transcript

Speaker 1:
[00:00] After many, many decades of people debating this, you might have figured out the reason why we dream.

Speaker 2:
[00:04] Yes, and it's a simple answer. So if you go blind, the visual cortex in the back of the brain gets taken over by hearing and by touch and by other things. In fact, our colleagues at Harvard did an experiment where they blindfolded normally sighted people, and you could start seeing that takeover happening after 60 minutes. And that's when we realized, wow, the purpose of dreaming is to defend the visual territory from takeover from the other senses. But what fascinates me about brain plasticity, and what I've devoted my career to is figuring out the way that we can be the sculptors of our own brains, and how it gives us an opportunity to become the kind of person we would like to be.

Speaker 1:
[00:40] I'm coming, dude.

Speaker 2:
[00:41] Yes, here's the thing. Your brain peaked at the age of two. Okay, so at the beginning, you've got fluid intelligence, meaning you could learn anything. But now that you have grown up in this world, you've got crystallized intelligence, meaning you know how to drive a car, you know how to operate a cell phone, you know how to run a business. And so your brain doesn't require as much change, which means that the structure of the brain is always degenerating.

Speaker 1:
[01:05] So what are the set of actions that will fundamentally change my brain and make me that type of person who's motivated in disciplines and who has high agency and attacks the world?

Speaker 2:
[01:13] So this is something I've studied in my lab for decades now. And the key is that...

Speaker 1:
[01:18] And what about AI and the social media debate as it relates to brain development?

Speaker 2:
[01:22] Well, I happen to be a cyber optimist for young people. I think it's gonna make them much smarter than the generation that came before. And here's why.

Speaker 1:
[01:31] Interesting. Guys, I've got a favor to ask before this episode begins. It's the algorithm, if you follow a show, will deliver you the best episodes from that show very prominently in your feed. So when we have our best episodes on this show, the most shared episodes, the most rated episodes, I would love you to know. And the simple way for you to know that is to hit that follow button. But also, it's the simple, easy, free thing that you can do to help us make this show better. And I would be hugely grateful if you could take a minute on the app you're listening to this on right now and hit that follow button. Thank you so, so, so much. Dr. David Eagleman, what made you so fascinated about the brain, and why should everybody listening be fascinated about the brain as well?

Speaker 2:
[02:17] Here's what I think it is. When I was eight years old, I fell off of the roof of the house that was under construction, and I fell 12 feet and broke my nose on the floor below. But the whole thing seemed to take a long time. I did the calculation and figured out that it only took 0.6 of a second to get from the top to the bottom. And I couldn't figure out why it seemed to have taken so long. So I think that got me really interested in perception and the machinery by which we view the world and taken in and what is actually real versus what's a construction of the brain. And that's what I've devoted my career to is figuring out how the brain, which is locked inside the skull, it's about three pounds, how it constructs this model of the world and which things we can take as reality and which things we shouldn't.

Speaker 1:
[03:06] I think most people don't even know that there's a brain there almost. It sounds like a strange thing to say, but most of us haven't really seen our own brains at all. We've never been able to touch our own brains at all. So it's easy to fall into the trap of thinking that everything I experience is true and is reality. So I'm wondering how a deeper understanding of all this stuff can help me live a better life.

Speaker 2:
[03:27] Yeah, one of the things that I started writing about years ago is that I think we're not, I think we often think of ourselves as individuals, meaning not divisible into other things. But really, you are a team of rivals. You've got all these neural networks that have different drives making different suggestions to you.

Speaker 1:
[03:48] What's a neural network?

Speaker 2:
[03:50] So in the brain, you've got 86 billion cells called neurons. And these are communicating with each other at a climbingly fast rate. Many of these cells are hooked up in networks. So they're, you know, this guy's talking to this guy and this guy, and they're all in particular networks. The thing is, you can actually get competing networks. So for example, Steven, if I drop some chocolate chip cookies in front of you, party brain wants to eat it. It's a good energy source. Party brain says, don't eat it. I'll gain weight. Party says, okay, I'll eat one, but I'll go to the gym tonight. The point is, you are arguing with yourself. You are conflicted. This is what makes humans so interesting is that we have all these voices trying to drive us to different conclusions about our behavior. So the way that your ship of state moves depends on the vote of the neural parliament at any time. So understanding this I think is really critical to navigating our own lives because all of us do things where retrospectively we regret it. We say, oh, I shouldn't have eaten that whole bag of chips or done the, the alcohol or the drugs or whatever. Like everybody has regrets all the time with things. And it's because you have different voices in charge at different times. Okay. Part of what this leads to is what we call the Ulysses contract. So a Ulysses contract is where you do something now to prevent yourself from behaving badly in the near future. Just as an example, you know, when people go to alcoholic synonymous, the first thing they're told is clear all the alcohol out of the house. Because even if you feel like, look, I'm in a moment of sober reflection, I don't want to ever drink again. If you have alcohol in the house, you're going to bust into that cabinet at some point on a festive Saturday night or a lonely Sunday night or whatever. So what you do is you constrain your future behavior by setting things up in the right way. So the future, you can't behave badly. We naively think, okay, well, I know who I am. I'm just one person, but you're not. And under different circumstances, you're tempted by different things and you'll do different kinds of behavior. So having a sense of what's going on under the hood gives us an opportunity to be more closely aligned with the kind of person we would like to be.

Speaker 1:
[06:04] Because it feels like there's just one, well, I do argue with myself in my head sometimes, but it feels like there is just one me. And so when I hear that voice say, Steve, you should have that cookie and it's 1 a.m. And then the other voice says, no, you shouldn't. I think it's kind of the same person just tussling with himself.

Speaker 2:
[06:21] Right. Well, but that tussling with himself implies different political parties that are all battling it out. When you look at a parliament, you've got all these political parties that all love their country. They just have different ideas of how to steer it. And this is what's going on in the brain all the time.

Speaker 1:
[06:37] So what does one do about that? How do I make, do I have to make a alicia's contract?

Speaker 2:
[06:41] I think it's very useful to make that sort of thing, but also just understanding what's, I mean, part of the, you know, there was this Greek admonition to know thyself. This was a sign they had in various places, various temples and stuff. But I think that becomes know thyselves. And the better we know ourselves, the more we can get rid of the illusion that we are one person, because all any of us need to do is look back on our behavior to say, oh yeah, in some circumstances I would do that, other circumstances I think is a terrible idea. So this is all to the goal of understanding who you are.

Speaker 1:
[07:16] What are the big misconceptions about the brain that people have gone through their life believing? I mean, that's one of them. Something that is true, that kind of could fall in place of that, is just this fundamental idea that our brains are plastic or sort of adaptable. Because when I found out that I could change my brain by what I do, I found that to be really, really inspiring.

Speaker 2:
[07:35] Yes, that's exactly right. So brain plasticity, if someone hasn't heard that term before, it sounds like a weird term. But the reason it came about a hundred years ago is because the great psychologist William James pointed out that if you take a piece of plastic, what we like about that material that we call plastic is that you can mold it into a shape and it'll hold that shape. And that's what your brain does. So if I ask you the name of your third grade teacher, you can remember that name, even though it's been a long time, because your neural networks changed and held on to that piece of information. Okay, well, our whole lives, our brains are changing every moment. Now, we have certain doors that close at different times. So just as an example, you need to learn language in the first several years of your life. If you don't learn language, you can never get the concept of language. Your brain will never figure that out.

Speaker 1:
[08:28] You're not saying you can't learn a new language as an adult. You're saying the concept of language.

Speaker 2:
[08:31] The concept of language, the concept that I can name things and I can ask for things and so on, that never clicks in the brain. For example, in Romania at the fall of Ceausescu, there were tens of thousands of kids in the orphanages because their parents had been killed. It was too many kids. And so the staff there said, look, the kids will get clingy if you pay too much attention to them. So here's what we're going to do. We're going to feed the kids, but we're not going to hold them. We're not going to talk to them. And all these children grew up with real cognitive deficits as a result. Here's the thing about brain plasticity. Human beings have a similar brain to all our neighbors in the animal kingdom. You compare our brain to a horse brain, a dog brain, anything like that. It's the same general structures and stuff. But what we have is much more of the wrinkly outer bit, called the cortex, it's the outer three millimeters, and maybe we'll come back to why that matters so much. But the other thing that Mother Nature tweaked with us, it's small genetic tweaks, but we have much more plasticity, adaptability, such that when a horse drops into the world, it's doing the same thing that horses did 100,000 years ago. It's just eat, mate. But when a human drops in the world, we learn everything that's happened before us, and then we springboard off the top of that. So we, living in the 21st century, we say, oh great, you know, physics, math, this, that, art, blah, blah, great, we got everything that's happened before us, now let's do our own thing. And that's what's so special about the plasticity of the human brain, the adaptability of it. The downside, the gamble, is that Mother Nature drops human brains into the world kind of half-baked. And we then get to absorb everything, but in the rare circumstance where you're not getting the right input, then that ends up really in trouble because it's only half-baked. So when it comes to language, we can learn multiple languages when we're young, that's very easy, but it gets harder and harder as that goes along. And various other things become harder, and here's why. It's because, I mentioned this earlier, the job of the brain is to make a model of the world so it can operate within it. So for example, you're an entrepreneur and you love doing business, so you get it. Okay, here's how, you know, here's a structure business, here's how you hire well, here's how you set up a board well, you're doing everything, because you've got a really rich internal model of how to structure a business. That's what the brain wants to do, is get that stuff right. As a result, if you suddenly ended up, you know, taking a trip to Mars, and there's a whole very different society there that does businesses very differently, you would have to relearn stuff really quickly. So here's the thing, you went from having a brain that had high fluid intelligence to now having a brain that has high crystallized intelligence. What that means is at the beginning, you can learn anything, you could learn any language you could have dropped into any era, you could have dropped into 13th century Japan.

