Dwarkesh Podcast - Scott Young - Ultralearning

Episode Date: November 16, 2020

Scott is the author of Ultralearning and famous for the MIT Challenge, where he taught himself MIT's 4 year Computer Science curriculum in 1 year.I had a blast chatting with Scott Young about aggressive self-directed learning. Scott has some of the best advice out there about learning hard things. It has helped yours truly prepare to interview experts and dig into interesting subjects.  Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform.Podcast website here.Check out Scott’s website. Follow me on Twitter for updates on future episodes.Buy Scott’s book on Ultralearning: https://amzn.to/3TuPEbfTimestamps(00:00) - Intro (01:00) - Einstein (13:20) - Age (18:00) - Transfer (24:40) - Compounding (34:00) - Depth vs context (40:50) - MIT challenge (1:00:50) - Focus(1:10:00) - Role models (1:20:30) - Progress studies (1:24:25) - Early work and ambition (1:28:18) - Advice for 20 yr old (1:35:00) - Raising a genius baby? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

feel like people are just way too unambitious in general and not in like the ambition like I want to be better than other people way but they just don't think of big projects they don't work on them they don't they don't have like you know big dreams to do cool things or if they are it's usually just something like i don't know you just boils down to something like social status like i want to be the you know the person that does this that's better than other people and i don't know i feel like i don't know how you change that, but I do think that rewarding kind of a culture where you want to do kind of ambitious, original things that are kind of interesting and you don't know where they're going to lead. I think that that's having that in you is kind of rare, and I think that cultivating it

is probably good for yourself and society. Okay, today I have the pleasure speaking with Scott Young, who is the author of the book, ultra-learning. Accelerate your career, master hard skills, and outsmart your competition. So Scott, I'll ask you some practical questions in a second. But first, let's talk about Einstein and Newton. So they both had an Annes Mirabulous, a miracle year within which many of their important contributions were concentrated. What explains this phenomenon of miracle years? Well, I don't know. I think whenever you look at these sort of outlier people, like Newton and Einstein, or certainly one, you have to realize,

that most people never have a year where they accomplish anything like that. So I think it's just it's just, I think it's a lot of its selection effect that you have a smart person who just happens to be working on the problem that will lead to a huge breakthrough. And so I mean, we could have lived in a world where Newton spent a lot of time on alchemy and then discover the way to turn lead into gold and then like that works, but that's not the world that we live in. And so I think that, you know, his work on physics and the Principia and stuff like that was what led to the breakthrough. And I think Einstein's a little rare. that he had kind of a couple key insights that led to physics.

Like, I mean, he discovers or sort of proves through Brownian motion, the existence of atoms, he just the photoelectric effect, which is the thing he actually won the Nobel for, not his relativity, which is what he's, you know, the thing you revolutionized physics for is not really even what he got the Nobel Prize for was the photoelectric effect, which, I mean, I guess it started quantum mechanics. So it's not really, can't downplay it too much.

But then special relativity. and then he struggles with him after like eight years to get general relativity. So I think Einstein's a, you know, he's a little bit of an exception in that he did have like multiple huge breakthroughs. And so when people are talking about like lists of geniuses or people who are important, sometimes that list gets populated by people who don't really deserve to be there. But Einstein is definitely like not, he's like an accurately rated genius that it's seen as being extremely smart and important and actually is extremely. smart and important. Right. But as you mentioned, they were different problems, right? And unless there's like a deeper principle, which I'm missing, I am having trouble understanding why many, like,

special relativity for an electric effect and brownian motion happened in the same year. Yeah. I don't know. I think that for Albert Einstein's case, the fact that the kinds of problems he was working on, I think were also amenable to his sort of style of thinking. So, you know, I'm a big of the Isaacson biography of Einstein. I talked about it. I did a little kind of summary post in my blog. And you can see that Einstein is really one of the great intuitive physicists. Like he's very much a spatial visual person.

And so like his thought experiments are really kind of the mechanism that he's using to generate these insights. And so, you know, putting the photoelectric effect aside for a second, you know, the special relativity is this kind of, all right, we have these weird experimental results that show. the speed of light doesn't vary depending on which direction you do it, which is weird, because if you think about a wave and it's going through some medium, then if you're moving relative to the medium, the speed should change, but it doesn't seem to do that. And so kind of like working through the geometric implications of that and then getting to this idea that like, well, lengths will contract as you go faster.

