although I'm beginning to come back a little bit. So let me tell you a little bit of my evolution. You know, as an economist, we have, by looking at the problem of people not having enough income, and the simplest thing is, well, why don't we write them a check? Problem solved. But then I talked to my sociologist friends and people being, and they really convinced me that just writing a check doesn't really get at the core values. You know, Voltaire once said that work solves three great ills: boredom, vice, and need. And, you know, you can deal with the need thing by writing a check,

• 1:14:14Speaker 01

  but people need a sense of meaning, they need something to do. And when, you know, say, steelworkers or coal miners lost their jobs and were just given checks, alcoholism, depression, divorce, all those social indicators, drug use, all went way up. People just weren't happy just sitting around collecting a check. Maybe it's part of the way they were raised, maybe it's something innate in people that they need to feel wanted and needed. So it's not as simple as just writing people a check. You need to also give them a way to have a sense of purpose. And that was important to me. And the second thing is that, as I mentioned earlier, you know, we are far from the end of work.

• 1:14:58Speaker 01

  You know, I don't buy the idea that there's just like not enough work to be done. I see, like, our cities need to be cleaned up. And robots can't do most of that. You know, we need to have better childcare, we need better healthcare, we need to take care of people who are mentally ill or older, we need to repair our roads. There's so much work that require, at least partly, maybe entirely, a human component. So rather than like write all these people off, let's find a way to repurpose them and keep them engaged. Now, that said, I do, would like to see more buying power from people who are sort of at the bottom end of the spectrum. The economy has

• 1:15:38Speaker 01

  Been designed and evolved in a way that's, I think, very unfair to a lot of hardworking people. I see super hardworking people who aren't really seeing their wages grow over the past 20, 30 years. While some other people who have been, you know, super smart and/or super lucky have, uh, have had, you know, have made billions or hundreds of billions. And, uh, I don't think they need those hundreds of billions to have the right incentives to invent things. I think if you talk to almost any of them, as I have, you know, they don't think that they need an extra $10 billion to, to do what they're doing. Most of them probably would, would love to do it for only a billion or maybe for nothing.

• 1:16:16Lex Fridman?

  For nothing, many of them, yeah.

• 1:16:18Speaker 01

  I mean, you know, a, a, a interesting point to make is, is, you know, like, do we think that Bill Gates would have founded Microsoft if tax rates were 70%? Well, we know he would have because they were tax rates of 70% when he founded it, you know? So, um, I don't think that's as big a deterrent and we could provide more buying power to people. My own favorite tool is, uh, the earned income tax credit, which is basically a way of supplementing income of people who have jobs and giving employers an incentive to hire even more people. The minimum wage can discourage employment, but the earned income tax credit encourages employment by supplementing, uh, people's wages, you know? If, uh, the employer can only afford to pay him $10 for a task, um, the rest of us pick it,

• 1:17:01Speaker 01

  kick in another five or $10 and bring their wages up to 15 or 20 total. And then they have more buying power than entrepreneurs are thinking, how can we cater to them? How can we make products for them? And it becomes a, a self-reinforcing system where people are better off. Andrew and I had a good discussion where he suggested instead of a, uh, universal basic income, he would suggest it, or instead of an unconditional basic income, how about a conditional basic income? Where the condition is you learn some new skills. We need to reskill our workforce, so let's make it easier for people to, uh, uh, find ways to get those skills and get rewarded for doing them. And that's kind of a neat idea as well.

• 1:17:40Lex Fridman?

  That's really interesting. So, I mean, one of the questions, one of the dreams of UBI is that you provide some little safety net while you retrain, while you're learning new skill.

• 1:17:53Speaker 01

  Yeah.

• 1:17:53Lex Fridman?

  But like, I think, I guess you're speaking to the intuition that. That doesn't always, like, there needs to be some incentive to reskill, to train, to learn new things.

• 1:18:02Speaker 01

  Well, I think it helps. I mean, there are lots of self-motivated people, but there are also people that, you know, maybe need a little guidance or help. And I think it's a really hard question for someone who is losing a job in one area to know what is the new area I should be learning skills in. And we could provide a much better set of tools and platforms that maps it. Okay, here's a set of skills you already have. Here's something that's in demand. Let's create a path for you to go from where you are to where, you know, you need to be.

