Making Sense with Sam Harris - #78 — Persuasion and Control

Episode Date: May 26, 2017

Sam Harris speaks with Zeynep Tufekci about "surveillance capitalism," the Trump campaign's use of Facebook, AI-enabled marketing, the health of the press, Wikileaks, ransomware attacks, and other topics. If the Making Sense podcast logo in your player is BLACK, you can SUBSCRIBE to gain access to all full-length episodes at samharris.org/subscribe.

Thank you. of the Making Sense podcast, you'll need to subscribe at SamHarris.org. There you'll find our private RSS feed to add to your favorite podcatcher, along with other subscriber-only content. We don't run ads on the podcast, and therefore it's made possible entirely through the support of our subscribers. So if you enjoy what we're doing here, please consider becoming one. Today's guest is Zeynep Tufekci. She is a contributing opinion writer at the New York Times, and she is an associate professor at the University of North Carolina at Chapel Hill, with an affiliate appointment in the Department of Sociology.

She is also a faculty associate at the Harvard Berkman Center for Internet and Society and was previously a fellow at the Center for Information Technology Policy at Princeton University. And her research interests revolve around the intersection of technology and society. Her academic work focuses on social movements, privacy and surveillance, and social interaction. She's also increasingly known for her work on big data and algorithmic decision-making. She's originally from Turkey and formerly a computer programmer but has taken that background in interesting and increasingly relevant directions. She's the author of Twitter and Tear Gas, The Power and Fragility of Networked Protest.

And we get into many interesting topics here, relevant to information security, and things like WikiLeaks, and ransomware attacks, the fake news phenomenon, all increasingly relevant, as we depend more and more on the internet and draw our beliefs about reality from what happens there. So without further preamble, I bring you Zeynep

Tufekci. I am here with Zeynep. Jesus, Zeynep. I know. I swear to you that no bungling is a fail because there is no baseline. So go for it. The mind was willing, but the tongue failed. I don't blame you. It really is. It's an isolate language.

It has no relatives either. We're a freak of nature language. All right. Well, I am here with Zeynep Tufekci. Zeynep, thanks for coming on the podcast. Thank you for inviting me. So we met at BAMF at the TED Summit where we actually were in the same session. We both gave talks on AI.

I gave a talk on sort of the further future and possible very scary outcome. And you gave a talk on the present, the way in which AI is becoming increasingly a topic of concern. We're not talking hypothetical human intelligence AI. We're talking specialized AI that can do many good things, but also many undesirable things if we're not careful. I'm sure we'll touch on that. But before we do, just introduce yourself to people, because you have an interesting backstory.

You didn't get into this by the most conventional path. How did you come to be an expert on the sorts of things we're going to talk about in cybersecurity and social persuasion and organizing movements through social media. Who are you, Zeynep? That's a very existential question to begin with. Who am I? Well, I'm not sure who I am, but I can describe the path that took me here. As you say, it is a little unconventional, partly because I'm the product of a historical transition, right? I'm still of the generation that grew up without the internet, especially since I grew up in the Middle East. And I grew

up in Turkey, which at the time was ruled in the aftermath of a military coup, which had brought about very heavy censorship. So I grew up watching a single TV channel, which made me acutely aware of censorship, especially since that TV channel didn't show us anything that seemed to be in the news or relevant to the country. Instead, we would watch Little House on the Prairie, because that's what they showed. And it made no sense in Turkey, but it didn't matter. And I started out as a kid really interested in math and science and physics and all the things that kind of geeky kids are interested in. And I really enjoyed learning about it. But early on in my life, I got terribly concerned about the ethical implications of technology, especially since what happens to many kids like me happened to me too.

I call it the atom bomb problem for kids into science. At some point, your excitement hits this wall because you learn about the atom bomb and that it was enabled by great physics. The very physics that you admire and think is amazing is also what enabled this. And so you go into this tailspin. And to my, I guess, kind of failure of imagination, I thought computers would be a great topic for me because they would have fewer ethical implications.

And I also needed to get into a job quickly because not only did I grow up in Turkey, I grew up in a pretty dysfunctional, broken home. I needed to start working as soon as I could. I started working literally as a 13-year-old and then as a programmer as early as a teenager, 16, 17. So I had this sort of very unusual path that I found myself in a technical job in a country still under pretty significant censorship and closed public sphere without the internet. And I found myself, because of my technical job, I found myself sort of glimpsing the future, kind of this parallel existence where I'd work at IBM, which had this amazing intranet that allowed me to talk with people around the world, I mean, here, there I am, a teen girl, kind of with all that goes into it in a country like that, pretty much anywhere in the world too. But I'm on the intranets kind of as a person and taken seriously. It was just, you know, the early promise of the internet people kind of laugh at right now.

