YouSaid · the spoken record

Erik Brynjolfsson

lines on the record: 118
first: 2020-11-25
most recent: 2020-11-25
sittings or episodes: 1
sources: podcast

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“It starts getting. I mean, I feel reasonably comfortable the next five, ten, twenty years in terms of that path. When you start getting truly superhuman artificial intelligence, kind of by definition, 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. So I'm not sure I can predict what that world is going to be like. One thing that AI researchers, AI safety researchers worry about is what's called the alignment problem. When an AI is that powerful, then they can do all sorts of things. And you really hope that their values are aligned with our values. And it's even tricky to finding what our values are. I mean, first off, we all have different values. And secondly, maybe if we were smarter, we would have better values. Like, you know, I like to think that we have better values than they did in 1860.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. No, I think that's very possible. I've played around with some of those VR headsets, and they're not great, but I mean, the average person spends many waking hours staring at screens right now. They're kind of low res compared to what they could be in 30 or 50 years. But certainly games, and why not any other interactions could be done with VR? And that would be a pretty different world. And we'd all, you know, in some ways be as rich as we wanted. You know, we could have castles and I could be traveling anywhere we want. And it could obviously be multi sensory. So that would be possible. You've had Elon Musk on and others, you know, there are people, Nick Bostrom, you know, makes the simulation argument that maybe we're already there.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Up. I mean, that should be like great, great news. And it kind of saddens me that some people see that as a big problem. I think I would be, should be wonderful if people have all the health and material things that they need and can focus on loving each other. Discussing philosophy and playing and doing all the other things that don't require work.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“As machines are able to do some tasks, people are going to have to reskill and move into other areas. And that's probably what's going to be going on for the next 10, 20, 30 years or more, kind of big restructuring of society will get wealthier and people will have to do new skills. Now, if you turn the Dow further, I don't know, 50 or 100 years into the future, then maybe all bets are off. Then it's possible that machines will be able to do most of what people do. Say one or 200 years, I think it's even likely. And at that point, then we're more in the sort of abundance economy. Then we're in a world where there's really little for the humans can do economically better than machines other than be human. And that will take a transition as well, kind of more of a transition of how we get meaning in life and what our values are. But shame on us if we screw that.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“When I look around the world and think of whether it's childcare or healthcare, clean the environment, interacting with people, scientific work, artistic creativity, these are things that for now machines aren't able to do nearly as well as humans, even just something as mandane as folding laundry or whatever. And many of these, I think, are going to be. Years or decades before machines catch up. I may be surprised on some of them, but overall, I think there's plenty of work for humans to do. There's plenty of problems in society that need the human touch.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a big question. I'm mostly a techno-optimist. I'm not at the extreme. You know, the singularity is near end of the spectrum. But I do think that we're likely in for some significantly improved living standards, some really important progress, even just the technologies that are already kind of like in the can that haven't diffused. You know, when I talked earlier about the J-curve, it can take 10, 20, 30 years for an existing technology to have the kind of profound effects. When I look at whether it's vision systems, voice recognition, problem solving systems, even if nothing new got invented, we would have a few decades of progress. So I'm excited about that. And I think that's going to lead to us being wealthier, healthier. I mean, the healthcare is probably one of the applications that I'm most excited about. So that's good news. I don't think we're going to have the end of work anytime soon. There's just too many things that machines still can't do.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Absolutely. No, and your point that we have to learn over time how to manage it. I mean, we can't put it all on the platform and say, you guys design it because if we're idiots about using it, nobody can design a platform that withstands that. And every new technology people learn, it's dangerous. When someone invented fire, it's great cooking and everything. But then somebody burned themselves. And then you had to learn how to avoid maybe somebody invented a fire extinguisher later and what stuff. So you kind of like figure out ways of working around these technologies. Someone invented seatbelts, et cetera. And that's certainly true with all the new digital technologies that we have to figure out, not just technologies that protect us, but ways of using them that emphasize that are more likely to be successful than dangerous.