Lex Fridman Podcast - #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology

Episode Date: November 25, 2020

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

The following is a conversation with Eric Brinjalsson. He is an economics professor, 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 eSponsor, followed by some thoughts related to the episode. Vincera watches, the maker of classy, well performing watches. Forsegmatic, the maker of delicious mushroom coffee, expressive pn, the VPN I've used for many years to protect my privacy on the internet and

cash app. The app I used has said many different. Please check out these sponsors in the description to get 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 of mongering camp or the technologically topianism camp.

As always, the future will land as the war in between. I prefer to wear two hats in these discussions and alternate between them often. 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, non-stop, staying isolated, 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, 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 will need to go to the end of the world for just such a moment. If you enjoyed this thing, subscribe on YouTube,

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Cash app lets you send money to friends by 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 there are also a lot of free thinkers and technical geniuses whose ideas are worth exploring in depth and with

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You posted a quote on Twitter by Albert Barlett saying that the greatest shortcoming of the human race is our inability to understand the exponential function. Why would you say the exponential growth is important to understand? Yeah, that quote, I remember posting that. It's actually a reprise of something Andy Mac if you 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 two or three days and I can see, oh my God, this is gonna be a catastrophe and it's gonna 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 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, because of COVID. And she's like, this is early March.

Early March. But, you know, I was worried because I knew I could see, or I suspect it, 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. would continue and it did and pretty soon we have 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 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, 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 that can be good in the case of things creating wonders, but it can be dangerous in the case of viruses and other things.

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 in which aspects of our society does exponential growth apply? Well, you can learn the math, but the truth is our brains, I think, tend to learn more from experiences. So we just start seeing it more and more often.

So, hanging around Silicon Valley, hanging around AI and computer researches, 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-3 and how rapidly natural language has improved. But I think that as the world becomes more exponential,

we'll all start experiencing 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. Well, the weird thing is that O's kind of looks linear in the moment. Like the, you know, it's hard to feel, it's hard to feel, it's hard to retrospect 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 the investments of AI or even just social media, all the various technologies that go into the digital umbrella. It feels pretty common, normal, and gradual. 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 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, other dysfunctions in our political and economic systems. So one guy that talks about exponential functions a lot of zeal musk, he seems to internalize

this kind of way of exponential thinking. He calls it first principles thinking, so to the kind of going to the basics, asking the question, like, what were the assumptions of the past, 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 often gets me in trouble because he overestimates like he doesn't meet

the initial estimates of the deadlines, but he seems to deliver late, but deliver. Right. And which is kind of interesting. Like, what are your thoughts about this whole thing? Well, no, I think we can all learn from Elon. I think going to first principles, I talked about two ways 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 can become thousands or tens or hundreds of thousands of them in a month, it makes sense when she 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 something we can all learn from. In my own life, I remember one of my first experiences, really seeing it was when I was a grad student and my advisor asked

me to plot the growth of computer power in the US economy in different industries. They're all these exponentially growing curves. I was like, Holy shit, look at this. In each industry, it was just taking off. You have to be a rocket scientist to extend that and say, wow, this means that this was in the late 80s and early 90s. 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. So when people look at Moore's Law, 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 stack on top of revolutions. Do you have any intuition on how the head humans keep finding ways to revolutionize things? Well, first let me just unpack that first point that I talked about exponential curves,

but no exponential curve continues forever. It's been said that if anything can't go on forever, eventually it will stop. And that's very profound. 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 of cap with COVID as well.

I mean, 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 more remarkable thing, as you alluded 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, and just extend it further and further. 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 in anyone who can do improvements in that part can reap huge returns so that the resources automatic get focused on whatever part of the system is in keeping up. Do you think some version of the Moore's Law will continue? 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 is where recording this. I'm not sure when you're

going to make it very simple after this. 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. 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, 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 more as law. So I think that the raw silicon isn't improving as much as it used to, but these other dimensions are becoming more important and we're seeing progress in them. 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. It's fascinating to think like, can I actually continue?

I was figuring out more and more tricks on how to train networks faster. 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 a wash and 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 probably 50 digital photos yesterday. I wouldn't have done that if it was chemical. And we have the internet of things and all sorts of other types of data.

