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The following is a conversation with Eric Brynjolfsson.
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He s an economics professor at Stanford and the director of Stanford s Digital Economy Lab.
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Previously, he was a long, long time professor at MIT, where he did groundbreaking work on the economics of information.
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He s the author of many books, including The Second Machine Age and Machine Platform Crowd, co-authored with Andrew McAfee.
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Quick mention of each sponsor, followed by some thoughts related to the episode.
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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.
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Like with many topics that are linked to predicting the future evolution of technology, it is often too easy to fall into one of two camps, the fear-mongering camp or the technological utopianism camp.
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As always, the future will land us somewhere in between.
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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.
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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.
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A conversation with Andrew has been on the table many times.
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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.
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I work hard to not compromise on this.
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Trust me, it s not easy, even more so in the times of COVID, which requires getting tested nonstop, staying isolated, and doing a lot of costly and uncomfortable things that minimize risk for the guest.
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The reason I do this is because to me, something is lost in remote conversation.
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That something, that magic, I think is worth the effort, even if it ultimately leads to a failed conversation.
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This is how I approach life, treasuring the possibility of a rare moment of magic.
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I m willing to go to the ends of the world for just such a moment.
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If you enjoy this thing, subscribe on YouTube, review it with Five Stars on Apple Podcasts, follow on Spotify, support on Patreon, connect with me on Twitter at Lex Friedman.
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I think from that ad read, you can tell that I have a second career, if everything fails, in reading infomercials.
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And now, here s my conversation with Eric Brynjolfsson.
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You posted a quote on Twitter by Albert Bartlett saying that the greatest shortcoming of the human race is our inability to understand the exponential function.
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Why would you say the exponential growth is important to understand?
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Yeah, that quote, I remember posting that.
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It s actually a reprise of something Andy McAfee and I said in The Second Machine Age, but I posted it in early March when COVID was really just beginning to take off, and I was really scared.
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There were actually only a couple dozen cases, maybe less at that time.
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but they were doubling every like two or three days and you know I could see oh my god this is going to be a catastrophe and it s going to happen soon but nobody was taking it very seriously or not a lot of people were taking it very seriously in fact I remember I did my last um
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in-person conference that week.
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I was flying back from Las Vegas, and I was the only person on the plane wearing a mask.
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And the flight attendant came over to me.
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She looked very concerned.
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She kind of put her hands on my shoulder.
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She was touching me all over, which I wasn t thrilled about.
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And she goes, do you have some kind of anxiety disorder?
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Are you okay?
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And I was like, no, it s because of COVID.
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And she s like- This is early March.
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Early March.
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But I was worried because I knew I could see
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or I suspected, I guess, that that doubling would continue and it did and pretty soon we had thousands of times more cases.
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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.
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But in either case, it can be very counterintuitive.
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I mean, if you
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If you walk for 10 minutes, you get about 10 times as far away as if you walk for one minute.
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That s the way our physical world works.
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That s the way our brains are wired.
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But if something doubles for 10 times as long, you don t get 10 times as much.
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You get 1,000 times as much.
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And after 20, it s a billion.
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After 30, it s a billion.
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No, sorry.
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After 20, it s a million.
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After 30, it s a billion.
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Pretty soon after that, it just gets to these numbers that you can barely grasp.
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Our world is becoming more and more exponential, mainly because of digital technologies.
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More and more often, our intuitions are out of whack.
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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.
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Do you think it generally applies, like is there spaces where it does apply and where it doesn t?
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How are we supposed to build an intuition about in which aspects of our society does exponential growth apply?
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Well, you can learn the math, but the truth is our brains, I think, tend to learn more from experiences.
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So we just start seeing it more and more often.
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So hanging around Silicon Valley, hanging around AI and computer researchers, I see this kind of exponential growth a lot more frequently.
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And I m getting used to it, but I still make mistakes.
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I still underestimate some of the progress in just talking to someone about GPT-3 and how rapidly natural language has improved.
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But I think that as the world becomes more exponential, we ll all start experiencing it more frequently.
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The danger is that we may make some mistakes in the meantime using our old kind of caveman intuitions about how the world works.
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Well, the weird thing is that always kind of looks linear in the moment.
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Like the, you know, it s hard to feel, it s hard to like introspect and really acknowledge how much has changed in just a couple of years or five years or 10 years with the internet.
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If we just look at advancements of AI or even just social media, all the various technologies that go under the digital umbrella.
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It feels pretty calm and normal and gradual.
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A lot of stuff, I think there are parts of the world, most of the world is not exponential.
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The way humans learn, the way organizations change, the way our whole institutions adapt and evolve, those don t improve at exponential paces.
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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.
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And that mismatch, I think, is at the root of a lot of the problems in our society, the growing inequality and other dysfunctions in our political and economic systems.
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So one guy that talks about exponential functions a lot is Elon Musk.
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He seems to internalize this kind of way of exponential thinking.
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He calls it first principles thinking, sort of the kind of...
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Going to the basics, asking the question, like, what were the assumptions of the past?
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How can we throw them out the window?
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How can we do this 10X much more efficiently?
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And constantly practicing that process.
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And also, using that kind of thinking to estimate, sort of,
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when, you know, create deadlines and estimate when you ll be able to deliver on some of these technologies.
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Now, it often gets him in trouble because he overestimates, like he doesn t meet the initial estimates of the deadlines, but he seems to deliver
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late but deliver, which is kind of an interesting, like what are your thoughts about this whole thing?
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Well, no, I think we can all learn from Elon.
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I think going to first principles, I talked about two ways of getting more of a grip on the exponential function.
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And one of them just comes from first principles.
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You know, if you understand the math of it, you can see what s gonna happen.
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And even if it seems counterintuitive that a couple of dozen of COVID cases could become thousands or tens or hundreds of thousands of them in a month, it makes sense once you just do the math.
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And I think Elon tries to do that a lot.
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In fairness, I think he also benefits from hanging out in Silicon Valley, and he s experienced it in a lot of different applications.
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So it s not as much of a shock to him anymore.
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But that s something we can all learn from.
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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.
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And there are all these exponentially growing curves.
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And I was like, holy shit, look at this.
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In each industry, it was just taking off.
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And you didn t have to be a rocket scientist to extend that and say, wow, this means that
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This was in the late 80s and early 90s that if it goes anything like that, we re going to have orders of magnitude more computer power than we did at that time.
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And of course we do.
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they often talk about it as just, so the exponential function is actually a stack of S-curves.
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So basically, it s you milk or whatever, take the most advantage of a particular little revolution, and then you search for another revolution, and it s basically revolutions stacked on top of revolutions.
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Do you have any intuition about how the heck humans keep finding ways to revolutionize things?
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Well, first let me just unpack that first point that I talked about exponential curves, but no exponential curve continues forever.
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It s been said that if anything can t go on forever, eventually it will stop.
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That s very profound.
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It s very profound, but it seems that a lot of people don t appreciate that half of it as well either.
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all exponential functions eventually turn into some kind of S-curve or stop in some other way, maybe catastrophically.
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And that s happened with COVID as well.
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I mean, it went up, and then at some point, it starts saturating the pool of people to be infected.
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There s a standard epidemiological model that s based on that.
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And it s beginning to happen with Moore s law or different generations of computer power.
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It happens with all exponential curves.
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The remarkable thing, as you allude in the second part of your question, is that we ve been able to come up with a new S-curve on top of the previous one and do that generation after generation with new materials, new processes, and just extend it further and further.
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I don t think anyone has a really good theory about why we ve been so successful in doing that.
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It s great that we have been, and I hope it continues for some time.
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But 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.
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If there s one component of it that s not keeping up,
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then the economic incentives become really large to improve that one part.
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It becomes a bottleneck and anyone who can do improvements in that part can reap huge returns so that the resources automatically get focused on whatever
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part of the system isn t keeping up.
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Do you think some version of the Moore s Law will continue?
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Some version, yes.
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It is.
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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.
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But he identified the fact that energy consumption has been declining by a factor of two.
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And for most of us, that s more important.
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The new iPhones came out today as we re recording this.
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I m not sure when you re going to make it available.
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Very soon after this, yeah.
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And for most of us, having the iPhone be
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twice as fast, it s nice.
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But having the battery life longer, that would be much more valuable.
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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.
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Other dimensions of Moore s law are in AI and machine learning.
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those tend to be very parallelizable functions, especially deep neural nets.
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And so instead of having one chip, you can have multiple chips, or you can have a GPU, a graphic processing unit that goes faster, and now special chips designed for machine learning, like tensor processing units.
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Each time you switch, there s another 10x or 100x improvement above and beyond Moore s law.
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So I think that the raw silicon isn t improving as much as it used to, but these other dimensions are becoming important, more important, and we re seeing progress in them.
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I don t know if you ve seen the work by OpenAI, where they show the exponential improvement of the training of neural networks.
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just literally in the techniques used.
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So that s almost like the algorithm.
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It s fascinating to think, like, can I actually continue us figuring out more and more tricks on how to train networks faster?
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Well, the progress has been staggering.
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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.
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One, we just talked about faster chips, not just Moore s law, but GPUs, TPUs and other technologies.
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The second is just a lot more data.
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I mean, we are awash in digital data today in a way we weren t 20 years ago.
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Photography, I m old enough to remember it used to be chemical.
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And now everything is digital.
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I took probably 50 digital photos yesterday.
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I wouldn t have done that if it was chemical.
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And we have the internet of things and all sorts of other types of data.
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When we walk around with our phone, it s just broadcasting huge amounts of digital data that can be used as training sets.
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And then last but not least, as they mentioned at OpenAI, there have been significant improvements in the techniques.
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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.
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So you multiply together a hundred-fold improvement
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computer power, 100-fold or more improvement in data, 100-fold improvement in techniques of software and algorithms, and soon you re getting into million-fold improvements.
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Somebody brought this up, this idea with GPT-3 that
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It s training in a self-supervised way on basically internet data.
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And that s one of the, I ve seen arguments made that seem to be pretty convincing that the bottleneck there is going to be how much data there is on the internet, which is a fascinating idea that it literally will just run out of human-generated data to train on.
