Technology, Connected - How AI Surveillance Works: Yuval Noah Harari on Metadata, Social Credit and Predictive Control

Episode Date: January 31, 2025

In Chapter 7 of Nexus, Yuval Noah Harari examines how artificial intelligence is transforming surveillance from passive observation into continuous analysis, prediction and control.Mark and Jeremy discuss how governments, companies and digital platforms collect data through phones, cameras, apps, online reviews and everyday transactions. AI makes it possible to combine these signals, identify patterns and act on them at a scale that older surveillance systems couldn’t match.In this episode, we discuss:How AI surveillance systems workHow Iran uses facial recognition and public cameras to enforce dress lawsWhy metadata can reveal behaviour, relationships and routinesHow predictive surveillance differs from traditional monitoringWhat China’s social credit initiatives reveal about data-driven governanceHow platforms use ratings and reviews to monitor workersWhy peer-to-peer surveillance has become part of daily digital lifeHow companies profit from collecting and analysing behavioural dataWhy Harari argues that modern technology exceeds the surveillance capabilities of the StasiWho controls the data produced by phones, apps, cameras and platformsThe central issue isn’t simply whether devices are listening or cameras are recording. It’s what happens when AI systems can connect that information, infer intent and influence decisions before people know they’re being judged.This episode examines the growth of AI surveillance and the political, commercial and personal consequences of living inside systems that continuously measure human behaviour.--Timestamps(00:00) Disruptors and curious minds(02:04) Apple, Siri And Your €20 Privacy(03:40) Ceausescu And The Secret Police(05:24) F.E.A.R(06:22) Why Does Yuval Reference Dictators In Nexus?(08:02) A Warning(11:22 A Ubiquitous Computer Network Powered By You(14:50) The NSA And Iran(19:36) The End Of Privacy(20:56) Tripadvisor And Peer-to-peer surveillance(24:59) Inflection Point(26:41) People Control(28:57) Black Mirror And Hell(31:06) AI School: No Human In The Loop--Read more: www.thinkingonpaper.xyzWatch on YouTube: https://www.youtube.com/@thinkingonpaper/videos

Disruptors and Curious Minds, CEOs, founders, book lovers. I'm Mark, this is Jeremy, and this is a thinking on paper, book club where critical thinkers like you come to read between the lines and work out the relationship between humanity and machine, between technology and what it means to be human. No pressure. We're reading Nexus by Yuval Noah Harari. We're on Chapter 7 and...

I don't know if I'm paranoid. Maybe I am. Maybe I'm not. But I'm sure that everybody is watching me. Everybody is monitoring my every movement. Ever think I say. Ever think I buy. Everywhere I go, every machine I interact with, every person I speak to.

It's recorded in a database somewhere waiting for the AI's plural to come in, sift through that data. and well I don't know what they're going to do with it all but that's going to happen we're talking about pattern recognition and the surveillance of modern humanity aren't we Jeremy chapter seven first impressions oh by the way if you're new like subscribe please yeah there you thank you and if you aren't if you aren't scared out of your brains yet hang with us because we're unpacking chapter 11 entitled relentless the network is already on it's scary It's a scary chapter and I kept questioning my, yeah, am I being paranoid? Is it, should we have this conversation? We'll scare people. I think the idea is that just enough, uh, scare to inform, right?

And, and spark some thinking and that sort of thing. I mean, I think chapter seven, I think the most informed individuals are kind of aware of the, the surveillance that, that is going on in our, in our world right now. And like Mark said, like and subscribe, thinking on paper, I'd, XYZ, you can see all the other stuff we're doing. Yeah, recently in the news, Apple had to pay, what, 90 million bucks, which is a drop in the damn bucket, because Siri was listening to our conversations without us knowing it and using that information to serve us with ads and do all the fun things.

Guess what, Mark, according to that payout, according to that payout, which I think I heard was like two hours of profit for for Apple. Comes down to 20 bucks a person. 20 bucks a person. Your privacy mark is worth $20. How about that? Well, that's why I don't have an apple. But $20, $20, the cost of my privacy.