When we walk around with our phone, it's just broadcasting a huge amounts of digital data that can be used as training sets. And then last but not least, as they mentioned in OpenAI, as they mentioned at OpenAI, there have been significant improvements in the techniques. 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 100-fold improvement in computer power, 100-fold or more improvement in data, hundredfold improvement in techniques of software and algorithms, and soon you're getting into millionfold improvements. You know, somebody brought this up, this idea with GPT-3 that it's, so it's training a self-supervised way

on basically internet data. And that's one of the, I've seen arguments made, and it seemed 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. It's a,

I know we make it to the point where it's consumed basically all of human knowledge or all digitized human knowledge. Yeah. And that will be the bottleneck. But the, 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 critics come up with, which is fascinating. I don't know how you overcome the data bottleneck, but probably more efficient training algorithms. Yeah, well, you already

mentioned 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, We also are just capturing a lot more data than we used to, especially in China, but all around us. So those are both important. In some applications, you can simulate the data, video games, some of the self-driving car systems or simulating driving, and of course that has some risks and weaknesses, but you can also, if if you want to, you know, exhaust all the different ways you could beat a video game, you could just simulate all the options. Can we take a step in that direction of autonomous vehicles?

Like you're talking the CTO of Waymo tomorrow. And obviously, I'm talking to Elon again in a couple of weeks. What's your thoughts on autonomous vehicles? Like where do we stand? Well, as a problem that has the potential of revolutionizing the world. Well, 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 there's a whole continuum of how much driving and assisting the car can do. I noticed that you're right next to your next door to Toyota Resursion too. There's a total accident. I love the TRI folks, but yeah. Have you talked to Gil Pratt? Yeah, we're going to, we're supposed to talk. It's kind of hilarious. So there's kind of the op, I think it's a good counterpart to see what Elon is doing. And hopefully they can be frank with they think about each other because I've heard both of them talk about it. But they're much more, this is an assistive, a guardian angel

that watches over you as opposed to try to do everything. I think there's some things like driving on a highway from LA to Phoenix, where it's mostly good weather, straight roads, that's close to a solve problem. Let's face it. In other situations, you know, driving through the snow and Boston, where the roads are kind of crazy, and most importantly, you have to make a lot of judgments about what the other drivers are going to do with these intersections that aren't really right angles and aren't

very well described. It's more like game theory. 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. What do you think about the Waymo? So as you mentioned, two companies that actually have cars on the road, there's the way more 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'd be perfect. Yeah. Well, I'm smart enough to be humble and not try to get between that. 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, I would shut down the industry, but it was more of a blip actually. That was interesting. Of course, there's still a concern that if there could be setbacks, if we do this wrong,

your listeners may be familiar with the different levels of self-driving, level one, two, three, four, five. I think Andrew Eng 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 is the way things are going to evolve. But you can 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.

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, and the Phoenix, Arizona, to, you know, you can get a ride in a Waymo car with no person, no driver. Oh, they've taken away the safety driver?

Oh, yeah, there's for a while now there's been no safety driver. Okay. Cause 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, then they added a second safety driver because the first safety driver would fall asleep. That's like I'm not sure they're going in the right direction with that. No, they, they've, uh, way more particular than a really good job of that. No, they they've way more particular than a really good job of that. They actually have a 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. Yeah, any time tune into the car, I bet they can probably remotely control it as well, but that's officially not the function that they can do. 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 just never been seen before in the training data.

And humans, more or less, can work around that, although let me be clear and note there are about 30,000 human fatalities. It's 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. And also the tricky thing that if I have a criticism for the Waymo folks, there is such a huge focus on safety, where people don't talk enough about creating products to people that customers love, like 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. 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 there were sort of wimpy cars that weren't fun. The first thing he did was, you know, he made a

roadster that when 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. Yeah, it's difficult to figure out what right marketing move is for AI systems. That's always been, I think it requires guts and risk taking, which is what Elon practices. I mean, to the degree of perhaps investors or whatever. Of course, guts and risk taking. It also requires, you know, 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, you know, it's not necessarily the worst thing in the world to do, but it's really not leading to a quantum change in a way you do things. You know, when Jeff Bezos said,