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I know, we may get to the point where it s consumed basically all of human knowledge or all digitized human knowledge.
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And that will be the bottleneck.
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But the interesting thing with bottlenecks is people often use bottlenecks as a way to argue against exponential growth.
1
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They say, well, there s no way you can overcome this bottleneck.
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But we seem to somehow keep coming up in new ways to overcome whatever bottlenecks the critics come up with, which is fascinating.
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I don t know how you overcome the data bottleneck, but probably more efficient training algorithms.
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Yeah, well, you already mentioned that, that these training algorithms are getting much better at using smaller amounts of data.
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We also are just capturing a lot more data than we used to, especially in China, but all around us.
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So those are both important.
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In some applications, you can simulate the data, video games.
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Some of the self-driving car systems are simulating driving.
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Of course, that has some risks and weaknesses, but you can also, if you want to exhaust all the different ways you could beat a video game, you could just simulate all the options.
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Can we take a step in that direction of autonomous vehicles?
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I m actually talking to the CTO of Waymo tomorrow, and obviously I m talking to Elon again in a couple weeks.
1
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What s your thoughts on autonomous vehicles?
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Where do we stand as a problem that has the potential of revolutionizing the world?
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Well, I m really excited about that.
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But it s become much clearer that the original way that I thought about it, most people thought about, will we have a self-driving car or not, is way too simple.
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The better way to think about it is that there s a whole continuum of how much driving and assisting the car can do.
1
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I noticed that you re right next door to Toyota Research Institute.
1
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That s a total accident.
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I love the TRI folks, but yeah.
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Have you talked to Gil Pratt?
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Yeah, we re going to, we re supposed to talk, it s kind of hilarious.
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So there s kind of the op, I think it s a good counterpart to what Elon is doing.
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And hopefully they can be frank in how they think about each other, because I ve heard both of them talk about it.
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But they re much more, you know, this is an assistive, a guardian angel that watches over you as opposed to try to do everything.
1
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I think there s some things like driving on a highway, you know, from LA to Phoenix, where it s mostly good weather, straight roads.
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That s
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close to a solved problem, let s face it.
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In other situations, driving through the snow in Boston where the roads are kind of crazy, and most importantly, you have to make a lot of judgments about what the other driver is going to do at these intersections that aren t really right angles and aren t very well described.
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It s more like game theory.
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That s a much harder problem and requires understanding human motivations.
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And
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So there s a continuum there of some places where the cars will work very well, and others where it could probably take decades.
1
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What do you think about the Waymo?
1
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So you mentioned two companies that actually have cars on the road.
1
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There s the Waymo approach that s more like, we re not going to release anything until it s perfect, and we re going to be very strict about the streets that we travel on, but it better be perfect.
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Yeah.
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I m smart enough to be humble and not try to get between.
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I know there s very bright people on both sides of the argument.
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I ve talked to them, and they make convincing arguments to me about how careful they need to be and the social acceptance.
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Some people thought that when the first few people died from self-driving cars, that would shut down the industry.
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But it was more of a blip, actually.
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And so that was interesting.
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Of course, there s still a concern that if,
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There could be setbacks if we do this wrong.
1
SPEAKER_00
Your listeners may be familiar with the different levels of self-driving, level 1, 2, 3, 4, 5.
1
SPEAKER_00
I think Andrew Yang has convinced me that this idea of really focusing on level 4, where you only go in areas that are well mapped rather than just going out in the wild,
1
SPEAKER_00
is the way things are gonna evolve.
1
SPEAKER_00
But you can just keep expanding those areas where you ve mapped things really well, where you really understand them, and eventually they all become kind of interconnected.
1
SPEAKER_00
And that could be a kind of another way of progressing to make it more feasible over time.
1
SPEAKER_01
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,
1
SPEAKER_01
and the Phoenix, Arizona, to, you know, you can get a ride in a Waymo car with no person, no driver.
1
SPEAKER_00
Oh, they ve taken away the safety driver?
1
SPEAKER_01
Oh yeah, for a while now, there s been no safety driver.
1
SPEAKER_00
Okay, because I mean, I ve been following that one in particular, but I thought it was kind of funny about a year ago when they had the safety driver and then they added a second safety driver because the first safety driver would fall asleep.
1
SPEAKER_00
direction with that.
1
SPEAKER_01
They ve way more particular than a really good job of that.
1
SPEAKER_01
They actually have a
1
SPEAKER_01
very interesting infrastructure of remote observation.
1
SPEAKER_01
So they re not controlling the vehicles remotely, but they re able to, it s like a customer service.
1
SPEAKER_01
They can anytime tune into the car.
1
SPEAKER_01
I bet they can probably remotely control it as well, but that s officially not the function that they.
1
SPEAKER_00
Yeah, I can see that being really, because,
1
SPEAKER_00
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.
1
SPEAKER_00
So you can deal with 90, 99, 99.99% of the cases, but then there s something that s just never been seen before in the training data.
1
SPEAKER_00
And humans, you know, more or less can work around that, although,
1
SPEAKER_00
Let me be clear and note there are about 30,000 human fatalities just in the United States and maybe a million worldwide.
1
SPEAKER_00
So they re far from perfect.
1
SPEAKER_00
But I think people have higher expectations of machines.
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SPEAKER_00
They wouldn t tolerate that level of death and damage from a machine.
1
SPEAKER_00
And so we have to do a lot better at dealing with those edge cases.
1
SPEAKER_01
And also the tricky thing that if I have a criticism for the Waymo folks, there s such a huge focus on safety.
1
SPEAKER_01
where people don t talk enough about creating products that people, that customers love, that human beings love using.
1
SPEAKER_01
It s very easy to create a thing that s safe at the extremes, but then nobody wants to get into it.
1
SPEAKER_00
Yeah.
1
SPEAKER_00
Well, back to Elon, I think part of his genius was with the electric cars.
1
SPEAKER_00
Before he came along, electric cars were all kind of underpowered, really light, and they were sort of wimpy cars that weren t fun.
1
SPEAKER_00
And the first thing he did was he made a Roadster that went 0 to 60 faster than just about any other car and went the other end.
1
SPEAKER_00
And I think that was a really wise marketing move as well as a wise technology move.
1
SPEAKER_01
Yeah, it s difficult to figure out what the right marketing move is for AI systems.
1
SPEAKER_01
That s always been, I think it requires guts and risk-taking, which is what Elon practices.
1
SPEAKER_01
I mean, to the chagrin of perhaps investors or whatever.
1
SPEAKER_00
It requires guts and risk-taking.
1
SPEAKER_00
It also requires rethinking what you re doing.
1
SPEAKER_00
I think way too many people are,
1
SPEAKER_00
unimaginative, intellectually lazy.
1
SPEAKER_00
And when they take AI, they basically say, what are we doing now?
1
SPEAKER_00
How can we make a machine do the same thing?
1
SPEAKER_00
Maybe we ll save some costs, we ll have less labor.
1
SPEAKER_00
And yeah, it s not necessarily the worst thing in the world to do, but it s really not leading to a quantum change in the way you do things.
1
SPEAKER_00
You know, when Jeff Bezos said, hey, we re going to use the internet to change how bookstores work, and we re going to use technology, he didn t go and say, OK, let s put a robot cashier where the human cashier is and leave everything else alone.
1
SPEAKER_00
That would have been a very lame way to automate a bookstore.
1
SPEAKER_00
He went from soup to nuts and said, let s just rethink it.
1
SPEAKER_00
We get rid of the physical bookstore.
1
SPEAKER_00
We have a warehouse.
1
SPEAKER_00
We have delivery.
1
SPEAKER_00
We have people order on a screen.
1
SPEAKER_00
And everything was reinvented.
1
SPEAKER_00
And that s been the story of these,
1
SPEAKER_00
general purpose technologies all through history.
1
SPEAKER_00
In my books, I write about electricity and how for 30 years, there was almost no productivity gain from the electrification of factories a century ago.
1
SPEAKER_00
Now, it s not because electricity is a wimpy, useless technology.
1
SPEAKER_00
We all know how awesome electricity is.
1
SPEAKER_00
It s because at first, they really didn t rethink the factories.
1
SPEAKER_00
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.
1
SPEAKER_00
But it s the combination of the technology with the new business models, new business organization.
1
SPEAKER_00
That just takes a long time, and it takes more creativity than most people have.
1
SPEAKER_01
Can you maybe linger on electricity?
1
SPEAKER_01
Because that s a fun one.
1
SPEAKER_00
Yeah.
1
SPEAKER_00
Well, sure.
1
SPEAKER_00
I ll tell you what happened.
1
SPEAKER_00
Before electricity, there were basically steam engines or sometimes water wheels.
1
SPEAKER_00
And to power the machinery, you had to have pulleys and crankshafts.
1
SPEAKER_00
And you really can t make them too long, because they ll break the torsion.
1
SPEAKER_00
So, all the equipment was kind of clustered around this one giant steam engine.
1
SPEAKER_00
You can t make small steam engines either because of thermodynamics.
1
SPEAKER_00
So, 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.
1
SPEAKER_00
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.
1
SPEAKER_00
And nothing much else changed.
1
SPEAKER_00
It took until a generation of managers retired or died, 30 years later, that people started thinking, wait, we don t have to do it that way.
1
SPEAKER_00
You can make electric motors big, small, medium.
1
SPEAKER_00
You can put one with each piece of equipment.
1
SPEAKER_00
There s this big debate, if you read the management literature, between what they call a group drive versus a unit drive, where every machine would have its own motor.
1
SPEAKER_00
Well, once they did that, once they went to unit drive, those guys won the debate, then you started having a new kind of factory, which is,
1
SPEAKER_00
sometimes spread out over acres, single story, and each piece of equipment had its own motor.
1
SPEAKER_00
And most importantly, they weren t laid out based on who needed the most power.
1
SPEAKER_00
They were laid out based on what is the workflow of materials?
1
SPEAKER_00
Assembly line, let s have it go from this machine to that machine to that machine.