Yeah. Yeah, so first impressions, let's dive right in. He always starts kind of high level and then trickles down into deeper things. A couple of things. things that I circled and highlighted just on the front end of this thing is, you know, bureaucrats role in monitoring populations, right? You know, since, since the beginning of time, really, there would be just, you know, liaisons of the crown, liaisons of the ruling party that kind of infiltrate and figure out what's going on and all these remote villages and that sort of

thing. And really, we're doing the same thing just with better technology now. It's gathering data. It's analyzing patterns. It's seeing if individuals are doing things according to the script or off script, right? Who writes the script? That's the thing, right? That's the thing. So I don't know, I should have done this. I should have done this in my show prep, but this guy, Nikolai, how do you pronounce his last name? Chalchescu? Chalchescu. I should know that. It's from the 70s. I was born in 76. You're American. I'm European. I know all to Dan well. who that guy is, unfortunately, but maybe he didn't hit the American media as much as it did over here.

And that's the definition of thinking on paper covering both sides of the coin, guys, you know, for the feeble-minded... Americans. Blinders, Americans, right? Yes, something slipped through the cracks for me. But let's break this thing down. So in 1970s, we're in Romania. a secret police just like every diabolical regime has a secret police, right?

And non-diabolical regime. Let's be, you know, let's be balanced. Yeah, today's, today's regimes too, and we can talk about that. So a few Romanians get together. They decide to write to Radio Free Europe. They're trying to try to break through a couple of things that they see going wrong. It causes this guy, Nikolai, to literally collect 20 million handwriting samples to find the culprit. What a great use of funds. what a great use of time and energy. Also requiring the registration of state-owned typewriters. And Mark, if you had a typewriter in Romania in the 1970s,

you'd be required to turn in its fingerprint, which is, I guess, that little thing that all typewriters have, that, you know, how it types, is that what that is? Yeah. And what you've just painted a really good picture of what it was, I mean, obviously, taking people's handwriting, having to register to use a typewriter. Like, it's, and what do you do with all that data?

It's not, it wasn't so much the surveillance. It was the fear that it instilled. And that, like, Romanian 17th, like, Chalachas, it was all about fear. It fear, fear, fear, you, okay, are they watching you? They might be, they've got your fingerprints, they've got your typewriter, they've got everything, you know. So it's fear, fear mongering, and that's what, it was, like, it terribly is. Yeah. Just like Stalin. We talked about that last time, you know, the, very similar, right? And it's like, yeah, it's to live in in that regime or to live in that world where, where that's happening, it's got to be, got to be crazy. And it's got to like, I don't know. I, the scary thing. Okay. So people who are just tuning into chapter seven haven't listened to chapter six, five, four, three, two, one. Why do you think that Yuval keeps reference?

referencing these horrible dictators from our past. In the last chapter, we were talking about Stalin, we've spoken about Nero, we've spoken about now Tricescu. Why do you think he keeps bringing it up over and over again? The thing that's really interesting to me with that question, and speaking of questions, drop them in the chat, guys. We'll jump on them as soon as we see him. These are patterns, and the reason why he brings up these individuals

using these patterns. They're the same patterns, man. Like it's the same framework, the same patterns that are being used. And they're actually being used today. And they're being implemented today. I mean, especially like what we're starting to witness,

you know, here in the U.S., like a lot of these things are being implemented and it's coming out of a similar playbook. And I think a lot of people are not in tune with that. I'm in tune with that more because I'm reading this book.

It's super timely. And it's super scary. Like it's a bit, it's a bit frightening, but understanding that the patterns are recognizable, just like AI recognizes patterns, we can recognize patterns and kind of predict where this train's going in a way. Did I answer your question? You did, yeah. I think that he keeps bringing up these because we kind of walk a very fine line humanity does. And we're always on the verge of falling off that line one way or the other. and he keeps bringing up these nefarious dictators from our past

to remind us of how often and how easily and how readily we fall the wrong way off this line. And it's all just hammering home, hammering home, hammering home the information networks, the analysis of data that we're just going into this age where there was two barriers to this kind of mass surveillance in the past, and that was there were legal limits placed on it and there were technical boundaries placed on it to various degrees.