• 1:18:30Lex Fridman?

  So I'm a total, how do I put it nicely about myself? I'm totally clueless about the economy. It's not totally true, but pretty good approximation. If you were to try to fix our tax system

• 1:18:48Lex Fridman?

  and, or maybe from another side, if there's fundamental problems in taxation or some fundamental problems about our economy, what would you try to fix? What would you try to speak to?

• 1:19:01Speaker 01

  You know, I definitely think our whole tax system, our political and economic system has gotten more and more screwed up over the past 20, 30 years. I don't think it's that hard to make headway in improving it. I don't think we need to totally reinvent stuff. A lot of it is what I've elsewhere with Andy and others called Economics 101. You know, there's just some basic principles that have worked really well in the 20th century that we sort of forgot, you know, in terms of investing in education, investing in infrastructure, welcoming immigrants, having a tax system that was more progressive and fair.

• 1:19:40Speaker 01

  At one point, tax rates were on top incomes were significantly higher, and they've come down a lot to the point where in many cases, they're lower now than they are for poorer people.

• 1:19:52Speaker 01

  So, and we could do things like earned income tax credit to get a little more wonky. I'd like to see more Pigouvian taxes. What that means is you tax things that are bad instead of things that are good. So, right now, we tax labor, we tax capital, and which is unfortunate because one of the basic principles of economics, if you tax something, you tend to get less of it. So, you know, right now, there's still work to be done and still capital to be invested in, but instead, we should be taxing things like pollution and congestion. And if we did that, we would have less pollution. So, a carbon tax is, you know, almost every economist would say it's a no-brainer, whether they're Republican or Democrat.

• 1:20:35Speaker 01

  Greg Mankew, who is head of George Bush's Council of Economic Advisers, or Dick Schmollensky, who is another Republican economist, agree, and of course, a lot of Democratic economists agree as well. If we taxed carbon, we could raise hundreds of billions of dollars. We could take that money and redistribute it through an earned income tax credit or other things so that overall, our tax system would become more progressive. We could tax congestion. One of the things that kills me as an economist is every time I sit in a traffic jam, I know that it's completely unnecessary. It's, this is complete waste of time.

• 1:21:12Lex Fridman?

  You could just visualize the cost and productivity that this is creating.

• 1:21:15Speaker 01

  Exactly, because they are taking cost from me and all the people around me. And if they charged a congestion tax, they would take that same amount of money and people would, it would streamline the roads. Like, when you're in Singapore, the traffic just flows because they have a congestion tax. They listen to economists. They invited me and others to go talk to them. And then, I'd still be paying, I'd be paying a congestion tax instead of paying in my time, but that money would now be available for healthcare, be available for infrastructure, or be available just to give to people so they could buy food or whatever. So, it's just, it saddens me when, you know, when you're sitting, when you're sitting in a traffic jam, it's like taxing me and then taking that money and dumping it in the ocean, just like destroying it. So there are a lot of things like that, that economists, and I'm not, I'm not like doing

• 1:22:00Speaker 01

  anything radical, or most, you know, good economists would, would, would, I probably agree with me point by point on these things. And we could do those things and our whole economy become much more efficient, it become fair, invest in R and D and research, which is close to a free lunch is what we have. My first wild MIT colleague, Bob Solow, got the Nobel Prize, not yesterday, but, but thirty years ago, for describing that most improvements in living standards from, come from tech progress. And Paul Romer later got a Nobel Prize for noting that investments in R and D and human capital can speed the rate of tech progress. So if we do that, then we'll be healthier and wealthier.

• 1:22:42Lex Fridman?

  Yeah, from an economics perspective, I remember taking an undergrad econ, you mentioned econ one on one.

• 1:22:47Speaker 01

  Mm hmm.

• 1:22:48Lex Fridman?

  It seemed, from all the plots I saw, that R and D is an obvious, you call it as close to free lunch as

• 1:22:55Speaker 01

  Right.