And these are all mind bending, but they come from this kind of rigorously working out the intuitions of this. And you can see that as being somewhat different, maybe. than the more mathematical physicists who were, you know, very, very strong at some of this advanced math. And it was a little bit less of a kind of like, well, what's my physical intuition about this? But more like, well, what is a way of representing this? And like, you know, I'm maybe getting a little bit outside of my comfort zone, but I'm imagining like people come up with matrix mechanics. This is just a little bit sort of like, oh, this is an interesting pattern. Or this is one way

you could do it that makes the math easier and stuff. And, and I think even, you know, Einstein, I believe it was Minkowski, who he worked with on like the tensor stuff because he kind of was like a little bit more limited there. Not that Einstein wasn't also brilliant at math, but it's definitely that's not what led to his huge breakthroughs was this kind of, he had some intuition and then he kind of worked to formalize it, whereas for other people might be like, oh, this is an interesting mathematical pattern. I wonder whether or not it would work for this particular problem. So I think that it may just be that these types of problems were kind of uniquely suited to his sort of style of doing physics. And I don't know if you look into the different time periods, it may just be the case that,

okay, there were a few insights that required someone with this kind of skill set to unlock. Yeah. This also relates to an essay you wrote called the Narrow Path of Success, where we explained that you kind of have to be following the right track, be successful in many ways. doesn't the example of Einstein confound that explanation because you have somebody who's a patent clerk couldn't get an academic job many years after he did special relatives he still couldn't get an academic job yeah yeah well i think when i'm talking about the the sort of surprisingly narrow path of success this is just sort of this kind of contrary to this idea that people have and it's sort of this

romantic idea that like in kind of high ambition fields you can kind of just do whatever you want and then like someone will recognize you and then it'll work. And if you actually get data on on how these careers actually work, it's clear that's not how they work. So the kind of motivating example that caused me to write that post is reading Jason, excuse me, Jason Brennan's book, Good Work if you can get it, which is about kind of a redated, driven approach to analyzing how careers work in academia. And the picture he paints is stark.

Like there's way more people going into academia than there are academic jobs. and the actual process of getting those jobs is quite rigid and that the filters are quite rigid. And so I think, you know, Einstein were using him as a bit of a counter example, but he kind of isn't because like he had some sort of bad things on his resume and he had a really hard time getting a physics job. So the right way of looking at that is that Einstein struggled to get through this because he didn't have the right resume. You're not Einstein.

So that's sort of the thing that I would kind of put. And I think there are always these exceptions where someone had kind of the wrong start to things and then had this like truly spectacular, you know, one in a million kind of result that brought them stardom. But you can't really count on that, right? And I think that's sort of the lesson here. And so like, you know, we kind of romanticize the Einstein stories. But if you were just doing it from a, okay, well, what is the typical path for people and how do, you know, 80, 99% of people find success in this field, that's what you should be betting on. You know, you shouldn't be going into a poker game, you know, well, if I get a royal flush,

then I'll be really good. You need to bet it on, well, you know, given that I have probably an average hand, what's the way I should play? And so I do think if you are in the top 0.01%, then don't listen to me because you're smarter than me, right? Like, you know what you should be doing. But if you're like everyone else and you're trying to figure out what you should be doing best, I think understanding the path to success in most fields and what is the typical path is so important because generally if you are outside of that path, you're going to be facing strong headwinds. And so, yeah, if you're a remarkable Nobel Prize winning genius, then maybe you will, you know, see some success.

But if you are not, then you're kind of stacking the odds against you for no reason. And, you know, one of my favorite examples from that post is I talk about how in nonfiction book publishing, there is an established path. And that path is you first get an agent because getting an agent is easier than getting a book deal. And then you work on a proposal, which is partly what the book is going to be. And partly also kind of like a business plan to show that you've thought out why this book will actually sell enough to interest the publisher. And then you pitch it and you get a book deal and then you write it. And so the writing it is coming way, way late in this process.