1
SPEAKER_00
Once they rethought the factory that way,
1
SPEAKER_00
huge increases in productivity.
1
SPEAKER_00
It was just staggering.
1
SPEAKER_00
People like Paul David have documented this in their research papers.
1
SPEAKER_00
And I think that is a lesson you see over and over.
1
SPEAKER_00
It happened when the steam engine changed manual production.
1
SPEAKER_00
It s happened with the computerization.
1
SPEAKER_00
People like Michael Hammer said, don t automate, obliterate.
1
SPEAKER_00
In each case, the big gains only came once smart entrepreneurs and managers basically reinvented their industries.
1
SPEAKER_00
I mean, one other interesting point about all that is that
1
SPEAKER_00
during that reinvention period, you often actually, not only you don t see productivity growth, you can actually see a slipping back, measured productivity actually falls.
1
SPEAKER_00
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.
1
SPEAKER_00
And that downward dip is when everyone s trying to like reinvent things.
1
SPEAKER_00
And you could say that they re creating knowledge and intangible assets, but that doesn t show up on anyone s balance sheet.
1
SPEAKER_00
It doesn t show up in GDP.
1
SPEAKER_00
So it s as if they re doing nothing.
1
SPEAKER_00
Take self-driving cars, we were just talking about it.
1
SPEAKER_00
There have been hundreds of billions of dollars spent developing self-driving cars.
1
SPEAKER_00
And basically, no chauffeur has lost his job, no taxi driver.
1
SPEAKER_00
I guess I got to check on the ones that could.
1
SPEAKER_00
Big J curve.
1
SPEAKER_00
Yeah, so there s a bunch of spending and no real consumer benefit.
1
SPEAKER_00
They re doing that in the belief, I think the justified belief, that they will get the upward part of the J curve and there will be some big returns, but in the short run you re not seeing it.
1
SPEAKER_00
That s happening with a lot of other AI technologies, just as it happened with earlier general purpose technologies.
1
SPEAKER_00
And it s one of the reasons we re having relatively low productivity growth lately.
1
SPEAKER_00
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.
1
SPEAKER_00
The economic productivity statistics are kind of dismal.
1
SPEAKER_00
We actually, believe it or not,
1
SPEAKER_00
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.
1
SPEAKER_00
And so that s not what you would have expected if these technologies were that much better.
1
SPEAKER_00
But I think we re in kind of a long J curve there.
1
SPEAKER_00
Personally, I m optimistic we ll start seeing the upward tick maybe as soon as next year.
1
SPEAKER_00
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.
1
SPEAKER_01
What would you place your biggest hope for productivity increases on?
1
SPEAKER_01
Because you kind of said at a high level AI, but if I were to think about what has been so revolutionary in the last 10 years, I would, 15 years, and thinking about the internet, I would say things like
1
SPEAKER_01
I hope I m not saying anything ridiculous, but everything from Wikipedia to Twitter.
1
SPEAKER_01
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.
1
SPEAKER_00
Yeah, well, that s another area I ve done quite a bit of research on actually, is these free goods like Wikipedia, Facebook, Twitter, Zoom.
1
SPEAKER_00
We re actually doing this in person, but almost everything else I do these days is online.
1
SPEAKER_00
The interesting thing about all those is most of them have a
2
SPEAKER_01
a price of zero, you know?
2
SPEAKER_01
What do you pay for Wikipedia?
2
SPEAKER_01
Maybe like a little bit for the electrons to come to your house?
2
SPEAKER_01
It s basically zero, right?
2
SPEAKER_00
Take a small pause and say, I donate to Wikipedia often, you should too, because that makes- It s good for you, yeah.
2
SPEAKER_01
So, but what does that do mean for GDP?
2
SPEAKER_01
GDP is based on the price and quantity of all the goods things bought and sold.
2
SPEAKER_01
If something has zero price, you know how much it contributes to GDP?
2
SPEAKER_01
to a first approximation, zero.
2
SPEAKER_01
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.
2
SPEAKER_01
It s like they don t exist.
2
SPEAKER_01
That doesn t mean they don t create value.
2
SPEAKER_01
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.
2
SPEAKER_01
So we ve got a mismatch there.
2
SPEAKER_01
Now, in fairness, economists, since Simon Kuznets invented GDP and productivity, all those statistics back in the 1930s, he recognized, he in fact said, this is not a measure of well-being, this is not a measure of welfare, it s a measure of production.
2
SPEAKER_01
But almost everybody has kind of forgotten that he said that, and they just use it.
2
SPEAKER_01
It s like, how well off are we?
2
SPEAKER_01
What was GDP last year?
2
SPEAKER_01
It was 2.3% growth or whatever.
2
SPEAKER_01
That is,
2
SPEAKER_01
how much physical production, but it s not the value we re getting.
2
SPEAKER_01
We need a new set of statistics, and I m working with some colleagues, Avi Kalas and others, to develop something we call GDP-B.
2
SPEAKER_01
GDP-B measures the benefits you get, not the cost.
2
SPEAKER_01
If you get benefit from Zoom or Wikipedia or Facebook,
2
SPEAKER_01
then that gets counted in GDP B, even if you pay zero for it.
2
SPEAKER_01
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.
2
SPEAKER_01
By the way, productivity is just defined as GDP divided by hours worked.
2
SPEAKER_01
So if you mismeasure GDP,
2
SPEAKER_01
You mismeasure productivity by the exact same amount.
2
SPEAKER_01
That s something we need to fix.
2
SPEAKER_01
I m working with the statistical agencies to come up with a new set of metrics.
2
SPEAKER_01
And over the coming years, I think we ll see.
2
SPEAKER_01
We re not going to do away with GDP.
2
SPEAKER_01
It s very useful.
2
SPEAKER_01
But we ll see a parallel set of accounts that measure the benefits.
2
SPEAKER_01
How difficult is it to get that B in the GDP?
2
SPEAKER_01
It s pretty hard.
2
SPEAKER_01
I mean, one of the reasons it hasn t been done before
2
SPEAKER_01
is that you can measure at the cash register what people pay for stuff, but how do you measure what they would have paid, like what the value is?
2
SPEAKER_01
That s a lot harder.
2
SPEAKER_01
How much is Wikipedia worth to you?
2
SPEAKER_01
That s what we have to answer.
2
SPEAKER_01
And to do that, what we do is we use online experiments.
2
SPEAKER_01
We do massive online choice experiments.
2
SPEAKER_01
We ask hundreds of thousands, now millions of people to do lots of sort of A-B tests.
2
SPEAKER_01
How much would I have to pay you to give up Wikipedia for a month?
2
SPEAKER_01
How much would I have to pay you to stop using your phone?
2
SPEAKER_01
And in some cases, it s hypothetical.
2
SPEAKER_01
In other cases, we actually enforce it, which is kind of expensive.
2
SPEAKER_01
Like we pay somebody $30 to stop using Facebook, and we see if they ll do it.
2
SPEAKER_01
And some people will give it up for $10.
2
SPEAKER_01
Some people won t give it up even if you give them $100.
2
SPEAKER_01
And then you get a whole demand curve.
2
SPEAKER_01
You get to see what all the different prices are.
2
SPEAKER_01
and how much value different people get.
2
SPEAKER_01
And not surprisingly, different people have different values.
2
SPEAKER_01
We find that women tend to value Facebook more than men.
2
SPEAKER_01
Old people tend to value it a little bit more than young people.
2
SPEAKER_01
It was interesting.
2
SPEAKER_01
I think young people maybe know about other networks that I don t know the name of that are better than Facebook.
2
SPEAKER_01
And so you get to see these patterns, but every person s individual.
2
SPEAKER_01
And then if you add up all those numbers, you start getting an estimate of the value.
2
SPEAKER_00
Okay, first of all, that s brilliant.
2
SPEAKER_00
Is this work that will soon eventually be published?
2
SPEAKER_01
Yeah, well, there s a version of it in the Proceedings of the National Academy of Sciences about, I think we call it Massive Online Choice Experiments.
2
SPEAKER_01
I should remember the title, but it s on my website.
2
SPEAKER_01
So yeah, we have some more papers coming out on it, but the first one is already out.
2
SPEAKER_00
You know, it s kind of a fascinating mystery that Twitter, Facebook, like all these social networks are free.
2
SPEAKER_00
And it seems like almost none of them, except for YouTube, have experimented with removing ads for money.
2
SPEAKER_00
Can you like, do you understand that from both economics and the product perspective?
2
SPEAKER_01
Yeah, it s something that, you know, so I teach a course on digital business models.
2
SPEAKER_01
I used to at MIT, at Stanford, I m not quite sure.
2
SPEAKER_01
I m not teaching until next spring.
2
SPEAKER_01
I m still thinking what my course is gonna be.
2
SPEAKER_01
But there are a lot of different business models.
2
SPEAKER_01
And when you have something that has zero marginal cost, there s a lot of forces, especially if there s any kind of competition, that push prices down to zero.
2
SPEAKER_01
But you can have ad-supported systems.
2
SPEAKER_01
You can bundle things together.
2
SPEAKER_01
You can have volunteer.
2
SPEAKER_01
You mentioned Wikipedia.
2
SPEAKER_01
There s donations.
2
SPEAKER_01
And I think economists underestimate the power of volunteerism and donations.
2
SPEAKER_01
National Public Radio.
2
SPEAKER_01
Actually, how do you, this podcast, how is this, what s the revenue model?
2
SPEAKER_00
There s sponsors at the beginning, and then, and people, the funny thing is, I tell people they can, it s very, I tell them the timestamps, so if you wanna skip the sponsors, you re free.
2
SPEAKER_00
But the, it s funny that a bunch of people, so I read the advertisement, and a bunch of people enjoy reading it.
2
SPEAKER_01
Well, they may learn something from it, and also,
2
SPEAKER_01
from the advertiser s perspective, those are people who are actually interested, you know?
2
SPEAKER_01
Like, I mean, the example I sometimes give is like, I bought a car recently and all of a sudden all the car ads were like interesting to me.