It had a reality to it. So I sort of just got enchanted by this possibility. And I was also fairly interested in how do we bring about change? How do we bring more freedom? How do we bring more compassion and reason to the world? And I thought, this is great. This is going to study it. And I switched to sociology kind of along the way, but not knowing what exactly would make sense, right? I was just trying to find my way. And because such things often happen in the United States, I found a way, I kind of stumbled into graduate school in the United States, trying to understand all this better. And in the meantime, as I was struggling with trying to understand and think through, the world was progressing. We started having more and more digital connectivity.

And sometime, I think around 2004, I stopped having to explain why computer science, computer programming and sociology and social science were related because Facebook happened. And it was, I think, the first time that a lot of people who are not specialists kind of had this very visceral reaction to how their social world is being changed by this new platform. Questions of privacy and other things became very prominent in people's mind. And then fast forward a little bit, Arab Spring happened, which is exactly what I study, social change and social movements. I started studying that. And then the Giza Park protests happened, which again happened three blocks from my place of birth, that close to home. So I went there and now I'm, you know, trying to focus on the future

and understand how the methods in both artificial intelligence, like machine learning and the Silicon Valley business models and the world we are in politically speaking, what does this intersection mean? You know, how do we understand the rise of authoritarianism? How do we think about technology's role in all of this? And the security part that you mentioned, I got into it partly because I work with so many people in social movements and journalists that they're kind of like the canary in the mine with the insecurity in the Internet affects them earlier on because they're targeted. So I got into that part too. I guess I'm a mutt in all of these particular fields. It's that intersection. And it turns out it's a relevant intersection. And so here I am, to the degree one can answer this question of what I'm studying right now and what I'm thinking about right now.

Well, it's all too relevant and only becoming more so. And as you say, the first blush of enthusiasm for the internet connecting us all as an unambiguous good, that has faded. And now we're discovering that as this technology connects people and empowers us, it's also fragmenting us in ways that are fairly difficult to correct for. And it's creating new levers of influence that could lead to more authoritarian control and perverse forms of persuasion. And you told me in the setup to this that you were worried about something you've called surveillance capitalism. How do you think about that?

What is surveillance capitalism? So here's what I think about this. We have this scary convergence of a couple of events. One of them is the business model on the internet for the sort of platforms that most of us use, like the Facebooks and Twitters of the world. It's capturing our attention and persuading us use, like the Facebooks and Twitters of the world, it's capturing our attention and persuading us to, at the moment, click on ads. So there's an enormous amount of brainpower going into how to make us buy 0.003 more shoes per person on average. You have this whole infrastructure that is collecting our data, that is doing, you know, hundreds of thousands of dynamic tests on the platform just to persuade us to act in a particular way for commercial reasons, right?

To make us purchase things. technologies, that are like machine learning, which is a form of computer programming that is different than the past in that we don't program it anymore. We feed the machines a lot of data and they create these large matrices and calculate certain things. And just like the brain, that we can't really see what a person is thinking if we slice their brain. With machine learning, you don't really see exactly what's going on. It just spews out classifications. It says, do this, do that, do this, do that. It's probabilistic, but it works pretty strikingly well for the things that we're using it for.

But it needs data to work, which means that we have a business model that is set both to figure out how to exactly push our buttons and also to use an enormous amount of data that is surveilled from us symmetrically. You don't get to see what they have. And this enormous amount of data can also be used to deduce things about us that we haven't disclosed, right? It's not just invading our privacy directly. When you have that much data, you can use computational inference to figure out who you think is the troublemaker, who's depressed, who might be on a manic swing in a manic depressive cycle. You can figure all these things out even if people don't disclose them or even know them. So this is kind of where things are at, this convergence. And the thing I fear is that this is a perfect setup for authoritarians because it allows them to survey the population and to nudge them and shape the opinions using this amount of information that's asymmetric that can figure things out and using machine learning at scale, that means you're like individually experimented on, figured out how to exactly scare you,

how to fear monger, how to, when you're vulnerable and what you're vulnerable for. And then this will come into politics as well. And there's nothing wrong with persuasion as a form of politics, but it's not happening openly, right? It's happening person by person.

