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Artificial Intelligence, Big Data & China | Martin Schmalz | Modern Wisdom Podcast 144

Martin Schmalz is a professor of Finance at Oxford University and an author. We're receiving constant warnings about the advent of Artificial Intelligence. And big data. And China. But how do all of these fit together? Expect to learn why your phone's GPS data on a night time is affecting your credit score, how the speed which you complete an online form in could change the price, where the REAL computing power behind AI is being deployed at the moment, and much more. Extra Stuff: Follow Martin on Twitter - https://twitter.com/martincschmalz Buy The Business Of Big Data - https://amzn.to/2HHg2Li Thank you to The Browser - https://thebrowser.com/ Take a break from alcohol and upgrade your life - https://6monthssober.com/podcast Check out everything I recommend from books to products - https://www.amazon.co.uk/shop/modernwisdom #bigdata #artificialintelligence #machinelearning - Listen to all episodes online. Search "Modern Wisdom" on any Podcast App or click here: iTunes: https://apple.co/2MNqIgw Spotify: https://spoti.fi/2LSimPn Stitcher: https://www.stitcher.com/podcast/modern-wisdom - Get in touch in the comments below or head to... Instagram: https://www.instagram.com/chriswillx Twitter: https://www.twitter.com/chriswillx Email: modernwisdompodcast@gmail.com

Martin SchmalzguestChris Williamsonhost
Feb 20, 20201h 9mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 0:56

    How unexpected data (like location) predicts credit risk

    1. MS

      ... if people start sleeping in two different locations interchangeably at night, so that tends to be a really bad credit risk. So these people tend to use up a lot of cash in the near future, more than they can afford. And the story they tell me behind that is, "Well, those are people who have lovers, and having lovers leads to divorces, and divorces are costly, and costly divorces lead to loan default."

    2. NA

      (laughs)

    3. MS

      So (laughs) whether that's a right story behind it or not, the, the boring fact is that using location data, um, is extremely useful in predicting default.

    4. CW

      Martin, how you doing, man? Welcome to show.

    5. MS

      Very good. Thank you very much, uh, for having me.

    6. CW

      Very, very happy to have you on today. We've recently been talking about some big data stuff. Seth Stephens-Svidiwitz was on recently, uh, and we were discussing about some of the interesting analysis that he'd done on Google searches and PornHub data as well. Um, maybe not PornHub today, but definitely some big data from yourself.

    7. MS

      That's right, yeah.

  2. 0:56 – 2:12

    Schmalz’s path: from mechanical engineering to finance + AI economics

    1. CW

      Lovely. So give us your background. What do you do?

    2. MS

      Well, my background, um, I, I, uh, grew up in Southwest Germany, and, uh, as everybody does who is from there, I, uh, and has any form of self-respect, I studied mechanical engineering.

    3. CW

      (laughs)

    4. MS

      Um, but at some point, I had the impression that, um, I can much better understand what happens in the world if I study how the financial system works, and thus made, made my way to, um, studying economics and, uh, going to the US and ended up being a finance professor. And in the course of that, I somehow stumbled across this topic of AI and big data and started teaching it, um, because I somehow felt, uh, that there was a bit of a discrepancy between the demands, uh, on our graduates in industry, you know, which concerns Python and big data skills, and what we taught them, which at the time was largely Excel. So I developed the ambition first to actually make MBA students teach, uh, learn some Python in class and apply it, and, uh, uh, of course, the ambition is not to turn them into data scientists, but to understand what the economics of data-driven businesses models is, um, and can be, and how we can understand, uh, the success of tech platforms, um, over the last, um, decade or so.

  3. 2:12 – 7:21

    Why companies need “translators” between data science and strategy

    1. CW

      So quite involved in the development of how we analyze the data and pushing that forward?

    2. MS

      Yes. So see, um, there are specialists on the analysis of data, and you call them data scientists or so, and what I'm trying to s- uh, spend time thinking about is predicting future directions of business models and the development of workplaces and, uh, yeah, just how, uh, jobs, firms, and industries get, uh, transformed as a result of the, of the big data revolution.

    3. CW

      Okay, so this is helping businesses to make decisions through big data?

    4. MS

      That's right, that help, that helps, um, businesses make strategic decisions on what the AI revolution means for them, uh, how they should position themselves. It also helps investors in deciding, um, what kind of businesses they, uh, should invest in or what kind of questions they might ask, uh, businesses they consider investing in, and distinguishing between the, you know, thousands of different fintech startups, so kind of figuring out which ones of those that just have AI in the name, which ones actually apply it-

    5. CW

      (laughs)

    6. MS

      ... and which ones apply it in a way that doesn't only solve a technical problem, um, but actually also has a decent chance of, uh, turning a profit at some point in the future.

    7. CW

      I understand. So is most of the challenge that you're coming up against here a technical one with regards to the way that you can statistically model things and the, um, the scripts and things that you can write? Or is it more so on the side of how you interpret that data, how you apply it to the market, and stuff like that?

    8. MS

      The latter. So the challenge is that there are very few people that have the combination of two skills or sets of knowledge. One is understanding what AI actually is and what machine learning algorithms do, and specifically what they don't do. And the spoiler alert is if you read the newspaper, um, you don't really get a good impression of what that is. And, uh, there are, of course, people who understand that, like the engineers that, uh, or data scientists that work with machine learning models. The problem with that is that very few of them have even basic, um, economics training or enough of, uh, economics training to kind of, uh, combine their data science skills, uh, with economic theory in order to, um, predict the future of industries and, and markets. So there doesn't seem to be a lot of structured thought about, uh, business models and how they will change in the age of AI. And indeed, there was no book out there, which is why we wrote one.

    9. CW

      (laughs)

    10. MS

      (laughs)

    11. CW

      Yeah, so that, by the way, Business of Big Data will be linked in the show notes below, of course. If you're interested in today, then head to the link there, and, uh, you'll be able to find out even more. Why do you need someone whose skill set straddles both areas? Why can't you just have the data science people feed out the data that the guys who understand economic theory and AI ... Like, why, why can't you have them just work in tandem as opposed to to have someone that bridges?

    12. MS

      Well, uh, well, I suppose you, you can have them work in tandem as long as they can communicate to each other, but, um, that is a challenge. Um, so if a data scientist talks to an economist who doesn't know what a feature is, um, that is a problem. If you explain to an economist that what the data scientist called feature, you call a variable, then all is good. But somebody needs to be there who translates between these two different worlds, and that's just, that's just a challenge that, that many firms have. So, so what happens if you don't have such a person? See, if you read The Financial Times or whatever the business press, you might get the impression that AI is about making computers to think and to replace, uh, human beings, um, and, uh, that the whole world is investing in it. So what do you do as a top executive...... trying to, um, allocate capital to various projects. You might call up the IT department and tell them, "Hey, why don't you, you know, do, do AI?"