2
SPEAKER_01
And then like, now that I have the car, like I d sort of zone out on it.
2
SPEAKER_01
But that s fine.
2
SPEAKER_01
The car companies, they don t really want to be advertising to me if I m not going to buy their product.
2
SPEAKER_01
So there are a lot of these different revenue models and, you know,
2
SPEAKER_01
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.
2
SPEAKER_01
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.
2
SPEAKER_01
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
2
SPEAKER_01
value for it, then you advertising isn t going to work as well.
2
SPEAKER_01
You re better off charging for it.
2
SPEAKER_01
Both of them have some inefficiencies.
2
SPEAKER_01
And then when you get into targeting and you get these other revenue models, it gets more complicated.
2
SPEAKER_01
But
2
SPEAKER_01
There s some economic theory on it.
2
SPEAKER_01
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.
2
SPEAKER_01
You know, Facebook tries to get more and more users, and then they harvest the revenue from advertising.
2
SPEAKER_01
So that s another way of kind of thinking about it.
2
SPEAKER_01
Is it strange to you that they haven t experimented?
2
SPEAKER_01
Well, they are experimenting.
2
SPEAKER_01
They are doing some experiments about what the willingness is for people to pay.
2
SPEAKER_01
I think that when they do the math, it s gonna work out that they still are better off with an advertising-driven model.
2
SPEAKER_01
What about a mix?
2
SPEAKER_00
This is what YouTube is, right?
2
SPEAKER_00
You allow the person to decide, the customer to decide exactly which model they prefer.
2
SPEAKER_01
No, that can work really well.
2
SPEAKER_01
And newspapers, of course, have known this for a long time.
2
SPEAKER_01
The Wall Street Journal, The New York Times, they have subscription revenue.
2
SPEAKER_01
They also have advertising revenue.
2
SPEAKER_01
And that can definitely work.
2
SPEAKER_01
Online, it s a lot easier to have a dial that s much more personalized, and everybody can kind of roll their own mix.
2
SPEAKER_01
And I could imagine having a little slider about how much advertising you want or are willing to take.
2
SPEAKER_01
And if it s done right and it s incentive compatible, it could be a win-win where both the content provider and the consumer are better off than they would have been before.
2
SPEAKER_00
Yeah, you know, the done right part is a really good point.
2
SPEAKER_00
Like with the 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
2
SPEAKER_01
I m totally with you.
2
SPEAKER_01
I don t understand why so many companies make it so hard.
2
SPEAKER_01
I mean, another example is when you buy a new iPhone or a new computer, whatever you, I feel like, okay, I m going to like lose an afternoon, just like loading up and getting all my stuff back.
2
SPEAKER_01
And, and.
2
SPEAKER_01
For a lot of us, that s more of a deterrent than the price.
2
SPEAKER_01
And if they could make it painless, we d give them a lot more money.
2
SPEAKER_01
So I m hoping somebody listening is working on, on making it more painless for us to buy your products.
2
SPEAKER_00
If we could just like linger a little bit on the social network thing, because, you know, there s this Netflix social dilemma.
2
SPEAKER_00
Yeah, no, I saw that.
2
SPEAKER_00
And Tristan Harris and company, yeah.
2
SPEAKER_00
And, you know, people s data,
2
SPEAKER_00
It s really sensitive and social networks are at the core, arguably, of many of societal tension and some of the most important things happening in society.
2
SPEAKER_00
So it feels like it s important to get this right, both from a business model perspective and just like a trust perspective.
2
SPEAKER_00
I still gotta, 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.
2
SPEAKER_01
Well, I m worried that people are afraid to try different business models.
2
SPEAKER_01
I m also worried that some of the business models may lead them to bad choices.
2
SPEAKER_01
And, you know,
2
SPEAKER_01
Danny Kahneman talks about System 1 and System 2, sort of like our reptilian brain that reacts quickly to what we see.
2
SPEAKER_01
We see something interesting, we click on it, we...
2
SPEAKER_01
retweet it versus our system two, you know, our frontal cortex that s supposed to be more careful and rational that really doesn t make as many decisions as it should.
2
SPEAKER_01
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.
2
SPEAKER_01
And that system, it tends to be driven by, you know,
2
SPEAKER_01
sex, violence, disgust, anger, fear, these relatively primitive kinds of emotions.
2
SPEAKER_01
Maybe they re important for a lot of purposes, but they re not a great way to organize a society.
2
SPEAKER_01
And most importantly, when you think about this huge, amazing information infrastructure we ve had that s connected billions of brains across the globe, not just so we can all access information, but we can all contribute to it and share it,
2
SPEAKER_01
Arguably, the most important thing that that network should do is favor truth over falsehoods.
2
SPEAKER_01
And the way it s been designed, not necessarily intentionally, is exactly the opposite.
2
SPEAKER_01
My MIT colleagues, Aral and Deb Roy and others at MIT, did a terrific paper on the cover of Science, and they documented what we all feared, which is that
2
SPEAKER_01
lies spread faster than truth on social networks.
2
SPEAKER_01
They looked at a bunch of tweets and retweets, and they found that false information was more likely to spread further, faster, to more people.
2
SPEAKER_01
And why was that?
2
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It s not because people like lies.
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It s because people like things that are shocking, amazing.
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SPEAKER_01
Can you believe this?
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Something that is not mundane, not that something everybody else already knew.
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SPEAKER_01
And what are the most unbelievable things?
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Well, lies.
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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.
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So they found that the emotional valence of false information was just much higher.
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It was more likely to be shocking and therefore more likely to be spread.
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SPEAKER_01
Another interesting thing was that that wasn t necessarily driven by the algorithms.
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I know that there is some evidence, you know, Zeynep Tufekci and others have pointed out in YouTube, some of the algorithms unintentionally were tuned to amplify more extremist content.
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SPEAKER_01
But in the study of Twitter that Sinan and Deb and others did, they found that even if you took out all the bots and all the
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automated tweets, you still had lies spreading significantly faster.
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It s just the problems with ourselves that we just can t resist passing on this salacious content.
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SPEAKER_01
But I also blame the platforms because, you know, there s different ways you can design a platform.
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SPEAKER_01
You can design a platform in a way that makes it easy to spread lies and to retweet and spread things on, or you can kind of put some friction on that and try to favor truth.
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SPEAKER_01
I had dinner with Jimmy Wales once, the guy who helped found Wikipedia.
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And he convinced me that, look, you can make some design choices, whether it s at Facebook, at Twitter, at Wikipedia, or Reddit, whatever.
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SPEAKER_01
And depending on how you make those choices,
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you re more likely or less likely to have false news.
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SPEAKER_01
Create a little bit of friction, like you said.
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SPEAKER_01
Yeah.
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SPEAKER_01
You know, that s the... It could be friction or it could be speeding the truth, you know, either way, but, and I don t totally understand... Speeding the truth, I love it.
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SPEAKER_01
Yeah, yeah, you know, amplifying it and giving it more credit and, you know, like in academia, which is far, far from perfect, but you know, when someone has a,
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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.
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SPEAKER_01
So you could try to do that too.
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SPEAKER_01
I don t know what the silver bullet is, but the meta point is that if we spend time thinking about it, we can amplify truth over falsehoods.
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SPEAKER_01
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.
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SPEAKER_01
And part of it going back to what we said earlier is, you know, these revenue models may,
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push them more towards growing fast, spreading information rapidly, getting lots of users, which isn t the same thing as finding truth.
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SPEAKER_00
Yeah.
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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
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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.
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SPEAKER_00
And as a thought experiment, I ve been thinking about if it s possible that our future will have, like the idea of truth is something we won t even have.
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SPEAKER_00
Do you think it s possible 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?
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Yeah, I think that s definitely possible.
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I m not a technological determinist, so I don t think that s inevitable.
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I don t think it s inevitable that it doesn t happen.
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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.
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We shape our destiny.
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SPEAKER_01
So just by us having this conversation, I hope
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that your audience is going to take it upon themselves as they design their products and they think about it, they use products as they manage companies, how can they make conscious decisions to favor truth over falsehoods, favor the better kinds of societies and not abdicate and say, well, we just build the tools.
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SPEAKER_01
I think there was a saying that
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was it the German scientists when they were working on the missiles in late World War II, you know, they said, well, our job is to make the missiles go up.
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Where they come down, that s someone else s department.
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And, you know, that s obviously not the, I think it s obvious, that s not the right attitude that technologists should have, that engineers
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SPEAKER_01
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 where the 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 or fame or money is orthogonal to truth
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SPEAKER_01
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.
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If they don t pass the scientific method, they re less likely to be true.
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And that has the societies and the people and the organizations that embrace that have done a lot better.
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um 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 the truth but methods that lead to the truth so maybe
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be on a more personal question.
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SPEAKER_01
If one were to try to build a competitor to Twitter, what would you advise?
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SPEAKER_01
Is there, I mean, the bigger, the meta question, is that the right way to improve systems?
3
SPEAKER_00
Yeah.
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SPEAKER_00
No, I think that the underlying premise behind Twitter and all these networks is amazing, that we can communicate with each other.
3
SPEAKER_00
And I use it a lot.
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There s a subpart of Twitter called Econ Twitter, where we economists,
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tweet to each other and talk about new papers.
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Something came out in the NBER, the National Bureau of Economic Research, and we share about it.
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People critique it.
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I think it s been a godsend, because it s really sped up the scientific process, if you can call economics scientific.
3
SPEAKER_00
Does it get divisive in that little?
3
SPEAKER_00
Sometimes, yeah, sure.
3
SPEAKER_00
Sometimes it does.
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SPEAKER_00
It can also be done in nasty ways, and there s the bad parts.
3
SPEAKER_00
But the good parts are great, because you just speed up that clock speed of learning about things.
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SPEAKER_00
Instead of
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like in the old, old days, you know, waiting to read in a journal or the not so old days when you d see it posted on a website and you d read it.
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Now on Twitter, like people will distill it down and there s a real art to getting to the essence of things.
3
SPEAKER_00
So that s been great.