• 1:22:55Lex Fridman?

  as we have. It seemed like obvious that we should do more research.

• 1:22:59Speaker 01

  It is.

• 1:23:00Lex Fridman?

  Like, what, what, like, you know?

• 1:23:03Speaker 01

  There's no Well, we should do basic research. I mean, so, well, let me just be clear. It'd be great if everybody did more research. And I would make this thing be to apply development versus basic research. So applied development, like, you know, how do we get this, this self-driving car, you know, feature to work better in the Tesla? That's great for private companies because they can capture the value from that. If they make a better self-driving car system, they can sell cars that are more valuable and they make money. So there's an incentive that, there's not a big problem there. And, and smart companies, Amazon, Tesla and others are investing in it. The problem is with basic research, like, coming up with core basic ideas, whether it's in nuclear fusion, or artificial intelligence, or biotech. There,

• 1:23:47Speaker 01

  if someone invents something, it's very hard for them to capture the benefits from it. It's shared by everybody, which is great in a way, but it means that they don't going to have the incentives to put as much effort into it. There you need, it's a classic public good. There you need the government to be involved in. And the US government used to be investing much more in R and D. But we have slashed that part of the government really foolishly. And we're all poorer, significantly poorer as a result. Growth rates are down. We're not having the kind of scientific progress we used to have. It's been sort of a, a short-term You know, eating the seed, corn, whatever metaphor you want to use, where people grab some money, put it in their pockets today,

• 1:24:30Speaker 01

  but five, 10, 20 years later, they're a lot poorer than they otherwise would have been.

• 1:24:37Lex Fridman?

  So we're living through a pandemic right now globally in the United States.

• 1:24:44Lex Fridman?

  From an economics perspective, how do you think this pandemic will change the world?

• 1:24:50Speaker 01

  It's been remarkable. And, you know, it's horrible how many people have suffered, the amount of death, the economic destruction. It's also striking just the amount of change in work that I've seen. In the last 20 weeks, I've seen more change than there were in the previous 20 years. There's been nothing like it since probably the World War II mobilization in terms of reorganizing our economy. You know, the most obvious one is the shift to remote work. And I and many other people stopped, you know, going into the office and teaching my students in person. I did a study on this with a bunch of colleagues at MIT and elsewhere. And what we found was that before the pandemic, in the beginning of 2020,

• 1:25:31Speaker 01

  about one in six, a little over 15% of Americans were working remotely. When the pandemic hit, that grew steadily and hit 50%. Roughly half of Americans working at home. So a complete transformation. And of course, it wasn't even, it wasn't like everybody did it. If you're an information worker, professional, if you work mainly with data, then you're much more likely to work at home. If you're a manufacturing worker, you know, working with other people or physical things, then it wasn't so easy to work at home. And instead, those people were much more likely to become laid off or unemployed. So it's been something that's had very disparate effects on different parts of the workforce.

• 1:26:11Lex Fridman?

  Do you think it's going to be sticky? In a sense that after vaccine comes out and the economy reopens, do you think remote work will continue?

• 1:26:22Speaker 01

  That's a great question. My hypothesis is yes, a lot of it will. Of course, some of it will go back, but a surprising amount of it will stay. I personally, for instance, I moved my seminars, my academic seminars to Zoom, and I was surprised how well it worked.

• 1:26:39Lex Fridman?

  So it works.

• 1:26:39Speaker 01

  Yeah, I mean, obviously, we are able to reach a much broader audience. So we have people tuning in from Europe and other countries, just all over the United States for that matter. I also actually found that it in many ways is more egalitarian. We use the chat feature and other tools, and grad students and others who might have been a little shy about speaking up, we now kind of have more of ability for lots of voices, and they're answering each other's questions. So you kind of get parallel. Like, if someone had some question about some of the data or a reference or whatever, then someone else in the chat would answer it. And the whole thing just became like a higher bandwidth, higher quality thing. So I thought that was kind of interesting. I think a lot of people are discovering that these tools that, thanks to technologists, have been developed over the past decade, they're