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, every one of them has at least like one nutty opinion. Exactly. It's like, there's like nobody who's completely, except me, of course. But I'm sure they thought that about me too. And so he just kind of like learned to be a little bit tolerant that like, okay, there's just, you know.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“And it's very worrying. These digital infrastructure, not just these disseminating things, but they're sort of permanent. So anything you say at some point someone can go back and find something you said three years ago, perhaps jokingly, perhaps not. Maybe you're just wrong and you made it, you know, and like that becomes, they can use that to define you if they have ill intent. And we all need to be a little more forgiving. I mean, somewhere in my 20s, I told myself, I was going through all my different friends and I was like.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“It's been super destructive for our democracy and our society and the people who run these platforms, I think have a social responsibility, a moral and ethical personal responsibility to do a better job and to shut that stuff down. Well, I don't know if you can shut it down, but to design them in a way that, as I said earlier, favors truth over falsehoods and favors positive types of communication versus destructive ones.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“And it's a great way to generate division. I talked to a friend who studied Russian misinformation campaigns, and they're very clever about literally being on both sides of some of these debates. They would have some people pretend to be part of BLM, some people pretend to be white nationalists, and they would be throwing epithets at each other, saying crazy things at each other. And they're literally playing both sides of it. But their goal wasn't for one or the other to win. It was for everybody to get behaving and distrusting everyone else. So these tools can definitely be used for that, and they are being used for that.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Good term. Nut picking is when you find an extreme nutcase on the other side and then you amplify them and make it seem like that's typical of the other side. So you're not literally lying. You're taking some Idiot, ranting on the subway, or just whether they're in the KKK or Antifa or whatever. And you normally nobody would pay attention to this guy. Like 12 people would see him and be the end. Instead, with video or whatever, you get tens of millions of people say it. And I've seen this. I look at it like I get angry. I'm like, I can't believe that person did such things. It's so terrible. Let me tell all my friends about this terrible”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Sometimes, yeah, sure. Sometimes it does. It can also be done in nasty ways, and there's the bad parts. But the good parts are great because you just speed up that clock speed of learning about. In the old days, you know, waiting to read it in a journal, or the not so old days when you'd see it posted on a website and you'd read it. Now on Twitter, people will distill it down and there's a real art to getting to the essence of things. So that's been great, but certainly we all know that Twitter can be a cesspool of misinformation. And like I just said, unfortunately, misinformation tends to spread faster on Twitter than truth. And there are a lot of people who are very vulnerable to it. I'm sure I've been fooled at times. There are agents, whether from Russia or from political groups or others that explicitly create efforts at misinformation and efforts at getting people to hate each other. Or even more importantly, I've discovered is nutpicking, you know, the idea of nut picking.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. No, I think that the underlying premise behind Twitter and all these networks is amazing that we can communicate with each other. And I use it a lot. There's a subpart of Twitter called Econ Twitter where we economists tweet to each other and talk about new papers. Something came out in the NBER, the National Bureau of Economic Research, and we share about it. People critique it. I think it's been a godsend because it's really sped up the scientific process, if you can call economic scientific.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Societies and the people and the organizations that embrace that have done a lot better than the ones who haven't. And so I'm hoping that people keep that in mind and continue to try to embrace not just the truth but methods that lead to the truth.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“That's obviously not the, I think it's obvious that's not the right attitude that technologists should have, that engineers should have, they should be very conscious about what the implications are. And if we think carefully about it, we can avoid the kind of world that you just described where truth is all relative. There are going to be people who benefit from a world of where people don't check facts and where truth is relative and popularity or fame or money is orthogonal to truth. But one of the reasons I suspect that we've had so much progress over the past few hundred years is the invention of the scientific method, which is a really powerful tool or meta tool for finding truth and favoring things that are true versus things that are false if they don't pass the scientific method. They're less likely to be true. And that, the...”