And what most people do is they write the book first and then they look around for people who will publish it, which is like screams amateur to publishers, and they don't like that. And so it's very difficult, even if your book is like pretty good to get a book deal that way. There are exceptions, but it's difficult, right? And so this is sort of, if you are serious about becoming a nonfiction author, the first step would be try to get an agent, right? Like obviously if you're self-publishing, none of this applies. But if you're trying to go through a traditional publisher, you want to have a published book

through an actual, you know, serious publisher, that's what you want to do. and I remember I wrote this and someone was kind of like, I'm set like, well, why do I have to do that? Why do I have to do it this way? And it's not that you have to do it that way. It's just that if you're not doing it that way, you're making it harder for yourself. So I think it's always fine to do something weird and, you know,

creative, but you should know that that's the strategy you're picking and that you're kind of making it harder for yourself in certain ways if you do it that way. And so I think this sort of narrow path of success kind of ties into one of my kind major points, not only in ultra learning, but in my sort of philosophy in general, which is that people really ought to do more research about what is the sort of typical way that these kinds of things succeed before they embark in projects. Because even if you decide, well, I'm not going to do it that way. I've got like kind of my own path that I think is going to work better for whatever

reason. But you should know what the status quo is. You should know what that works. You shouldn't just like, oh, well, I didn't know that that's how people did this or that's how you go. success in this field. And that this research is not actually even that hard to do for most things. It is not like it requires. Super secret knowledge is just most people don't know how to do it or they don't care to do it or they don't want to find out the answer because maybe it's not something that they want to hear. Right. Okay. So maybe we shouldn't be studying the exceptions that hard. But let me just ask you one more question about how Einstein's way of learning contrast with ultra learning. So he seems to have lacked the sort of structure and discipline and organization, a sort of ultra learning project kind of requires, right?

Is that evidence for a more like fun-based and excitement and curiosity-based form of learning? Well, I don't know whether that's true. Like, I mean, again, I'm going off the Isaacson biography here, but I mean, Einstein did an enormous amount of kind of deep thinking on hard problems when it wasn't necessary. So he's the patent clerk and he's working through some of these hard ideas. So I don't know whether it like, you know, fits this mold. I think this is important to keep in mind, too, that when you write a self-improvement book like ultra-learning, you do kind of want to create it in a somewhat structured way. So you're taking what was sort of in some ways the kind of spontaneous, intuitive approaches

that people who are successful are using. And you're trying to turn it into a structure that someone who wouldn't do that automatically could follow. And so similarly, you know, if you were to try to analyze any. any kind of domative of importance and then relate it to someone, you have to make it into a format that, okay, well, you need to follow these steps. So in my mind, like Einstein was one of the people that I researched for the book and I would have maybe considered him as one of the vignettes in the book if he wasn't like way too famous and people have already heard too much about him. So like, it

wasn't because I thought he wasn't suitable. Now, there's certainly people that I think do kind of break the mold because they're doing a lot of things that, you know, cognitive science, seriously are like bad practice and yet they're really successful. And so like I kind of give Terence Tao as like the counter example of being someone who like just seems to be really, really, really, really smart. And like there's he's not really doing anything particularly special. And I mean, there's other people that I talk about as well that they're kind of mild counter examples. But in my mind, Albert Einstein definitely isn't one of them. He seems to be someone who followed a lot of the ideas, even if he wasn't necessarily doing them,

doing them in a kind of structured approach. He was doing them sort of a little bit more. They just came naturally to him. Interesting. What's the relationship between age and ultra learning? Is there a prime age where you're at the peak of both your plasticity, but also your maturity to be able to learn things really fast? Well, there's kind of two questions here.

So one of the questions is, are the kinds of principles and techniques that I talk about in ultra learning age specific, and I would say probably not. It seems to be the case that retrieval works better than review. I don't see a reason why that would change as you get older. Similarly, like feedback is going to be important whether you're 79 or 17. Like they're not, I don't think that those things are going to change. So you could read ultra learning and if you were sort of like, okay, what practical step should I take to learn X or Y? I don't think that the advice would change too too much. Now, the other point you're kind of making is ultra learning as this kind of like can you be really successful at an ambitious learning project? How does that change