• 1:27:24Speaker 01

  a lot more powerful than we thought. I mean, of all the terrible things we've seen with COVID and the real failure of many of our institutions that I thought would work better, one area that's been a bright spot is our technologies. Bandwidth has held up pretty well, and all of our email and other tools have just scaled up kind of gracefully. So that's been a plus. Economists call this question of whether it'll go back a hysteresis. The question is, like, when you boil an egg, after it gets cold again, it stays hard. And I think that we're going to have a fair amount of hysteresis in the economy. We're going to move to this new, we have moved to a new remote work system, and it's not going to snap all the way back to where it was before.

• 1:28:06Lex Fridman?

  One of the things that worries me is that the people with lots of followers on Twitter

• 1:28:16Lex Fridman?

  And people with voices, people that can, voices that can be magnified by, you know, reporters and all that kind of stuff are the people that fall into this category that we were referring to just now, where they can still function and be successful with remote work. And then there is a kind of quite quiet suffering of what feels like millions of people whose jobs are disturbed profoundly by this pandemic, but they don't have many followers on Twitter.

• 1:28:53Lex Fridman?

  What do we, and, and again, I apologize, but I've been reading the rise and fall of the Third Reich, and there's a connection to the depression on the American side. There's a deep, complicated connection to how suffering can turn into forces that potentially change the world in, in destructive ways. So like, it's something I worry about is like, what is this suffering going to materialize itself in five, 10 years?

• 1:29:25Speaker 01

  Yeah.

• 1:29:25Lex Fridman?

  Is that something you worry about, think about?

• 1:29:28Speaker 01

  It's like the center of what I worry about. And, and let me break it down to two parts. You know, there's a moral and ethical aspect to it. We need to relieve this suffering. I mean, I'm, I'm share the values of, I think, most Americans. We like to see shared prosperity or most people on the planet. And we would like to see people not falling behind, and they have fallen behind, not just due to COVID, but in the previous couple of decades, median income has barely moved, you know, depending on how you measure it. And the incomes of the top 1% have, have skyrocketed. And our, part of that is due to the ways technology has been used. Part of this has been due to frankly, our political system has continually shifted more wealth into those people who have the powerful interest.

• 1:30:12Speaker 01

  So there's just a, I think a moral imperative to do a better job. And ultimately, we're all going to be wealthier if more people can contribute, more people have the wherewithal. But the second thing is that there's a, a real political risk. I'm not a political scientist, but you don't have to be. One, I think, to see how a lot of people are really upset with their getting a raw deal and they are going to, you know, they want to smash the system in different ways in 2016 and 2018 and now I think there are a lot of people who are looking at the political system and they feel like it's not working for them and they just want to do something radical.

• 1:30:52Speaker 01

  Unfortunately, demagogues have harnessed that in a way that is pretty destructive to the country. And an analogy I see is what happened with trade. You know, almost every economist thinks that free trade is a good thing, that when two people voluntarily exchange, almost by definition, they're both better off if it's voluntary. And so generally, trade is a good thing, but they also recognize that trade can lead to uneven effects, that there can be winners and losers in some of the people who didn't have the skills to compete with somebody else or didn't have other assets. And so trade can shift prices in ways that are averse to some people.

• 1:31:36Speaker 01

  So there's a formula that economists have, which is that you have free trade, but then you compensate the people who were hurt. And free trade makes the pie bigger, and since the pie is bigger, it's possible for everyone to be better off. You can make the winners better off, but you can also compensate those who don't win, and so they end up being better off as well. What happened was that we didn't fulfill that promise. We did have some more increased free trade in the 80s and 90s, but we didn't compensate the people who were hurt. And so they felt like the, you know, the people in power reneged on the bargain, and I think they did. And so then there's a backlash against trade.