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I think that's definitely possible. I'm not a technological determinist, so I don't think that's inevitable. I don't think it's inevitable that it doesn't happen. I mean, the thing that I've come away with every time I do these studies and I emphasize in my books and elsewhere is that technology doesn't shape our destiny. We shape our destiny. Just by us having this conversation, I hope that your audience is going to take it upon themselves as they design their products and they think about the use products, as they manage companies, how can they make conscious decisions to favor truth over falsehoods, favor the better kinds of societies, and not abdicate and say, well, we just build the tools. I think there was a saying that was it the German scientists when they were working on the missiles in late World War II, you know, they said, well, our job is to make the missiles go up where they come down, that's someone else's department.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, yeah, you know, amplifying it and giving it more credit. And like in academia, which is far, far from perfect, but when someone has an important discovery, it tends to get more cited and people kind of look to it more and sort of it tends to get amplified a little bit. So you could try to do that too. I don't know what the silver bullet is, but the meta point is that if we spend time thinking about it, we can amplify truth over falsehoods. And I'm disappointed in the heads of these social networks that they haven't been as successful or maybe haven't tried as hard to amplify truth. And part of it, going back to what we said earlier, is these revenue models may push them more towards growing fast, spreading information rapidly, getting lots of users, which isn't the same thing as finding truth.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Wikipedia. And he convinced me that look, you know, you can make some design choices, whether it's at Facebook, at Twitter, at Wikipedia or Reddit, whatever. And depending on how you make those choices, You're more likely or less likely to have false news.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Another interesting thing was that that wasn't necessarily driven by the algorithms. I know that there is some evidence to Feki and others have pointed out in YouTube some of the algorithms unintentionally were tuned to amplify more extremist content. But in the study of Twitter that Sinan and Deb and others did, they found that even if you took out all the bots and all the automated tweets, you still had lies spreading significantly faster. It's just the problems with ourselves that we just can't resist passing on this salacious content. But I also blame the platforms because, you know, there's different ways you can design a platform. You can design a platform in a way that makes it easy to spread lies and to retweet and spread things on. Or you can kind of put some friction on that and try to favor truth. I had dinner with Jimmy Wales once, you know, the guy who helped found”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Already knew. And what are the most unbelievable things? Well, lies. And so if you want to find something unbelievable, it's a lot easier to do that if you're not constrained by the truth. So they found that the emotional valence of false information was just much higher. It was more likely to be shocking and therefore more likely to be spread.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Billions of brains across the globe, not just that we can all access information, but we can all contribute to it and share it. Arguably, the most important thing that that network should do is favor truth over falsehoods. And the way it's been designed, not necessarily intentionally, is exactly the opposite. My MIT colleagues, Son RL and Deb Roy and others at MIT did a terrific paper in the cover of science. And they document what we all feared, which is that lies spread faster than truth on social networks. They looked at a bunch of tweets and retweets, and they found that false information was more likely to spread further, faster to more people. And why was that? It's not because people like lies. It's because people like things that are shocking, amazing. Can you believe this? Something that is not mundane, not that something everybody else.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Sex, violence, disgust, anger, fear, you know, these relatively primitive kinds of emotions, maybe they're important for a lot of purposes, but they're not a great way to organize a society. And most importantly, when you think about this huge, amazing...”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I'm worried that people are afraid to try different business models. I'm also worried that some of the business models may lead them to bad choices. And Danny Kahneman talks about system one and system two, sort of like a reptilian brain that reacts quickly to what we see, see something interesting, we click on it, we retweet it versus our system two, our frontal cortex that's supposed to be more careful and rational that really doesn't make as many decisions as it should. I think there's a tendency for a lot of these social networks to really exploit system one, our quick instant reaction make it, so we just click on stuff and pass it on and not really think carefully about it. And that system, it tends to be driven by, you know,”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“I'm totally with you. I don't understand why so many companies make it so hard. I mean, another example is when you buy a new iPhone or a new computer, whatever, I feel like, okay, I'm going to like lose an afternoon, just like loading up and getting all my stuff back. And for a lot of us, that's more of a deterrent than the price. And if they could make it painless, we'd give them a lot more money. So I'm hoping somebody listening is working on making it more painless for us to buy your products.