Speaker 1:
[11:30] When I was young.

Speaker 2:
[11:30] When you were young, when you were a baby, if you had dropped out of the womb in 10th century Mongolia, you would have said like, okay, cool, learn a language. You would be a 10th century Mongolian. But as it happens, you dropped into this era, a certain place and time and neighborhood and culture and family. And so you learn that. That's who you become, is that person. We often think that plasticity diminishes as you age, but it's not simply that it's diminishing, it's that you are getting the right answers about how you operate in the world. And so you don't have to change as much. Your brain doesn't require as much change.

Speaker 1:
[12:08] What if I want to change?

Speaker 2:
[12:09] Yes. So it turns out you still can change. That's the key, is that the reason brains change less and less is because they don't have to. But when things get upside down, just as one example, everything about the pandemic really stunk. Except for one thing, I think the tiny silver lining is that all of us had to reassess, oh my gosh, wait, how is the world working? I thought I knew how the world worked. But now, I don't know if there's going to be toilet paper at the store. I don't know if the bank's going to be open. I don't know if I can get coffee at the coffee shop. Like everything was different. As awful as it was, it's really useful to challenge your internal model of the world and get to do that as an adult. We don't usually get to.

Speaker 1:
[12:54] So if I want to change, what would you recommend that I do? If I want to change who I am? Say I'm stubborn, I'm not motivated, and I want to be a different person.

Speaker 2:
[13:03] The key is challenge. The key is seeking challenge. So it turns out that where we always want to be is in between the levels of frustrating but achievable. And you want to take on new tasks. You want to seek novelty to find yourself in that zone and push yourself to do things that you just haven't done before. And one of the things that's so wonderful about the modern world, everyone's got complaints about the Internet and social media and stuff like it. But the good news is it exposes you to so much more than you ever even knew was out there. The key is to actively seek those challenges and seek new things and seek to become expert in various sorts of fields. And I think the key is that once you become good at something, you have to drop that and take on something you're not good at. This is the best thing you can do for your brain. The reason is because what you're doing is you're constantly building new roadways and pathways in the brain. There's a study that's been going on for decades now called the Religious Orders Study, where a bunch of Catholic nuns agreed to donate their brains for autopsy when they passed away. What the researchers discovered when they look at the brain carefully is that some fraction of these nuns had Alzheimer's disease. Their brains were physically degenerating with the ravages of this dementia. But they didn't show any of the cognitive deficits that one normally has. They didn't seem to be having memory problems and so on. It turns out it's because all these nuns lived in these convents till the day they died. They had social challenges and they had fights with their fellow sisters, and they played games with their fellow sisters, and they had chores and responsibilities, and they were doing stuff. What that means is even as the tissue, the brain tissue was physically degenerating, they were making new roadways and bridges all the time. And so that's what kept them cognitively healthy. We call that cognitive reserve. Contrast this with people who retire at 65, and they go home and they watch television, and their social circles shrink and so on. That's when you've really got concerns because you're not building the new pathways.

Speaker 1:
[15:09] Is there data to support that, that when you retire, if you retire early or if you retire say in your 60s, it increases your probability of an earlier death or cognitive decline?

Speaker 2:
[15:20] Almost certainly with cognitive decline because you're just not getting the challenge. At that point, you're just coasting on your internal model. It's tragic, but what happens often is that people's hearing gets worse. And so by the time they retire, let's say in their mid 60s, it's not really that fun for them to go out to parties and restaurants anymore because they can't quite hear. And so there are all these converging reasons why their social lives shrink. But it turns out, social life is one of the most important things that we can do for our brains because there's an expression we sometimes use in neuroscience, which is that nothing is as hard for the brain as other people, because you never know what the other person is going to say and do and how they'll react emotionally and so on. So you're constantly on your toes with other people. And if you're not doing that anymore, that ends up being a problem.

Speaker 1:
[16:07] Interesting. And as I'm 33 years old, so if you were to plot where my brain is on a graph of decline, is it the case that I should be doing as much as I can now to build as many pathways as I can so that when I'm 80, my decline sort of levels out in a better place?

Speaker 2:
[16:26] Oh, yeah. For sure. But this is true for many reasons, actually. Okay, so the truth is your brain peaked at two, at the age of two, because that's when you get the most connections between neurons, between these cells in the brain. You get this, at first you're born with these 86 billion neurons and they connect and connect and connect, and it finally becomes like a overgrown garden at the age of two. And from there, you're pruning, from there you're taking connections away. Now, it happens that that's not a bad thing. It's a good thing, because that's how you're resonating with the world that you are in, you know, 21st century London and LA versus, you know, 10th century Mongolia, because you're just strengthening those pathways that resonate and you're getting rid of everything else. Okay, fine. But over time, your brain cells die, you know, every time you hit your head on something or whatever, your brain cells are going down. So in that sense, you've peaked. But your crystallized intelligence that you've been building your whole life, you know, that keeps going. And you'll have decades ahead of you where you can start doing stuff. But yes, the reason to learn everything you can is because all that stuff caches out at various points in your life. When you're starting your next business, or you're, you know, wanting to do the next great thing where you're surfing the web of AI, you know, you'll say, oh, I learned this thing when I was 16. I learned this thing when I was 22. And these are paying off now.

Speaker 1:
[17:50] I think I heard Andrew Huberman say that one of the most fascinating discoveries of the last century is a particular part of the brain called the anterior mid-singular cortex. And it links to what you were saying a second ago about challenge and doing things that are difficult.

Speaker 2:
[18:05] Yeah. It turns out that area of the brain is involved and other networks as well. Because when you're doing something new and challenging and difficult, you have stress and anxiety. Your whole brain is active. Let's say I measure your brain, even with something like EEG, Electroencephalography. That's where I stick electrodes on the outside. Let's say I measure your brain and my brain. We're doing something that let's say you're an expert at. What's something you're really good at? Juggling. I don't know.

Speaker 1:
[18:34] Let's go for juggling.

Speaker 2:
[18:34] Okay. Let's say you're an expert juggler. Let's say I've never juggled. Okay. If we're both juggling, you're going to be much better than I am. But your brain will be less active. You won't have as much activity in your brain. My brain is on fire with activity because why? I'm trying to figure out, okay, where do I put my hand? How do I throw this? And blah, blah, blah. So when I'm in novice at something, my brain is using much more activity, not just the anterior made cingulate, but tons of activity all over because I'm trying to figure out the rules. I'm trying to figure out what's going on. You as an expert, you know, you got it. You don't need to burn much activity. This is what the brain's goal is, is to say, hey, once I've practiced something a lot, once I get something about the world, I'm going to burn it deeper and deeper into the circuitry so I don't have to burn a lot of energy on it.

Speaker 1:
[19:18] On this part of the brain, the anterior medsingulate cortex, Andrew Huberman was saying it's larger in people that do things that they basically don't want to do, hard things. If you spend your life doing things you don't want to do, then it happens to be bigger. And so people have now thought of this part of the brain almost like the willpower muscle, because for some reason, those that are doing hard things have bigger ones and those that are not have smaller ones.

Speaker 2:
[19:38] I mean, it wouldn't be so much the willpower muscle. It would be some indication retrospectively of how hard you have worked. Look, the fact is, you can see changes in brain size with lots of things. I'll give you an example. If you are a pianist, if you play piano, then we can actually see physical changes in your motor cortex. This is the part of the brain is essentially underneath where you would wear headphones. For those who are looking visually, it's this red part here. You actually get a bigger loop of tissue here than you do in a normal brain. Why? Because you're doing so much fine motor activity with your fingers, with both hands. In contrast, if you're a violinist, you're only really doing that kind of detailed activity with one hand, the other hand is just bowing. So you only get that activity here in one half of the brain for violinists. So I can look at a brain and tell, hey, is the person a pianist or a violinist or neither? I can tell just by looking at the visual cortex because you see changes in the brain based on what you do. For example, jugglers, people who play music, even you can tell this with medical students who study for final exams. You actually see changes in the distribution of their cortex.

Speaker 1:
[20:50] Why would it be getting bigger?

Speaker 2:
[20:51] The reason is the brain's devoting more real estate to that. In this case, let's say we're talking about fingers on a piano or a violin. The brain is devoting more. There's more relevance to that. And so it more real estate so that you can do it better in the future. Exactly. The key about the cortex, this wrinkly outer part, is that it is a one-trick pony. This is often overlooked because even this brain that I'm holding here is color-coded so that we think, oh, okay, that's clearly labeled this, that's clearly labeled that, and so on. But in fact, it's all the same stuff and it can change. So for instance, if you are born blind, then this area that we normally call the visual cortex gets taken over by the rest of the brain. If you're born deaf, then this part that we call auditory cortex gets taken over and gets devoted to other tasks. And so this whole system is very, very fluid. And this is what fascinates me about brain plasticity, is the way that we can be the sculptors of our own brains, because we can devote ourselves to particular things and have the brain's real estate get involved in that.

Speaker 1:
[21:58] So if I was currently someone that couldn't get out of bed, I didn't have a lot of discipline and motivation, and I wasn't very good at committing myself to hard things. With everything you know about the brain, is it possible to take a set of actions that will fundamentally change my brain and make me that type of person who runs marathons, who does hard things, who's motivated in disciplines, and who has high agency and attacks the world?

Speaker 2:
[22:21] Yes. But it's much more than simply resolve. Because, I mean, just look at New Year's resolutions. Yeah, by February, most people have dropped most of them. So it's really a psychology problem about figuring out, okay, what are the things that motivate me? So let's say you want to become a marathon runner. You've got that distant dream. You figure out, like, what actually motivates me in the short term? Who am I trying to impress? What am I trying to accomplish in my life? How can I structure things like this Ulysses contract that I talked about earlier, where I'm actually locking myself into a contract? Like, you know, I call Bob and I say, I will meet you every morning at 7, and we're going to run until we drop. Like, once I've committed to those sorts of things, that's how you set things up so that you do the right thing.