new age? And that certainly does change. So we know, for instance, that like fluid intelligence probably declines from your early 20s. It kind of straight lines downward. Things like working memory and stuff also declined. I did an essay where I dove into some of the research on aging and learning. And that's a really interesting subfield. And I didn't, I did it after the book. So I didn't include any of it in the book. But. one of the things that's a key finding is that the frontal areas of the brain seem to be the parts that deteriorate faster as we age. And so the two main things that tend to be harder as you get older is one is the kind of frontal areas. So think about this as kind of like your executive control

of like dictating where you should focus your attention, being able to switch rules for things. So if it's like I give you a puzzle where you have to apply a certain rule to a problem, but then I switch it suddenly and you have to apply a different. different rule. There's more perseverance, like people who are older have a harder time switching because this kind of like, oh, the habit that they want to override, they're having a harder time switching back and forth. And so this does suggest, for instance, in areas of focus that if you're older, you need to pay more attention to having an environment that's conducive focus, because if you're 21, maybe you can have the television on in the background and just tune it out,

but if you're maybe 75, you may just find it impossible to keep from being distracted because of that frontal area stuff. There's also stuff on chunking that there seems to be difficulties with like the medial temporal lobe areas which are involved with like binding information. So that's a big part of the intuition chapter. I talk about chunking where you're kind of

assembling pieces of information together so that they can be attached. And it seems like people that are older have more difficulty doing that, which would obviously impact learning. But it's also why like, you know, someone who is older might recognize someone but they can't remember their name is easily because the name and the facial recognition

just don't bind as strongly as like, oh, it's, I recognize this person and their name is so and so, and I know that they went to school here and this is how I know them and all of that kind of stuff. And so there might be some benefit of being more explicit in how you kind of put information together so that you can bind it more easily. For instance, there's studies that show that like if you're making flashcards, for instance, and you have to do more manipulation, like, show how the parts connect with the flashcards. So like in this particular example, I'm, I'm working off of memory here. They were talking about like learning Chinese words. And so there's the characters in the pronunciation. There's two characters and two pronunciation. And you put them

side by side, what you have to do is take like the first part of one and link it with the first part of the other and the second part of one and link it with the other. And it turns out this makes it somewhat harder to remember than if they're on top of each other. And there's just an easy visual link. And so you could think of maybe if you're older and you're struggling more with these kinds of issues, organizing your material better, making the connections between things you have to learn more explicit. Having that kind of pre-processing work might be a little bit better. But I think those are also things that would benefit people who just struggle with learning more. So if you have kind of, if you feel like you're more distractible, if you feel like you struggle to understand concepts, I mean, those are also things that would apply. So that would be my connection between age and learning.

But I think, you know, the broader thing that I think matters is just that when you're learning something, using the correct strategy is mostly going to be the same. So I don't think it's the case that there's some strategy that works for really smart people and some strategy that works for people that don't. The same things probably work. It's just it's going to be easier if you're more intelligent or you're younger or you have those advantages. Sure. Yeah. Although the principle of transfer seems to be injured by your explanation that your capacity to look beyond superficial differences and recognize deeper principles is harmed by age, right?

Or connect different concepts. Yeah, I don't know whether it's the case that you're like, so there's two issues there. So the issue of being able to look at what the deeper principle is, I think is part of this problem that you need a lot of exposure to a field and to the knowledge in the field in order to build up this kind of repertoire of patterns so that when you see it, you can actually see what the principles are. And so that's one of those sort of things that like there's, I forget the name of the study, but it was like one of those classic cog size studies where they took physics novices and experts and showed how they look at problems and the novices focus on stupid stuff like,

oh, this involves a pulley or an inclined ramp, whereas the experts are like, oh, this is a conservation of energy problem. And so the kind of naive way is thinking, oh, well, it would be nice. We should just tell people how to recognize whether it's a conservation of energy problem. But the problem is that the conservation of energy aspect of it is a kind of abstract property of the problem. And so whether it involves a pulley is an obvious aspect. And so what you need to be able to do is look at all those obvious aspects and then figure out what the abstract or a higher thing is. And it seems like you probably do this through chunking. So you probably learn all these smaller patterns and you build it up so that when you see it, it just all kind of