3
SPEAKER_00
But it certainly, we all know that Twitter can be a cesspool of
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misinformation.
3
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And like I just said, unfortunately, misinformation tends to spread faster on Twitter than truth.
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And there are a lot of people who are very vulnerable to it.
3
SPEAKER_00
I m sure I ve been fooled at times.
3
SPEAKER_00
There are agents, whether from Russia or from political groups or others, that explicitly
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create efforts at misinformation and efforts at getting people to hate each other, or even more important lately I ve discovered is nut picking.
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SPEAKER_00
You know the idea of nut picking?
3
SPEAKER_00
It s a good term.
3
SPEAKER_00
Nut picking is when you find an extreme nut case on the other side
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And then you amplify them and make it seem like that s typical of the other side.
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SPEAKER_00
So you re not literally lying.
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SPEAKER_00
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 just, and you normally, nobody would pay attention to this guy, like 12 people would see him and it d be the end.
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with video or whatever, you get tens of millions of people say it, and I ve seen this.
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I look at it, I get angry.
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I m like, I can t believe that person did such things.
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It s so terrible.
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Let me tell all my friends about this terrible person.
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It s a great way to generate division.
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SPEAKER_00
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.
3
SPEAKER_00
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.
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SPEAKER_00
And they re literally playing both sides of it.
3
SPEAKER_00
But their goal wasn t for one or the other to win.
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SPEAKER_00
It was for everybody to be hating and distrusting everyone else.
3
SPEAKER_00
So these tools can definitely be used for that, and they are being used for that.
3
SPEAKER_00
It s been super destructive for our democracy and our society.
3
SPEAKER_00
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.
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SPEAKER_00
Well, I don t know if you can shut it down, but to design them in a way that, as I said earlier, favors truth over falsehoods and favors positive types of
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communication versus destructive ones.
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SPEAKER_01
And just like you said, it s also on us.
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I try to be all about love and compassion, empathy on Twitter.
3
SPEAKER_01
I mean, one of the things, not picking is a fascinating term.
3
SPEAKER_01
One of the things that people do that s, I think, even more dangerous is
3
SPEAKER_01
nut picking applied to individual statements of good people.
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SPEAKER_01
So basically, worst case analysis in computer science is taking sometimes out of context, but sometimes in context.
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SPEAKER_01
a statement, one statement by a person, like I ve been, because I ve been reading The Rise and Fall of the Third Reich, I often talk about Hitler on this podcast with folks, and it is so easy.
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SPEAKER_00
That s really dangerous.
3
SPEAKER_01
But I m all leaning in, I m 100%, because, well, it s actually a safer place than people realize, because it s history, and history in long form is actually very fascinating to think about, and it s,
3
SPEAKER_01
But I could see how that could be taken totally out of context, and it s very worrying.
3
SPEAKER_00
It s the thing about these digital infrastructures, not just they disseminate things, but they re sort of permanent.
3
SPEAKER_00
So anything you say, at some point, someone can go back and find something you said three years ago, perhaps jokingly, perhaps not.
3
SPEAKER_00
Maybe you re just wrong, and that becomes, they can use that to define you if they have ill intent.
3
SPEAKER_00
And we all need to be a little more forgiving.
3
SPEAKER_00
I mean, somewhere in my 20s, I was going through all my different friends, and I was like,
3
SPEAKER_00
You know, every one of them has at least like one nutty opinion.
3
SPEAKER_00
There s like nobody who s like completely except me, of course.
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SPEAKER_00
But I m sure they thought that about me, too.
3
SPEAKER_00
And and so you just kind of like learn to be a little bit tolerant that like, OK, there s just, you know.
3
SPEAKER_01
Yeah, I wonder who the responsibility lays on there.
3
SPEAKER_01
I think ultimately it s about leadership, like the previous president, Barack Obama, has been, I think, quite eloquent at walking this very difficult line of talking about cancel culture.
3
SPEAKER_01
But it s difficult, it takes skill.
3
SPEAKER_01
Because you say the wrong thing and you piss off a lot of people.
3
SPEAKER_01
And so you have to do it well.
3
SPEAKER_01
But then also the platform, the technology is,
3
SPEAKER_01
should slow down, create friction and spreading this kind of nut picking in all its forms.
3
SPEAKER_00
Absolutely.
3
SPEAKER_00
And your point that we have to learn over time how to manage it.
3
SPEAKER_00
We can t put it all on the platform and say, you guys design it.
3
SPEAKER_00
Because if we re idiots about using it, nobody can design a platform that withstands that.
3
SPEAKER_00
And every new technology people learn, it s dangerous.
3
SPEAKER_00
When someone invented fire,
3
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It s great cooking and everything, but then somebody burned himself.
3
SPEAKER_00
And then you had to like learn how to like avoid, maybe somebody invented a fire extinguisher later.
3
SPEAKER_00
So you kind of like figure out ways of working around these technologies.
3
SPEAKER_00
Someone invented seat belts, et cetera.
3
SPEAKER_00
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.
3
SPEAKER_01
So you ve written quite a bit about how artificial intelligence might change our world.
3
SPEAKER_01
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?
3
SPEAKER_01
That s a big question.
3
SPEAKER_00
You know,
3
SPEAKER_00
I m mostly a techno-optimist.
3
SPEAKER_00
I m not at the extreme, you know, the singularity is near end of the spectrum.
3
SPEAKER_00
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.
3
SPEAKER_00
You know, when I talked earlier about the J curve, it can take 10, 20, 30 years for an existing technology to have the kind of profound effects.
3
SPEAKER_00
And when I look at whether it s, you know,
3
SPEAKER_00
vision systems, voice recognition, problem solving systems.
3
SPEAKER_00
Even if nothing new got invented, we would have a few decades of progress.
3
SPEAKER_00
So I m excited about that.
3
SPEAKER_00
And I think that s going to lead to us being wealthier, healthier.
3
SPEAKER_00
I mean, the health care is probably one of the applications I m most excited about.
3
SPEAKER_00
So that s good news.
3
SPEAKER_00
I don t think we re gonna have the end of work anytime soon.
3
SPEAKER_00
There s just too many things that machines still can t do.
3
SPEAKER_00
When I look around the world and think of whether it s childcare or healthcare, cleaning the environment, interacting with people, scientific work, artistic creativity, these are things that for now machines aren t able to do nearly as well as humans, even just something as mundane as folding laundry or whatever.
3
SPEAKER_00
And many of these I think are gonna be
3
SPEAKER_00
years or decades before machines catch up.
3
SPEAKER_00
I may be surprised on some of them, but overall, I think there s plenty of work for humans to do.
3
SPEAKER_00
There s plenty of problems in society that need the human touch.
3
SPEAKER_00
So we ll have to repurpose.
3
SPEAKER_00
We ll have to, as machines are able to do some tasks, people are going to have to reskill and move into other areas.
3
SPEAKER_00
And that s probably what s going to be going on for the next 10, 20, 30 years or more, a kind of big restructuring of society.
3
SPEAKER_00
We ll get wealthier and people will have to do new skills.
3
SPEAKER_00
Now, if you turn the dial further, I don t know, 50 or 100 years into the future, then, you know, maybe all bets are off.
3
SPEAKER_00
Then it s possible that machines will be able to do most of what people do.
3
SPEAKER_00
You know, say one or 200 years, I think it s even likely.
3
SPEAKER_00
And at that point, then we re more in the sort of abundance economy, then we re in a world
3
SPEAKER_00
there s really little that humans can do economically better than machines other than be human.
3
SPEAKER_00
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.
3
SPEAKER_00
But shame on us if we screw that up.
3
SPEAKER_00
I mean, that should be great, great news.
3
SPEAKER_00
And it kind of saddens me that some people see that as a big problem.
3
SPEAKER_00
I think it should be wonderful if people have all the health and material things that they need and can focus on
3
SPEAKER_00
loving each other and discussing philosophy and playing and doing all the other things that don t require work.
3
SPEAKER_01
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 a week, would you be able to understand what the hell s going on?
3
SPEAKER_00
You mean if I was there for a week?
3
SPEAKER_01
Yeah, if you were there for a week.
3
SPEAKER_00
a hundred years in the future.
3
SPEAKER_01
Yeah.
3
SPEAKER_01
So like, so I ll give you one thought experiment is like, isn t it possible that we re all living in virtual reality by then?
3
SPEAKER_01
Yeah.
3
SPEAKER_00
No, I think that s very possible.
3
SPEAKER_00
You know, I ve played around with some of those VR headsets and they re not great, but I mean, the average person spends
3
SPEAKER_00
many waking hours staring at screens right now.
3
SPEAKER_00
You know, they re kind of low res compared to what they could be in 30 or 50 years.
3
SPEAKER_00
But certainly games and why not any other interactions could be done with VR and that would be a pretty different world that we d all
3
SPEAKER_00
in some ways, be as rich as we wanted.
3
SPEAKER_00
We could have castles, we could be traveling anywhere we want.
3
SPEAKER_00
And it could obviously be multi-sensory.
3
SPEAKER_00
So that would be possible.
3
SPEAKER_00
And of course, if there s people, you ve had Elon Musk on and others, there are people.
3
SPEAKER_00
Nick Bostrom makes the simulation argument that maybe we re already there.
3
SPEAKER_01
We re already there.
3
SPEAKER_01
But in general, or do you not even think about it in this kind of way?
3
SPEAKER_01
You re self-critically thinking, how good are you
3
SPEAKER_01
as an economist at predicting what the future looks like?
3
SPEAKER_00
Well, it starts getting, I mean, I feel reasonably comfortable the next five, 10, 20 years in terms of that path.
3
SPEAKER_00
When you start getting truly superhuman artificial intelligence,
3
SPEAKER_00
kind of by definition, I ll be able to think of a lot of things that I couldn t have thought of and create a world that I couldn t even imagine.
3
SPEAKER_00
And so I m not sure I can predict what that world is going to be like.
3
SPEAKER_00
One thing that AI researchers, AI safety researchers worry about is what s called the alignment problem.