    13. CW

      (laughs)

    14. MS

      "Do, do big data."

    15. CW

      Yeah (laughs) .

    16. MS

      At which point the IT department, uh, says, "Well, yes," uh, and then they build a data pool. And two or three years later, they ask themselves, "So why exactly did we do that? And how does it fit into our overall strategy? And, and what's the product? Who's the customer? Uh, what produces the value in this, in this case?" Or, uh, you know, the problem here being that, uh, in many cases, top management doesn't really understand what is at the core of AI and machine learning. On the other hand, um, engineers tend to get very excited about to- solving technical problems. And the technical problems ML solves is prediction. You predict some stuff. But then again, there's many things it could predict, and there's a lot of data you could use for prediction. But, uh, ideally, that should be in- so- informed, in a business that strives to make a profit, by what economically important and valuable parts, um, of, of prediction are. And this is where the communication between the engineers and the economists or the strategists has to come in. And that communication suffers from just, you know, basically language barriers or, or lack of knowledge on both sides about what the other side is doing.

  4. 7:21 – 10:10

    What AI really does: cheap, scalable prediction (not “thinking”)

    1. CW

      I get it. I get it. So, what does AI do? You've mentioned some of the things that it doesn't do.

    2. MS

      Right.

    3. CW

      What's AI being used for mostly at the moment, other than preparing for a global takeover and to make all of our jobs obsolete?

    4. MS

      Voila. There you go. Exactly. So that's the kind of thing you read about, right? So when Elon Musk talks about the singularity and stuff. And my point is not so much about whether that's a real thing and whether it might happen in 30 years. The point is that 95% of currently used ML and AI techniques is basically about a pretty boring technical problem, which is you use a bunch of data to predict some sort of outcome variable. So, what could that be? Um, I know everything about, you know, uh, your, uh, listener's PornHub watching habits as well as their shoe size, uh, hair color, uh, social network, phone records, and I don't know, a whole bunch of different variables, as well as their heart rate variability, um, at night between 2:00 and 3:00 AM, all their sleeping habits and exercise habits. And I might use all that information to predict stuff. What I could have possibly predict? Well, I could want to predict obviously what kind of, um, ads they're likely to click on. So what kind of products they like, what kind of movies they like to watch. But not only that, but also how much are they willing to pay for it, you know? So if I know that, uh, you have a certain travel schedule because I tracked your cellphone location data in a, in a precise way, I might be able to start to infer how much you're willing to pay for airline tickets on a certain, uh, route at particular times. And obviously, that information gets valuable if you manage to exploit it in, in, in, uh, in a profitable way. Um, so, uh, what was the question? (laughs)

    5. CW

      (laughs) What does AI do and what does it not do?

    6. MS

      Right. So what AI does is, uh, machine prediction that is generic in a sense of you have a bunch of data from the past and in context where the past predicts the future, or the future works according to similar rules as the past. Uh, the generic types of prediction machines can do faster, better, and cheaper than human beings, and increasingly so, simply because there's more and more data around, more data than a human could possibly analyze.

    7. CW

      Mm-hmm.

    8. MS

      Uh, computation power gets cheap. These algorithms are very computationally intensive.

    9. CW

      Mm-hmm.

    10. MS

      Uh, but yeah, that stuff got cheap. So data collection, data storage, and computation got cheap. And as a result of it, machine prediction got cheap at a given level of quality. That's what it does. And it doesn't do anything else really.

    11. CW

      (laughs)

    12. MS

      Computers, computers don't think. Like artificial intelligence has very little to do with intelligence in a, in a broader sense. So it is true that a pretty big part of human intelligence these days is being used for generic prediction. But that's exactly what will change, okay? So this part will change.

  5. 10:10 – 15:33

    Generic vs non-generic prediction: where humans still dominate

    1. CW

      What like?

    2. MS

      What like? Well, there used to be a job description called loan officers. Their job is to know your dad and your, uh, your, your mother, your, your friends, and your personal character as evidenced on the Newcastle soccer team or whatnot, um, and from all this information, kind of predict wh- how likely you are to repay a loan, and hence, whether they should extend a mortgage to you and at what interest rate and which, which collateral. That is a prediction exercise. The thing is, nowadays, there are companies that know much better what your social network is and whether your dad and sister and social network repaid loans and, uh, what your current health and mental health status is. And those companies are called tech companies or data aggregators and so forth. So they are just becoming much better and cheaper and faster at predicting loan default. What else might you predict? You might predict health outcomes, um, you know, or the, the, the life expectancy. That's kind of useful when you run a life insurance company.

    3. CW

      Mm-hmm.

    4. MS

      Um, i- i- you might, uh, as I said, movie-watching habits. Um, a lot of things. If you drive a car, you might predict whether this cyclist you see in the right of the road is likely to make a left turn anytime soon and get in your lane or not. All of these are prediction exercises. Um, yeah, once you spend some time thinking about this topic, you see that, um, prediction exercises all, are all over what we do in real life these days. And some of them are gen- generic and based on a whole bunch of data and a whole bunch of experience, and those are gonna get replaced. And others are non-generic.

    5. CW

      Mm-hmm.

    6. MS

      They're not based on a whole lot of data-

    7. CW

      (laughs)

    8. MS

      ... um, but are, like, fundamentally new. Here's an example. What about predicting how AI will disrupt your job or your firm or your industry? The thing is, that's n- never really happened before. So a computer cannot possibly analyze a past dataset when this has happened before and therefore predict what happens now. It has to be a human being. And that's a uniquely human strength.

    9. CW

      One of the blog posts that I read last year, which was really illuminating to me, was talking about the role of a top-end executive, and they classified a top-end executive as a difficult-to-replace complex decision engine.

    10. MS

      Right.

    11. CW

      That was what they said that the- the absolute top-end executives are.

    12. MS

      Right.

    13. CW

      And when you look at it from ... You know, I appreciate that the (laughs) listeners haven't read that blog post, but it absolutely blew my mind. You've got to think, the real value that is added by Tim Cook at Apple or by Elon Musk, it's not particularly his engineering ability, it's not particularly his marketing ability, it's not this, that, and the other. It is their ability to compile a lot of disparate, complex, interwoven variables and make the best decision based on all of those. So that's prediction data, right? That's prediction.

    14. MS

      It is absolutely prediction. I would, however, say it's non-generic prediction. In particular, um, they- they tend to create new products, no?