And the technical boundaries are no more, or will soon be, no more. The things that stopped Stalin, the things that stopped Chalchescu, the things that stopped Nero going right back, they no longer or will soon no longer exist. Well, but check this out. This hit me right between the eyeballs, man, in a big way. So you look at, you know, imagine being, you know, in Romania in the 70s, imagining being in Russia, you know, in the 20s, the 30s, whatever, 40s.

Yeah, I mean, just, you know, imagine, you know, Germany in the 30s and 40s, you know, that kind of thing. But guess what? Like these surveillance systems, these things that were put in place were done in a not very scalable and a little bit of a clunky way. but what do we do now? We carry these things around that like enable all of the shit that we were trying to avoid back in the day. Like that stuff is now automatically enabled by devices.

And this I keep pointing back to this like this tradeoff. And we talk about it a lot. This like security for convenience tradeoff, right? And I use ways as an example, not because I hate ways because I put in ways a lot. And it gets me from point A to point. be, but in doing so, my data on where I go, when I go, how I go, how fast I go is going somewhere else. But it's easier than me pulling out Rand McNally's Atlas and trying to get to my business meeting, right?

Sorry, I just got distracted by a thought I had. You were talking, you mentioned Germany in the 1930s, and I just want to throw a book that might be for you. It's called Hitler Land, and it's by a guy called Andrew Nagorski. And it's basically letters and correspondence written by people in Germany in the 1930s about Hitler and the German government. And it's fascinating. Like in a real time? Yeah, it's all like real documents. Oh my gosh.

Wow. And how they were thinking and how they were reacting to it. and almost systematically across the board, you can probably imagine what they were all saying. It's not that bad. Right. Yeah, it's like bull and a frog. Are we frogs in a pot of water that's slowly been getting, you know,

cranked up temperature-wise? But yeah, so Hitler-Land, read that. But jumping back to what you were saying, I want to read a quote, by 2024, we are getting close to the point when a ubiquitous computer network can follow the population of entire countries 24 hours a day. This network doesn't need to hire and train millions of human agents to follow us around. It relies on digital agents instead. And the network doesn't even need to pay for these digital agents. Citizens pay for the agents on our own initiative and we carry them with us wherever we go.

And I, obviously I know that. We all know this. And you mentioned earlier with Apple, they were, You know, okay, so Siri's listening. So it's not, like, am I paranoid that when I speak about skateboard, suddenly I get adverts for skateboards on my, everywhere I look. Is that a coincidence? Is that my paranoia? Is that really happening? And I know we keep, everyone knows.

Oh, it's, sorry to interrupt, Mark. It's happening for sure. Yeah. I have a personal kind of, I like to, I like to talk about 1984 and George Orwell and the surveillance state a lot. And I think, oh, I'm really cool because I'm referencing George Orwell. I'm referencing 1984. Look at me.

My ego takes over. And then, you know, the brave new world. Aldous Hookley. And then, but I'm just doing, I'm carrying the phone in my pocket. I am, I am the creator of my own demise. I am the creator of my own dislike for the system. Like, what do I do? How many things, how many things knowingly do we do that are, that are bad for us?

So check this out. Like we had, um, we had, uh, the XPRIZE, uh, technical prize director for Kwanamon to one of our episodes.

And we referenced, you know, Peter Diamandis because he was one of the, one of the folks that got X Prize going. And, you know, I saw a quick interview with Peter D. Amanda's,

uh, on, it was a social media clip. And he talks about like sugar, like in us eating sugar. And like back in the day, I don't know if these numbers are super accurate, but it's an order of magnitude.

Back in the day, you know, we would, eat, you know, one pound of sugar every year, you know, as a society. And, and now we're upwards of eating 100 pounds of sugar, uh, every year, right? And we know sugar is bad for us, but what do I do? Like, I'm a cereal. I love cereal, man. Like that's my, that's my, that's my thing, you know, and, and a nice big bowl of cereal, like I'm a kid, man, but piles of sugar in that. But I'm like, you know what? It's all good. You know, I'm just like,

one bowl of cereal will kill me, but, you know, maybe four thousand. bowls of cereal might. Well, it's compounding. You're compounding, compounding, compounding. Diamandis has been drinking from the Brian Johnson tap, I think. He's big into longevity now. He's doing a lot of, I think, I think that XPRIZ might be moving into longevity science. He wants to live forever.