hey, we're gonna use the internet to change how bookstores work. And we're going to use technology. He didn't go and say, okay, let's put a robot cashier where the human cashier is and leave everything else alone. I would have been a very lame way to automate a bookstore. He's like, went from soup to nuts and said, let's just rethink it. We get rid of the physical bookstore. 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. And that'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 takes more creativity than most people have. Can you maybe linger on electricity because that's a fun one? Yeah, well, 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 crankshafts. And you really can't make them too long because 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'd 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. It took until a generation of managers retired or died three years later that people started thinking, wait, we don't have to do it that way. You can make electric motors, 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 they call group drive versus unit drive, where every machine would have its own motor. Well once they did that, 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 has 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. 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 I think that there's a lesson you see over and over. It happened when the steam engine changed manual production. It's happened with the computerization. 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 don't see productivity growth, you can actually see a slipping back, measured productivity actually falls. I just wrote a paper with Chad Severson 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're just talking about it. There have been hundreds of billions of dollars spent developing self-driving cars. Basically, no chauffeur has lost his job, no taxi driver. I got to check out the ones that are in the curve. Yeah, so there's a bunch of spending and no real consumer benefit. 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 they 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 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. And so that's not what you would have expected 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 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. Well, what would you place your biggest hope for productivity increases on? Like 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 have 15 years and thinking about the internet.

I would say things like hope for them not saying jacuz 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. Yeah, well, that's another area of 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 online. The interesting thing about all those is, most of them have a price of zero. What do you pay for Wikipedia? Maybe a little bit for the electrons to come to your house. Basically zero, right? Take a small pause and say, I donate to Wikipedia often you should do.

Because I'm actually... For you. Yeah. But what does that do mean for GDP? GDP is based on the price and quantity of all the good 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 of 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 crave 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. Now, in fairness, economists, since Simon Kusner's 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 used it, like how well off are we? Well, it 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, Abby Collis and others to develop something we call GDP-B. GDP-B measures the benefits you get, not the cost. If you get benefit from Zoom or Wikipedia or Facebook, then that gets counted in GDPB even if you pay zero for it. So, 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 GDB? It's pretty hard. I mean, one of the reasons it hasn't been done before is that 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 is?

That's a lot harder. You know, how much is Wikipedia worth to you? That's what we have to answer. To do that, what we do is we can use online experiments. We do massive online choice experiments. We ask hundreds of thousands, not millions of people, to do lots of sort of A, B tests. How much would I have to pay you to get up Wikipedia for a month? How much would I have to pay you to stop using your phone? In some cases, it's hypothetical. In other cases, we actually enforce it, which is kind of expensive.

Like we pay somebody $30 to stop using Facebook and we see if they'll do it. And some people will give it up for $10. Some people won't give it up even if you give them $100. That's awesome. And then you get a whole demand curve. You get to see what all the different prices are and 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. I was interesting. I think young people may be know about other networks that I don't know the name of that are better than Facebook. And so you get to see these patterns, but every person's individual.

And then if you add up all those numbers, you start getting an estimate of the value. Okay, first of all, that's brilliant. Is this work that you will soon eventually be published? Yeah, well, there's a version of it in the, using the National Academy of Sciences about, I think we call it massive online choice experiments. I 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. 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 both economics and the product perspective? It's something that, you know, so I teach a course on digital business models,

so I used to do it at MIT at Stanford. I'm not quite sure. I'm not teaching until next spring. I'm still thinking what my course is gonna be. But there are a lot of different business models. And we 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, your national public radio. Actually, this podcast, how is this revenue model? There's sponsors at the beginning, and then, and people, the funny thing is,

I tell people, it's very, I tell them the time stamp, so if you wanna skip the sponsors, you're free, but it's funny that a bunch of people, so I read the advertisement, and a bunch of people enjoy reading it. 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 saw things gave, I bought a car recently, and all of a sudden, all the car ads were like, interesting to me. Exactly. And then, like, now that I have the car, that I just sort of zone out on again, 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. Exactly. And then like now that I have the car, that I just sort of zone out on again, 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 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 is going to work as well.

You're better off charging for it. 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. 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.

Is it strange to you that they have an experimented? Well, they are experimenting. So they are doing some experiments about what the willing is for people to pay. I think that when they do the math, it's gonna work out that they still are better off with an advertising driven model. But-

What about a mix? Like this is what YouTube is, right? Yeah. You allow the person to decide, the customer to decide exactly which model they prefer. Yeah, no, that can work really well. 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 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 have mentioned having a little slider about how much advertising you want or willing to take.