    15. CW

      Mm. Mm.

    16. MS

      So what they're trying to predict is, what is demand going to be for a phone that doesn't have a keypad?

    17. CW

      Mm.

    18. MS

      I don't know if you remember that. I'm old enough to remember how the press was all over how there's absolutely not gonna be demand, and obviously BlackBerry was on the wrong end of this. There's absolutely not gonna be demand for a phone where you'd have to key in stuff on a touch pad. Well, how do you predict that? Yeah, it is a prediction exercise, but it's not that a computer can do, because it's not, and it hasn't happened before in the past. So an- an AI algorithm, machine learning algorithm cannot analyze the past dataset to make that prediction.

    19. CW

      Absolutely, yeah. That's the- the- the safety mechanism, I suppose, that is inherent in being an executive. Being a top-end executive allows you to protect your own job by being the only person on the planet that knows that very particular set of variables and is able to extrapolate forward from that and say, "This is what I think that we need to do." And I suppose that's where some people talk about business as an art form or being an exec as a- as be- or a CEO, board member as being- being a creative artist in one way or another, and it is that, isn't it? It's taking all of these different variables and seeing something that potentially isn't in the data.

    20. MS

      Uh, so I agree with that. Now, let me add one level of nuance. It's not so much about a job of a top executive, but it's about particular tasks that the executive might take, um, that is more useful to think about. So, we- we right now talked about predicting the demand for a no-keyboard, um, cell phone. Well, what about coming up with the creative process of even coming up with the possibility of having a no-touch, uh, touchphone?

    21. CW

      Mm-hmm.

    22. MS

      That, so far, is a rather uniquely human kind of activity that a computer has absolutely no (laughs) , um, advantage- advantage doing. So, the kind of, uh, I don't know, just intuitive creativity, um, I guess you're referring to synthesis as well.

    23. CW

      Mm.

    24. MS

      Seeing a bunch of trends in the world and somehow synthesizing them in an intuitive way, um, in order to make these creative, um, predictions, um, that is something that, um, humans are uniquely good at, so far at least.

  6. 15:33 – 17:18

    Behavioral micro-signals: typing speed, typos, and insurance/credit models

    1. CW

      Mm. Yeah. Looking at some of the stuff that we were talking about before, wha- why does my bank care how fast I fill in a form?

    2. MS

      Why does he have to know that, or why does he want to know that?

    3. CW

      Why does a bank care how fast I fill in a form?

    4. MS

      Right. So see, this is an example I write about in the book. Um, I spent a lot of time talking to, uh, friends in China. China is, let's say, at least half a decade ahead in the AI, um, and big data game-

    5. CW

      Is it really?

    6. MS

      ... due- due to various reasons. And, um, a, uh, yeah, a friend of mine who- who, uh, is a head of AI research at a company called Huawei, that, you know, recently was in the news once or twice-

    7. CW

      Mm-hmm.

    8. MS

      ... um, tells me, "Yeah, you know, one really important variable these days is how fast people fill out online forms." And I say, "What? Wait, why?" And he says, "Well, see, first of all, if you type your Social Security number or your National Insurance number," or however it's called in the country from which you're listening, um, "that kind of tells you a lot about the person. If you can't fill in your Social Security number real fast, then you're probably a fraud or, I don't know, not particularly intelligent or something. I don't know. It tells you something about people's, uh, willingness or ability to repay a loan or how big of an insurance risk, um, they are. Or similarly, if you make lots of typos while filling out a form, perhaps you're not a particularly careful person." Um, but whatever, you know, the theory is, uh, behind why these variables predict outcomes well, they appear to work really well in predicting loan default, um, as well as insurance risks. So companies start collecting it. I never thought until I heard it, uh, this story, uh, about that anybody might track how fast I fill out online forms, but they do.

  7. 17:18 – 23:25

    Dynamic pricing, willingness to pay, and the ethics of personalization

    1. CW

      Wow. Yeah, that is- that is super interesting. I was on Skyscanner, uh, a flight comparison site only a couple of months ago, and the particular flight that I was looking at, I watched, I refreshed the page, and in front of my eyes, it went up by 300, 300 pounds. And I was like-

    2. MS

      (laughs)

    3. CW

      ... "There's not- there's not this many people looking at this particular flight to Vegas right now. There's me. There's me and there's the guy sat behind the screen that's just said, 'I'm gonna- I'm gonna screw him to the tune at 250 quid.'" And then I went back, and I ended up booking the flight to Vegas just in between Christmas and New Year, and I booked it, and it had gone back down to below the original price, but it changed as I was looking at it. Refreshed the page, changed as I was looking at the page, and I was like, "There's a guy at Skyscanner that's just gone, 'Ah, fuck it.'"

    4. MS

      Well, a guy or a machine.

    5. CW

      Or- the machine's a guy as well. Yeah, yeah, you are right. There is a- there's a- there's a- there's a guy with a button, and the button made it happen.

    6. MS

      Mm-hmm.

    7. CW

      Yeah.

    8. MS

      So see, um, I have never...... I asked myself for years why nobody does that to me. (laughs) So you're the first time that, uh, uh, the first data point that I hear, uh, reporting, um, such an incident. Um, so I don't want to extrapolate too much about it, but see, it's an, it's an, it's an interesting point, right? So, uh, the website can see how interested, um, you are and, uh, how desperate you seem to go in a particular date and particular time, no? So what they're predicting is really your willingness to pay, uh, for a particular ticket at a particular time. Now obviously, that can be very useful. But I imagine that you may not have felt super happy about that price increase and obviously they increased it so much that you didn't buy the ticket.

    9. CW

      Mm-hmm.

    10. MS

      I might even suspect that you might be less willing to use that particular site in the future if you suspect that they're using your search information against you.

    11. CW

      Yeah.

    12. MS

      So when you think about this from a business perspective, what a business executive might want to keep in mind as they s- seek to exploit the data various users generate is ethical considerations. If not, because they themselves care about the ethics, but their customers might. And that might-

    13. CW

      (laughs)

    14. MS

      ... turn out to be business risk, no? And to get the kind of intuition of what makes people upset, that's kind of like really valuable, no?

    15. CW

      Absolutely.

    16. MS

      So people tend to get really upset at being charged a different price from another person. But you know what people really love? Getting individualized discounts. So you get a discount, but your neighbor didn't.

    17. CW

      (laughs)

    18. MS

      Now when you think about this for more than like two or three seconds, you're like, "But that's the same thing." Yeah, but it feels very different to people, no?

    19. CW

      Absolutely.