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“No, that can work really well. And newspapers, of course, have known this for a long time. The Wall Street Journal, the New York Times, they have subscription revenue. They also have advertising revenue. And that can definitely work. Online is a lot easier to have a dial that's much more personalized and everybody can kind of roll their own mix. And I could imagine having a little slider about how much advertising you want or are willing to take. 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.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Are experimenting. So they are doing some experiments about what the willingness is for people to pay. I think that when they do the math, it's going to work out that they still are better off with an advertising driven model.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“There's some economic theory on it. I also think, to be frank, there's just a lot of experimentation that's needed because sometimes things are a little counterintuitive, especially when you get into what are called two-sided networks or platform effects where you may grow the market on one side and harvest the revenue on the other side. Facebook tries to get more and more users and then they harvest the revenue from advertising. So that's another way of kind of thinking about it.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“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. In short, when the demand curve is relatively flat and wide, like generic news and things like that, then you tend to do better with advertising. If it's a good that's only useful to a small number of people, but they're willing to pay a lot, they have a very high value for it. Then you advertising isn't going to work as well. You're better off charging for it. Both of them have some inefficiencies. And then when you get into targeting and you get these other revenue models, it gets more complicated.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“They may learn something from it, and also from the advertiser's perspective, those are people who are actually interested. I mean, the example I sometimes gave, I bought a car recently. All of a sudden, all the car ads were like interesting to me. Exactly. And then now that I have the car, I sort of zone out on it. But that's fine. The car companies, they don't really want to be advertising to me if I'm not going to buy their product. So there are a lot of these different revenue models.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, it's something that, you know, so I teach a course on digital business models. I used to get used to it at MIT at Stanford. I'm not quite sure. I'm not teaching until next spring. I'm still thinking what my course is going to be. But there are a lot of different business models. And we have something that has zero marginal cost. There's a lot of forces, especially if there's any kind of competition that push prices down to zero. You can have ad supported systems. You can bundle things together. You can have volunteer, you mentioned Wikipedia. There's donations. And I think economists underestimate the power of volunteerism and donations. National public radio. Actually, how do you do this podcast? What's the revenue model?”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. Well, there's a version of it in the Proceedings of the National Academy of Sciences about, I think we call it massive online choice experiments. I should remember the title, but it's on my website. So, yeah, we have some more papers coming out on it, but the first one is already out.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“It's pretty hard. I mean, one of the reasons it hasn't been done before 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? That's a lot harder. How much is Wikipedia worth to you? That's what we have to answer. And to do that, what we do is we can use online experiments. We do massive online choice experiments. We ask hundreds of thousands, now millions of people to do lots of sort of A-B tests. How much would I have to pay you to give up Wikipedia for a month? How much would I have to pay you to stop using your phone? And in some cases, it's hypothetical. In other cases, we actually enforce it, which is kind of expensive. We pay somebody $30 to stop using Facebook and we see if they do it. And some people will give it up for $10. Some people won't give it up even if you give them $100.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Hours worked. So if you mismeasure GDP, you misheasure productivity by the exact same amount. That's something we need to fix. I'm working with the statistical agencies to come up with a new set of metrics. And over the coming years, I think we'll see we're not going to do away with GDP. It's very useful, but we'll see a parallel set of accounts that measure the benefits.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“But almost everybody has kind of forgotten that he said that. And they just use it. How well off are we? What was GDP last year? It was 2.3% growth or whatever. That is how much physical production, but it's not the value we're getting. We need a new set of statistics. And I'm working with some colleagues, Avi Kollis and others, to develop something we call GDP-B. GDP B measures the benefits you get. not the cost. If you get benefit from Zoom or Wikipedia or Facebook, then that gets counted in GDPB even if you pay zero for it. So back to your original point, I think there is a lot of gain over the past decade in these digital goods that doesn't show up in GDP, doesn't show up in productivity. By the way, productivity is just defined as GDP divided by.