comes together and you can see what the situation is. And so I do think it's probably the case that if you're learning something that has a lot of abstraction, really spending time to kind of familiarize yourself with the ins and outs of the more basic pieces, allow those things to kind of lock together faster. And so, you know, the vignette I chose for that one was fine. And just because he had spent so much time kind of playing around with math and he just had so much familiarity that he was like this encyclopedia of like weird math trivia so you know like just random stuff comes up and he can come up with the answer because he has all these patterns that are stored in memory right so that was a part of the book that um I was thinking about a lot because there was that part of intuition

where which explains that your memories in area one can influence and help your knowledge and area too but then the chapter on directness talks about the failures of transfer learning and so I would just trying to put those two things together. Yeah, I mean, I think so if we're kind of visualizing it a little bit here, the principle of intuition is sort of recognizing that that because of this sort of hierarchical structure of chunking, you have this kind of building up layers of abstraction on top of each other. And what the thing on transfer is showing is that when you go from one domain to another, the problem with the problem with the transfer idea is that when we talk about learning skills, we tend to use fairly general labels for things.

So we say kind of like, well, I'm going to get good at X. And X is just this sort of broad category of skills. And what is sort of missing from that is that to actually perform those skills quite well, you have to do something very, very precise. And so it seems to be a general feature of the brain that, it learns things quite specifically and that is how it works. It's not just like a defect, but like that's why we're smart is because we can make very fine grain discriminations between things. And so you can get transfer if you're thinking about it in terms of like, well,

I understand domain one well enough that I can see abstract pattern here. And I understand domain too well enough that I can understand abstract pattern here. And I can see that these are the same abstract pattern and I can make that linkage. But the problem is that if you haven't chunked the first domain enough to get up to that pattern and you're trying to talk about the next, they don't match because superficially they're quite different, right? So this is like this issue where, yes, if you understand physics problems well enough that you can see that this conservation of energy is this principle that holds. And then you start learning a new domain and you realize, oh, this is actually like conservation of energy. You get it up to that level of abstraction. Then yes, you can do that transfer.

but the problem is that most people don't have this sort of like richly abstract sort of principle-based reasoning about things. And so they just see all these superficial details and they have nothing to do with each other. And if the superficial details of one don't have to do with the superficial details of another, there's zero transfer there because they don't like it all. And so I think, you know, one of the books that I read that was sort of a major source of the research on transfer was, I think it was called Transfer of Cognitive Skill. And I'm blanking on the author's name right now. but it's mentioned in my, I think it's Haskell maybe,

is mentioned in ultra learning. And so that's his point later in the book, is that his idea about how we overcome this transfer problem is that we teach more theory because ideally if we have this sort of richer, more abstract ideas about things, then we're getting it to a level where it can transfer above these superficialities. I'm a little bit more skeptical of that because I feel like, well, that's kind of what universities do is teach theory

and it doesn't seem to work very well. So you're kind of suggesting what we're already doing. But I think the point that I want to make in the directives chapter is, presumably you're reading this book and there's something you want to be good at, right? And so if you want to be good at it, then make sure that those like specific skills, the micro skills that need to be in place are the ones that you actually need in the real situation because if there's mismatch, your performance is going to go down considerably. And there's lots of real world situations where, you know, just having a theoretical insight is not enough.

you need to actually perform all of these small sub-skills. And if you don't have the subskills, your performance is zero. And so if you're doing some training that doesn't work on the subskills you need, you're not actually going to be able to perform. So I'm talking about it very abstractly, but language learning is the example I use there, that if you only learn how to recognize sentences, you can't actually recall them, then that's useless for you when you're speaking because you're not recognizing sentences. What you're doing is actually speaking.