    20. MS

      And, and what we just described, all of the above, but with coming up with variables that you never thought about, uh, could pos- potentially predict your credit risk, or the intuition about what people get upset about. These are completely human exercises and it is precisely about predicting the future of AI and how they transform industries and jobs and websites and searching for flights and so forth, where humans have a huge edge over computers, because it hasn't happened before, you know? So educating oneself about those particular areas is actually a great skill to have as the AI revolution takes off.

    21. CW

      Am I right in thinking that Amazon split tests its prices? I've heard some, some s- some, uh, reports about two people going onto the same product and Amazon doing a, whatever it was that you mentioned, desirability to buy or desire to buy, um, split tests on some of its prices for things. Just by pens.

    22. MS

      So I don't know that for a fact. What I have noticed myself is that when I put a certain item in my basket, um, in my shopping basket, but I don't immediately buy it, and I come back a while later to take a look at it, I'm frequently, uh, confronted with the fact that the price of that item may have changed, increased or decreased.

    23. CW

      Mm-hmm.

    24. MS

      Now I don't know that this is because Amazon is running an experiment on me. What I do know is that this creates data. It does create data about whether I'm buying a certain item at a certain price or whether I'm not. And that can be analyzed and used, um, in order to infer individual's willingness to pay for certain items. Now again, I can't tell you how much deliberation is behind that and Amazon would likely not tell me if I asked, um, but for s- for sure this is one area where, uh, listeners are becoming aware right now that they're generating data that people can go and analyze and use to, um, estimate, you know, predictive future behavior.

    25. CW

      Mm-hmm. Why, uh, why does my car insurer care about my email address? Is that the same thing as the bank, the bank filling in a bank form?

    26. MS

      Yeah, kind of, kind of like that. I mean, I'll just ask this, uh, (laughs) to the listeners and, you know, do you know anybody who has an AO- AOL address or-

    27. CW

      My, my dad did until I forced him to get rid of it and then he m-

    28. MS

      (laughs)

    29. CW

      ... he upgraded to I think what he considered to be modern, which was a Yahoo email address.

    30. MS

      Very good. (laughs) So, suppose that I was offering an insurance product that I really don't want to offer to older people. Based on this particular, um, uh, example that you just offered, uh, I would ass- assume that offering that to AOL and Yahoo users would not be in my best interest, because I would be targeting an older audience, no?

  8. 23:25 – 26:37

    Location data, ride-hailing, and why Uber/Didi move into lending

    1. CW

      ... make, make a judgment. It's starting, it's starting to fit together. Um, what else have we got? Oh yeah, checking your phone in the morning and where you sleep at night. What about that? Tell us about that.

    2. MS

      So that's another story of China, uh, where people tell me-

    3. CW

      It's, everything's coming out of China, Martin.

    4. MS

      (laughs)

    5. CW

      Everything is at the moment.

    6. MS

      Yeah. N- oh-

    7. CW

      Fucking Huawei. Huawei now, I mean, what's, I'm recording on a Logitech at the moment. What's that? What's Logitech? Is Logitech Chinese?

    8. MS

      Uh, I don't know.

    9. CW

      I bet, I bet they, it will be. Someone, someone behind the scenes will own Logitech. They'll ha- they'll have a, a controlling share from China.

    10. MS

      Uh, well, okay, so you have to update me here on, on what you're after here.

    11. CW

      Just Logitech. Who are they? Where are they, where are they from?

    12. MS

      Uh, it, I, I thought they were Swiss, but I don't know who's actually behind them, so.

    13. CW

      Well, that's it, China. I'm telling you, I'm telling you that.

    14. MS

      Ah-hah.

    15. CW

      Anyway, so China, what time you check your phone and where you sleep at night. Tell us about that.

    16. MS

      Well, so, uh, so, so, so what they tell me is that, um, if people start sleeping in two different locations interchangeably at night, that that tends to be a really bad credit risk. So these people tend to use up a lot of cash in the near future, more than they can afford. And the story they tell me behind that is, well, those are people who have lovers and having lovers leads to divorces, and divorces are costly and costly divorces lead to loan default. (laughs)

    17. CW

      Oh.

    18. MS

      So whether that's the right story behind it or not, the, the boring fact is that using location data, um, is extremely useful in predicting your fault. Let's have a less juicy story and just say, see. Say you're Uber or DiDi when you want to go to China, and you know where a person lives because, you know, you have stored a home address in your, in your app, as well as where you work. That gives you a really good idea about what the guy's annual income is, no? If the person moves to a richer and more prosperous neighborhood, that tells you something about whether their salary went up, uh, what kind of restaurants they eat at, which of course as a ride-hailing company, you know, because that's where you drop them off or pick them up. It tells you a lot about a particular person as well, no? Uh, simply how fancy the restaurant is. So obviously, you can infer a lot of things about the person's current, um, liquidity (laughs) situation from simply analyzing the location data and nothing else. And guess what? DiDi, a year ago or two, um, actually started a lending arm as you would expect from a company that knows so many things about individuals that are relevant for predicting loan default.

    19. CW

      Wow. (laughs) You-

    20. MS

      Well, see, okay, so, you know, it- it's fun how I can impress people. Um, the, the way I do this to, in MBA classes is to tell them the economic theory of how clearly a ride-hailing company, um, should be offering, uh, lending products sometime soon. And I did that in the class, and I, I, I, I earned all the respect by my students because a week later, Uber started, um, announcing pla-

    21. CW

      No way.

    22. MS

      See? Now the thing is, I didn't actually predict that. I just knew that DiDi had done it a year earlier (laughs) in China. So-

    23. CW

      Got you.

    24. MS

      ... as I s- (laughs) as I said, it's not very hard to predict what happens in this world as long as you basically follow what's happened in China over the last five years.

  9. 26:37 – 36:42

    Why China is ahead: super-apps, lax constraints, and engineering scale

    1. CW

      Wow. Why, uh, why is China so far ahead with this?

    2. MS

      Well, I think there's at least four reasons. Let's see. Uh, we, we talk about some of them in the book. Let me see if I can still get them together. I mean, number one, I said at the beginning what machine learning is really useful for is analyzing huge datasets. Otherwise, you can use conf- conventional statistics. Now China has a large population in a reasonably homogenous economic system, you know. So, well, just by virtue of that, you have a lot of data. But also for each of these individuals, you have lots of different data points because for various reasons the Chinese, uh, the Chinese people appear to tend to be less, uh, privacy concerned or if they're privacy concerned, they just get a lot more convenience out of the apps they have. So with the kind of apps, uh, that, that WeChat, say, offers, um, you can do a lot more things than with WhatsApp in the US, right? So if you think of, uh, Mark Zuckerberg, uh, trying to merge Instagram with, with, uh, WhatsApp and Facebook data, that's what happened in China many, many years ago.