• 1:32:16Speaker 01

  And now both political parties, but especially Trump and company, have really pushed back against free trade. Ultimately, that's bad for the country. Ultimately, that's bad for living standards, but in a way I can understand that people felt they were betrayed. Technology has a lot of similar characteristics. Technology can make us all better off. It makes the pie bigger. It creates wealth and health. But it can also be uneven. Not everyone automatically benefits. It's possible for some people, even the majority of people, to get left behind while a small group benefits. What most economists would say, well, let's make the pie bigger, but let's make sure we adjust the system

• 1:33:00Speaker 01

  so we compensate the people who are hurt. And since the pie is bigger, we can make the rich richer. We can make the middle class richer. We can make the poor richer. Mathematically, everyone could be better off. But again, we're not doing that. And again, people are saying, this isn't working for us. And again, instead of fixing the distribution, a lot of people are beginning to say, hey, technology sucks. We've got to stop it. Let's throw rocks at the Google bus.

• 1:33:26Lex Fridman?

  Let's blow it up.

• 1:33:27Speaker 01

  Let's blow it up. And you know, there were the Luddites almost exactly 200 years ago who smashed the looms and the spinning machines because they felt like those machines weren't helping them. We have a real imperative, not just to do the morally right thing, but to do the thing that is going to save the country, which is make sure that we create not just prosperity, but shared prosperity.

• 1:33:50Lex Fridman?

  So you've been at MIT for over 30 years.

• 1:33:54Speaker 01

  Ah, don't tell anyone how old I am. Yeah, that's true.

• 1:33:57Lex Fridman?

  And you're now moved to Stanford. I'm going to try not to say anything about how great MIT is. What's that movement like? What it's East Coast to West Coast.

• 1:34:12Speaker 01

  Well, MIT is great. MIT has been very good to me. It continues to be very good to me. It's an amazing place. There's I continue to have so many amazing friends and colleagues there. I'm very fortunate to have been able to spend a lot of time at MIT. Stanford's also amazing. And part of what attracted me out here was not just the weather, but also, you know, Silicon Valley, let's face it, is really more of the epicenter of the technological revolution. And I want to be close to the people who are inventing AI and elsewhere. A lot of it is being invented, MIT for that matter, in Europe and China and elsewhere, India. But but being a little closer to some of the key technologists was something that was important to me. And, and, you know, it may be shallow, but I also do enjoy the good weather.

• 1:34:57Speaker 01

  And I'm, you know, I felt a little ripped off when I came here, you know, a couple of months ago. And immediately there are the fires and, you know, my eyes were burning, the sky was orange. And there's the heat waves and, you know, so it wasn't exactly what I've been promised. But I'm, fingers crossed it'll, it'll get back to better.

• 1:35:15Lex Fridman?

  Maybe on a brief aside, there's been some criticism of academia and universities and different avenues. And I, as a person who's gotten to enjoy universities from the, the pure playground of ideas that it can be,

• 1:35:33Lex Fridman?

  always kind of try to find the words to tell people that these are magical places. Is there something that you can speak to that is beautiful or powerful about universities?

• 1:35:50Speaker 01

  Well, sure. I mean, first off, I mean, economists have this concept called revealed preference. You can ask people what they say, or you can watch what they do. And so obviously by revealed preferences, I love academia. I'm out here, I could be doing lots of other things, but it's something I enjoy a lot. And I, and I think the word magical is exactly right, at least it is for me. I do what I love, you know, hopefully my dean won't be listening, but I would do this for free.

• 1:36:14Speaker 01

  You know, it's, it's just what I like to do. I like to do research. I love to have conversations like this with you and with my students, with my fellow colleagues. I love being around the smartest people I can find and learning something from them and having them challenge me. And that just gives me, gives me joy. And every day I find something new and exciting to work on. And a university environment is really filled with other people who feel that way. And so I feel very fortunate to be part of it. And I'm lucky in a, that I'm in a society where I can actually get paid for it and put food on the table while doing the stuff that I really love. And I hope someday everybody can have jobs that are, are like that. And I appreciate that it's not necessarily easy for everybody to have a job that they both love. And also they get paid for.

• 1:36:58Speaker 01

  So there are things that don't go well in academia, but by and large, I think it's a kind of, you know, kinder, gentler version of a lot of the world.

• 1:37:05Lex Fridman?