It's happening in the person. It's happening in the dark. You don't see what other people are seeing. You don't see what is being targeted at you. And think of China, right, with hundreds of millions of people online. And it's not like they censor everything. They censor a few things, but we know from research they usually don't censor government criticism. I feel like it might have even made them more stable because an authoritarian's blind spot is not knowing what people are up to. And this is perfect for knowing exactly what people are up to and individually pushing their buttons. that the Silicon Valley business model and the Silicon Valley workforce, which is uniquely liberal or progressive

or libertarian in general, pro-science, empirically oriented, they're geeky in many ways. And I say it as a positive, I find that's my tribe too. We may well be building the infrastructure of authoritarianism. And I think they're under this impression that they'll never lose control of

these tools, that they built them and they won't let them be used for evil, so to speak. And I look at history and that's never how it works. You build infrastructure, it gets taken over by the people with money, with power, with authority. So that's kind of what I've started really worrying about. My first book was about social moon, social change and digital tech and the complexities there. I'm now thinking, let's look at this from the point of view of power, the powerful, not the challengers. You know, we spend a lot of time thinking about digital media and digital technology and challengers really need to start thinking about digital technology and the powerful and how they're converging historically. And I've been thinking a lot about the way in which digital media is co-opting our attention and causing us to spend our lives in ways that we will later regret. And actually, I had another guest on the podcast, Tristan Harris, who spoke a lot about that. He's great on that. Yeah.

Yeah. Yeah. But I haven't really thought as much about the authoritarian misuse of this. I mean, obviously, there's a lot in the news and a lot of talk about fake news and the Russian meddling in our election, and we should probably get to that. So there's been obvious political issues here. But what's your view on social media in particular? I mean, I noticed you use Twitter with a fair degree of enthusiasm. I see you have 74,000 tweets. I do. It's also my research area. So it's kind of, it's a special thing that I'm usually watching things on Twitter too. So I have this deal thing. I may be keeping an eye on part of my research project. I think I would use it less if it weren't part of my research. In fact, I don't do Facebook research as much, and I'm on Facebook a lot less, partly because, as Tristan points out, it's a medium designed to capture your attention, right? And it's a medium, like every incentive

there is to try to capture your attention. And there are times when I'm fine with that, but how do you keep autonomy and agency in an architecture that's designed to get you to do something that maybe you don't want to do if you ask me in the morning, right? I might be wanting to do it then, but if you ask me in the morning, is this how much of my day I want to spend? So I try to sort of judge that. And outside of my own research and my job researching this stuff, I try to be sort of more mindful of when am I not going to be on this? And when am I going, how am I going to relate to these technologies that I know are designed to grab me. One of the things I've started trying to do is not use services if there's an alternative that I don't pay for. I feel like I want to be the consumer. I want to be the one they're catering to rather than being the person whose attention they want to grab so they can sell to

people trying to manipulate me into buying 0.003 more shoes. So that's kind of, it's part, and the problem is, of course, it's part of life. I work with refugees and I do, you know, I try to sort of, the unluckiest people, right? I try to sort of see if I can be of some help. And I couldn't do that work if I weren't on Facebook, because that's where the groups are and that's where the organization goes on a lot of times. And I couldn't do that work if I weren't on Facebook, because that's where the groups are. And that's where the organization goes on a lot of times. So, you know, to be in the civic world today, you use these platforms because that's where billions of people are. On the other hand, they're not designed with the kind of goals I have in mind when I'm engaging the world. And it's this huge challenge. It's this huge tension.

And it feeds into what I just said, which is that the people in power are increasingly looking at this world and saying, what can we do with this? How can we use it to consolidate power? Do you have any thoughts about what recently happened in the election and the role that social media played there, and then the larger fake news phenomenon, and just this issue with respect to how we are getting siloed in echo chambers. It's like it's an illusion of being open to information, but in fact, people are just ramifying their worldview by use of these tools. ramifying their worldview by use of these tools? Yeah. So let me just say a couple of things. With a lot of these tools, if you talk to the companies, the first thing they will tell you is that's what people are doing. You know, that's not us. Now, on the one hand,

it's certainly true that this is driven by people, right? This is like you would not, it would not be fair to say that, you know, the social media platforms are generating this from whole cloth. They're not. It's more like we have certain human tendencies. We have, you know, if we see something that we agree with, it's more pleasant. We have, you know, if we see something that we agree with, it's more pleasant. If we see something we're angry, it's kind of captures our attention. And you see this in the research on perception, right?