Speaker 1:
[23:09] It's a bit of a cycle, right? Because then my brain will adapt, and then presumably that will make it easier for me to run.

Speaker 2:
[23:14] Yeah.

Speaker 1:
[23:14] And then I'll run more, and then my brain will adapt.

Speaker 2:
[23:17] That's right.

Speaker 1:
[23:17] And the cycle continues.

Speaker 2:
[23:18] And it's not just your brain, of course. In this case, it's your body. You're getting better, you're getting stronger, you don't get us out of breath. And so all these things help. Exactly. But in order to keep the cycle going, you need to figure out what is spinning this flywheel, and what are the all the other things in your life, whether good motivations or bad, it doesn't matter. You just figure out what it is that you can do to get there.

Speaker 1:
[23:39] Are there some physical exercises that are particularly good for the brain from what you've understood?

Speaker 2:
[23:45] The general story is exercise is really important for the brain. I'll give you just one example of that, which is there's still this debate going on about whether we get new neurons in the brain. The general story has always been, you're born with 86 billion neurons and those slowly die with time. But in rats, for example, there is a little trickle of new cells, new brain cells. There's been a debate for a long time about whether that little trickle happens in humans or not. Still unresolved. But in rats, what you can see is that exercise causes the trickle to increase. If you stick the rat on the wheel and is doing physical exercise, you get more new brain cells. Now, we don't know for sure that this happens in humans, but lots of things about physical fitness and exercise matter a lot to the brain. This is nothing new. Exercise, sleep, diet. These are really important things for keeping the health of this organ.

Speaker 1:
[24:38] Is there anything else that's important to know for someone that is trying to change and improve and keep their brain in a healthy state as they age that we haven't touched on?

Speaker 2:
[24:47] There is something that all of us are thinking about, which is about social media and the Internet in general. I do think one of the interesting things about the Internet and social media is that if we were growing up in a village 500 years ago, you just know the people in the village and what they can do and so on. But let's say no one in the village was an entrepreneur or a neuroscientist. So we can't even picture that as a thing. We don't know anything about that. One thing that the Internet has done for kids growing up in the digital age is they have a lot more exposure to things. You have so much more exposure. I actually think this is one of the positive things that I would say about social media, is that you not only get exposure, wow, that kind of thing is possible and that kind of thing is possible, but you also have people teaching you how to get there. They say like, hey, I'm a fitness influencer, I'm going to show you exactly how to do the thing. Or you say, hey, here's exactly how you start a business. Or I say, hey, here's the route that you go through undergrad and grad school to become a neuroscientist. And that's great. I mean, there's just, there's so much more of a talent window now that everyone gets exposed to. So I think that makes a better brain.

Speaker 1:
[25:55] What are we doing to our children that you think we probably shouldn't be doing as it relates to brain development?

Speaker 2:
[26:01] Here's the thing that's really important about this debate is that nobody really knows. And I'll tell you why. It's because to do anything in science, when you're saying something about a group, you need to have a control group that you're comparing against. And when it comes to asking the question of, hey, kids growing up now with social media or the internet, how do they compare to other brains of kids who don't grow up with that? Well, we don't have a control group unless you look at kids who are incredibly impoverished or, let's say, Quakers who don't believe in technology. And with both those groups, there's a hundred other important differences. So you can't just say, oh, look, I'm comparing to this kid who grew up without food, and I'm going to say there's this difference. Who the heck knows why the difference is there? Even a generation ago, there's so many differences in terms of diet and pollution and politics and blah, blah, blah, like everything that you can't do it. So I only mention this because I think it's very important. A lot of people pipe off with things about, oh, the younger generation, their brain, this, that, but we don't actually know. And I will tell you that I happen to be a cyber optimist on this point about what growing up with the internet does for young people. I think it's going to make them much smarter than the generation that came before. And here's why. It has to do with the size of the intellectual diet that they can bring in. So when I was a kid, I grew up pre-internet. You know, I wanted to know stuff. So my mom would drive me to the library, which was 25 minutes away, and I would pick up the Encyclopedia Britannica, and I would flip through it and hope they had an article about the thing that I wanted to know about. And that's how I was able to get my little straw of knowledge. But now kids are growing up with access to anything they're interested in. This is so good for the brain. And from a plasticity point of view, the reason this matters is because change happens in the brain when you are curious about something. So when a kid asks a question to Alexa or Siri or whatever, and they get the answer, that sticks because they have the right cocktail of chemicals going on in their head. In contrast, when I grew up, I learned tons of just-in-case knowledge. I mean, that's all that the teachers could teach us is just in case you ever need to know this fact, here it is. But kids are in a really great situation now. So there are pros and cons to all this stuff, but I think I'm very optimistic about what this means for the warehouse of knowledge that kids can build up now. And by the way, I saw an interview with Isaac Asimov in 1988. He was the great science fiction writer who wrote Foundation and so many other books. And he was saying on the show in 1988, he said, Look, I envision a day when there will be one central supercomputer and every house will have a cable running to that supercomputer. And you can ask any question you want and it knows the entirety of humankind's knowledge on that computer. You know, what he was foreseeing here was the internet. He got the details wrong, which doesn't matter. But the idea is he saw how this would be so incredible for education because he pointed out, look, in any classroom, it's going too fast for half the kids, too slow for the other half of the kids. And if you could just pursue the sphere of humankind's knowledge, if you could enter in whatever door you wanted to, that's the way to do it because you'll be motivated. Now, he wasn't talking about brain plasticity or anything, but this is exactly what I'm seeing from a brain plasticity point of view. It really matters. I'll just mention something, which is a lot of people are concerned that, oh, with AI, we're going to get lazy. We won't know how to do anything anymore because we can outsource it. It just so happens that I love doing some improvement. I'm always fixing my house. I have 3X to myself in the last half year because of AI. Because I take a picture of something, I say, hey, I've never seen this kind of thing before. How does this work? Whatever. And ChatGPT says, oh, you do this and you take this out, and here's the bolt and blah, blah. It's not me outsourcing it. It's me being curious about something. And so I remember how to do everything now. I know how to do much more than I used to because I like it.

Speaker 1:
[29:59] What about the, there's been a couple of studies that have come out that say things like your brain's going to atrophy if you don't continue to write or if you just defer all of your learning to things like Chachiputti or other AI models. I guess one of the areas that I think in one of the studies, was it a Stanford study that everyone was talking about, where the participants used Google and AI and then they'd learned something themselves? But one of the things I've wondered is if I'm going through my business life and I'm encountering hard problems, and every time I encounter a hard problem, I drop it into an AI, the AI spits out a text-based answer, I copy and paste that and send it as my response. Presumably, there's some important part of the learning cycle, the neurological development that I'm foregoing there, I'm missing, that I probably should, said earlier about doing hard things. What I'm doing there is I'm avoiding the hard thing, which is thinking about it and trying to understand it. Yeah.

Speaker 2:
[30:56] Here's, I think, the really important distinction. There's vicious friction in our lives, and there's virtuous friction. So vicious friction is all the stupid stuff that you have to do like, hey, Steven, for your business, I need you to copy this spreadsheet over here and fill in all these cells and do your taxes or whatever. Okay. That, if we can push that off to AI, is massively important for improving human lives. There's really not benefit in vicious friction, but virtuous friction is, hey, Steven, I really want you to think about what is the optimal way to do this business? What is the best structure for this? How do we actually go D to C? How do we go B to B on this? What's the approach here that we're gonna take that you haven't done before? That would be amazing. That's virtuous friction because you're really using your brain to learn stuff that way. So that's the first distinction that matters is get rid of all the busy work. There's no honor in that. I mean, I'll just mention in the 1990s, there was this big debate about whether we should have kids use desk calculators or not. And thank god that finally got resolved. We let kids use calculators so that we can learn, we can spend a couple of days learning long division, but you don't have to spend six months on it because who cares? With the virtuous friction, there's real opportunity to surf the wave of AI so that you are figuring out these tough problems with the aid of somebody who cares about your problem and is willing to talk with you 24-7, and never gets tired of talking to you about it. And so, you are not just copying and pasting, but you're working with the AI to come up with ideas that were beyond what you would have come up with. Because I mentioned earlier about internal models, we have pretty narrow fence lines, and you can think of all these things, but you don't even know what you don't know. So if you can have somebody who's willing to talk with you, an expert in all of humankind's knowledge, willing to talk to you about it as much as you want, there's a real opportunity there to have a synergy where collectively, you both come up with a better idea than either of you could have alone.

Speaker 1:
[32:58] But is there a way for that relationship to take place that I actually benefit? Because in the example I gave, I take the question I was asked, I put it into an AI, it gives me an answer, I copy and paste it back to the person that asked me the question.

Speaker 2:
[33:10] That would happen if you really didn't care about the person asking you the question or the question.

Speaker 1:
[33:14] I mean, this is what a lot of people are doing. I get so many emails, because we interview a lot of candidates who join the business, so I see tens of thousands of emails, sometimes a week. I mean, I don't see all of them, but the ones that I see, I often know that... Because we've sent them five questions or a task, and I look at it and go, I can almost predict the exact model that sent it to me, because they will have a different personality. So I go, oh, this one, the person put it into Gemini, or this one, the person put it into ChatGBT.

Speaker 2:
[33:39] Yeah, exactly. And it's full of contrastive construction, like it's not this, it's that. Yeah, exactly. And then the M dash is exactly.

Speaker 1:
[33:47] I'm really asking, is the person that did that benefiting from it?