And if it's done right and it's insensitive compatible, it can be a win-win where we're both a content provider and the consumer are better off than they would have been before. Yeah, the done right part is a really good point. Like with Jeff Bezos and the single click purchase on Amazon, the frictionless 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 to subscribe to them that I literally don't subscribe

just because of the number of times I have to click. I'm totally with you. to subscribe to them, that I literally don't subscribe just because of the number of times I have to click. 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, okay, I'm gonna like lose an afternoon, just like loading up and getting all my stuff back.

And for a lot of us, that's more of a deterrent than the price. And if they could make it painless, we'd give them a lot more money. So I'm hoping somebody listening is working on making it more painless for us to buy your products. If we could just linger a little bit on the social network thing because, you know, there's this Netflix social dilemma. Yeah, no, I saw that. And, and, and, and, interest in Harris and company, yeah. And, you know, people's data,

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

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, you know, Danny Conneman talks about System 1 and System 2, 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, 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 and not really think carefully about it. And that system, it tends to be driven by sex, violence, disgust, anger, fear, 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 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, is exactly the opposite. My MIT colleagues are all in Debrau and others at MIT did a terrific paper in the cover

of science. And they document what we all feared, which is that lies spread faster than truth on social networks. They looked at a bunch of tweets and weep tweets, 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 that are shocking, amazing. Can you believe this?

Something that is not mundane, not that something that everybody else already knew. And what are the most unbelievable things? Well lies. And so 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. Another interesting thing was that that wasn't necessarily driven by the algorithms.

I know that there are some evidence, you know, xenopetefici 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 Sinon 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 problems with ourselves that we just can't resist passing on the salacious content.

But I also blame the platforms because there's different ways you can design a platform. 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 did it with Jimmy Wales once, you know, the guy who helped found Wikipedia. And he convinced me that, you know, you can make some design choices, whether

it's at Facebook, Twitter, Wikipedia, or Reddit, whatever. And depending on how you make those choices, you're more likely or less likely to have false news. Create a little bit of friction, like you said. Yeah, you know, that's the, and sorry, if friction could be speeding the truth, you know, either way, but, and I don't totally understand. And you're speeding the truth, I love it. 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 the meta point is that if we spend time thinking about it, we can't amplify truth over falsehoods. And I'm disappointed 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 these revenue models may push them more towards growing fast, spreading information rapidly, getting lots of users, which isn't the same thing as finding truth. Yeah, I mean, implicit in what you're saying now is a hopeful message that with platforms we can 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 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, but 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 toys that are not at all connected to reality?

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 the use products as they manage companies. How can they make conscious decisions to favor truth over falsehoods? It's favor the better kinds of societies and not abdicate and say, well, we just build the tools. I think there was a saying that was at the German scientists when they were working on the missiles in late World War II, they said, well, our job is to make the missiles go up

where they come down that's someone else's department. And that's obviously not the, 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

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 not just a truth but methods that lead to the truth So maybe on a more personal question and continue to try to embrace not just the truth, but methods that lead to the truth. So maybe on a more personal question, if one were to try to build a competitor to Twitter, what would you advise? Is there, I mean, the bigger the matter question, is that the right way to improve systems? 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 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. Does it get divisive in that little? Sometimes, yeah, sure. Sometimes it does. It can also be done in nasty ways, and there's a bad parts. But the good parts are great because you just speed up that clock speed of learning

about things. 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.

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 importantly, I've discovered is not picking. The idea of not picking? No, what's that? The good term.

Not picking is when you find an extreme nutcase on the other side and then you amplify them and make it seem like that's typical of the other side. 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 at 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 at it and I'm like, I can't believe that person did

said things 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, 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 get 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, you know, as I said earlier, favors truth over falsehoods and favors positive types of communication versus destructive ones. And just like you said, it's also on us. I try to be all about love and compassion and empathy on Twitter. I mean, one of the things, not picking is a fascinating term. One of the things that people do

that's I think even more dangerous is not 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've often talked about Hitler on this podcast with folks, and it is so easy. I'm really dangerous. But I'm all leaning in. I'm 100%. Because, well, it's actually a safe 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. Yeah, totally out of context and it's very worrying about the you know these digital infrastructures not just these disembowhe things, but they're sort of permits or 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 may, and like that becomes, they can use that to define you if they have an intent. And we all need to be a little more forgiving. I mean, somewhere in my 20s, I told myself,

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. Yeah. Exactly. There's like nobody who's like completely, except me, of course. Yeah. But I'm sure they thought that about me too.