When you look at a crowd, you're a lot more likely to notice the angry face. Because, I mean, in an in-person thing, if you think about the evolutionary process, the Pleistocene, if you live in a small group, it kind of probably made sense to know exactly who was mad at you, because that could be a threat. So there are all these things that we already have tendencies for. I liken it to having an appetite for sugar and salt, right? It's a perfectly reasonable thing, given our evolutionary history, to be for sugar and salt, right? It's a perfectly reasonable thing, given our evolutionary history, to be into sugar and salt. The problem is, very rapidly, without any time to adjust, forget evolutionarily, culturally, we have shifted to a world where we're supplied with, not only, we're not just supplied with extra sugar and salt, social media platforms use sugar and salt to keep you there.

Kind of like a salt lake used to shoot animals. But instead of shooting us, they're just capturing our attention. They're selling our shoes. And that's, I think, big part of what's going on with the election too. What happened is we got siloed, of course. And and I for my because I'm because of my work and because of my sensitivities to authoritarians, I guess I started following social media, Trump social media very early on because I thought, whoa, this is an interesting thing.

And I argued he was liable when everybody was laughing at him, exactly because I was following his base on social media. And a couple of things happened. I saw how and why he resonated. I also saw an enormous amount of misinformation that ranged from distortions to fake news sort of proliferate there. news sort of proliferate there. I also saw that once when I wasn't making a conscious effort to follow these people, which I did as a part of work, I did it every day for, you know, almost two years now. Like if I went on Facebook, I had friends who were Trump supporters, although they were in the minority because, you know, I'm a college professor in a blue part of a purple state. And it kind of makes sense for most of my friends not to be Trump supporters, but I have friends from middle school and elsewhere, and some of them, turns out, were sympathetic to Trump. I never saw their posts, right? I just sort of thought about it, you know, halfway through. And I'm like, whoa, do I not have a single person I friended on Facebook? Because I friend lots of people and Facebook is not very personal

for me. And I had to hunt them. I guessed them and I hunted their posts down. And yeah, there were people who had sympathy and Facebook's algorithm never showed it to me. And I'm guessing it's not, I mean, obviously it's not a conscious decision. Once again, it's these machine learning algorithms. They know that if you give people sugar and salt, which I just, in case of Facebook, for me, it's cuddly stuff or outrageous stuff, right? Babies, cats, cute things, happy news. Also things we're angry about, outrageous. Babies, cats, cute things, happy news. Also things we're angry about, outrageous. Babies eating cats.

I think those both polar sides attract attention. So they just feed us that. And I think that's really destructive, especially given it's a way to make money for people. So you could just be a spammer and figure out, hey, look, I can just feed people fake news about Hillary Clinton. That's what a lot of people did. I interviewed a bunch of these people. Some of them were even liberals. They were just like, it works, it spreads, and we make money from Facebook. So not only does it allow, not only does the algorithm kind of amplify this, it allows you to make a lot of money from doing exactly this.

And I'm not saying mass media was ever perfect. Many failures there, but this is a new, unmoored world to have no checks on, no ethos against this kind of misinformation. So about four or five years ago, five years ago now, I wrote this article for the New York Times worried about the Obama campaign's use of data, right? Because they were already sort of developing all these methods to target people and to try to persuade people using statistical data they had on them.

And I said, look, you know, I understand campaigns want to win, but this kind of, you know, asymmetric accumulation of data where it goes far beyond just, you know, which magazine you're subscribed to. And the kind of smart targeting has the potential to gerrymander us down to the person and have politics only be about people who are persuadable and all these sort of downside effects of having the public sphere become more and more private and more and more asymmetric in how it operates. And I got a lot of pushback from people in the Obama campaign and people in the data science world. And one of the things I was told was, one, I was told this will always be on the side

of people we like, people told me. They said, you know, this is something that, you know, people who like science, people who like data, people who are empirical, this is only going to be their tool because the other side, they told me, doesn't like data, they can't do this. The second thing I was told was, this is just a form of persuasion, no different than any other. Now, fast forward just four years after that, and what I saw was in the 2016 election, the Ted Cruz's data people ended up being Donald Trump's data people. And I'm going to recount something they claimed they did. Now, I don't have access to the internal data, so I can't vouch they did this, but I have some independent evidence that they at least tried. But just outlining is enough to explain what the issue is. So they claim that they used people's Facebook