  Yeah, that's true.

• 1:37:06Speaker 01

  You know, we sort of cut each other a little slack on things like, you know. On just a lot of things, you know, of course, there's harsh debates and discussions about things and some petty politics here and there. I personally, I try to stay away from most of that sort of politics. It's not my thing. And so it doesn't affect me most of the time, sometimes a little bit, maybe. But, but, you know, being able to pull together something, we have the digital economy lab. We get all these brilliant grad students and undergraduates and postdocs that are just doing stuff that I learned from. And every one of them has some aspect of what they're doing that's just, I couldn't even understand. It's like way, way more brilliant. And that's really, to me, actually, I really enjoy that, being in a

• 1:37:50Speaker 01

  room with lots of other smart people. And, and Stanford has made it very easy to attract, you know, those people. I just, you know, say, I'm going to do a seminar, whatever, and the people come, they come and want to work with me. We get funding, we get data sets, and it's, it's come together real nicely.

• 1:38:08Lex Fridman?

  And the rest is just fun.

• 1:38:11Speaker 01

  It's fun. Yeah. And we feel like we're working on important problems, you know, and we're doing things that, you know, I think are first order in terms of what's important in the world. And that's very satisfying to me.

• 1:38:23Lex Fridman?

  Maybe a bit of a fun question. What three books, technical fiction, philosophical,

• 1:38:31Lex Fridman?

  you've enjoyed, had a big, big impact in your life?

• 1:38:34Speaker 01

  Well, I guess I go back to like my, my teen years and, and, you know, I read Siddhartha, which is a philosophical book and kind of helps keep me, keep me centered.

• 1:38:42Lex Fridman?

  By Herman Hesse.

• 1:38:43Speaker 01

  Yeah, by Herman Hesse, exactly. Don't get too wrapped up in material things or other things and just sort of, you know, try to find peace on things. A book that actually influenced me a lot in terms of my career was called The Worldly Philosophers by Robert Howbrenner. It's actually about economists. It goes through a series of different economies, written in a very lively form. And it probably sounds boring, but it did describe whether it's Adam Smith, or Karl Marx, or John Maynard Keynes, and each of them, sort of what their key insights were, but also kind of their personalities. And I think that's one of the reasons I became an economist was, was just. Understanding how they grapple with the big questions of the world.

• 1:39:20Lex Fridman?

  So would you recommend it as a good whirlwind overview of the history of economics?

• 1:39:25Speaker 01

  Yeah, yeah, I think that's exactly right. It kind of takes you through the different things and, you know, so you can understand how they reach, thinking some of the strengths and weaknesses. I mean, probably is a little out of date now, it needs to be updated a bit, but, you know, you could at least look through the first couple hundred years of economics, which is not a bad place to start. More recently, I mean, a book I really enjoyed is by my, my friend in college, Max Tegmark, called Life 3.0. You should have on your podcast if you haven't already.

• 1:39:50Lex Fridman?

  It was episode number one.

• 1:39:53Speaker 01

  Oh, my God.

• 1:39:54Lex Fridman?

  And he's back, he'll be back, he'll be back soon.

• 1:39:57Speaker 01

  Yeah, no, he's terrific. I love the way his brain works and he makes you think about profound things. He's got such a joyful approach to life. And so that's been a great book. And, you know, you learn, I learn a lot from it. I think everybody, but he explains it in a way, even though he's so brilliant, that, you know, everyone can understand, that I can understand. You know, that's three, but let me mention maybe one or two others. I mean, I recently read More From Less by my, my sometimes co-author Andrew McAfee. It made me optimistic about how we can continue to have rising living standards while living more lightly on the planet. In fact, because of higher living standards, because of technology, because of digitization that I mentioned, we don't have to have as big an impact on

• 1:40:40Speaker 01

  the planet. And that's a great story to tell, and he documents it very carefully.

• 1:40:47Speaker 01

  You know, a personal kind of self-help book that I found kind of useful, people is Atomic Habits. I think it's, what's his name, James Clear?

• 1:40:53Lex Fridman?

  Yeah, James Clear.