    3. CW

      Mm-hmm.

    4. MS

      So their messaging app, you can already transfer money, so essentially you have a, a useful currency there. You can make your doctor's appointment, and you can buy your life insurance. So you can do all kinds of things.

    5. CW

      Through WeChat?

    6. MS

      Yeah. Basically, it's all in the same app. They call it the super app. Okay? So, um-

    7. CW

      Shit, the bed.

    8. MS

      Um, now... So, so I said, "Well, uh, why is it useful to have all these apps within one app?" Wait, think about this. I just said you make the doctor's appointment in the same app that you buy life insurance. Well, you know, guess what? You can probably predict the remaining life expectancy of a particular person a lot better if you know what kind of doctor's appointments they're in the process of making, no?

    9. CW

      Mm-hmm.

    10. MS

      Um, so this just illustrates that, uh, using data that you generate in one part of the business can be really useful in another part of the business. So having all these diverse sets of businesses under one roof, and you might say, "Why doesn't messaging service, you know, help-"

    11. CW

      Mm-hmm.

    12. MS

      "... with merging that with Facebook or Instagram data?" That's kind of-

    13. CW

      Yeah.

    14. MS

      ... like the answer. You get different types of variables about the same people, and that turns out to be incredibly useful for predicting, well, all kinds of stuff.

    15. CW

      The thing that is coming to mind now as we talk about this more and more, it's obvious that there's massive amounts of data that are being collected. It's also obvious that some of these data correlates are really useful for companies.

    16. MS

      Right.

    17. CW

      That makes them powerful.

    18. MS

      Right.

    19. CW

      One of the problems that everyone that's listening can, will think of is, right, okay, so let's say, um, my life insurance provider has direct access to my doctor bookings. From the company's side, I can see why that would be great.

    20. MS

      Right.

    21. CW

      From the customer's side, that could be great-

    22. MS

      Right.

    23. CW

      ... or it could m- leave me suffering at the hands of higher insurance premiums that potentially aren't fair or anything else. And based on the company's ethics, based on something that's completely out of my control now, I'm at the mercy of this company which knows more about me than I do.

    24. MS

      I couldn't agree more. (laughs)

    25. CW

      It's-

    26. MS

      So where does... So I think you mentioned a lot of different things. So one is fairness. Some peo- some alarm bells might ring, and you might, people might find, find things unfair. Well, the company might say, "Well, you're, you're free to not use your, our app." But the problem is it's kind of hard to be a functional use human being in today's society without any using some sort of technology, no? I know some people who try-... my, my, my cycling buddy from grad school is now a computer science professor at Berkeley, teaching machine learning, and in particular, fairness, by the way. And, uh, his wife works at Google in an AI team, and he does not have a smartphone. (laughs)

    27. CW

      Fuck, man. This is the same as-

    28. MS

      That, that scares me.

    29. CW

      ... is it, is it Elon, is it Elon Musk or Tim Cook that doesn't let their kids have iPads in the house?

    30. MS

      Yeah. Uh, well, I, yeah, you know, it's kind of- is, uh, has, has a similar spirit to it, no? Now, here's the thing. I, I thought about the same, because I don't particularly like the idea of my, uh, privacy being invaded by, I don't know whom. But on the other hand, I really like to use Google Maps because otherwise I'm having a hard time traveling and getting to places in a reasonable amount of time, you know? So basically, there's a trade-off between privacy and convenience, you might call it. And an anecdote I like to tell is in, in 2006 or so, the US started taking fingerprints when you entered their country. And there was a huge, um, uh, outcry in Germany about how this is outrageous, and how can they, and so forth, and people should just boycott traveling to the US and so forth. I said, "Well, that's all very nice, but I just got an offer to go to grad school in the US. Am I actually not going to go to grad school in the US because of the fingerprint thing?" And obviously, the answer is no, no? Now, uh, uh, fast-forward 12 years later, I travel across, um, uh, country border- borders like twice a week, um, and use these automatic, you know, um, scan your passport, scan your face kind of portals. And it goes a lot faster than the old passport control and I'm perfectly happy about it, no? So yes, they use my personal information. They know all kinds of things about me, all my biometrics.

  10. 36:42 – 41:35

    Antitrust, GDPR, and regulators blocking data mergers (Facebook example)

    1. CW

      Mm.... okay, so antitrust. What is antitrust? What's that?

    2. MS

      Well, antitrust is, uh, otherwise, otherwise known as, uh, competition law or the enforcement of, of competition laws. And this relates to this topic, um, in, in various ways. Uh, one of it is, uh, we talked about Facebook previously and their desire to merge WhatsApp, Instagram, and, and Facebook data, which previously they said they would never do, but now apparently they changed their mind. Um, and the German competition watchdog, the Bundeskartellamt, um- (laughs)

    3. CW

      What a name. What an-

    4. MS

      Of course.

    5. CW

      ... unbelievable name.

    6. MS

      (laughs)

    7. CW

      Can you say that again? What is it?

    8. MS

      Bundeskartellamt. That's really just one word.

    9. CW

      Oh my God. Th- that is amazing.

    10. MS

      Um ... (laughs)

    11. CW

      It's so badass in German, isn't it? Fuck, man.

    12. MS

      They made the following argument. They say, "See, um, it's not illegal to be a monopolist. What is illegal is to abuse a dominant position." And here's the argument they made. They said, "See, people care about privacy. If there was a social media network site, uh, similar to Facebook that offers a similar benefit, but that is not Facebook, and that actually cares about people's privacy, people would potentially, uh, uh, choose that alternative site over Facebook. But such a site doesn't exist, and Facebook does not honor the privacy preferences of the users. Hence, it's f- Facebook is abusing the dominant position." So this is obviously paraphrasing what the argument is, is, uh, roughly is, and they simply prohibited, um, Facebook from merging these, these diverse datasets as it pertains to users that are under their jurisdiction. And I have absolutely no idea how that is actually measured. I'm German, but I don't live in Germany, so I don't particularly know whether I fall under that or not. The point being-

    13. CW

      Is it where your signal is coming from? Is it where your phone is registered?

    14. MS

      (laughs)

    15. CW

      Is it where your phone was bought? Blah, blah, blah.

    16. MS

      Is it where you currently are? I have no clue, um, h- how that actually works. The, the point is just to illustrate that, uh, competition enforcers can actually come up with effective arguments in order to throw roadblocks into business models that try to pry on, um, uh, getting information from these different, uh, parts of the business. You know, as we just described is happening in China right and left. Facebook is trying to do it as well, but, uh, especially in Europe, I suppose, uh, uh, regulators are, are throwing roadblocks into these kind of attempts.