And so I'm very critical of dualingo there because a lot of the exercise they do are not recall. They're just they're just, you know, doing multiple choice from a word bank, but actually speaking involves recall. It also involves pronunciation. It also involves working around words you don't understand and these kinds of things. And so I think the more you analyze skills you're trying to learn, you realize, oh, this is why this thing doesn't work because the actual thing that I need to do in the performance situation is not what I've been training. this makes you wonder if this explains why very widely read people and very broadly knowledgeable people don't seem that much smarter than just generally widely read people. I had Tyler Cowen on the podcast and I asked him, did he learn more between in the last 10 years than he did between

the ages of say 15 and 25 and he said obviously 15 and 25 and asked him if you take the concept of compounding growth of knowledge seriously shouldn't you expect to be learning more in the last 10 years. And he said it's diminishing returns when it comes to learning. But I wonder if just the fact that the compounding just doesn't work because there's a lack of transfer once you know enough. Well, I don't think that I don't think compounding works generally. Like I think compounding is this very seductive idea where you you just get more and more returns. But really the areas where there is true nonstop exponential growth are like vanishingly rare. And they're vanishingly rare because when they apply. They, like, totally transform the situation you're dealing with. So, like, startup growth is an

example of compounding return. And it's where, like, one guy in his basement can rule the world after, like, after 10 years of grinding or 20 years of grinding. So, like, that's a situation that's, you know, vanishingly rare. Most things have kind of regions of exponential growth and then regions of diminishing returns. I would say that, you know, classic cowan economics thinking is that you tend to think in terms of diminishing returns. So that diminishing returns tends to be the kind of default way of viewing things that you get most of your growth in the beginning. But I think it's certainly about like truths about the world. There are some basic mental models or concepts which are very fundamental. And once you understand them, you kind of, you get like the 80, 20, you get quite a bit of understanding.

And then you're getting to like the esoterica of academia. And now suddenly they're like debating the finer points of some BS problem that doesn't really matter. And like, yeah, spending a lot of time studying that may be necessary to advance the field, but like clearly from a utilitarian standpoint of like, where are you going to get the most benefit from your learning? It was in that first phase. And so maybe that's the kind of thesis and ultra learning too, is that, you know, having kind of like, okay, I can capture the 60% of the value of like or utility of this field in a relatively short period of time, because if I do kind of plan my learning out very effectively, I can kind of capture it. And I don't want to say that that's always the case. And

certainly for professional careers, you often need to be in the top like 1% of a skill for it to matter at all. So I do think there's differences. But, you know, definitely there's the case that if you wanted to become good enough as a programmer to do a lot of your own programming for personal tasks, but maybe you're not like what you do is programming full time, you can easily do that in a year. Like that's something a person could easily do in a year. Whereas the way people often and think about it as well, I have to go to school for four years and then maybe work in an office for, so I have to do it for like a decade to be good at. And similarly with language learning, like, yeah, if you want to be able to lecture in a language or speak so fluently that like your

entire life has lived in that language, and yeah, it's going to take you probably a decade. But you get to a level where like traveling in the country is pretty frictionless after maybe a couple months. And I think that's the kind of interesting zone of like, oh, I could get like decent at these skills in a relatively short period of time. How? does that change my calculus about what kinds of skills I pursue or what kinds of things I invest in in terms of projects? Does this diminishing returns apply to consecutive ultra-learning projects as well? I think you implied in the book that there's a cumulative advantage of doing one ultra-learning project and then you have the meta-skills and confidence to go from one to another.

Well, so they're probably S-c curves. They're probably S-c curves. There's probably a case that like you're so one of the main points that I try to make is I think that there is this kind of compounding confidence curve and especially at the beginning which is sort of where I'm focused on which is that if you've never done sort of aggressive self-directed learning projects before and you try to do this and I mean you're reasonably smart you have the kind of background um of stick tuativeness to like actually get a project done in this kind of thing which is is not a small assumption, but I mean, I'm kind of assuming you have enough self-efficacy to like, okay, I'm going to set and work on this for three months and then you actually do it.

It's not like a week later. And you're like, ah, I just threw that book in the garbage. That was too boring. But if you're actually able to get through it and follow up on it and actually kind of do the things that I'm talking about in the book, then you can often get to this sort of like, oh, oh, wow, I didn't know that I could do that. Like this was a lot of the people that I interviewed and I talked to who kind of went through this in the book.

Like Tristan de Montibella was just this great example. I mean, his outcome was pretty extreme, but his was kind of like, he was a, you know, kind of reasonably competent, smart guy, but he'd never try to do things this way before. And then he does them and he gets this huge result and he's like, oh, this is like crazy. I could think of all these other types of projects that I could tackle now if I was really serious about them. And so I think there's this sort of improvement not only in your confidence, but in your overall strategy.