I think Peter DeAndes. Well, let's talk about, let's talk about more known, some known. Let's get back on track. Yeah, more known surveillance kind of stuff, right? So we went through the idea that, you know, these regimes went to great lengths and great resources to collect and surveil people. Nowadays, the resources are things that we play with every day, which is kind of ironic. This is really interesting. So that, and I don't know if this is going to automatically shut down our podcast or like scramble us right when I say this.

But in the book, he references the NSA, right? in the NSA's monitoring of, of quote-unquote terrorist organizations, right? And there's a, there's a quote in here. I'm going to paraphrase it, but you know, mass surveillance of Pakistan's mobile phone network, 55 million people, and this the surveillance network attempts to rate their likelihood as being a terrorist or not, okay? So we all talk about the ability, like, when trust comes up. a whole lot, right? So we're not we, not you and I, but, but organizations are trusting these machines,

trusting these computers, trusting this metadata, right? There's a quote in here, we kill people based on metadata. You know, that's, that's, that's interesting because then you get, get to thinking like, can AI be really objective? Can, can data and algorithms and that sort of thing be really objective in creating the parameters of what it means to be something like a terrorist? define terrorists so one man's terrorist is another person's peace maker absolutely one of the issues he brings up in the book is that yeah you define terrorism and the algorithms go looking for patterns of terrorist behavior but what is terrorist behavior it looks different to the person programming the computer so that's a big worry um i don't

want to go to Iran and I think every podcast maybe we kind of knock off a country that we won't ever be able to go to but Iran what an horrendous state he paints that in absolutely like I knew it's bad but I didn't realize it was that bad are you going to the hijab law yeah yeah yeah there's there's some there's some there's some pretty crazy stats um in with that related to um these these laws that were based on the fact of, you know, women's heads being covered up or being offensive for not being covered up. And again, going back to intersubjective realities to pull groups of people together in the balance of truth and order, right? This one lying more on order than truth necessarily. But check it out. 133,000 text messages, immobilized vehicles for two weeks, confiscated

2,000 cars sent 4,000 people to trial with the potential of up to 10 years in prison for not covering up their head. And they were doing this in cars, right? They're not out in public. They're driving their car down the road, right? So the surveillance capability of catching people without their hijabs on, like, yeah, that's a hell of an illustration of kind of their surveillance piece that's happening. Yeah, they were literally using face recognition algorithms through car windows as they were driving along and then finding that person and then texting them before they even got out of the car with a fine or with a warning, however it might be.

And then in September 23 on the anniversary of Maha Amanin's death, Iran's parliament passed a new instructor-Hihab bill. According to the new law women who failed to wear the hijab can be punished by heavy fines and up to 10 years in prison. additional penalties including confiscation of cars and communication devices, driving bands, deductions and salary and employment benefits, dismissal from work and prohibition from accessing banking services. So you mentioned Brave New World, Aldous Huxley. I automatically go to the pop culture version of that, more digestible version of that with Wesley Snipes and Sylvester Stallone and Demolition Man.

And, you know, all I think about is like... Demolition Man. All I think of, you've seen that one, right? Yeah. Yeah. So you know, you're, you're sitting there and, you know, uh, John Spartan's character says, you know, F bomb or whatever and all of a sudden out of some random speaker, John Spartan, you've been assigned four demerits for breaking the code of X, Y, Z.

And like, that casts a hell of a vision to like, you know, we always talk about, you know, looking at science fiction stories, uh, as a projection of future realities. And some of them come true. Some of them don't. Some of them are just funky enough to end up being something that actually happens. Yeah. Wow. Where am I going with this?