• 1:40:55Speaker 01

  He's just, yeah, it's a good name because he writes very clearly. And, you know, most of the sentences I read in that book, I was like, yeah, I know that. But it just really helps to have somebody like remind you and tell you and kind of just reinforce it. And

• 1:41:08Lex Fridman?

  So build habits in your life that you, you hope to have, that have a positive impact and don't have to make it big things. It could be just tiny little.

• 1:41:16Speaker 01

  Exactly. I mean, the word atomic, it's a little bit of a pun, I think he says, you know, one atomic means a really small, you take these little things, but also like atomic power, it can have like, you know, big impacts.

• 1:41:27Lex Fridman?

  That's funny. Yeah. The biggest ridiculous question, especially to ask an economist, but also a human being, what's the meaning of life?

• 1:41:36Speaker 01

  I hope you've gotten the answer to that from somebody. I think we're all still working on that one. But what is it? You know, I actually learned a lot from my son, Luke, and he's nineteen now, but he's always loved philosophy. And he reads way more sophisticated philosophy than I do. I went and took him to Oxford, and he spent the whole time like pulling all these obscure books down and reading them. And a couple of years ago, we had this argument, and he was trying to convince me that hedonism was the ultimate, you know, meaning of life, just pleasure seeking. Well, how old was he at the time? Seventeen.

• 1:42:11Speaker 01

  But he made a really good like intellectual argument for it, too. But it just didn't strike me as right. And I think that, you know, while I am kind of a utilitarian, like, you know, I do think we should do the greatest good for the greatest number, that's just too shallow. And I think I've convinced myself that real happiness doesn't come from seeking pleasure. It's kind of a little, it's ironic, like, if you really focus on being happy, I think it doesn't work. You gotta like be doing something bigger. It's, I think the analogy I sometimes use is, you know, when you look at a dim star in the sky, if you look right at it, it kind of disappears, but you have to look a little to the side, and then the parts of your retina that are better at absorbing light, you know, can pick it up better. It's the same thing

• 1:42:54Speaker 01

  with happiness. I think you need to sort of find something other goal, something, some meaning in life, and that ultimately makes you happier than if you go squarely at just pleasure. And so for me, you know, the kind of research I do that I think is trying to change the world, make the world a better place, and I'm not like an evolutionary psychologist, but my guess is that our brains are wired not just for pleasure, but we're social animals, and we're wired to like help others. And ultimately, you know, that's something that's really deeply rooted in our psyche. And if we do help others, if we do, or at least feel like we're helping others, you know, our reward systems kick in, and we end up being more deeply satisfied than if we just do something

• 1:43:39Speaker 01

  selfish and shallow.

• 1:43:41Lex Fridman?

  Beautifully put. I don't think there's a better way to end it. Eric, you're one of the people when I first showed up at MIT that made me proud to be at MIT. So it's so sad that you're now at Stanford, but I'm sure you'll do wonderful things at Stanford as well. I can't wait till future books and people should definitely read.

• 1:43:59Speaker 01

  Well, thank you so much. And I think we're all, we're all part of the invisible college, as we call it. You know, we're all part of this intellectual and human community where we all can learn from each other. It doesn't really matter physically where we are so much anymore.

• 1:44:12Lex Fridman?

  Beautiful. Thanks for talking today.

• 1:44:14Speaker 01

  My pleasure.

• 1:44:15Lex Fridman?

  Thanks for listening to this conversation with Eric Binjalsen, and thank you to our sponsors. Insera Watches, the maker of classy, well-performing watches. Four Sigmatic, the maker of delicious mushroom coffee. ExpressVPN, the VPN I've used for many years to protect my privacy on the internet. And Cash App, the app I use to send money to friends. Please check out these sponsors in the description to get a discount and to support this podcast. If you enjoy this thing, subscribe on YouTube, review it with five stars and up a podcast. Follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Fridman. And now let me leave you with some words from Albert Einstein.

• 1:44:57Lex Fridman?

  It has become appallingly obvious that our technology has exceeded our humanity. Thank you for listening and hope to see you next time.