Speaker 2:
[33:50] No. Well, no, but for a couple of reasons. One is that you know, and it triggers your red flag, and so that does not do anyone any good. I see so many of my colleagues posting on LinkedIn. These are obvious AI things, and it irritates me because I feel like I'm not gonna spend my time reading that because of, I call this the effort phenomenon, which is in psychology, we care a lot about things that seem like they took a lot of effort. And there's something about seeing an AI post that's just irritating because it's so obviously AI.

Speaker 1:
[34:22] That's a really interesting idea, the effort phenomenon.

Speaker 2:
[34:25] Yeah, I've been writing about this for a while because it turns out there are psychology studies where if I offer you two pieces of art, and one of them looks like, you know, let's say it's a red dot in the middle of a white canvas, and the other one is, you know, bottle caps stacked up and glued in this great shape or whatever. You'll pay much more for the thing that looks like it took a lot of effort. People will pay more for a real diamond than a synthetic lab-grown diamond, which is exactly the same thing. It's just carbon in the matrix, but they feel like, oh, Mother Nature took hundreds of millions of years of effort on this one, but not over here. It just took a few days in the lab. So there's a million ways where we care about that a lot. When it comes to this AI thing, yes, anybody who's just popping back some thing to you, it just feels like, all right, they took the path of least resistance and I'm not so interested.

Speaker 1:
[35:13] I want to know from a neuroscience perspective whether they benefit.

Speaker 2:
[35:17] Presumably, they don't benefit too much either. I mean, it's hard to know exactly how many times they went back and forth with it. They could have said, hey, Chachapiti, thank you for this, but I'm more of this person. When I really think about it, this is the thing that inspires me, not what you suggested. So somebody could put effort into it, it's just that we can't know that when we get the AI response.

Speaker 1:
[35:37] It seems to be a pretty consistent principle of life generally, that when you do something hard and when you put in effort, as you say, you tend to get back an equal and opposite return, relatively. So I would think that if I fought through, maybe even using AI as a companion, but I fought then to write it out myself instead of just copying and pasting. One of the things I've learned from doing this podcast, all these episodes, is everything is a trade-off. Yeah. And if you don't know what the trade you're making, then you're often at great risk. And so some of my friends will say, oh, I take this pill and it's amazing. It does all these things for me. It's the most amazing thing ever. I can just focus for 24 hours a day and I'm so productive now. And I go, what's the downside? And they go, oh, there's no downside. And I go, hmm. So that's what I mean. It's even worse when you don't know the trade you're making. And so with AI, I go, okay, if it's making me wildly more efficient or productive, what trade am I making?

Speaker 2:
[36:34] I think understanding this, it's probably not two categories, but a spectrum from vicious friction to virtuous friction. But really paying attention to what is virtuous friction, what would make me a better person if I actually put the effort into this. That matters a lot. And I will say for us as professors, for you looking for job candidates, we need to change how we're asking the questions. If we just say, hey, answer these five questions, of course, everyone's going to use it. For example, in my classes at Stanford, I don't have people turn in a final paper anymore. That was from previous life before AI. Now I have them do projects as their final thing, where they're running an experiment on something. Of course, they use AI to help them generate some of the issues, but they have to deal with other people and look at the data and figure out what's wrong and that kind of stuff.

Speaker 1:
[37:23] I worry that it's getting into the age of the whole calculator thing you said where maybe, actually, it is now you need to assess them on their ability to use the AI not to succeed without it.

Speaker 2:
[37:34] Yeah, great. This is the whole game for all of us, I think, is figuring out how to surf this wave of AI where it can make us superhuman. We can just be better, so much better than anything we ever were doing before because we have immediate access to knowledge and facts that either we had forgotten or we never knew existed. We should be surfing that wave. I totally groove you on that point. If you can figure out how to change your interview questions so that you're seeing, hey, can this person really get the speed?

Speaker 1:
[38:01] With everything you know about learning and neuroplasticity and expanding one's brain, is there anything else you can say to the audience about how they should use AI so that they become a superhuman?

Speaker 2:
[38:13] Interesting. Look, I have been talking to my friends about this issue a lot lately, and I mentioned how I've become so much better at home improvement stuff. Each one of my friends has something like that. We're like, hey, you know what? I've actually gotten so much better at this super random thing that I never even thought about it explicitly. But because I'm always asking AI questions about that, and it's giving me the answers, it's not simply that it gives me the answers and I forget it. It gives me the answers and I remember it. I become better and better because it's like the way that Alexander the Great had Aristotle as his tutor and could ask him anything and learn great stuff from him. We've all got Aristotle in our pocket now and we can become better at the things that we want to do, the things that resonate with us for whatever reason.

Speaker 1:
[38:59] If everyone's got Aristotle in their pocket, how does one create an edge?

Speaker 2:
[39:03] I think it has to do with we're all just going to be running faster. And the same way that when Steve Jobs introduced Apple computers, he said, this is like a bicycle for the mind. What he meant by that was that for millions of years, we've been walking by pitily and then just in the last nanosecond of evolution, we invented the bicycle. And suddenly, humans can move faster because of the bicycle. And he said, having a personal computer is like a bicycle for the mind. And I think of AI now as like a motorcycle for the mind. It allows us to move so much faster. So now it's a motorcycle race. And there will be people who are much faster than other people because they're really using that optimally.

Speaker 1:
[39:42] And that's what I mean. It's like, how do I create an edge versus my whoever I'm competing with in whatever industry I'm in?

Speaker 2:
[39:48] Well, for sure, the people who are just copying and pasting the AI slop, that'll be easy to beat that crowd. But otherwise, I think it's just a matter of, hey, these are the newest things. It's like in history when the new sword gets invented or the new gun or the new cannon, you have to keep improving and using that. And that's what's going on now with AI.

Speaker 1:
[40:08] And from a neuroscience perspective, if I wanted to use AI to, based on all these things you've told me about novelty and all these other points and expand the connections across my brain and give me a big cognitive reserve, what might I install as a practice every week when I'm speaking to my AI?

Speaker 2:
[40:25] Oh, ask it questions that you're curious about, about anything, just asking questions. Here's one thing I do all the time. I'll say, hey, I've been thinking about this. On my podcast, I do a lot of monologues. And so I'll start talking to it and I'll say, hey, I've got this idea that I'm thinking about. What if blah, blah, blah, blah, blah? And then I'll say, here's my idea. Give me pros and cons. Tell me why this is wrong. And I do that pretty much with everything that I ask it, if I'm proposing some stupid seed of an idea. And it really gives me the counterarguments. And I really engage with it. That is the important part, I think. And by the way, I just want to say, I think for the next generation that we're teaching, there are really only two things we can teach because all the details of, hey, let's teach computer programming or something, it's probably already gone as a useful thing. So what we can teach is critical thinking and creativity. That's it.

Speaker 1:
[41:20] I think that's such an important point, this point about asking your AI why you might be wrong. I think I had most of my paradigm shifting moments when I've come to an AI model that I was using with very high conviction. And the prompt that always I think is most expansive in terms of my intellectual knowledge is when I say to it, be brutally honest about your opinion, think for yourself and be objective, and tell me where my blind spots are. There's something innate within us all where we don't actually want to be wrong. We often, I think, as a natural reflex, and this is why people get really sort of trapped in echo chambers of political opinion. And Leon Fessinger talked about the side of cognitive dissonance when something you believe contrasts with new information and how it makes you feel uncomfortable. There's something, when I type that out, when I love the idea or the thing I've written or the memo I've written, this new idea, and I go, go on, tell me why I'm completely, completely wrong, and it eviscerates me, it is both uncomfortable, but feels incredibly important, because then it's like I've grown. But these AIs, they're programmed almost to kiss my ass.

Speaker 2:
[42:29] Yes, although, you know, Chachi Petit released a very sycophantic version, I don't know, maybe a year ago, meaning it complements you, you give some idea and it says, oh, Steven, that's the best idea I've ever heard, you're a genius, and blah, blah. And that didn't last very long, that model, because nobody actually liked it. So you're exactly right. And I'm sure most listeners know this, but you can tell your AI to be brutally honest with you all the time. You can tell them to do that all the time, and it'll do that. So you can you can establish the kind of person that you're talking to. Here's the thing. You're right, of course, people don't like to be wrong. It can be socially embarrassing, it can be uncomfortable. And yet, there's something very different when you're talking to your AI. It's a very private thing. And you say, hey, tell me why I'm brutally wrong. And when it tells you, you think, oh, thank God it's telling me that instead of like a real human. So I think a lot of that is alleviated with AI. We don't feel as bad about being wrong there.

Speaker 1:
[43:24] As you were saying, I just went on ChatGPT, and I typed this in. Is my joke funny? And the joke I typed in is knock knock, who's there? A lettuce, lettuce who? Lettuce in and I'll tell you. Okay, you didn't laugh, I didn't laugh.

Speaker 2:
[43:38] Okay.

Speaker 1:
[43:38] ChatGPT said, yes, it works as a joke, a solid structure, uses the classic pun payoff, which is exactly how most knock knock jokes land. And then it's done a laughing emoji. I then said, be brutally honest and completely objective. Was that funny? It said, it's not very funny.

Speaker 2:
[43:55] Interesting. That's interesting because it depends, right? A little child actually finds that joke funny. And for a little child, they then get to repeat that to their classmate and they're learning how to do a joke and so on. So I'm not sure I think there's a single answer to whether that can be funny or not.

Speaker 1:
[44:14] But the interesting thing is just reinforcing what I already believed. And therefore, when we think about growth or having a growth mindset, if someone's just always reinforcing what you already believe and know, I don't know if it's ever going to be a growth mindset. I mean, I just asked it again, I said, be really honest. And it said, it's absolutely not funny.