    17. CW

      Is part of that because in Europe you've got this ... You could look at Europe as one big country that's federal essentially, and you'd have to jump through the hoop of France, then the UK, then Germany, then Norway, as opposed to in America where you've just got one thing? Or is there something systemic about Europe that is anti-data, anti-big tech?

    18. MS

      Uh, that's a good question. See, GDPR is a Europe-wide thing, you know? An example I just gave is-

    19. CW

      And there's no equivalent, no equivalent in the US, right?

    20. MS

      There's no equivalent in the US. Um, uh, in the, in the US, particular states are starting to get very sensitive about these issues as well. Um, but it's certainly, it's certainly very different. So I think, yeah, it's both a federal and a European-wide, uh, issue that there seems to be more, uh, care taken with respect to, uh, data privacy. It's surprising to some, you know? Um, Norway, for example, if I'm correctly informed, having Nest, you know, those home video camera kind of things-

    21. CW

      Mm-hmm. Mm-hmm.

    22. MS

      ... is kind of illegal here for privacy reasons. You can't have that. Uh, which obviously is not the case in the US.

    23. CW

      Mm-hmm.

    24. MS

      And people are surprised about this because they say, "Wait, is Norway a country where everything is transparent, you can see your neighbor's salary and I don't know what, and the col- the, the government collects all kinds of data about you?" That's what my American friends says. And I say, in response I tend to say, "Wait, have you heard of the NSA?"

    25. CW

      (laughs)

    26. MS

      It's not like, it's not like your government doesn't collect the data about you, they just don't make it available for research, you know? (laughs)

    27. CW

      They just don't tell you. They don't, they don't tell you about it. Yeah, exactly. Have you heard, uh, I don't know what the new product is from Amazon, one of their new products, uh, maybe like one of the Echo Dots or something like that. And as everyone was opening it up for Christmas, there's some stories of Amazon engineers being on the other side of it, um, pretending to be Santa Claus and doing all sorts of weird stuff. There was a lady who got death threats from the other side of there-

    28. MS

      What?

    29. CW

      Yeah. Yeah, yeah, yeah. This is-

    30. MS

      Oops.

  11. 41:35 – 51:37

    The privacy–convenience tradeoff and the coming societal decision

    1. CW

      The bizarre thing is, for the people like myself I guess who I don't fully understand all of the privacy concerns, but I, I've, I've s- watched enough Netflix documentaries to be scared, right?

    2. MS

      (laughs)

    3. CW

      But for me, for me, in the back of my mind I'm always thinking about that trade-off with something. So I don't have a, a Amazon Alexa or a Google Home Pod or any of that sort of stuff. I don't have that.

    4. MS

      Right.

    5. CW

      I don't have it particularly b- because of a privacy concern.

    6. MS

      Right.

    7. CW

      But if I was to buy one, I would think about it.

    8. MS

      Right.

    9. CW

      I'd be like, "Right, okay, well am I, am I prepared to forego my privacy for that?" For me, it's not a massive, a massive element of my decision-making, but it would be in there.

    10. MS

      Right.

    11. CW

      And what's interesting is that when you get to see behind the curtain, which is my Skyscanner example, right?

    12. MS

      Mm-hmm.

    13. CW

      Had I have been anchored at the higher price, had I have logged on and refreshed the page and it had stayed at the lower price, or I'd logged on the first time and it was at 700 pounds, the higher price, and then refreshed the page and it had stayed the same, I, first off, I wouldn't have this story to tell you. Second, I wouldn't have mistrust in the site.

    14. MS

      Right.

    15. CW

      So part of...... the game that's being played here with regards to our privacy and the way that data is being used, is the transparency of our cognizance about it happening, not whether or not it is happening, it's whether or not the user becomes aware of it. And that-

    16. MS

      Right.

    17. CW

      ... creates, that creates an environment for the company to purposefully try and be as obfuscating around what's being on, b- b- what's happening with your data as possible.

    18. MS

      Well, it's true, or to frame it differently, I previously said that people hate individualized pricing but they love individualized discounts, you know?

    19. CW

      Mm-hmm.

    20. MS

      So it's a question of how you frame stuff as well. And, yeah, I mean, you know, some companies at the cutting edge of this are, are way ahead of that (laughs) consideration already, but you know many others will follow and have to make these similar decisions. I wanna, I wanna throw in, um, like, um, switch sh- uh, switch sides a bit and make an argument to not scare people. It's pretty easy to scare people-

    21. CW

      Yeah.

    22. MS

      (laughs)

    23. CW

      Yeah.

    24. MS

      ... with these privacy concerns, but here's another one. So say, why do I have a cell phone? I do have a cell phone, I have a smartphone, um, among others because I find they're really useful. But even if that wasn't a dominant consideration, do I really help myself by, uh, not generating all this data? So say, um, say I was concerned that a health insurer is, um, is using all kinds of data about myself to price my health insurance. Am I actually concerned that I'm a worse health insurance risk than the average person in the society I live in? Probably not. I'm kind of like in shape and, you know, do sports and I'm n- generally healthy and all that good stuff. So, I might actually want the health insurance company to know all these things about me if that makes me get a cheaper price than I would get without the phone. So true, they might ju- still exploit my will- you know, my, uh, uh, extract some of these rents and, uh, but on the other hand, I might still get a much better deal than I would without generating all this data for them. So it's not entirely clear that, uh, you know, living in autarchy actually, uh, prevents you from leaving a, a, a trail, because if you then apply that you belong to the, to, to the set of people who don't have a smartphone-

    25. CW

      (laughs) Yeah.

    26. MS

      ... and think about who that is.

    27. CW

      Yeah, the people that live in the woods, all they're doing is feeding on whatever they can find around the outside of their shed. Yeah, no, I, you, you, you totally correct.

    28. MS

      It's not entirely clear that I want to be counted as those, you know?

    29. CW

      Yeah. Which side of the fence do you fall on? Because previously, when it's us versus company, there's always been these tricks of the trade, you know, like sort of between maybe the s- the '70s, and you hear these stories about, um, (inhales sharply) Bill Gates getting free phone calls by, like, phone hacking, putting in particular tones back through the receiver to be able to make free international calls, li-

    30. MS

      (laughs)

  12. 51:37 – 57:36

    AGI vs reality: why ‘boring statistics’ drives profits (and jobs) now

    1. CW

      Got you. So one of the things that you've mentioned so far is computers struggle to predict in situations that there's no, essentially no precedent for.