Let's see. Are you going towards surveillance, capitalism, biometric capitalism, the end of privacy. Is that way you go? Let me read you this quote. Let me read you this quote. This was pretty good. As fish live in water, humans live in a digital bureaucracy, constantly. inhaling and exhaling data. Facts, spot on, like, whole, that, that makes me like,

that makes me claustrophobic just like thinking about it, but it's super true, man. Like, so that, that's, that's the piece of the puzzle, right? And, and from there, it kind of, go ahead, Mark, you were going to say something. I said, say it again for the people at the back. Oh my gosh. Yeah. We're, we're swimming through a digital bureaucracy, see friends and neighbors we are we're we're we're sucking in stuff as we as we swipe swipe swipe swipe or we get fed on autoplay which we talked about last episode clock or corns our eyes are wide open right we're auto fed stuff so much data coming at us but then there's so much data coming out of our interactions with that data yeah piles of it last week we spoke about the

end of humanity or human history or our part in human history and this week we talk about the end of privacy and one of the parts of chapter seven which i took great pleasure in reading about was the peer-to-peer surveillance systems and trip advisor especially and how much trip advisor and peer-to-peer surveillance systems like TripAdvisor have changed our relationships is absolutely mind-bending because I don't know what percentage. It's very, very high. People go on holiday. They check TripAdvisor. They go to a restaurant. They check TripAdvisor. They go to a bar. They check trip advisor. Me one shortcut. Me one shortcut. I don't want to think for themselves. TripAdvisor says this restaurant is good, even though the score's been made by outliers

who either love it or hate it and most probably hate it, so you probably shouldn't trust it. But it's got waiters and waitresses, like, scared to, although I have to say that I don't think TripAdvisor's quite reached France because the waiters and waiters don't give a crap how they treat you, but beyond the borders of my country I live in. Yeah. No, it's yeah. And how does that, how does that affect things in the whole, you know, there's a, there's a construct of like, it's easier to leave a bad review than a good review, right? So if you leave a good one, it's like, great job. If you leave about, oh, I walked in and this guy, you know, there was a hair in my salad and, you know, then the fork was dull and my napkin was slightly tinted a different color than my pants. And like it, it's just easier. It's facts. It's easier to say something negative about something. than something positive. So those large systems, and could that be a self-correcting mechanism, though? That's an interesting piece.

Like, could that rating system, in a way, be labeled? Because we talk about self-correcting mechanisms. Our host and author talks about self-correcting mechanisms. Is that one? No. You didn't sound very confident in that response. I don't think. So a self-correcting mechanism going back.

to chapter 5, four, three, two, and one, allows us to learn from our mistakes, to make mistakes and make the system better. I don't see how, it kind of removes human nature from the conversation a little bit. If you've got peer-to-peer surveillance systems akin to TripAdvisor, where you're biometrally hooked up

to something, it can track your every mood and how you feel and how your emotions related to a restaurant, to an experience, to a person. Or maybe that is self-correcting, yeah, like it would, I take it back, I don't know. I always think about, I guess eBay was the first one to kind of fold that, you know, how do we make sure people on an open platform do the right thing? How do we create trust in a way that, you know, people don't see each other in person, right? And that was the review system. That was the rating system.

But, you know, as soon as a system comes out, then we figure out, well, how can we game it? And then how can we use technology to game it faster? Maybe that's when a lot of this stuff happens. I'm going to tell you what, Mark, the one thing that's pretty scary, and I wrote, like, on here, this is probably going to show it backwards, but I wrote on here inflection point. And the inflection point, I think, is when this data, this information that's being captured, where we go, my eyeballs, you know, looking at a website, look, my eyeballs looking at a social media post being tracked, you know, whether it's neuralink or whether it's a smartphone or whatever it is, when that data is actually coupled and matched with the real-time biological data, that's when it's. it's scary. So like your heart goes from steady resting at 70 beats per minute to 175 while watching Fox News, you know, ooh, this one's, we really got Mark now. Let's keep firing this stuff at him or whatever it is. And, you know, it could be CNN. It could be whatever, whatever, you know,

whatever thing you're talking about. But what do you, how do you feel about that? I use Brave browser and I have no recommended videos on YouTube and it doesn't auto play the next video. I'm building in automata what's the word? Automated security provisions, automated privacy conditions. Yeah, which I don't have to think about it

because they're there. So if I do go on YouTube and watch 20 minutes of Fox News, God forbid, I won't be hounded by the algorithms who try to change what I watch. Like for the next video, I'm going to have to choose for myself what I want