likes and other kind of indicators, social media data, or whatever it is they use, because social media data is very good for this, to try to figure out people's psychological profile. Now, we know from research that if I just have, say, what you liked on Facebook, or even just your tweet stream, we can guess using these sort of complex algorithms, we can guess with pretty high probability where you fit on the big five personality traits like neuroticism, extroversion, et cetera. We can guess your sexual orientation. We can guess whether you're religious and what religion. We can guess a lot of things, even if you never disclose them, right? These are not things that you needed to

put on your profile. So we can figure this out. And we know also from research that some people will vote more authoritarian if they're scared. Other people get pissed off at fear mongering. And the problem with advertising on TV is, you know, you're advertising to everyone at the same time, right? But what if you could go on Facebook and target only the people that would be prone to a particular kind of message, say fear-mongering? Now, again, because Facebook won't tell us, we don't know the exact story here, but Donald Trump's campaign claims they try to demobilize particular segments of the population against voting. So it's important. This isn't persuasion.

They weren't trying to persuade them to vote for him. They were just trying to tell them Hillary Clinton's just as bad. Stay home. For example, one of the targeted constituencies was black men in Philadelphia. And for example, one of the targeted constituencies was black men in Philadelphia and Philadelphia was, you know, just very little difference in Pennsylvania, which was a major electoral gain for Trump. Right. And I have independent confirmation they did target black men in Philadelphia. We've come across instances. So what they tried to do was to demobilize those people. What did they tell them? We don't know, right? Only Facebook knows. Did they tell them things that were correct, things that were false, things that were completely made up of whole cloth? Were they just scary commercials? Who knows? They were just targeted at them. And so the census data from the election just came in.

And it's pretty clear that the biggest difference between 2012 and 2016 is the black turnout in the country was depressed in lots of places. Now, clearly, there are multiple possible explanations for this. It could be the Obama effect has worn off, right? It's kind of reasonable to expect the first African-American president would gather a bigger share and enthusiasm from the African-American population. suppression-oriented laws that cut the amount of hours, that cut the number of voting machines in minority districts. It could be the gerrymandering. It could be the voter ID laws that are especially problematic with elderly Black people who don't necessarily have the birth certificates and etc. But it could also be this. We could also have a world in which large segments of the population were psychologically profiled and otherwise profiled and silently targeted through Facebook dark ads in a way that would push their buttons and do it one by one. Like if you needed people to figure out what everybody needed, you'd never manage it because to target 100,000 people, you'd need 10,000 people. Whereas right now, we're at a world where machine learning is designing machine learning

experiments to experiment on us. It's already out of our control, right? And you can do this at scale. You can figure out people one by one using these technologies. So what if that is part of what swung a very close election? Clearly, it's multi-causal, so anything could have swung it. But what if this is part of what made the difference? Now, this is a small example. And the question, I mean, the objection I hear to this is they probably didn't manage this. We don't know. And if they didn't manage it, this is where things are going. You see what I'm saying? This is what my concern with surveillance capitalism meets authoritarianism is that the business model of capturing your attention, profiling you and trying to persuade you to buy that extra shoe is very compatible with a manipulative public sphere where you don't get to see what is even contested because it's so segmented person by person. And then buttons are pushed person by person. That make sense? Yeah, yeah, it's all very interesting. I think people, most people at first glance

will understand what's wrong with targeting people, however individually, with fake information, with lies, with fake news stories, and persuading them that way. That's clearly a problem, and we have to figure out some way to correct for it. But as you said earlier, persuasion is just persuasion. There's nothing wrong in principle with persuasion. And so it's not, it may not be clear to people why there is a special concern around the segmentation of the population with these tools when you are validly persuading them. Well, even if you're validly persuading people, right, even if you're just sort of, I mean, in some ways, obviously, this is just more of what just political campaigners

and marketers and everybody have always tried to do, right? In many ways, there is no difference from what they try to do. The big difference is it's doable now, right? This is what past marketers, you can go back and you can look at, you know, sort of how political campaigns have always tried to do this. I'm just reading this Rick Pearlstein's biography of Goldwater, and he's got a campaign manager that's saying the indifference, we got to target the indifference. And he has to figure out who they are and what's, you know, how to target these people. They had baseball bats. They could advertise on TV.