And, and he just kind of like learned to be a little bit tolerant that like, okay, there's just, you know, uh, yeah, I wonder who their responsibility lays on there. Like, I think ultimately it's about leadership, like the previous president, Barack Obama's been, I think quite eloquent at walking this very difficult line of talking about cancel culture, but it's difficult.

It takes skill, because you say the wrong thing and you piss off a lot of people, but then also the platform of the technology is should slow down, create friction spreading this kind of nut picking in all its forms. Absolutely. No. And in your point that we have to learn over time how to manage it.

And we can put it all in the platform and say, you guys design it. And because if we're idiots about using it, nobody can design a platform that withstands that. Every new technology people learn, it's dangerous, when someone invented fire, it's great cooking and everything, but then somebody burned himself. Then you had to learn how to maybe somebody invented a fire extinguisher later and what so.

You figure out ways of working around these technologies, someone invented seat belts, et cetera. And that's certainly true with all the new digital technologies that we have to figure out, not just technologies that protect us, but ways of using them that emphasize that are more likely to be successful than dangerous. So you've written quite a bit about how artificial intelligence

might change our world. 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? That's a big question. 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-Curb, 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. I mean, the health care is probably one of the applications that I'm most excited about.

So that's good news. I don't think we're going to have the end of work any time 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 child care or health care, clean 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 folding laundry or whatever. And many of these, I think, are going to be years or decades before machines catch up. 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. As machines are able to do some tasks, people are going to have to re-skill and move into other areas.

That's probably what's going to be going on for the next 10, 20, 30 years or more kind of big restructuring of society will 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 maybe all bets are off. Then it's possible that machines will be able to do most of what people do. Say, one or 200 years, I think it's even likely. At that point, then we're more in the sort of abundance economy, then we're in a world where there's really little for the humans can do economically better than machines, other than

be human. And 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 shame on us if we screw that up. I mean, that should be like great, great news. And it kind of saddens me that some people see that as like a big problem. I think I would be should be wonderful 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. Do you think you'll 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 week, would you be able to understand what the house going on? You mean if I was there for a week? Yeah, if you were's say, week. Would you be able to understand what the hell's going on? You mean, if I was there for a week? Yeah, if you were there for a week. 100 years in the future? Yeah. So, like, I'll give you one thought experiment.

It's like, isn't it possible that we're all living in virtual reality by then? Yeah. No, I think that's very possible. 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 during a screen right now. 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. That would be a pretty different world than we'd all, in some ways be as rich as we wanted. We could have castles, and it could be traveling anywhere we want. And it could obviously be multi-sensory. So that would be possible. And of course, there's people,

you've had Elon Musk on and others, there are people, Nick Bostrom, the simulation argument that maybe we're already there. We're already there. So, but in general, or do you not even think about it in this kind of way, you're self-critically thinking how good are you as an economist at predicting what the future looks like? Yeah. Well, it starts getting, I mean, I feel reasonably comfortable next, you know, five,

ten, twenty years in terms of that path. When you start getting truly superhuman artificial intelligence, by definition, I'm 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 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. We really hope that their values are aligned with our values.

And it's even tricky to find 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, I like to think that we have better values than it did in 1860. And or in the year 200 BC, 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 involved. Maybe I'd be a vegetarian or do other things that right now, whether my future self would consider kind of a moral. 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, whether it's a nanotechnology or biotech and weaponized viruses, AI, and other things that... Nuclear weapons. Nuclear weapons, of course.

The old school technology. Yeah, good old nuclear weapons. Nuclear weapons, of course. The old school technology. Yeah, good old nuclear weapons that could be devastating or even existential and new things yet to be invented. So that's a branch that, you know, I think is pretty significant. And there are those who think that one of the reasons we haven't been contacted by other civilizations,

is that once you get to a certain level of complexity and 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. It's a great filter. 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

yeah no i think i think is robin handsome as 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 a hundred or a thousand or ten thousand years, things just get destroyed for some reason. The other one is that 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 evolve intelligent life.