3
SPEAKER_00
When an AI is that powerful, then they can do all sorts of things and you really hope
3
SPEAKER_00
that their values are aligned with our values.
3
SPEAKER_00
And it s even tricky defining what our values are.
3
SPEAKER_00
I mean, first off, we all have different values.
3
SPEAKER_00
And secondly, maybe if we were smarter, we would have better values.
3
SPEAKER_00
I like to think that we have better values than we did in 1860 or in the year 200 BC on a lot of dimensions, things that we consider barbaric today.
3
SPEAKER_00
And it may be that if I thought about it more deeply, I would also be morally evolved.
3
SPEAKER_00
Maybe I d be a vegetarian or
3
SPEAKER_00
or do other things that right now, whether my future self would consider kind of immoral.
3
SPEAKER_00
So that s a tricky problem, getting the AI to do what we want, assuming it s even a friendly AI.
3
SPEAKER_00
I mean, I should probably mention, there s a non-trivial other branch where we destroy ourselves, right?
3
SPEAKER_00
I mean, there s a lot of exponentially improving technologies that could be
3
SPEAKER_00
ferociously destructive, whether it s in nanotechnology or biotech and weaponized viruses, AI, and other things that- Nuclear weapons.
3
SPEAKER_00
Nuclear weapons, of course.
3
SPEAKER_00
The old school technology.
3
SPEAKER_00
Yeah, good old nuclear weapons that could be devastating or even existential, and new things yet to be invented.
3
SPEAKER_00
So that s a branch that,
3
SPEAKER_00
you know, I think is pretty significant.
3
SPEAKER_00
And there are those who think that one of the reasons we haven t been contacted by other civilizations, right, is that once you get to a certain level of complexity and technology, there s just too many ways to go wrong.
3
SPEAKER_00
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.
3
SPEAKER_00
the great filter.
3
SPEAKER_00
The great filter.
3
SPEAKER_01
I mean, there s an optimistic view of that.
3
SPEAKER_01
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.
3
SPEAKER_00
No, I think it s Robin Hanson has a good way of, maybe others, they have a good way of thinking about this.
3
SPEAKER_00
If there are no other intelligence creatures out there that we ve been able to detect, one possibility is that there s a filter ahead of us, and when you get a little more advanced, maybe in 100 or 1,000 or 10,000 years, things just get destroyed for some reason.
3
SPEAKER_00
The other one is the great filter s behind us.
3
SPEAKER_00
That ll be good, is that most planets don t even evolve life, or if they don t evolve life, they don t evolve intelligent life.
3
SPEAKER_00
Maybe we ve gotten past that, and so now maybe we re on the good side of the great filter.
3
SPEAKER_01
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 10 years out, you ve written about jobs and the impact on sort of our economy and the jobs in terms of artificial intelligence that it might have.
3
SPEAKER_01
It s a fascinating question of what kind of jobs are safe, what kind of jobs are not.
3
SPEAKER_01
Can you maybe speak to your intuition about how we should think about AI changing the landscape of work?
3
SPEAKER_00
Sure, absolutely.
3
SPEAKER_00
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.
3
SPEAKER_00
But we do have
3
SPEAKER_00
human level or superhuman level narrow intelligence, narrow artificial intelligence.
3
SPEAKER_00
And you know obviously my calculator can do math a lot better than I can and there s a lot of other things machines can do better than I can.
3
SPEAKER_00
So which is which?
3
SPEAKER_00
We actually set out to address that question with Tom Mitchell.
3
SPEAKER_00
I wrote a paper called what can machine learning do that was in science and we went and interviewed a whole bunch of
3
SPEAKER_00
AI experts and kind of synthesized what they thought machine learning was good at and wasn t good at.
3
SPEAKER_00
And we came up with what we called a rubric, basically a set of questions you can ask about any task that will tell you whether it s likely to score high or low on suitability for machine learning.
3
SPEAKER_00
And then we ve applied that to a bunch of tasks in the economy.
3
SPEAKER_00
In fact, there s a data set of all the tasks in the US economy.
3
SPEAKER_00
Believe it or not, it s called ONET.
3
SPEAKER_00
The US government put it together, part of Bureau of Labor Statistics.
3
SPEAKER_00
They divide the economy into about 970 occupations, like bus driver, economist, primary school teacher, radiologist.
3
SPEAKER_00
And then for each one of them, they describe which tasks need to be done.
3
SPEAKER_00
Like for radiologists, there are 27 distinct tasks.
3
SPEAKER_00
So we went through all those tasks to see whether or not a machine could do them.
3
SPEAKER_00
And what we found, interestingly, was- Brilliant study, by the way.
3
SPEAKER_00
That s so awesome.
3
SPEAKER_00
Yeah, thank you.
3
SPEAKER_00
So what we found was that there was no occupation in our data set where machine learning just ran the table and did everything.
3
SPEAKER_00
And there was almost no occupation where machine learning didn t have a significant ability to do things.
3
SPEAKER_00
Take radiology.
3
SPEAKER_00
A lot of people I hear saying, it s the end of radiology.
3
SPEAKER_00
And one of the 27 tasks is read medical images.
3
SPEAKER_00
Really important one.
3
SPEAKER_00
It s kind of a core job.
3
SPEAKER_00
And machines have basically gotten as good or better than radiologists.
3
SPEAKER_00
There was just an article in Nature last week, but they ve been publishing them for the past few years.
3
SPEAKER_00
showing that machine learning can do as well as humans on many kinds of diagnostic imaging tasks.
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But other things radiologists do, you know, they sometimes administer conscious sedation, they sometimes do physical exams, they have to synthesize the results and explain to the other doctors or
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or to the patients.
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In all those categories, machine learning isn t really up to snuff yet.
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So that job, we re going to see a lot of restructuring.
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Parts of the job, they ll hand over to machines.
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Others, humans will do more of.
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And that s been more or less the pattern in all of them.
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So to oversimplify a bit, we re going to see a lot of restructuring, reorganization of work.
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And it s real going to be a great time.
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It is a great time for smart entrepreneurs and managers to do that reinvention of work.
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We re not going to see mass unemployment.
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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 Xs and the Ys, the inputs and the outputs, you can do that kind of mapping and find the relationships.
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They tend to not be very good at, even now, fine motor control and dexterity, emotional intelligence and human interactions.
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Thinking outside the box, creative work.
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If you give it a well-structured task, machines can be very good at it.
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But even asking the right questions, that s hard.
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There s a quote that Andrew McAfee and I use in our book, Second Machine Age.
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Apparently, Pablo Picasso was shown an early computer.
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And he came away kind of unimpressed.
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He goes, well, I don t see all the fusses.
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All that does is answer questions.
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And to him, the interesting thing was asking the questions.
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Yeah, try to replace me, GPT-3.
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I dare you, although some people think I m a robot.
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You have this cool plot that shows, I just remember where economists land, where I think the x-axis is the income.
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And then the y-axis is, I guess, aggregating the information of how replaceable the job is.
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Or I think there s an index.
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That s the suitability for machine learning index, exactly.
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So we have all 970 occupations on that chart.
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And there s scatters, and all four corners have some occupations.
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But there is a definite pattern, which is the lower-wage occupations tend to have more tasks that are suitable for machine learning, like cashiers.
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I mean, anyone who s gone to a supermarket or CVS knows that they not only read barcodes, but they can recognize an apple and an orange and a lot of things that cashiers, humans, used to be needed for.
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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.
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A lot of those tasks are.
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And then, yeah, you mentioned economists.
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I couldn t help peeking at those.
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paid a fair amount, maybe not as much as some of us think they should be.
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But they have some tasks that are still for machine learning.
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But for now, at least, most of the tasks that economists do didn t end up being in that category.
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And I should say, I didn t create that data.
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We just took the analysis.
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And that s what came out of it.
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And over time, that scatterplot will be updated as the technology improves.
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But it was just interesting to see the pattern there.
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And it is a little troubling insofar as if you just take the technology as it is today, it s likely to worsen income inequality on a lot of dimensions.
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So on this topic of the effect of AI on our
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on the landscape of work.
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One of the people that have been speaking about it in the public domain, public discourse, is the presidential candidate, Andrew Yang.
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What are your thoughts about Andrew?
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What are your thoughts about UBI, that universal basic income that he made?
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One of the core ideas, by the way, he has like hundreds of ideas about like everything.
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It s kind of interesting.
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But what are your thoughts about him and what are your thoughts about UBI?
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Let me answer the question about his broader approach first.
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I mean, I just love that.
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He s really thoughtful, analytical.
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I agree with his values.
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So that s awesome.
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And he read my book and mentions it sometimes, so it makes me even more excited.
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And the thing that he really made the centerpiece of his campaign was UBI.
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And I was originally kind of a fan of it.
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And then as I studied it more, I became less of a fan, although I m beginning to come back a little bit.
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So let me tell you a little bit of my evolution.
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You know, as an economist, by looking at the problem of people not having enough income, and the simplest thing is, well, why don t we write them a check?
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Problem solved.
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But then I talked to my sociologist friends, and they really convinced me that,
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just writing a check doesn t really get at the core values.
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Voltaire once said that work solves three great ills, boredom, vice, and need.
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And you can deal with the need thing by writing a check, but people need a sense of meaning.
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They need something to do.
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And when, say, steel workers or coal miners lost their jobs and were just given checks,
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alcoholism, depression, divorce, all those social indicators, drug use, all went way up.
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People just weren t happy just sitting around collecting a check.
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Maybe it s part of the way they were raised.
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Maybe it s something innate in people that they need to feel wanted and needed.
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So it s not as simple as just writing people a check.
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You need to also give them
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a way to have a sense of purpose.
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And that was important to me.
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And the second thing is that, as I mentioned earlier, we are far from the end of work.
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I don t buy the idea that there s just like not enough work to be done.
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I see like our cities need to be cleaned up.
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And robots can t do most of that.
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We need to have better childcare.
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We need better healthcare.
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We need to take care of people who are mentally ill or older.
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We need to prepare our roads.