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Good for you. Yeah. So, but what does that do mean for GDP? GDP is based on the price and quantity of all the good things bought and sold. If something has zero price, you know how much it contributes to GDP, to a first approximation, zero. So these digital goods that we're getting more and more of, we're spending more and more hours a day consuming stuff off of screens, little screens, big screens, that doesn't get priced into GDP. It's like they don't exist. That doesn't mean they don't create value. I get a lot of value from watching videos and reading Wikipedia articles and listening to podcasts, even if I don't pay for them. So we've got a mismatch there. Now, in fairness, economists, since Simon Kuzmus 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.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. Well, so that's another area I've done quite a bit of research on, actually, is these free goods like Wikipedia, Facebook, Twitter, Zoom. We're actually doing this in person, but almost everything else I do these days is online. The interesting thing about all those is most of them have a price of zero. What do you pay for Wikipedia? Maybe like a little bit for the electrons to come to your house. Basically zero, right?”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“But the past decade has been a bit disappointing if you thought there was a one to one relationship between cool technology and higher productivity.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“They're doing that in the belief, I think the justified belief that they will get the upward part of the J curve and there will be some big returns. But in the short run, you're not seeing it. That's happening with a lot of other AI technologies, just as it happened with earlier general-purpose technologies. And it's one of the reasons we're having relatively low predictivity growth lately. As an economist, one of the things that disappoints me is that as eye-popping as these technologies are, you and I are both excited about some of the things they can do. The economic productivity statistics are kind of dismal. Actually, believe it or not, have had lower productivity growth in the past about 15 years than we did in the previous 15 years, in the 90s and early 2000s. And so that's not what you would have expected if these technologies were that much better. But I think we're in kind of a long J curve there. Personally, I'm optimistic. We'll start seeing the upward tick. Maybe as soon as next year.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Interesting point about all that is that during that reinvention period, you often actually not only don't see predictivity growth, you can actually see a slipping back, measured predictivity actually falls. I just wrote a paper with Chad Sieverson and Daniel Rock called The Productivity J curve, which basically shows that in a lot of these cases you have a downward dip before it goes up. And that downward dip is when everyone's trying to reinvent things. And you could say that they're creating knowledge and intangible assets, but that doesn't show up on anyone's balance sheet. It doesn't show up in GDP. So as if they're doing nothing, like take self-driving cars, we're just talking about it. There have been hundreds of billions of dollars spent developing self-driving cars. And basically no chauffeur has lost its job, no taxi driver. Yeah, so there's a bunch of spending and no real consumer benefit.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, once they did that, once they went to Unit Drive, those guys won the debate, then you started having a new kind of factory, which is sometimes spread out over acres, single story, and each piece of equipment had its own motor, most importantly, they weren't laid out based on who needed the most power. They were laid out based on what is the workflow of materials, you know, assembly line, let's have it go from this machine to that machine to that machine. Once they rethought the factory that way, huge increases in productivity was just staggering people like Paul David have documented this in their research papers. And I think that there's a lesson you see over and over. It happened when the steam engine changed manual production. It happened with the computerization, you know, people like Michael Hammer said, don't automate, obliterate. In each case, the big gains only came once smart entrepreneurs and managers basically reinvented their industries.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I'll tell you what happened before electricity there were basically steam engines or sometimes water wheels. And to power the machinery, you had to have pulleys and crankshafts. And you really can't make them too long because they'll break the torsion. So all the equipment was kind of clustered around this one giant steam engine. You can't make small steam engines either because of thermodynamics. So you have one giant steam engine, all the equipment clustered around it, multi-story. They'd have it vertical to minimize the distance as well as horizontal. And then when they did electricity, they took out the steam engine. They got the biggest electric motor they could buy from General Electric or something like that. And nothing much else changed It took until a generation of managers retired or died”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Electricity and how for 30 years there was almost no productivity gain from the electrification of factories a century ago. Now it's not because electricity is a wimpy useless technology. We all know how awesome electricity is. It's because at first they really didn't rethink the factories. It was only after they reinvented them and we describe how in the book then you suddenly got a doubling and tripling of productivity growth. But it's the combination of the technology with the new business models, new business organization that just takes a long time and it takes more creativity than most people have.