They could just try to send a message out. And it was really difficult to send the message out to one person and not the other. And to push one person's buttons without upsetting the other. without upsetting the other. And also because it was public, if you put out an ad like that on TV, it was plausible that the other side would mobilize and say, this isn't true. Here's how to do this, right? It's all possible that, you know,

we could have this contestation. And if you go back to the idea of the public sphere, right? It was never as, you know, nice and as clean as the Habermasian version of it, where people are just having recent discussions regardless of who they are and their status. But it was really sort of, at least in ideal, we would have this world. sort of, at least in ideal, we would have this world. Right now, it's gone exactly in the opposite direction. Instead of sort of wishing to persuade us like that and only having baseball bats to act with, they have scalpels that they can use to get at us one by one, right?

So instead of baseball bats that would both provoke a reaction and weren't as effective, they have quite scalpels that they can do this with without provoking the reaction, without being public and without sort of having us be able to oppose it.

And so that's kind of my worry is that, yes, we have antecedents of this as we have everything, but it's now effective. And it's also asymmetric. I don't ever see what data they have on me. I don't ever see what they're trying to push my buttons. Right. I don't have any meta idea of like I don't have perspective and I don't have defenses against it because if it was you know if I had defenses against it let me liken it this way when movies first came out people ran away when they saw a train coming at them on the screen right now right now if you see

a movie you know and there's a train or a car coming at you, you don't even flinch, right? You know, it's a movie screen and nothing's coming at you. For the ordinary person, it was perfectly understandable to be scared of this new phenomenon, not understand how to deal with because it wasn making, you see that it was greatly intertwined with extreme violent, racist, fascist ideologies. If you look at people like, say, Lene Riefenstahl, this German filmmaker, actress, who was great behind the camera. She invented a lot of the shots. If you watch ESPN, she's probably invented half their shots covering first the Munich Olympics for Hitler. But that craft got adopted into authoritarianism because it was very impressive and very effective

in persuading the masses in ways that isn't as apparent to us now because we kind of got used to the format and we have a lot more cynicism and defense against the format. So that's where I think we are with these sort of dark technologies asymmetrically aimed at persuasion and manipulation is that we don't really understand their power. We don't get to see it. It's all private data. Like, so we don't get to see Facebook knows what happened last election, not telling anyone, not letting any independent researchers kind of add it. And we don't have a way to defend ourselves against it. And people will say, you know, I'm not manipulated. I'm not manipulated. And everybody thinks that, but you know, we're all people. We're all persuadable in particular ways. And if there's

a science and a craft of doing it with massive surveillance of us and testing of us and finding the exact way, we're all going to be vulnerable. And I think that's where we are is that, in fact, if you look at it, Facebook's business model is telling advertisers and political campaigns that it's a great platform for persuading people. and political campaigns, that it's a great platform for persuading people. And it's telling us it's a lousy platform. It won't change any minds. It's just us. And like both of those things can happen at the same time. And I think it works to a degree. And I think we need to sort of really think about how do we deal with this new threat to free conversation that is not so asymmetrically controlled. Well, listen, with 74,000 tweets, Zeynep, I would say the AIs have

already gotten to you. You might have a problem. I'll just point them at you. When they come for me, I'll say it was them, it was them. I think I only have 6,000. Well, yeah. So the thing is they, they probably have my number in terms of what kind of a person I am, a lot of things. Although on the other hand, um, I studied these things a lot. So I'm always watching like every time I'm advertised, every time there's a dynamic change, every time something happens, I'm constantly trying to probe and get at it. And despite that, I wouldn't trust myself to be immune to it at all. And that's the reason, I mean, there's a strong reason to construct, for example, I think places for children that are free of advertisements directed at them.

I think children don't have yet, like especially younger children, don't have the way to assess the credibility. And it's something that part of parenting is to teach them how to assess manipulative messages directed at them. So it starts from protecting them to educating them. directed at them. So it starts from protecting them to educating them. And hopefully by the time they're out in the world on their own, they realize manipulative messaging. And I feel like it's the same thing, except this is on steroids. This is much more effective, much more database, much more empirically strong and machine learning based ways of manipulating us that we don't yet have means to defend ourselves properly

because we don't even have a full picture of what's going on. The degree to which our economy depends on advertising, in particular the digital economy, it's really stunning, and most people are fairly oblivious to the downside, apart from not liking some annoying ads. But they don't see how the incentives get aligned perversely. Absolutely, absolutely. I mean, advertising, if it was done kind of very low-key, I didn't really... If you'd like to continue listening to this conversation, you'll need to subscribe at SamHarris.org. Kind of very low-key. The Making Sense Podcast is ad-free and relies entirely on listener support. And you can subscribe now at SamHarris.org.