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There s so much work that require at least partly, maybe entirely a human component.
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So rather than like write all these people off, let s find a way to repurpose them and keep them engaged.
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Now that said, I would like to see more buying power from people who are sort of at the bottom end of the spectrum.
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The economy has
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been designed and evolved in a way that s, I think, very unfair to a lot of hardworking people.
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I see super hardworking people who aren t really seeing their wages grow over the past 20, 30 years, while some other people who have been super smart and or super lucky have made billions or hundreds of billions.
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And I don t think they need those hundreds of billions to have the right incentives to invent things.
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I think if you talk to almost any of them, as I have,
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you know, they don t think that they need an extra $10 billion to do what they re doing.
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Most of them probably would love to do it for only a billion, or maybe for nothing.
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For nothing, many of them, yeah.
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I mean, you know, an interesting point to make is, you know, like, do we think that Bill Gates would have founded Microsoft if tax rates were 70%?
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Well, we know he would have because they were tax rates of 70% when he founded it.
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So I don t think that s as big a deterrent.
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And we could provide more buying power to people.
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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.
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The minimum wage can discourage employment, but the Earned Income Tax Credit encourages employment by supplementing people s wages.
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If the employer can only afford to pay him $10 for a task,
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The rest of us kick in another $5 or $10 and bring their wages up to $15 or $20 total.
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And then they have more buying power.
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Then entrepreneurs are thinking, how can we cater to them?
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How can we make products for them?
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And it becomes a self-reinforcing system where people are better off.
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Andrew Ng and I had a good discussion where he suggested instead of a 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 reskill our workforce, so let s make it easier for people to find ways to get those skills and get rewarded for doing them.
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That s kind of a neat idea as well.
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That s really interesting.
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So, I mean, one of the questions, one of the dreams of UBI is that,
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you provide some little safety net while you retrain, while you learn a new skill.
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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.
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I think it helps.
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I mean, there are lots of self-motivated people, but there are also people that maybe need a little guidance or help.
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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.
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And we could provide a much better set of tools and platforms that maps it.
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Okay, here s a set of skills you already have.
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Here s something that s in demand.
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Let s create a path for you to go from where you are to where you need to be.
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So I m a total, how do I put it nicely about myself?
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I m totally clueless about the economy.
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It s not totally true, but pretty good approximation.
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If you were to try to fix our tax system, or maybe from another side, if there s fundamental problems in taxation or some fundamental problems about our economy, what would you try to fix?
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What would you try to speak to?
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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.
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I don t think it s that hard to make headway in improving it.
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I don t think we need to totally reinvent stuff.
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A lot of it is what I ve been elsewhere with Andy and others called economics 101.
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You know, there s just some basic principles that have worked really well in
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the 20th century that we sort of forgot in terms of investing in education, investing in infrastructure, welcoming immigrants, having a tax system that was more progressive and fair.
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At one point, tax rates 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.
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So, and we could do things like an earned income tax credit to get a little more wonky.
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I d like to see more Pigouvian taxes.
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What that means is you tax things that are bad instead of things that are good.
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So right now we tax labor, we tax capital.
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which is unfortunate because one of the basic principles of economics, if you tax something, you tend to get less of it.
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So right now, there s still work to be done and still capital to be invested in.
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But instead, we should be taxing things like pollution and congestion.
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And if we did that we would have less pollution.
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So a carbon tax is a you know, almost every economist would say it s a no-brainer whether they re Republican or Democrat Greg Mankiw who s head of George Bush s Council of Economic Advisers or or or Dick Schmalensee who is the another Republican economist degree and of course a lot of Democratic
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economists agree as well.
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If we taxed carbon, we could raise hundreds of billions of dollars.
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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.
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We could tax congestion.
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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.
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This is complete waste of time.
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You just visualize the cost and productivity.
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Exactly, because they are taking
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Cost 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 it would streamline the roads like when you re in Singapore the traffic just flows because they have a congestion tax They listen to economists.
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They invited me and others to go talk to them and then
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I d still be paying, I d be paying a congestion tax instead of paying in my time, but that money would now be available for healthcare, be available for infrastructure, or be available just to give to people so they could buy food or whatever.
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So it saddens me when you re sitting in a traffic jam, it s like taxing me and then taking that money and dumping it in the ocean, just like destroying it.
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So there are a lot of things like that, that economists, and I m not doing anything radical here, most good economists would probably agree with me point by point on these things.
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And we could do those things and our whole economy would become much more efficient, it d become fairer.
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invest in R&D and research, which is close to a free lunch, is what we have.
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My erstwhile MIT colleague, Bob Sola, got the Nobel Prize, not yesterday, but 30 years ago, for describing that most improvements in living standards come from tech progress.
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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.
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So if we do that, then we ll be healthier and wealthier.
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Yeah, from an economics perspective, I remember taking an undergrad econ, you mentioned econ 101.
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It seemed from all the plots I saw that R&D is an obvious, that s close to free lunches as we have.
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It seemed like obvious that we should do more research.
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It is.
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Like what?
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Well, we should do basic research.
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I mean, so let me just be clear.
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It d be great if everybody did more research.
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And I would make this contribution to apply
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development versus basic research.
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So applied development, like how do we get this self-driving car feature to work better in the Tesla, that s great for private companies because they can capture the value from that.
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If they make a better self-driving car system,
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They can sell cars that are more valuable and then make money.
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So there s an incentive that 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
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There, if someone invents something, it s very hard for them to capture the benefits from it.
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It s shared by everybody, which is great in a way, but it means that they re not going to have the incentives to put as much effort into it.
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It s a classic public good.
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There you need the government to be involved in it.
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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.
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significantly poorer as a result.
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Growth rates are down.
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We re not having the kind of scientific progress we used to have.
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It s been sort of a short-term eating the seed corn, whatever metaphor you want to use, where people grab some money, put it in their pockets today, but five, 10, 20 years later, they re a lot poorer than they otherwise would have been.
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So we re living through a pandemic right now globally in the United States.
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From an economics perspective, how do you think this pandemic will change the world?
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It s been remarkable and it s horrible how many people have suffered, the amount of death, the economic destruction.
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It s also striking just the amount of change in work that I ve seen.
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In the last 20 weeks, I ve seen more change than there were in the previous 20 years.
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There s been nothing like it since probably the World War II mobilization in terms of reorganizing our economy.
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The most obvious one is the shift to remote work.
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And I and many other people stopped going into the office and teaching my students in person.
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I did a study on this with a bunch of colleagues at MIT and elsewhere.
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And what we found was that before the pandemic, at the beginning of 2020, about one in six, a little over 15% of Americans were working remotely.
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When the pandemic hit, that grew steadily and hit 50%, roughly half of Americans working at home.
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So a complete transformation.
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And of course it wasn t even, it wasn t like everybody did it.
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If you re an information worker, a professional, if you work mainly with data, then you re much more likely to work at home.
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if you re a manufacturing worker, you know, working with other people or physical things, then it wasn t so easy to work at home.
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And instead, those people were much more likely to become laid off or unemployed.
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So it s been something that s had very disparate effects on different parts of the workforce.
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Do you think it s going to be sticky in a sense that after vaccine comes out and the economy reopens, do you think remote work will continue?
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That s a great question.
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My hypothesis is yes, a lot of it will.
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Of course, some of it will go back.
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But a surprising amount of it will stay.
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I personally, for instance, I moved my seminars, my academic seminars, to Zoom.
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And I was surprised how well it worked.
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That s how it works?
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Yeah.
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I mean, obviously, we were able to reach a much broader audience.
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So we have people tuning in from Europe and other countries, just all over the United States for that matter.
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I also actually found that it, in many ways,
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more egalitarian.
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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.
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And they re answering each other s questions, so you get parallel.
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If someone had a question about some of the data or a reference or whatever, then someone else in the chat would answer it.
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And the whole thing just became a higher bandwidth, higher quality.
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So I thought that was kind of interesting.
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I think a lot of people are discovering that these tools that, you know, thanks to technologists have been developed over the past decade, they re a lot more powerful than we thought.
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I mean, all the terrible things we ve seen with COVID and the
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real failure of many of our institutions that I thought would work better.
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One area that s been a bright spot is our technologies.
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You know, bandwidth has held up pretty well, and all of our email and other tools have just, you know, scaled up kind of
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Gracefully, so that s been 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 gonna have a fair amount of hysteresis in the economy We re gonna move to this new we have moved to it a new remote work system and it s not gonna snap all the way back to where it was before one of the things that worries me is
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is that the people with lots of followers on Twitter,
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and people with voices, voices that can be magnified by 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.
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And then there is a kind of quiet suffering of what feels like millions of people
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whose jobs are disturbed profoundly by this pandemic, but they don t have many followers on Twitter.
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What do we...
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And again, I apologize, but I ve been reading the rise and fall of the Third Reich, and there s a connection to the depression on the American side.
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There s a deep, complicated connection to how suffering can turn into forces that potentially change the world.
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in destructive ways.
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So like it s something I worry about is like, what is this suffering going to materialize itself in five, 10 years?
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Is that something you worry about, think about?
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It s like the center of what I worry about.
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And let me break it down to two parts.
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There s a moral and ethical aspect to it.
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We need to relieve this suffering.
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I mean,
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I share the values of I think most Americans, we like to see shared prosperity or most people on the planet.
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And we would like to see people not falling behind and they have fallen behind, not just due to COVID, but in the previous couple of decades, median income has barely moved, depending on how you measure it.
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and the incomes of the top 1% have skyrocketed.
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And part of that is due to the ways technology has been used.
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Part of this has been due to, frankly, our political system has continually shifted more wealth into those people who have the powerful interest.
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So there s just a I think a moral imperative to do a better job and ultimately We re all going to be wealthier if more people can contribute more people have the wherewithal But the second thing is that there s a real political risk.
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I m not a political scientist, but you don t have to be
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one I think to see how a lot of people are really upset with their getting a raw deal and they are going to you know they want to smash the system in different ways in 2016 and 2018 and now I think there are a lot of people who are looking at the political system and they feel like it s not working for them and they just want to
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do something radical.