    2. MS

      Yes.

    3. CW

      Will that change?

    4. MS

      Well, people are trying. But let's, let's say, this is ... The ma- the main point we're trying to make in the book is, that's 95% of what you read about in the newspaper, like thinking computers essentially, but it's very, a very small percentage of what actually happens in the real world. What happens in the real world is that people assemble humongous datasets about your behavior and predict which movie you want to watch next. Okay? So that's the stuff that actually happens that's economically viable. So as people think about what actually affects their real life in the next five or 10 years, it's definitely not thinking computers. It most definitely is a bunch of fancy statistics that is really boring, uh, to, to talk about. It's a bunch of engineers, yeah, analyzing big datasets, and that's it. It has nothing to do with, with thinking computers and all these, uh, you know, more exciting and hence more headline generating, um, things that you, that you read about elsewhere.

    5. CW

      I suppose, you know, thinking about Nick Bostrom or Max Tegmark, they're, they're kind of either utopia or dystopia depending on how you look at it for the future of AI-

    6. MS

      Right.

    7. CW

      ... when you look at what they talk about, they're not talking about essentially the, the world's best statistical modeler.

    8. MS

      Right.

    9. CW

      What they're talking about is artificial general intelligence, right?

    10. MS

      Exactly.

    11. CW

      They're talking about a thinking, a thinking computer. But it was-

    12. MS

      That's right.

    13. CW

      ... it would seem at the moment, you know, there was that, there was that, um ... At a particular conference a few years ago, someone asked a room of experts how long do they think it would be before we reached artificial general intelligence.

    14. MS

      Right.

    15. CW

      I don't know if it was the singularity, but it was certainly artificial general intelligence. And like the, the consensus was like 50 years, like before, definitely-

    16. MS

      Right.

    17. CW

      ... before 2100, right?

    18. MS

      And by the way, that was the consensus in the 1950s too. (laughs)

    19. CW

      The same... Oh, the same distance from the nine- so it's 50 years from 1950s and now 50, and it's just moving as we, as time goes on.

    20. MS

      (laughs)

    21. CW

      Okay. (laughs) That's hilarious.

    22. MS

      So, see, see, so whatever the right number here is, the point here is people have talked about this for decades and decades.

    23. CW

      Yeah.

    24. MS

      And the horizon has shifted out. See, uh, y- y- you can call me wrong if it happens next year and, and I ridicule the idea. But as you say, um, the closer people are actually to the, the forefront of research, the further out in the future it seems to be that they expect artificial general intelligence to be there.

    25. CW

      Yeah.

    26. MS

      So our point is exactly, hey, this is very exciting to talk about, and people have, and it's a very appealing thought to human beings apparently, but what actually matters in the real world right now and what affects people's lives is boring statistics.

    27. CW

      Mm. And that, th- my point here is, we've got this is when it's gonna happen, artificial general intelligence, you know, this is what ... Superintelligence, which is one of my favorite books, and everyone who's listening who really, really wants to kind of get a good grasp of how artificial general intelligence might come about should read Superintelligence by Nick Bostrom. It's awesome. Um ...... this is kind of the terminator, uh, uh, romantic view that is being put in the press. But that's not what's being economically rewarded right now. What's being economically rewarded right now are big datasets being analyzed cleverly so that you can make good predictions. So you have to presume that as we move forward, the resources are going to be, um, disproportionately moved in that direction. They're gonna be discriminated towards what can make Amazon make more money, or what can make my insurance company make more money? Not, should we throw a trillion dollars into maybe making artificial general intelligence when we don't know if it's possible?

    28. MS

      Tha- that's exactly right. So this is exactly the point. See, see, if you want to see what currently makes money for businesses, look at the market cap of companies. And you'll find companies such as Amazon, Google and Facebook out there, don't you? Um, or of course the Chinese tech- tech giants. So what do they do? Well, so one thing I showed my MBA students is an annual report of Amazon in 2006. And so that's, you know, like 14 years ago, or 13. And what you see is Jeff Bezos talking about that they have the ability, the data and the technical ability to analyze what economists essentially call, uh, price elasticities for demand. So if you change the price of a certain good, how are people going to react to it? Are they gonna buy more or less of it, and how much more and how much less? The techniques by which you analyze that, that's basically just dead boring PhD level, or actually more like tenured US top university professor level, um, s- uh, statistics and econometric, uh, modeling. And this is exactly the people that Amazon hired over the last few years, is literally like hundreds of PhD economists that analyze these datasets. So if you want to use what produces a trillion dollar market cap, you just have to see what they do. And it's just, you know, um, econometric or economic modeling, a whole bunch of data science on huge datasets has absolutely nothing to do with, uh, uh, artificial general intelligence.

    29. CW

      And that's what's-

    30. MS

      It's kind of... yeah.

  13. 57:36 – 1:09:37

    Business models powered by data extraction—and closing thoughts

    1. MS

      So I'm a finance professor, so there's a risk, uh, of me delving into, into finance topics a little too much for, for, um, for this audience. In financial markets, uh, the most successful hedge funds and, and market participants are precisely those that have used computer science and, uh, models for the last few decades, um, predicting, uh, future stock returns and it works like a charm and, um, and people are rich. Okay, fine. So in financial markets, there's a huge transformation happening, let's not get too much into that. You're asking much broader, uh, a much broader question. So I get invited a lot to like investor conferences, um, that are like, "I read about AI in the newspapers a lot and there are thousands of startups. But first of all, is this just a fad? And second, if it's not just a fad, then which of these thousands of startups am I supposed to invest in? How do I think about this?" And, and what I tell them is, "Well see, why is there so much AI? It's because there's a lot of data, hence big data. Why is there so much data? Well, because it's cheap to collect data, it's cheap to store data, and it's become cheap to analyze it." Okay? And when a product is cheap, people buy a lot of it. And economist says, "Demand curves are downward sloping," but okay, you can forget about this again if you, if you don't care. When things are cheap, people buy a lot of it. It's really cheap to collect data. I can get your cellphone location data if I pay a data ag- aggregator a few hundred bucks and they will tell me where you are. It's very simple. Um, it used to be im- implausibly expensive and I need to hire a private detective. No longer.

    2. CW

      (laughs)

    3. MS

      Okay?

    4. CW

      Yeah. Some guy, some guy in a leather, a, a leather suit and a big sort of-

    5. MS

      Sitting in a corner.

    6. CW

      ... wide-brimmed hat. Yeah. Smoking.