Maybe we've gotten past that, and so now maybe we're on the good side of the great filter. So if we sort of rewind back and look at the thing where we could say something a little bit more comfortably at five years and ten years out. You've written about jobs and the impact on sort of our economy and the jobs in terms of artificial intelligence that it might have. It's a fascinating question of what kind of jobs are safe, what kind of jobs are not. He may be speak to your intuition

about how we should think about AI changing the landscape of work. 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 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. And we came up with, 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 U.S. economy, believe it or not, it's called ONet. The U.S. government put it together, part of Bureau of Labor Statistics, and 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 really and study whether that's so awesome. 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 a significant ability to do things.

Like take radiology. A lot of people, I hear it saying, you know, the end of radiology. And one of the 27 tasks is read medical images. Really important one. 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 they've been publishing them for the past few years,

showing that machine learning can do as well as humans on many kinds of diagnostic imaging tasks. But other things are radiologists do, 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 to oversimplify a bit, we're going to of restructuring reorganization of work. And it's a real, going to be a great time, it is a great time for smart entrepreneurs and managers to do that reinvention of work. 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. Even now, find 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. 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 to him, the interesting thing was asking the questions. 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, I just remember where economists land, where I think the X-axis is the income. Yes.

And then the Y-axis, I guess, aggregating the information of how replaceable the job is, or I think there's an index. I think there's an individual machine learning index. Exactly. So we have all 970 occupations on the chart. It's a cool plot. And there's gathers and 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 cash shares. I mean, anyone who's gone to a supermarket or CVS knows that they not only read barcodes, but they can recognize you on Apple and on Orange. And a lot of things cash's humans used to be needed for

At the 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 peaking at those and and they're paid a fair amount. Maybe not as much as some of us think they should be. But they have some tasks they're still 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 didn't like create that data. We just took the analysis and that's what came out of it.

And over time that scatter plot will be updated as the technology improves, but it was just interesting to see the pattern there. And it is a little troubling in so far as if you just take the technology as it is today, it's likely to worsen income inequality on a lot of dimensions. So on this topic of the effect of AI and our 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. What are your thoughts about Andrew? What are your thoughts about UBI that universal basic income that he made one of the core. By the way, he has like hundreds of ideas about like everything. It's kind of interesting. Yeah, but what are your thoughts about him and what are your thoughts about UBI?

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

became less of a fan, although beginning to come back a little bit. So let me tell you a little bit of my evolution. As an economist, we have, by looking at the problem of people not having enough income and the simplest things, 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. Voltaire once said that works all three great ills, boredom, vice, and need. You can deal with the need thing by writing a check, but people need a sense of meaning. They need something to do. When steel workers or coal miners and, you know, say, steel workers 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 at 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, we are far from the end of work. I don't buy the idea that there's just 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 prepare 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, well, let's find a way to repurpose them

and keep them engaged. Now that said, I would like to see more buying power from people who are sort of at the bottom end of the spectrum. The economy has been designed and evolved in a way that's I think very unfair to a lot of hard working people. I see super hard working people aren't really seeing their wages grow over the past 20, 30 years, while some other people who have been super smart and or super lucky have made billions or hundreds of billions.

And 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, they don't think that 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, they don't think that they need an extra $10 billion to do what they're doing. Most of them probably would love to do it for only a billion, or maybe for nothing. First, for nothing, many of them, yeah. I mean, a interesting point to make is, do we think that Bill Gates would have founded Microsoft of tax rates were 70%. Well, we know he would have, because they were tax rates of 70% when he founded it. You know, so I don't think that's as big a deterrent, and we could provide more buying-powered

people. My own favorite tool is 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 people's wages. If the employer can only afford to pay him $10 for a task, the rest of us kick in another $5 or $10 and bring your 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 self-reinforcing system where people are better off.

And I had a good discussion where he suggested instead of a universal basic income, he suggested, or instead of an universal basic income, he suggested, 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 re-skill our workforce. So let's make it easier for people to find ways to get those skills and get rewarded for doing them. That's kind of a neat idea as well. 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 learn a new skill. 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 a new thing. Well, I think it helps. I mean, there are lots of self-motivated people, but there are also people that 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 map it.

Okay, here's the 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 need to be. 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 tech system

If you were to try to fix our tax system, and 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? 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 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.

We could do things like earning income tax credit to get a little more wonky. I'd like to see more pegoovian 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 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 almost every economist would say it's a no-brainer, whether they're Republican or Democrat, Greg Mancue who is head of George Bush's Council of Economic Advisers or Dick Schmollancy who is the 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. You could just visualize the cost and productivity

that this is great. Exactly, because they are taking costs for 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 streamline the roads, like when you're in Singapore, the traffic just flows because they have a congestion tax.