watch. I have to search for it manually. There's no recommendations. So what do I think about it? I think it's pretty scary and I'll go back to the beginning and I read it. I think I started with this is scary. Unfortunately, social credit algorithms combined with ubiquitous surveillance technology now threatened to merge all status competitions into a single never-ending race. Even in their own homes or while trying to enjoy a relaxed vacation, people would have to be extremely careful about every deed and word, and if they were performing on stage in front of millions. This could create an incredibly stressful lifestyle destructive to people's well-being as well as to the functioning of society. If digital bureaucrats use a precise point system to keep

tabs on everybody all the time, the emerging reputation market could annihilate privacy and control people far more tightly than the money market ever did. Even if the network is potentially benign, the very fact that it is always on might be damaging to you. organic entities like humans because it will take away our opportunities to disconnect well this is happening already you know China has the social credit system I don't know a ton about it just you know tangentially aware of what's going on over there but you know kind of the idea of assigning points for everything you know Harari talks about it is as being this this new kind of money this new value exchange, right? A standardization, he calls it, a standardized valuation of the reputation

market. And as I think about that initially, the idea of creating value to things that don't have inherent financial value could be interesting in one way. Like doing something really good for the world, right? You know, do, you know, helping a grandma across the road, you know, basically teaching, you know, teaching people who don't know how to speak French in France, you know, to do so. You know, doing these things for the betterment and for the good. But then I start thinking about like, okay, like, how is that going to be gameed and played? And, you know, this quote, a comprehensive social credit system, you reference this will annihilate privacy and effective. turn life into a never-ending job interview.

That sounds like hell to me. Oh, my God, right? Yeah. Jeez. That sounds like a black mirror episode, doesn't it? Where you help the old lady across the road and you get 10 tokens and then you walk into a shop and you hold the door for someone. You get 10 tokens and you say please and you say thank you. You get 10 tokens and everyone's walking around this black mirror episode with.

the little bars, energy bars going up above their heads. And then when they get home, they cash in those points for, I don't know, some kind of strange reward. Didn't religion, doesn't religion do this to a certain extent, right? So if you're, if you're an ass. The Catholic church again, Jeremy. If you're an ass, you know, if you throw a beer bottle at somebody's head, then you go into church the next day and you confess.

And, you know, I go, Mark, hey, I've, I've, I did this thing yesterday. You assigned me a couple of things like some Hail Mary's. And I'm, listen, I want to be clear. I'm not trying to debase or, or be super sarcastic towards,

uh, towards religious, uh, organizations. I just, I'm, I'm pointing out, there's an interesting correlation between how information

networks, um, are used in the right ways, in the wrong ways. So going back into this point. system like you're talking about going around going around helping grandmas across the street all over your city and you're getting points

and all that stuff then can you look at that and go well I've got enough points I can go smart I can go I can go punch mark right in the throat and you know I might lose some points but everything's okay like I don't know that that part of it that's been around for years

right I'm going to pull this all back together now for the people who's tuning you I thought this was about artificial intelligence what the hell of these guys rambling on about. Okay, the point to remember in all of these examples we've given is that the algorithms will be making the decisions. So in all of these dystopian futures that we're painting or this dystopian present that we're painting, the idea is read the book that the person is out

of the loop, the human is out of the loop. And we spoke about this last week, about these self-learning algorithms and I was reading something this week about language models becoming teachers and teaching other language models and like taking the learning and taking the data sets away from the human input and the language models training other language models a school of AI teaching other AIs in the ways of the algorithm and once once the trajectory has been programmed, depending on whose view you take, it's kind of set. I mean, there are people who say that that's not the case, and you can edit, and you can change, and you can update, and you can remove from the lounge model.

But I'm not sure that we should be taking that risk, even though we probably are going to be. And I think that's the key point of Nexus, isn't it? This chapter, for me at least, anyway. Yeah, I think you're absolutely right. And, man, it's hard to not live in this. uh, dystopian discussion space, this dystopian thinking space,

um, while we're reading this. And, you know, I hope, I hope there's a, as we get towards, uh,

the later chapters, we get into a point where it's like, okay, but there's still time, Mark, there's still time and here's what we can do. And, uh,

I don't know. I hope to, I'm not saying, yeah, and Harari's not like a hyper negative, you know, he's not hyper negative. or negative in a lot of it, but it's like, yo, this is the real thing, guys.