Speaker 2:
[44:31] Yeah. But remember, all it's doing is it's just, it's a statistical parrot. And so when you say, be really honest, it thinks that's what it should answer.

Speaker 1:
[44:41] I said, be even more honest. It says, it's basically not funny at all and shouldn't say that to people. And it says, comedic originality one out of 10, likelihood of real laughter one out of 10.

Speaker 2:
[44:51] Well, that's quite good. That's quite accurate. Here's the thing. I've been thinking about this issue a lot about whether AI can be funny. And at the moment, it can't be. It's great at repeating jokes, but it doesn't understand humor on its own. What it knows, if you ask it to make up a new joke, what it'll do is it'll have the first guy walks in the bar, then the second guy walks in the bar and does X and that establishes the pattern. But then the third guy, it'll have break that pattern, which is the structure of a joke. But it doesn't know how to break the pattern in a way that's funny. It's just the third guy does some random thing. So AI as it stands now, the way it's structured with what's called the transformer model, doesn't know how to think of the punchline and then go back and make the joke lead to that punchline.

Speaker 1:
[45:37] A lot of people don't either. I say that not in an offence way, but just to say that like, I don't know, I often hear the claim that AI could never be creative.

Speaker 2:
[45:46] It's massively creative. Here's why, creativity in the brain, all creativity is, is you absorb your world, the whole world around you, every experience you've ever had, and then you're bending and breaking and blending those cognitive concepts into new remixes. That's all creativity is, and you're doing that all the time, whether you're just trying to think of what to say next or what recipe to make next or what patent to do or what company to start, you're just remixing the stuff that you already know. And that's why, I don't know, take Beethoven. He could have written any kind of music that was being done anywhere in the world. But of course, he didn't like that's what he grew up with, there's the music in his local culture and so on. What we have now is a much broader diet, as I mentioned before, where we can get everything going in. But the point I want to make here is that AI, that's what it does. It remixes stuff that's come in. So AI is massively creative. The part of creativity that AI can't do right now is selection, meaning it can generate 100 pictures, but it doesn't know which one to pick. It doesn't know which one is going to be the most appealing to you, but it can remix beautifully.

Speaker 1:
[46:53] But neither do humans, right? So if I asked an intern to make me 100 pictures, I mean I could get my AI to pick one, but it wouldn't know what the intern or the AI wouldn't know which one I loved.

Speaker 2:
[47:03] The intern would have a much better shot at it. And as the intern is there for a while, he or she becomes quite good at getting, oh, okay, I get Steven's taste. It would be this one.

Speaker 1:
[47:12] And AI can't learn that? What my taste is?

Speaker 2:
[47:14] I don't think the AI could learn that about visual images because when it generates the pixels, it's doing this magical stuff under the hood where it's deciding which pixels and how they diffuse together and mix the image. But it doesn't know how to read that image like, oh yeah, the way this is and blah, blah, that'll really appeal to Steven. It's not seeing the image except as a bunch of pixels. You need to be a human for that.

Speaker 1:
[47:37] Because I feed, I was doing an experiment recently where I took my behind-the-scenes channel, which is a 30-minute-long video, and I dropped it into Gemini. And I'd say things to it like, predict where people would drop off on the video. And then we upload the video to YouTube, we get the retention data back. And Gemini, in the last two times that I've done it, has a 100% record of knowing that at minute seven, where in-set person talked for too long, and might have been a bit more selly, might have tried to sell a hoodie, for example, in that part, it would say, you're going to lose people here. And it would very accurately say why. It would say, because you talked for 74 seconds, and it was jarring versus the moment that came before it. And when I feed the AI, I don't know, let's say thumbnails and say, which thumbnail is going to perform the best? We did a test recently where we put four thumbnail test results that we knew the answer to into Gemini and said, which one's going to win on YouTube baby testing? And it got 100% accuracy of predicting on data we already had, which one would win. And so now, I don't know, I keep having these paradigm-shifting moments where only humans could do that. But increasingly, the AIs that we're experimenting with are making better creative decisions, then now I can make myself as if the outcome of that creative decision is which one is people going to prefer. I'd say a year ago that wasn't the case.

Speaker 2:
[48:58] Okay, so I totally agree with you, but let me just mention one thing, which is fascinating, which is that often the way it's doing it is not at all the way that a human would do it, which might be fine for our purposes, but the data and the way that it's picking up on it, it might be something about how much green was in the YouTube thumbnail image, or how much red or whatever the thing is, or just noticing that there's big font versus smaller font or whatever. The next time you try it, it says, oh yeah, this thumbnail is going to be great, and it's some ridiculous thumbnail that doesn't make any sense to you as a human, nor to your fellow humans, but it might say, oh yeah, this would be great because it's judging things on very weird dimensions that we can't always see.

Speaker 1:
[49:40] The example you gave about maybe it's because the text is bigger or the color red, but those are the same factors we think about. As a human, we think if we know that if the font is bigger, it performs better. We know that red performs better than green.

Speaker 2:
[49:51] Quite possibly, but here's the interesting thing. Human art constantly evolves and all AI is trained on is what has been done before and what has worked. And so if I asked it, let's say we composed five different songs and said, hey, AI, which song is going to be better? It's going to say something that's right in the middle of the distribution of popular songs. But that's not what actually makes it next year and the year after. It's new things, it's new twists that nobody has seen before. That's what we love. That's what we seek as consumers. And so because AI can only be trained up on what already exists, it's never going to get the new thing at the edge.

Speaker 1:
[50:28] But if the AI was asked to... Because I think the reason why a new song would break out, let's say a new Drake song comes out and it's a smash hit. If we think about that distribution curve, so like if I draw it on the graph, you're saying that this middle section here is what sort of AI will aim at because it's the popular and the known. Well, if I tell AI to make a million songs, which is kind of what I guess is what's going on every day around the world, if you scattered them on this graph, it like, you know.

Speaker 2:
[50:57] Absolutely.

Speaker 1:
[50:59] And then the AI's most unusual song ends up taking off. But it's just because there's so many of them.

Speaker 2:
[51:04] Quite right. But that's the human selection part that we're seeing over there. If you asked, okay, out of all of these dots, which do you think AI is going to be best? It's going to have to tell you the middle of the curve. But the surprising part is the part that you circle there, which is the one on the edge is the one that humans like. Why? Because we're constant novelty seekers. We care about the things that are new.

Speaker 1:
[51:24] I think the point I'm getting at is that the creation of it, the creative process is still the same, which is like AI or humans just trying a bunch of shit, and then the world going, oh, that one.

Speaker 2:
[51:39] I totally agree. This is consistent with what I was saying, which is that AI can be massively creative in terms of the generation of something, but you need humans to do the selection. I'm only arguing the point that AI is not good at saying, okay, I've generated 100 songs, this is the one humans will choose. We end up saying, hey, wait, this one is just weird and unique enough that I really like that.

Speaker 1:
[51:59] It's interesting because when you speak to record labels about music, what they're often doing is getting a format of a song that they know will work. So they're like, right, so it's going to be eight bars here, it's going to be this here, you're going to have a chorus, that's like hooky, it's going to come back round, it's going to build up pace, and there's like a rough format to it. And it's no surprise that someone like Ed Sheeran has written so many songs for so many fucking people. When I spent some time working with Sony, they had a brand new boy band in the wake of One Direction, and when I sat with the boy band and was introduced to myself, they said to me, oh yeah, so here are the boy band's first three songs, and Ed Sheeran has written all of them. And I was like, what? I thought like, they're like, no, Ed Sheeran's written all of them, and then what we do is we give them to the boy band, and then the boy band sing them, and they're pretty much guaranteed to be hits, because Ed Sheeran has like a formula. The way he writes is really in like vogue right now. People tend to think a lot that the songs that are number one in the charts are there because, just because someone had Creative Genius, and of course that is the case sometimes. But there is a lot of this writing going on, and then handing the formula over, because someone has cracked the code of a hit.

Speaker 2:
[53:11] Right, but here's the thing, and you know that we all know this, which is that the code never lasts. So, humans have this pull, where they're always seeking things between novelty and familiarity. So we like things where we recognize the brand, and we recognize what the singer has done before, but there has to be novelty, or else we're not gonna go for it. We're not gonna listen to that boy band for the next 10 years doing the same song over and over. So, you're of course right that we want a bit of familiarity, we wanna be anchored, but we definitely seek the new. This is what humans always do, this is why car companies always release the next model, even though the current model is perfectly fine, this is why haircuts evolve, this is why fashion evolves through the years, because we always care about novelty.

Speaker 1:
[53:56] And the other thing in the music industry that I think is also creating a hit is I was reading many years ago about some psychology, which you'll probably know much more about, that says exactly what you just said, which is, we love something when it is familiar, but new.

Speaker 2:
[54:11] Exactly.

Speaker 1:
[54:12] So, the way that the record industry and the radio industry make something familiar is they blast the same song at you on every radio station for a long period of time until it breaks past being just novel, just new, and it becomes familiar. And I saw this graph which shows that a song that you'll love is right there in the middle. It's new enough that you're still into it, but it's familiar now because you've heard it so many times that you love it. And if anyone listening, the first time you hear a song, you might not love it as much as once you've heard it like 20 times. And then at some point, you've heard it too much. And it comes back down the other side of the curve where it's now too familiar.

Speaker 2:
[54:52] Yeah, that's exactly right. And so we're always seeking that tension in the middle. And companies run into this all the time. Sometimes they try things that are too novel that just completely fail. Coca-Cola tried this long time ago introducing New Coke and no one liked it or whatever. And other companies like, what was that company? Blackberry with the little thumb things where you can press the physical keyboard on the phone. They failed because they wouldn't change fast enough. But anyway, companies that make it are always staying in that sweet spot.