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Guts and risk, it also requires rethinking what you're doing. I think way too many people are unimaginative, intellectually lazy. And when they take AI, they basically say, what are we doing now? How can we make a machine do the same thing? Maybe we'll save some cost, we'll have less labor. And yeah, you know, it's not necessarily the worst thing in the world to do, but it's really not leading to a quantum change in the way you do things. When Jeff Bezos said, hey, we're going to use the internet to change how bookstores work, and we're going to use technology. He didn't go and say, okay, let's put a robot cashier where the human cashier is and leave everything else alone. That would have been a very lame way to automate a bookstore. He went from soup to nuts said, let's just rethink it. We get rid of the physical bookstore. We have a warehouse. We have delivery. We have people order on a screen. And everything was reinvented. And that's been the story of these general purpose technologies all through history. In my books, I write about like”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, back to Elon, I think part of his genius was with the electric cars before he came along, electric cars were all kind of underpowered, really light, and they were sort of wimpy cars that weren't fun. And the first thing he did was, you know, he made a roadster that went zero to 60 faster than just about any other car and went the other end. And I think that was a really wise marketing move as well as a wise technology move.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, I can see that being really because I think the thing that's proven harder than maybe some of the early people expected was there's a long tail of weird exceptions. So you can deal with 90, 99, 99.99% of the cases, but then there's something that just never been seen before in the training data. And humans more or less can work around that, although let me be clear and note there are about 30,000 human fatalities just in the United States and maybe a million worldwide. So they're far from perfect. But I think people have higher expectations of machines. They wouldn't tolerate that level of death and damage from a machine. And so we have to do a lot better at dealing with those edge cases.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“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. I'm not sure they're going the right direction with that.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. Well I'm smart enough to be humble and not try to get between. I know there's very bright people on both sides of the argument. I've talked to them and they make convincing arguments to me about how careful they need to be and the social acceptance. Some people thought that when the first few people died from self-driving cars, that would shut down the industry, but it was more of a blip, actually. And, you know, so that was interesting. Of course, there's still a concern that if there could be setbacks if we do this wrong, your listeners may be familiar with a different levels of self-driving, you know, level one, two, three, four, five. I think Andrew Rang has convinced me that this idea of really focusing on level four, where you only go in areas that are well mapped rather than just going out in the wild, is the way things are going to evolve. But you can just keep expanding those areas where you've mapped things really well, where you really understand them and eventually all become kind of inter.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“So they're kind of the opposite. I think it's a good counterpart to say what Elon is doing. And hopefully they can be frank in what they think about each other, because I've heard both of them talk about it. But they're much more, you know, this is an assistive, a guardian angel that watches over you as opposed to try to do everything. I think there's some things like driving on a highway from LA to Phoenix where it's mostly good weather, straight roads. That's close to a solved problem, let's face it. 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. It's more like game theory. That's a much harder problem and requires understanding human motivations. So there's a continuum there of some places where the cars will work very well and others where it could probably take decades.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, I'm really excited about that, but it's become much clearer that the original way that I thought about it, most people thought about it, like, you know, will we have a self-driving car or not, is way too simple. The better way to think about it is that there's a whole continuum of how much driving and assisting the car can do. I noticed that you're right next door to Toyota Research Institute.”
2020-11-25 · Lex Fridman Podcast · #141 – Erik Brynjolfsson: Economics of AI, Social Networks, and Technology · IDENTIFIED FROM THE TRANSCRIPT · source