I'm laying it out. I'll call him a dystopian futurist. That's what I would call him. Like, if you run the show, I don't think he's coming on the show. I'm going to just read just in our defense because people think that we're mega, paranoid, dystopian peddlers of doom. Of course, pattern recognition also has enormous positive potential. Algorithms can help identify corrupt government officials, white-collar criminals and tax evading

corporations. The algorithms can similarly help flesh and blood sanitation officials to spot threats to our drinking water, help doctors to discern illnesses and bludgeoning epidemics, and help police officers and social workers to identify abused spouses and children. In the following pages, I dedicate relatively little attention to the positive potential of algorithmic bureaucracies because the entrepreneurs leading the AI revolution already bombard the public with enough rosy predictions about them. My goal here is to balance these utopian visions by focusing on the more sinister potential of algorithmic pattern recognition.

Not just us. Transition. I wish I had some sinister music in my little transition buttons here, but I don't. What would you play? I don't. We could go with this one.

All right. We have to do this, actually. That's a great idea. Can we have some really upbeat kind of little red riding hood music? Little Red Riding Hood. I love it. some really nefarious doom music.

And whenever one of us speaks about something really positive and utopian, we play the one. And whenever we go, or dystopian and doom, we play the other. Challenge, challenge accepted, Mark. Challenge accepted. Let's see. Last quick thing, I think, to talk about, I think, as we wrap this, as we wrap this up, I don't know, this stuff's been going on for a while, right? You know, throughout history, surveillance has been going on.

It's getting a hell of a lot more efficient. we're enabling it as users of devices and platforms and that sort of thing. Here's one quick thing to take away. In 2023, there was roughly one camera per every eight people across the globe. One camera per every eight people across the globe. One billion CCTV cameras monitoring our every move. And I bet that's 20%, 30% higher now. Yeah.

I guess the last quick thing. And I don't want to get too deep because we're already at 35 minutes. We try to keep these relatively accessible and digestible. Sorry, guys. The idea that about exceptional times. And he defined, you know, exceptional times often require more surveillance, right? Or exceptional time because of this thing that happened, I need more access to everyone all the time. and at that point there's an intersubjective reality created that people buy into and go yeah i guess

that's right yeah okay here you go oh shes that's yeah here you go so that's that's something to think about um guys we're really chipper individuals uh we we really we really are positive guys we're we're trying to work our way through this we're trying to inform folks um about these these kind of systems and processes that relate to to AI because it's a topic that we're all talking about in a number of ways. Thinking on paper. That xyz, you can see some of our happier episodes.

We talked to some great minds. We have Kevin Kelly. Kevin Kelly is coming on the show, and that's going to be great. It's in April. We're going to do a big special event for that. How do we want to close this up? I'm trying to keep.

Let's stay upbeat, Mark. Let's stay upbeat. Okay. I won't read the first line of chapter eight, which is in the gulag. Oh. I'm not laughing at. No, definitely not.

Yeah, let's end with Kevin Kelly. So we're looking for 10 of the biggest Kevin Kelly fans on the internet. If you are a Kevin Kelly fan, get in touch with us because we've got a little competition, which you'll want to hear all about. So calling all Kevin Kelly fans. Should we, as a thought experiment, should we spin up? an algorithm to determine the number one Kevin Kelly fan of all time. Well, yes.

I'll lose that to you. We need 10. We're going to have 10. We need 10. The top 10. I like it. I like it. Kevin Kelly fans come see us thinking on paper.

At xyz chapter 8 coming next week. We've got some guests coming up that we're excited about. Thanks for listening. Mark, closing thoughts. next week on our main show we are interviewing a very special guest who is building data centers in space I'm so excited that I'm so excited that these guys are coming on like I haven't data center nerd I worked in the data center space for so long and to have these guys take time time out of day to jump in and talk about how we're putting data centers in lower earth orbit to take

advantage of all kinds of fun stuff up there. I can't wait to hear you nerd out and tell us everything you know about data centers on earth and how they will or won't work or function in space. This is going to be epic. This is going to be epic. Thinking on paper. That x, y, Z. Be curious.

Stay disruptive. Keep thinking on paper. See you next week. Bring your books.