They listened 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 to give to people so they could buy food or whatever.

So it's just, it saddens me when you're sitting in traffic jam, it's like taxing me and then taking that money and dumping in the ocean, just like destroying it. So there are a lot of things like that that economists, and I'm not like doing anything radical or most could economists would probably agree with with me point by point on these things. And we could do those things in our whole economy become much more efficient,

it become fair, invest in R&D and research, which is close to a free lunch is what we have. My first while MIT colleague Bob Solo got the Nobel Prize not yesterday, but 30 years ago, Bob Solok got the Nobel Prize not yesterday, but 30 years ago, for describing that most improvements in living standards have come from tech progress. And Paul Romer later got a Nobel Prize for noting that investments in R&D and human capital can speed the rate of tech progress. So if we do that, then we'll be healthier and wealthier. Yeah, from an economics perspective, I remember taking an undergrad, you mentioned Econ 101,

it seemed from all the plots I saw that R&Ds and all the, you call it as close to free lunches as we have, it seemed like obvious that we should do more research. It is. Like what, like. I don't know. There's no, what we should do basic research. I mean, so let me just be clear. It'd be Like. I don't know. There's no, what should do basic research?

I mean, so let me just be clear. It'd be great if everybody did more research. And I'd make this things be to apply development versus basic research. So apply development like, you know, how do we get 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 for that. If they make a better self-driving car system, they can sell cars that are more valuable and then make money. So there's an incentive, there's not a big problem there. 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, 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're not gonna 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&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 to kind of scientific progress we used to have.

It's been sort of a short term, you know, eating the seed corn, whatever, you know, metaphor you want to use, where people grab some money, put it in their pockets today, but five, ten, twenty years later, they're a lot poorer than the otherwise would have been. So we're living through a pandemic right now globally in the, in the United States. From an economics perspective, how do you think this pandemic will change the world? It's been remarkable. And 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.

The most obvious one is the shift to remote work. And I and many other people stopped 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, 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, 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 has had very disparate effects on different parts of the workforce. Do you think it's gonna be sticky in a sense that after vaccine comes out and the economy reopens, do you think remote work will continue? 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. So it works? Yeah, 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 in many ways, there's 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 have more of ability for lots of voices. And they're answering each other's questions. So you get parallel. If someone had some question about, you know, 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. And I think a lot of people are discovering that these tools that, you know, thanks to technologies have been developed over the past decade. They're a lot more powerful than we thought.

I mean, 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. You know, bandwidth has held up pretty well and all of our email and other tools that 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. One of the things that worries me is that the people with lots of followers on Twitter 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 quiet, 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. What do we, and again, I apologize, but I've been reading the rise and fall of the third rake, 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 destructive ways. So like it's something I worry about

is like what is this suffering going to materialize itself in five, 10 years? Is that something you worry about? Think about. It's like the center of what I worry about. And let me break it down to two parts. There's a moral and ethical aspect to it. We need to relieve this suffering. I mean, 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 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 a meeting income has barely moved in depending on how you measure it And the incomes of the top 1% 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. So there's just, 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 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 they're 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. 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. So there's a formula that economists have, which is that you have free trade, but then you compensate the people who are hurt. And free trade makes the pie bigger.

And since the pie's 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 people in power were negged on the bargain. And I think they did. And so then there's a backlash against trade. 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 a 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 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 saying, hey, technology sucks. We've got to stop it.

Let's throw rocks at the Google bus. Let's blow it up. 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 to do the morally right thing, but to do the thing that is gonna save the country, which is make sure that we create not just prosperity,

but shared prosperity. So you've been at MIT for over 30 years. I think. Don't I want to hold it? Yeah, that's true. And you're now moved to Stanford. I'm gonna try not to say anything about how great MIT is. What's that move been like? What is East Coast, the West Coast?

Well, MIT is great. MIT has been very good to me. It continues to be very good to me. It's an amazing place. 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 I tracked him out here was not just the weather, but also 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 invested at MIT for that matter in Europe, in China, and elsewhere, in India. But being a little closer to some of the key technologists was something that was important to me. And it may be shallow, but I also do enjoy the good weather. And I felt a little ripped off when I came here

a couple of months ago. And immediately, there are the fires. And my eyes were burning. The sky was orange, and there's the heat waves. It wasn't exactly what I've been promised, but fingers crossed, it'll get back to better. 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 pure playground of ideas that it can be,

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? 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 think the word magical is exactly right. At least it is for me. I do what I love. Hopefully my dean won't be listening, but I would do this for free. You know, 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 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 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.