    7. MS

      That, that's right. (laughs) So, hmm, okay, so that's why that's the year. Now, is this just a fad? Well, unless you tell me a story why data collection and data analysis is suddenly going to become way more expensive again, the answer is no, it's not just a fad. It's gonna stay here. Now let me, let me put a small disclaimer in there, which is, I can actually tell you a story why it becomes more expensive to collect and, and process data. And it's called legal constraints. Think GDPR in Europe, you know? So sure, if politicians wake up t- or, uh, to, to these concerns or are convinced by these concerns, they might throw roadblocks in it, and it's gonna differ from jurisdiction to jurisdiction. And therefore, if you're in this business, that's one really important consideration you should have in mind as you get in this field. Well, which leads to the second question, which kind of businesses should you invest in given that you want to invest in this space in general? Uh, or which one, companies should you want to work for and so forth? I mean, one, uh, one question is concerns of legal jurisdiction, but the other one is understanding what the economics of their business model is. Is it a business model to make better predictions than the competitors, and therefore like offering a better product? Okay, that sounds great. Is their business model to have some sort of, uh, useful thing people like and that people are willing to give up their personal information for? What, the, the example I always think of is like, uh, uh, scooters, you know, inner city scooters or bike sharing and stuff. The question of these business models has very little to do with whether...... the fee people pay to rent a bike actually pays for buying the bikes and, I don't know, keeping them charged and maintained. The economics of it is that you sell the people cellphone location data to a data aggregator who sells it to Facebook or Google or whoever wants to buy it, which they use to target ads (laughs) . Okay, so let me, let me go backwards in this value chain. Obviously, correctly targeting ads is a valuable thing to... from a business perspective, but you need a bunch of data for it. Where does the data come from? Well, in some cases, the companies like Google and Facebook collect the data themselves. But in other cases, for other variables, like how fast do you type your name in an online form-

    8. CW

      Mm-hmm.

    9. MS

      ... um, they might buy it from a data aggregator. The data aggregator buys it from whoever first came up with the idea of measuring how people... how fast people buy stuff in an online form. And whether that's an online form for car insurance or an online form for, "I want to rent a scooter in Madrid," doesn't matter the first bit, no?

    10. CW

      Mm-hmm.

    11. MS

      And this is why I'm saying, hey, the logic, the economic logic of business models really changes, um, as data itself becomes valuable, because the whole point of your business model might be to collect a bunch of data, and it has absolutely nothing to do with the product. If you program a flashlight app for a phone, but somewhere in the terms of conditions you say that you also want their entire address book and their, their, their, their, their location data, um, how fast do they type their names, and track all their activity on the internet-

    12. CW

      (laughs)

    13. MS

      ... I mean, if you get people to sign up for that, um, well, good for you. You don't need-

    14. CW

      More power to you, yeah.

    15. MS

      (laughs) You don't need it for the flashlight app. Now, so I'm sounding very cynical, and I suppose I am. This is not an endorsement of these kind of practicals- practices or me saying, uh, this is ethical. All I'm saying is, like, this is what is happening out there in the real world. Indeed, there are ethical constraints to it. Indeed, legal constraints are there and start popping up, but that's the economic logic of how business models, um, are changing.

    16. CW

      Presumably as well, because this is such a fledgling market that's moving so quickly, the... there's an asymmetry in terms of how many people want data to be more available from the company side, uh, how sophisticated they want the modeling to be able to be, and the decisions that can be made off the back of that, versus (clears throat) me and you thinking, "I, I don't want my data to be sold to these people. I should speak to my local councilor who's busy, like, dealing with, like, a, you know, a proper... like a flood or, like, a real, a real-"

    17. MS

      Right.

    18. CW

      "... sort of real-world problem that's gonna make news." As opposed to-

    19. MS

      Right.

    20. CW

      ... me and you saying, "I think if this keeps going over the next s- 20 years, this could be like Orwell's 1984." And, but you know, that, like, so where does the rubber meet the road with regards to protection for users?

    21. MS

      Well, I think it's a societal decision, see, um, and, and in, in the end, a political game. So as we actually alluded to previously, in Europe, uh, both individuals and politicians seem to be more on the cautious side concerning, uh, data collection, uh, and in some ways and just in a vague sense. And as I said previously, I don't know, maybe that's a bad thing. I mean, first order, uh, having more data enables better decisions. So as long as it doesn't get abused in some sort of way, uh, having companies that, I don't know, give me... I love Uber. Uh, uh, having taxis in, in the small town in the US where I lived for l- uh, long time was a disaster.

    22. CW

      Mm-hmm.

    23. MS

      I loved Uber coming along and offering me cheap rides, you know? So, so first order, this is all great. Uh, the question is just when the concerns come in, and that depends a lot on how many companies start abusing, uh, the power they, they, they have thanks to the information and, uh, how politicians react to it, either to the company's concerns or lobbying efforts, (laughs) or-

    24. CW

      Yeah, the, the lobbying, the lobbying must be so... Uh, that must be where I would imagine companies like Amazon and Facebook are really ratcheting up their spend. You know, if first off they needed to get a lot of computer scientists, up next they need to get the lawyers and the lobbyists who can protect the work that the computer scientists are doing.

    25. MS

      Yeah, and the economists I suppose. So see, all... what do you get from all of this is a sense that there's gonna be a few winners in this space, and there's gonna be a bunch of losers, you know? And obviously, that's a societal concern. Everybody talks about inequality, and this is one mechanism by which, you know, which can drive it. Um, and as I said, I don't expect this to stop anytime soon. It's gonna go faster in some jurisdictions than in others. Uh, it, it goes fastest in China for the reasons we've discussed. So if you want to see whether you want to live in a society, um, you know, that has very little regulation around this, these topics, uh, you can look there and see if you like it. There's many things I like about it. Go to Germany and try to pay with a credit card in a supermarket. It's gonna be hard because they like cash. Meanwhile, in China, you walk past a supermarket counter, they scan your face, and that was it.

    26. CW

      You're kidding me.

    27. MS

      Oh my god, is that beautifully convenient. You know, uh, it's similar like the airport, the airport face scanning technologies, whatever. It's, it's not a technical problem that has been unsolved. I mean, how much would you like to not have to stand in line at a supermarket because, uh, some person in front of you is trying to, you know, count individual coins to get the precise amount, you know, like in the '80s.

    28. CW

      Scan my face. I've put my stuff in my bag, scan my face.

    29. MS

      Scan my face, and then there. You know, it's just incredibly convenient. So I, I do not want to be paternalistic about which kind of society we want to live in. It has huge benefits, um, to live in this more technologically advanced world.

    30. CW

      Mm-hmm.

Episode duration: 1:09:34

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