Speaker 1:
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Speaker 2:
[57:41] It's similar, but it's not the same, which is why with AI you get what we call jagged intelligence, meaning that it can do something so extraordinarily smart, and then in the next moment give an answer that's weird and doesn't make any sense. AI still is doing this. It's not even thinking like we think. Okay, why? It's because AI as we think about it now, really started, of course, decades and decades ago, where people said, look, you've got all these billions of cells, neurons in the brain that are connected to each other. What if we ignore all that complexity and we just say, look, imagine that you have units that are connected to each other. We're going to forget about a single cell in the brain is as complicated as a city. It's got the entire human genome, it's trafficking millions of proteins. Let's put all that aside, just imagine it's a circle and it's connected to other cells and each connection has a certain strength. That's what we call an artificial neural network. Now, that went off in its own direction. The amazing surprising part is how successful it's been to just get rid of all the detail. But it's still super different than what human brains are like. Just an example, this thing I mentioned at the very beginning about how we're a team of rivals under the hood. You've got all these different competing neural networks that are trying to drive your behavior and so on. The fact that we're emotional, the fact that we are driven by different appetites, whether food or sexuality or whatever it is. But you're a ChatGPT, you don't want that in the ChatGPT. So it's just an artificial neural network, many layers deep, and it's extraordinary at what it does. But it's so different than a human. For example, the fact that it's read everything on the planet and remembers it, and you have it, you would need to lead a thousand lifetimes to read that much. Of course, you wouldn't remember much of it. It's very different is the point I'm making. They both have converged on something that we would call intelligence, but it's a pretty different structure, even though AI was inspired by the brain.

Speaker 1:
[59:33] That's what Jeffrey Hinton was telling me. He was telling me that much of the breakthroughs that have made AI what it is today came from understanding how the brain works.

Speaker 2:
[59:42] Yeah, but that's interesting, because Hinton is incentivized to say that, but a neuroscientist...

Speaker 1:
[59:48] He's incentivized to say that.

Speaker 2:
[59:50] People doing AI, of course, are paying a lot of attention to how this is structured like the brain, because before that, people would do things like probability theory or rules. They were trying to do AI by trying to say, okay, if this, then do that. But when people started doing artificial neural networks, that led to a lot of success. I'm only pointing out that the artificial neural network looks a lot like the brain on the surface. You say, hey, you've got units and you've got connections. But beyond that, there's a lot of differences.

Speaker 1:
[60:21] And why are those differences significant as it relates to what's possible?

Speaker 2:
[60:25] Because what we've developed is a new species, essentially, that is incredibly impressive, but it ain't a human brain. It's different than a human brain. There may be all kinds of similarities, things that we even come to understand are similar, but there are so many differences. Here's an example. Humans do one trial learning all the time. Meaning, if I say, or when you were a kid and your mom said, hey, Steven, this is a pomegranate, you say, OK, pomegranate, got it. But when you're training up an artificial neural network, like at OpenAI or Gemini or Anthropic, you have to give thousands or millions of examples of everything for it to learn anything. There's no one trial learning on those systems, and they have to be trained at the cost of billions of dollars. Then they can do a run where you ask a question and it answers the question. But brains in the real world don't have that luxury of having a training phase and then an action phase. We have to learn on the fly. It's very different.

Speaker 1:
[61:23] So I guess the point in question is, does it change what's possible for the brain versus the artificial neural networks we see in AI? Like is there some limitation based on what you've just said, that means this brain in front of me, this human brain in front of me will always be better than the AI at something? Because I'm trying to track forward about what this means for the future of humans.

Speaker 2:
[61:46] Yeah. I think it's an interesting question that we'll have to see, but it's clearly the case that we know what it is to be a human from the inside. When I'm making a model of you and who you are and you're making a model of me, we have assumptions about what it is like to be a human. AI only watches human behavior from the outside, and so it can tell a lot of great stuff, but it doesn't really know what it is to be a human. If I ask it some question about what would it be like if this or that happened, it can answer based on observing lots of things. But it can only ever know from the outside.

Speaker 1:
[62:22] In terms of why that matters.

Speaker 2:
[62:24] Yeah.

Speaker 1:
[62:24] Because if I ask my AI, my fiance is being like this today, or if I ask my best friend, my fiance is being like this today, if both of them give me the same useful answer, it doesn't really matter what's going on.

Speaker 2:
[62:34] I agree with you. I agree. I am actually writing a new podcast on this about what you can tell from the outside and what you can tell from the inside, and whether that difference matters. Look, an example is, last week I got a Tesla with full self-driving, and I was watching as it was full self-driving, I was coming up on a very complicated traffic situation. I thought, well, what's my car going to do here? How is it possibly going to understand? But what it did is it slowed down and came to a stop, which was exactly the right thing. I thought, oh, that's interesting. Algorithmically, it might think of it very differently than I am thinking about the situation. Doesn't matter. It comes to the same conclusion, ends up in the same place. We have yet to see where these differences matter, and what it is to be a human. But I can tell you one thing, we care about other humans. So, here's my little prediction, is that there's going to be actually a renaissance in things like live theater and live performances. When things first came out like Napster, everyone thought, okay, that's the death of concerts. That's the death of musicians. But in fact, you look at a Taylor Swift concert, gajillions of people there, paying lots of money. Everyone loves the thing. Why? Because they're going to see the real Taylor Swift in person. And I have noticed, I give a lot of talks on the road, I have noticed an increase in the number of talks since AI came out a few years ago. The first thing that my friend said to me is, hey, did you know, David, that you can use 11 Labs and Hey, Jen, and you can make an avatar of yourself and you can use your voice and use Chachi Petit to generate what you're going to say and have a fully virtual version of you? He said, my friend who gives talks to you, he said, maybe we can start doing this and do virtual talks. I said, nobody's going to want that. In fact, what's happened is more people want to fly us across the country to have a stand there in person because it really matters to see fellow humans. And I think that's only going to increase.

Speaker 1:
[64:25] I completely agree with you. I think it's so funny, I did a post on my LinkedIn the other day saying that maybe the like interesting paradox or interesting outcome of AI is that every other iteration of technology made us less human. And maybe the intelligence now has gotten to a point where it's now forcing us to be more human because that is all that kind of remains in a way that maybe the technology has gotten so good. Like social media didn't make us more human in any capacity, but maybe this is the moment where it goes, we've got this now. Go do what only you as a human can do, which is like go out there Taylor Swift and sing in front of people IRL. Go and do something in the real world. Even for like nurses and doctors, maybe they shouldn't be filling out admin paperwork anymore. Maybe they should be holding your hand and giving you in real life care that only a human could do.

Speaker 2:
[65:18] I totally agree.

Speaker 1:
[65:19] And so maybe that's the positive upside to all of this is finally, we've been on this journey with technology and finally it's delivered upon its promise.

Speaker 2:
[65:27] I totally agree. And by the way, you know, AI relationships, by one estimate, there's a billion people having relationships with AI. Like a girlfriend or boyfriend kind of thing.

Speaker 1:
[65:37] Okay.

Speaker 2:
[65:37] And so for people like us who grew up before that existed, we think, oh my gosh, that's weird. But in fact, I think it might become helpful because it can be a sandbox as long as we have the proper feedback. In the end, we have millions of years of evolution driving us towards being with the person you love, touching another human being, watching the stars, taking her out to dinner with your parents. We care about that. And so this worry that people sometimes talk about, about, oh, people are just going to be on their phone with their AI relationship, I don't think is realistic for almost everybody. Because it gives us the chance to hopefully sandbox some things about relationships and get over some dumb things with relationships. And then we can actually be with our fellow humans.

Speaker 1:
[66:20] Counter-argument would be that maybe there's going to be a bifurcation, a splitting of society where some people are going to become even more addicted to the technology because the AI is now much smarter at retention. Like I know exactly what I need to say to you based on your brain, Dr. David, to make you not put this device down.

Speaker 2:
[66:42] Yes. But fundamentally, I want to be in contact with my wife. I mean, that's the evolution of hundreds of millions of years, is that I want to make babies, and I want to go and eat dinner with somebody. And as much as I might find my phone appealing, I'm not going to sit it across from me at a nice Italian restaurant and sit there like that.

Speaker 1:
[67:06] A lot of people do. Me and my friends are at restaurants, because we have a rule where we don't touch our phones when we're at date night. And I have to look around and I'm like, oh my God, how are all these guys getting away with this? But do you see what I'm saying? Some people, they just have a different sort of proclivity. They have a different wiring, which means that instead of doing the hard thing of going out there and going on a first date and being rejected, pornography or a virtual wife might be a substitute for that.

Speaker 2:
[67:33] Yeah. No, I agree with you. There will be bifurcations. One question, I don't know the answer to, but one question is, what would that person have done in previous generations? Is it really the case that person would have gone out and had a great, successful relationship? Or would they always have had troubles relating to people?

Speaker 1:
[67:52] I sat with a few neuroscientists and experts that studied dopamine. Dr. Anna Lemke was one.

Speaker 2:
[67:59] She's my colleague.

Speaker 1:
[68:00] She's a colleague. She talks a lot about how we all have different types of addictive substances. We will think heroin is addictive for everybody and alcohol is addictive. I used to think of it on a spectrum, but actually she said, for her, her addiction was romantic erotic novels. She almost ruined her relationship because of erotic novels, which is something that I would read and just throw in a bit. Maybe this new technology is particularly addictive to a certain type of person.

Speaker 2:
[68:31] Yeah, I think that's exactly right. I think we're going to see that with everything. The wild part about human society is that there's so little that we have in common, meaning everybody is really different. This is something I've studied in my lab for decades, is this issue about what are the subtle differences from person to person. Not big things like, oh, this person is a psychopath, this person has schizophrenia. But the more subtle things, I'll just give you an example. If I ask you to imagine, to visualize, let's say, an ant on a purple and white tablecloth crawling towards a jar of red jelly, do you see that in your head like a movie or do you have like no particular picture at all or somewhere in between? What do you experience?