I hope someday everybody can have jobs that are like that. I appreciate that it's not necessarily easy for everybody to have a job that they both love and also they get paid for. There are things that don't go well in academia, but by and large, I think it's a kind of kinder, gender version of a lot of the world. Yeah, that's true. You know, we sort of cut each other a little slack on things like, you know, honest 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 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 the stuff that I learn from. And every one of them has some aspect of what they're doing that's just, I couldn't even understand.

It's way more brilliant. And that's really, to me, actually, I really enjoy that. Being in a room with lots of other smart people. And Stanford has made it very easy to attract those people. I just say I'm going to do a seminar or whatever, and the people come, they come and want to work with me. We get funding, we get data sets, and it's come together real nicely. Yeah, the rest is just fun.

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 our first order in terms of what's important in the world, and that's very satisfying to me. Maybe a bit of a fun question. What three books, technical, fiction, philosophical? You've enjoyed, had a big impact in your life. Well, I guess I go back to my teen years and I read Sid Arthur, which is a philosophical book and kind of helps keep me centered

about my own destiny. Yeah, my Herman has 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 Howe, Brenner. It's actually about economists. It goes through a series of different companies written in a very lively form. And it probably sounds boring.

But it did describe whether it's Adam Smith, that Karl Marx, that John Maynard Keynes, and each of them, what their key insights were, but also their personalities. I think that's one of the reasons I became an economist was just understanding how they grappled with the big questions of the world. Would you recommend it as a good whirlwind overview of the history of economics? Yeah, yeah. I think that's exactly right. It kind of takes you through the different things.

So you can understand how they reach thinking some of the strengths and weaknesses. I mean, probably it's a little out of date now. It needs to be updated a bit, but 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 enjoy is by my friend and colleague Max Tagmark called Life 3.0. You should have on your podcast if you haven't already. He was episode number one. Oh my God. And he's back. He'll be back. He'll be back soon. 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, I learn a lot from it. I think everybody

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 last 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 the planet. And that's a great story to tell. And he documents it very carefully. A personal kind of self-help book

that I found kind of useful people is atomic habits. I think it's James Clear. Yeah, James Clear. He's just, yeah, it's a good name because he writes very clearly. And most of the senses 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 so build habits in your life that you hope to have a positive impact and don't have to make it big things that could be just tiny little. Exactly, I mean, the word atomic, it's a little bit of a pun, I think he says, one atomic means a really small, you take these little things,

but also like atomic power, it can have like, you know, it's big impacts. That's funny. Yeah. The biggest ridiculous question, especially to ask an economist, but also a human being. What's the meaning of life? I hope you've gotten the answer that from somebody else. I think we're also working on that one. But what is it? You know, I actually learned a lot from my son, Luke, and he's 19 now, but he's always loved philosophy. And he reads way more sophisticated philosophy than I do. I went to come to Oxford and he spent the whole time 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 and, well, how old was he at the time? 17, 17. But he made a really good like intellectual argument for it too. And you know, I 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, 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 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. 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 Selfish and shallow 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, they made me proud to be at MIT. So sad that you're now a Stanford, but I'm sure you'll do wonderful things as Stanford as well. I can't wait till future books and people should definitely read together. Well, thanks so much. And I think we're all part of the invisible college, as we call it. You know, we're all part of the invisible college as we call it. 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. Beautiful.

Thanks for talking to me. My pleasure. Thanks for listening to this conversation with Eric Benjalsen. And thank you to our sponsors. And Sarah Watches, the maker of classy, well performing watches, force-sigmatic, the maker of delicious, mushroom coffee, express VPN, the VPN I've used for many years to protect my privacy on the internet,

and cash out. The app I used is in money to friends. Please check out the sponsors in the description to get discount and to support this podcast. If you enjoyed this thing, subscribe on YouTube, review it with 5 stars on an Apple podcast, follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Friedman. And now let me leave you with some words from Albert Einstein. It has become a polingly obvious that our technology has exceeded our humanity. Thank you for listening and hope to see you next time. you