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Unfortunately, demagogues have harnessed that in a way that is pretty destructive to the country.
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And an analogy I see is what happened with trade.
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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.
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And so generally, trade is a good thing.
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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.
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And so trade can shift prices in ways that are adverse.
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some people.
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So there s a formula that economists have, which is that you have free trade, but then you compensate the people who were hurt.
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And free trade makes the pie bigger.
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And since the pie is bigger, it s possible for everyone to be better off.
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You can make the winners better off, but you can also compensate those who don t win.
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And so they end up being better off as well.
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What happened was that
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we didn t fulfill that promise.
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We did have some more increased free trade in the 80s and 90s, but we didn t compensate the people who were hurt.
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And so they felt like the people in power reneged on the bargain.
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And I think they did.
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And so then there s a backlash against trade.
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And now both political parties, but especially Trump and company, have really pushed back against free trade.
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Ultimately, that s bad for the country.
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Ultimately, that s bad for living standards.
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But in a way, I can understand that people felt they were betrayed.
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Technology has a lot of similar characteristics.
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Technology can make us all better off.
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It makes the pie bigger.
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It creates wealth and health.
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But it can also be uneven.
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Not everyone automatically benefits.
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It s possible for some people, even a majority of people, to get left behind while a small group benefits.
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what most economists would say, well, let s make the pie bigger, but let s make sure we adjust the system so that we compensate the people who are hurt.
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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.
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Mathematically, everyone could be better off.
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But again, we re not doing that.
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And again, people are saying, this isn t working for us.
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And again, instead of fixing the distribution, a lot of people are beginning to say, hey, technology sucks.
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We ve got to stop it.
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Let s throw rocks at the Google bus.
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Let s blow it up.
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Let s blow it up.
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And 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.
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We have a real imperative, 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.
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So you ve been at MIT for over 30 years, I think.
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Don t tell anyone how old I am.
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That s true, that s true.
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And you re now moved to Stanford.
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I m gonna try not to say anything about how great MIT is.
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What s that movement like, what, it s East Coast to West Coast?
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One thing that you just turned up.
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MIT is great.
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MIT has been very good to me, it continues to be very good to me.
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It s an amazing place.
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I continue to have so many amazing friends and colleagues there.
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I m very fortunate to have been able to spend a lot of time at MIT.
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Stanford s also amazing.
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And part of what attracted me 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.
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And I want to be close to the people who are inventing AI and elsewhere.
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A lot of it is being invented at MIT, for that matter, in Europe and China and elsewhere, India.
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But being a little closer to some of the key technologists was something that was important to me.
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It may be shallow, but I also do enjoy the good weather.
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I felt a little ripped off when I came here a couple months ago, and immediately there are the fires, and my eyes were burning, the sky was orange, and there s the heat waves, and so it wasn t exactly what I d been promised, but fingers crossed it ll get back to better.
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Maybe on a brief aside, there s been some criticism of academia and universities and different avenues.
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And I, as a person who s gotten to enjoy universities from the pure playground of ideas that it can be,
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I always kind of try to find the words to tell people that these are magical places.
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Is there something that you can speak to that is beautiful or powerful about universities?
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Well, sure.
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I mean, first off, economists have this concept called revealed preference.
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You can ask people what they say, or you can watch what they do.
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And so obviously, by revealed preferences, I love academia.
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I m out here.
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I could be doing lots of other things, but it s something I enjoy a lot.
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And I think the word magical is exactly right, at least it is for me.
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I do what I love.
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Hopefully, my dean won t be listening, but I would do this for free.
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You know, it s just what I like to do.
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I like to do research.
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I love to have conversations like this with you and with my students, with my fellow colleagues.
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I love being around the smartest people I can find and learning something from them and having them challenge me.
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And that just gives me joy.
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And every day I find something new and exciting to work on.
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And a university environment is really filled with other people who feel that way.
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And so I feel very fortunate.
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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 and I hope someday everybody can have jobs that are like that and I appreciate that it s not necessarily easy for everybody to have a job that they both love and also they get paid for.
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So there are things that don t go well in academia, but by and large, I think it s a kinder, gentler version of a lot of the world.
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We sort of cut each other a little slack on just a lot of things.
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Of course, there s harsh
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debates and discussions about things and some petty politics here and there.
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Personally, I try to stay away from most of that sort of politics.
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It s not my thing.
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And so it doesn t affect me most of the time, sometimes a little bit maybe.
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But being able to pull together something, we have the Digital Economy Lab.
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We get all these brilliant grad students and undergraduates and postdocs that are just doing stuff that I learn from.
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And every one of them has some aspect of what they re doing that s just
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I couldn t even understand.
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It s like way, way more brilliant.
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And that s really, to me, actually, I really enjoy that, being in a room with lots of other smart people.
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And Stanford has made it very easy to attract those people.
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I just say, I m going to do a seminar, whatever, and the people come.
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They come and want to work with me.
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We get funding.
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We get data sets.
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And it s come together real nicely.
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And the rest is just fun.
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It s fun, yeah.
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And we feel like we re working on important problems, you know, and we re doing things that, you know, I think are first order in terms of what s important in the world, and that s very satisfying to me.
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Maybe a bit of a fun question, what three books, technical, fiction, philosophical, you ve enjoyed, had a big impact in your life?
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Well, I guess I go back to my teen years, and I read Siddhartha, which is a philosophical book, and kind of helps keep me centered.
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Herman Hest.
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Yeah, Herman Hest, exactly.
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Don t get too wrapped up in material things or other things, and just sort of try to find peace.
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on things.
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A book that actually influenced me a lot in terms of my career was called The Worldly Philosophers by Robert Heilbrenner.
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It s actually about economists.
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It goes through a series of different companies written in a very lively form.
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It probably sounds boring, but it did describe whether it s Adam Smith or Karl Marx or John Maynard Keynes and each of them sort of what their key insights were, but also kind of their personalities.
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I think that s one of the reasons I became an economist was just
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understanding how they grapple with the big questions of the world.
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So would you recommend it as a good whirlwind overview of the history of economics?
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Yeah, yeah, I think that s exactly right.
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It kind of takes you through the different things and you know, so you can understand how they reach thinking some of the strengths and weaknesses.
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I mean probably is a little out of date now, it needs to be updated a bit, but you know, you could at least look through the
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first couple hundred years of economics, which is not a bad place to start.
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More recently, I mean, a book I really enjoyed is by my friend and colleague Max Tegmark called Life 3.0.
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You should have him on your podcast if you haven t already.
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It was episode number one.
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Oh my God.
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And he s back, he ll be back, he ll be back soon.
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Yeah, no, he s terrific.
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I love the way his brain works, and he makes you think about profound things.
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He s got such a joyful approach to life.
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And so that s been a great book, and I learn a lot from it.
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I think everybody explains it in a way, even though he s so brilliant, that everyone can understand, that I can understand.
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You know, that s three, but let me mention maybe one or two others.
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I mean, I recently read More From Less by my sometimes co-author, Andrew McAfee.
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It made me optimistic about how we can continue to have rising living standards while living more lightly on the planet.
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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.
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And that s a great story to tell, and he documents it very carefully.
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You know, a personal kind of self-help book that I found kind of useful, people, is Atomic Habits.
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I think it s, what s his name, James Clear.
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Yeah, James Clear.
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He s just, yeah, it s a good name, because he writes very clearly.
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And, you know, most of the sentences I read in that book, I was like, yeah, I know that, but it just really helps to have somebody, like, remind you and tell you and kind of just reinforce it.
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So build habits in your life that you hope to have, that have a positive impact and don t have to make it big things.
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It could be just tiny little.
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Exactly.
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I mean, the word atomic, it s a little bit of a pun.
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I think he says, you know, one atomic means are really small and you take these little things, but also like atomic power, it can have like, you know, big impacts.
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That s funny.
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Yeah.
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The biggest ridiculous question, especially to ask an economist, but also a human being, what s the meaning of life?
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I hope you ve gotten the answer to that from somebody else.
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I think we re all still working on that one.
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What is it?
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I actually learned a lot from my son, Luke.
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He s 19 now, but he s always loved philosophy.
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He reads way more sophisticated philosophy than I do.
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I once took him to Oxford and he spent the whole time pulling all these obscure books down and reading them.
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A couple years ago, we had this argument, and he was trying to convince me that hedonism was the ultimate meaning of life, just pleasure-seeking.
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And- Well, how old was he at the time?
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17.
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But he made a really good intellectual argument for it, too.
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Of course.
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But it just didn t strike me as right.
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And I think that while I am kind of a utilitarian, like I do think we should do the grace, good for the grace number,
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That s just too shallow and I think I ve convinced myself that real happiness doesn t come from seeking pleasure.
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It s kind of a little, it s ironic, like if you really focus on being happy, I think it doesn t work.
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You gotta like be doing something bigger.
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I think the analogy I sometimes use is
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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.
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It s the same thing with happiness.
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I think you need to sort of find something other
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goal, some meaning in life, and that ultimately makes you happier than if you go squarely at just pleasure.
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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
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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.
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Beautifully put.
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I don t think there s a better way to end it, Eric.
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You re one of the people when I first showed up at MIT that made me proud to be at MIT.
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So it s so sad that you re now at Stanford, but I m sure you ll do wonderful things at Stanford as well.
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I can t wait till future books and people should definitely read
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Well, thank you so much.
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And I think we re all we re all part of the invisible college, as we call it.
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You know, we re all part of this intellectual and human community where we all can learn from each other.
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It doesn t really matter physically where we are so much anymore.
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Beautiful, thanks for talking today.
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My pleasure.
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Thanks for listening to this conversation with Erik Brynjolfsson, and thank you to our sponsors.
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If you enjoy this thing, subscribe on YouTube, review it with Five Stars on Apple Podcasts, follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Friedman.
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And now, let me leave you with some words from Albert Einstein.
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It has become appallingly obvious that our technology has exceeded our humanity.
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Thank you for listening, and hope to see you next time.