Speaker 1:
[69:18] An ant crawling towards a jar of jelly.

Speaker 2:
[69:20] Yes.

Speaker 1:
[69:22] Yeah, I see a big black ant and then this jar of jelly is like overflowing down the sides with a wooden lid on top of it and the ant is almost there.

Speaker 2:
[69:30] Oh, wow. Okay. So you have a, okay. So what you have, I'm just guessing where you are, but you are on the end of the spectrum that we call hyperphantasia, which means you have very rich visualization. You're like seeing it like a picture or a movie. Is that accurate?

Speaker 1:
[69:44] Yes.

Speaker 2:
[69:44] Okay. I happen to be at the other end of that spectrum called aphantasia, where I don't have any visual images at all. There's no, I don't see things visually in any way. And it turns out the whole population is spread evenly along the spectrum. I'll just give a quick side note, which is that for many years, I've been talking with Ed Catmull about this. He's the guy who started Pixar films. So he's got all the patents on how to do ray tracing and how to make these beautiful animated characters, right? Ed Catmull is aphantagic, like I am. And when he learned about this, he got really interested and he gave the questionnaire to everybody at Pixar. It turns out many of his best animators and directors are aphantagic. They don't picture anything inside their heads. Now this seems surprising and strange, right? But it turns out that if you are an aphantagic kid, you're going to become better at drawing because you have to really pay attention to the subject out there and really have a dialogue with the page with your pencil. Whereas a kid who's hyperphantagic might say, I know what a horse looks like, and just draws it. So anyway, so it turns out there's a real spectrum across the population, meaning inside your head and my head, we're having pretty different experiences. But I've studied this along dozens of different axes, and everyone's got different things going on. Just as one example, do you know about synesthesia? Have you ever heard of this?

Speaker 1:
[70:59] Is that forgetting or something?

Speaker 2:
[71:00] No, synesthesia is having a blending of the senses. So someone with synesthesia might look at letters, and it triggers a color experience in their head. They look at J, and that triggers green, and they look at M, and that triggers blue, and whatever. It's different for each person. Or you might hear music, and it triggers a visual experience. Or you might taste something, and it puts a feeling on your fingertips, or whatever. It's a blending of the senses. At least 3% of the population has this. It's not a disease or a disorder. It's just an alternative perceptual reality.

Speaker 1:
[71:28] So if you have aphantasia, does that mean that you can't picture your kids?

Speaker 2:
[71:33] It means that the way I picture them is not visually. I mean, there's sort of a very general... But for me, it's more motoric imagery and audio imagery. I'm imagining talking to them and being with them and being close to them and probably some olfactory imagery, meaning how they smell and the whole thing. Like I have a very rich notion of what it is to be with my kids, but it's a pretty terrible visual picture. Not much there.

Speaker 1:
[71:59] So imagine people at home have done that same experiment while they were listening. Could they picture an ant walking towards a jar of jam? And if they find themselves on the a-phantagic, I can't remember the two.

Speaker 2:
[72:12] A-phantagic, yeah, or hyperphantagic.

Speaker 1:
[72:14] So hyperphantagic is you can picture it, a-phantagic is you can't.

Speaker 2:
[72:17] Yes.

Speaker 1:
[72:18] What does that potentially suggest about?

Speaker 2:
[72:21] Nothing. Now here's the interesting part. So we've done lots of studies about what this translates to in terms of your capacities in the world. Nothing. Why does it translate to nothing? Because you can accomplish tasks in a hundred different ways. And so some people are doing this very visually. Other people are doing it where they're like picturing it with their motor systems. Others are doing it, as I mentioned, with sound or smell or whatever, or others are doing it just purely conceptually, just thinking through how the steps would go. But there's nothing obvious other than this thing I mentioned about visual artists often being a-phantagic. Otherwise, you can kind of accomplish anything.

Speaker 1:
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Speaker 2:
[75:19] Yeah. Yeah. It's actually after millennia of people debating this. This is the cool part. So, okay, remember I mentioned earlier that if you go blind, the visual cortex at the back of the brain gets taken over by hearing and by touch and by other things. And it's no longer visual cortex. Well, what we realized is that because we live on a planet that rotates into darkness for half the time, the visual cortex, the visual part of your brain is at a disadvantage. So what I realized is that the purpose of dreaming is to defend the visual territory from takeover from the other senses. So every 90 minutes, you've got these, you've got this very ancient thing in your midbrain that shoots random activity into the visual system. And only the visual system, only this very tiny part of the visual system, every 90 minutes you just blast random activity in here. And the reason is you are just defending that territory against takeover. Now, the reason that all this came together is because our colleagues at Harvard did an experiment where they took normally sighted people and they blindfolded them tightly for 60 minutes. And it turns out that 60 minutes was sufficient for the visual cortex to start responding to sound and to touch. You could start seeing that takeover happening after 60 minutes. And that's when we realized, wow, this part of the brain really needs a way of defending itself. Now, because the brain is a natural storyteller, if you blast random activity in there, it'll put that together in some sort of visual story about what's happening, mostly based on what connections are hot from the day. But that's why we dream.

Speaker 1:
[76:57] So we dream to stop the other parts of our brain overtaking the visual part of our brain, overpowering it, and I guess ultimately making us go blind.

Speaker 2:
[77:08] Yeah, that's exactly right. If we lived on a different kind of planet that did not rotate into darkness, then we presumably wouldn't dream.

Speaker 1:
[77:16] Would we even need to close our eyes?

Speaker 2:
[77:18] I mean, not necessarily. Yeah, it may be that in the sleeping state, in the state of deep sleep, the brain is doing particular things like taking out the trash and cleaning some things up. That might be necessary. Who knows? But yeah, I don't think we would need to dream. We wouldn't need to blast random activity in there if our eyes were always open, for example, and it was always light out.

Speaker 1:
[77:39] Are there other examples in the animal kingdom? Yes. Support this.

Speaker 2:
[77:44] Yes. Thank you for asking that. This is why this new theory about why we dream is taken off, because we can make quantitative predictions across animal species. So for example, in our last paper, we looked at 25 different species of primates, apes and monkeys, and we looked at how plastic their brains are. In other words, how flexible the whole circuitry was, and how much they dream at night, which you can tell by looking at rapid eye movements. When you dream at night, your eyes are shooting back and forth like that. It's called REM, rapid eye movement sleep. You can measure that in other animals. Their eyes move back and forth. We correlated how plastic the brain is and how much dream sleep you have. It correlates perfectly, which is to say, humans, which are the most plastic, have dream sleep all the time. By the way, when you're an infant, you have dream sleep for half of your sleep time, 50% of the time. As you get older, you get less and less dream sleep because you just don't need it as much anymore. Anyway, when we look across species, it correlates perfectly. If you're a monkey that drops into the world sort of already fully baked and you don't need it to have much plasticity, you don't have much dream sleep either.

Speaker 1:
[78:47] Interesting. Seems like a very strange thing. It sounds like a very strange thing for the brain to do, but it also is perfectly plausible based on everything you've said.

Speaker 2:
[78:58] Yeah. By the way, I just want to mention dreaming is across the animal kingdom. Everybody dreams. All animals dream at night.

Speaker 1:
[79:03] Even like animals at the bottom of the ocean?

Speaker 2:
[79:05] Yes. It's harder to measure stuff all the way at the bottom of the ocean, but fish do have what is equivalent to dream sleep where you're just zapping activity in there. By the way, even animals that have gone blind, like there's a mammal called the blind mole rat which lives in darkness and has eyes, but they're blind because over evolutionary time they've lost vision, but they still dream because the dream circuitry is so ancient. This is so ancient that all animals have to defend themselves against the darkness by keeping their visual systems going. And so even though the animal went blind, the rest of the brain didn't catch up. I mean, that's how evolution goes.

Speaker 1:
[79:42] So funny. It's funny because it's kind of like that evolution gave us this TV that comes on at nighttime when the real TV, our real life turns off and it just puts on this fake TV set to keep that part of the brain doing something so that it doesn't deteriorate and atrophy.

Speaker 2:
[80:01] It's exactly right. Yeah, it's exactly right.

Speaker 1:
[80:04] Which means dreams are quite pointless outside of just protecting on neurological matter.

Speaker 2:
[80:10] I suspect so. It might be that the particular pathways that could travel down, you know, maybe there's some meaning there. My own suspicion is that it's like if I went to your bookshelf and I picked a random book up and I flipped to random page and picked a random sentence, I might find some meaning in that. I might say, well, that was just the sentence that I needed to hear. But it's not really. It's just that it has some meaning to me. Anyway, the point is if you blast random activity in there, I might dream about something where I wake up and say, oh, that was pretty useful. But the thing that I think it's overlooked is that most dreams are totally useless and bizarre.

Speaker 1:
[80:46] Dr. David, what is the most important thing we haven't talked about that we should have talked about? As it specifically relates to people that are trying to improve their lives, get better at whatever their subjective mission is, and the brain.

Speaker 2:
[81:00] There are probably a lot of things. But I got to say, the thing that I've been thinking about so much lately is just about our political interfacing with one another. And so I do feel that really learning the skills of dialogue with our fellow humans, where we listen to what they're saying and try to better understand what their internal model is. It's not equivalent to agreeing with them, but it is saying, hey, somebody is coming from this perspective. Let me see if I can understand that. I think that matters a lot. And I also think that because we're so highly predisposed for in groups and out groups, it's really useful to figure out how to complexify those relationships. Meaning, how do you figure out all the things that cross cut in the relationship so that you say, hey, you know what, I shouldn't dismiss this person as a member of my out group right away, because actually they belong to the same group I do. And they love surfing as much as I do. And they love golden retriever dogs. And they grew up in my hometown. And whatever. Like, finding those things explicitly helps the brain to keep these circuits on that are involved in seeing another person as a person. We have all the social circuitry that is all about understanding other people. And when things get dehumanized, that actually gets dialed way down. When we look at, you know, let's say, a homeless person or a drug addict or someone who we think of as our enemy or an outgroup, that gets dialed down. So we don't think of them as a person anymore. We think of them as an object to get around. So this is what I think is really important, is figuring out what we can do to keep that social circuitry still going, which includes the things like eye contact and conversation. And this is one of the most important things we can do as citizens in a rapidly changing world.

Speaker 1:
[82:55] As it relates to things like dementia, which I know is a fear that a lot of people have, a lot of people are suffering with dementia, I think increasingly, in fact, if I was trying to stave off dementia, what advice would you give me, David?

Speaker 2:
[83:08] Yeah, keep your brain active. Keep it active till the day you die, take on new challenges. And as soon as you get good at something like, you know, Sudoku, drop it and pick up some that you're not good at.

Speaker 1:
[83:20] And in simple terms, why?

Speaker 2:
[83:21] It's because you're forcing your brain to make changes. Otherwise, your brain says, OK, I got this. I got the world. I understand what's going on. There's no real particular need for me to change. And the fact is that the structure of the brain is always degenerating. And when you get something like a disease, like Alzheimer's disease, it degenerates much faster. And what you want to always be doing is building new roadways and fashioning new paths that had not been walked before.

Speaker 1:
[83:46] So that there's more to degenerate, which gives me more leftover once that degeneration begins.

Speaker 2:
[83:54] Yeah, I think that's a good way to look at it. Your pathways are falling apart. And if you can build new pathways, which requires effort, you have to actually care and pursue and do the thing. Even as parts of the thing have fallen apart, you still have ways of getting from A to B.

Speaker 1:
[84:09] What do I need to stay away from in terms of chemicals or supplements or food?

Speaker 2:
[84:14] Yeah, obviously, there's just been a lot more emphasis on getting good sleep and good diet. And this stuff really matters. I think that's really useful for the brain. I mean, it's fascinating to watch what's happened in the latest generation in terms of alcohol consumption. I live up in Silicon Valley. And there's a lot of people who have wineries just north of me. And they're selling half their acreage. It's absolutely fascinating to see what's happening there. I will say I have a friend who's in her 20s who said that she's in favor of bringing drinking back.

Speaker 1:
[84:44] Why?

Speaker 2:
[84:44] Because she said, we go to parties and everything's so awkward and no one knows how to talk to one another. And so they're missing something else. They're missing the dumb mistakes category that we all got to enjoy growing up. So it is a really interesting balance of how abstemious one wants to become.

Speaker 1:
[85:03] David, we have a closing tradition where the last guest leaves a question to the next guest, not knowing who they're leaving it for. The question left for you is, what do you wish most for our planet over the next 10 years?

Speaker 2:
[85:18] The whole list of the top 10.

Speaker 1:
[85:20] Yeah. Can't be well-pissed.

Speaker 2:
[85:23] You know, I think I would come back to this piece about the complexification of relationships, which is to say, if we could just get a little bit smarter about understanding people in our outgroups as being humans with lives, with their own thing going on, doesn't mean we have to love them or agree with them. But if we can just get to that point, I don't think we'll ever hit world peace, but at least we'd have slightly less polarization. So I'm definitely in favor of that. And I do think it's possible, and I do think AI can help us get there. By challenging us on these points and saying, hey, that group that you've already dismissed as an out group, what if I told you this story about this person? What if I introduced you to this person? That kind of stuff. And there's all kinds of social movements that have sprung up that allow people of different political opinions to come together in a room and talk with one another. Again, it's not that anyone has to change their mind, but they can say, hey, you know what? I really like that person. I thought that was a cool person, a sweet person, a nice person. And now I understand that somebody who I have seen with my own eyes has a different opinion on this than I do.

Speaker 1:
[86:30] Is that wishful thinking to some degree?

Speaker 2:
[86:32] I don't think so, because these things are happening all over the place.

Speaker 1:
[86:36] But the macro is division, isn't it? It's polarization, echo chambers. I think there's now 20 social networks, some crazy number that have more than 20 million people on them, which means that social networks are splintering off into niches and interests. There's like Rumble and Bumble and there's like Threads and X and Facebook, Snap, Instagram, and what we're seeing is more and more interest. Also, the other thing with algorithms is we went from having a social graph, where if I had a thousand people follow me, those thousand people would see my stuff, to now these interest graphs where it doesn't matter if I have one follower or one million followers, the algorithm is going to decide who's interested in that thing, and it's going to serve it to them because that's the most retentive thing. If you're a publicly listed company that's driven by ad revenue, so you've got this algorithm that's actually forcing you into tighter and tighter echo chambers. And even as someone that's been on social media 15 years and ran social media companies, this is one of the great things I've noticed is when I had a million followers back in the day, I would reach those people because they had hit follow or subscribe. Now, even on our YouTube channel, 61% of you don't subscribe, and please subscribe. And that's in part because the algorithm is now doing the work of deciding who to show it to, who it will, on the basis of who will be retained. Yeah.

Speaker 2:
[87:52] Here's what I would say. There's absolutely nothing new about echo chambers because it was always the case that your neighbors and your community and whatever, that's what you thought was reality. I'm actually quite optimistic about the existence, the mere existence of the internet, because at least we are exposed to the fact that there are lots of different points of view. It used to be in places like the USSR, they controlled the media tightly so that everything you saw was a news-approved story. But now you see all the points of view. Now, many of them might drive you crazy and whatever, but at least you know that there are people out there that believe in that, and I think that's really useful. If I had to decide between state control where there's a single story or seeing the whole messy spectrum of opinions, I'd rather see the latter.

Speaker 1:
[88:37] What about the middle? One of the phrases that's, again, a principle that's helped me think is that the truth is in the middle. Generally, I try to understand what the middle looks like. So you've got state controlled over here. You've got aggressive algorithm that's reinforcing whatever you currently believe. Is there not some middle ground where the algorithms have to let up a little bit? Of course, we're not going to go for state controlled.

Speaker 2:
[88:59] Here's my prediction in 2026 is that there is a market opportunity for a new social media company to come along because everybody is aware of exactly this problem that you're pointing out. Everyone hates when they surf and they get served exactly what they're supposed to get served and they get off after an hour or two, and they feel like they've wasted their lives. I think it's a real opportunity for a social media company to come along and say, you know what, we're not building our algorithm like the other guys. It's not about just trying to get engagement at any cost with incendiary posts, but instead, we're looking for ways to connect people. So if you and I both love this particular thing, this particular cuisine or location or whatever it is, we get connected, we see each other's stuff, and the algorithm carefully, temporally sequences things so that we come to have a certain connection threshold before we find out, whoa, you have a totally different political opinion than I do on subject X. Wow, I didn't know that, but I really like Steven. So I'm going to lean in and listen a little bit more. I think this is very easy to do, and I think it can actually be part of the selling point of the media company is saying, hey, we are here not to enrage you, but to actually build connection.

Speaker 1:
[90:15] It sounds like how social media started.

Speaker 2:
[90:17] Yeah, maybe it's a return.

Speaker 1:
[90:19] I think there's probably a neuroscience basis as to why we ended up here.

Speaker 2:
[90:25] No, it's an economics basis. But the fact is, there's now an economic opportunity now that everyone sees the landscape.

Speaker 1:
[90:31] What I'm trying to say is that that social network wouldn't be that retentive by design, because it wouldn't trigger my dopamine. It wouldn't be a slot machine. TikTok is a slot machine. Ping ping, randomized returns. Ping ping ping, dopamine hit. Ping ping ping. So this other social network that wasn't playing with my dopamine in such a way, I don't know whether I'd be addicted enough to return. Therefore, they wouldn't sell their ads, the economic return. Therefore, they wouldn't do very well.

Speaker 2:
[90:55] Here's the thing. I don't know if the story is that simple that we all want to do slot machines all the time. I don't think we do. Exactly. Because the fact is that a lot of people go to Las Vegas and do slot machines sometime, but we don't do that all the time. It's kind of rare, actually. What we really desire are meaningful connections. We really desire feeling like, hey, you know what? I met this person online that I'm following and he's following me. And we really connect on all these points. And oh, by the way, I then found out, interestingly, he's got a totally different opinion about Iran or abortion or whatever than I do, but that's cool. Now we're listening to each other.

Speaker 1:
[91:31] It kind of goes back to your point earlier about the very start where we were talking about the brain having an internal battle. Do I want the cookie or do I want the salad? And unfortunately, in the world, we live in, the cookie is going to give me a dopamine hit.

Speaker 2:
[91:43] Yes, but we don't eat cookies all the time. This is the point. We do eat salads much of the time because we're not just unconscious automaton that are doing the cookies.

Speaker 1:
[91:53] Dr. David Eagleman, thank you so much for the work that you do. I'm going to link your book below so everyone can read this book. You've got a new book on the way, which I'm very excited about as well. What's that book going to be about and when is that out?

Speaker 2:
[92:03] That's about the Ulysses contract and that'll come out in 2027.

Speaker 1:
[92:06] For anyone that wants to know how to change your life by changing your brain, I think this is the perfect book to read. It's a New York Times bestselling author. The book is absolutely fascinating. It was actually learning about this subject matter in Livewired that helped me to pursue more of a growth mindset and just a growth mentality across my life, and to realize that if I'm not something now, it doesn't mean that I can't be tomorrow. Thank you so much for the work that you do, David, and it's been truly illuminating. I'm sure my neural pathways have expanded in really important ways because of this.

Speaker 2:
[92:39] Great. Thank you, Steven.