You know, if you've learned a couple languages, then learning more languages just becomes like this routine activity. I mean, it still takes you the same amount of time to get to like that level of mastery. But the way people feel and talk about like, oh, I really want to learn Spanish, but I haven't made any progress in like five years. Like they don't deal with that, right? Like, okay, yeah, I'm going to work on Spanish. And I know I'll be able to get to a level where I'm having a conversation after, you know, a few months just because that's just how it works. Right. And so I think that there is this benefit of getting this compounding. Now, if we're talking about like do just the people who become smarter just become this like eclipse level and it's like that

you know i don't know the johnny debt movie where he becomes like he becomes the universe computer like no obviously not like there's obviously some kind of okay now you've learned and mastered sort of the edge of what we know about like performance from memory and so now you're doing little tweaks you know you're it's a little bit like i would say athletics is similar that if you've never gone running before and then someone first says hey there's a this thing called jogging. And if you put your shoes on and you kind of run for a bit, like you can probably get quite a bit better for a while. Like there's quite a bit of gains. But then once you're at the level where you're, okay, you're regularly competing in,

you know, marathons, you've run the Boston marathon. Well, now you're kind of shaving seconds off your time. So there's probably an S curve there. I think the argument I'd like to make is that like most people are before the gains. They're the people who've like, I've maybe jogged a couple times in my life, but they've never taken it seriously. So I think that for the intended audience, I think this book is, is, that that's true. But I think, again, whenever we talk about compound growth, we just have to keep in mind that like an unending compound growth is a very, very scary thing in the world because it just implies that, you know, one person controls the entire universe or is smarter than

every other person on earth. Like, they tend to diminish at some point. You know, it's interesting. I had Robin Hanson on the podcast. He's written this here as a blog post about the long view, which is that we do have, You couldn't just invest in the stock market. And like a thousand years down the line, you'll be like the richest person in the world by a big factor. And he asked, like, why is it not the case that some organization says, it doesn't matter if we die.

We're just going to put like a couple million dollars in this fund. And we're going to control the universe in a few thousand years. Well, yeah, I mean, the classic reason for that is that that's one of those sort of engineering fallacies where it's sort of like, well, yeah. But obviously once an organization started to have that kind of power, not only. would you have corruption from the inside so that like the human agents that are controlling the agency would start diverting the resources and breaking the company's mission but everyone from the outside would start to kind of dominate it like you don't just dominate the world without also having an army and also having like wide popular appeal and like doing this kind of thing and so I mean

I could see some scenario where that happens where you're creating an organization that's like like a new church or something where it like becomes the mass religion and then controls the universe but there's no kind of secular, like no, no kind of political ambitions organization that manages to concentrate so much wealth for, you know, thousands of years. And people are like, oh, yeah, well, we'll just honor that contract. So I do really think that, you know, no disrespect to Robin Hanson, because I think he's a brilliant thinker. But I really admire him because he's the one who kind of like points out these sort of, well, why don't people just do this? And he has the kind of, he has the right kind of mind to even like consider things that seem absurd to normal people. But, but I would say that's the

issue for, for something like that. And I think it's probably true for all kinds of compound growth. Like at some point, at some point it has to stop, you know. Right. You can't just go on forever. But if there's some meaningful range where you get compound growth, even if it's a short range, it can still be super, super important to pay attention to. Okay, so let me intersperse some practical questions here. So following your advice, I've been on my own ultra-learning projects. When I'm learning, I sometimes have a lot of questions about like, how does this work on a deep level, right? And I'm wondering whether you should, is there value in first of all getting the whole context, like kind of building a rough map?

Or should you, as you're learning, kind of question each piece of knowledge as you're accumulating it? So I think the way I view sort of projects and learning projects in general is that they all kind of have to start with this, what is what does success look like? What is the outcome that I'm trying to generate from this learning project? Because analyzing things like this abstractly are hard because if you're optimizing for a different goal, the answer might be different, right? So, you know, just even to take the language learning as an example, I found for the goals that I had that trying to figure out, let's say, the etymology of words or spending a lot of time figuring out where words come from and what it is, it quickly hits a point where you can be wasting a lot of time. So it quickly hits a point where, okay, yes, but actually what I need to do is just memorize more words. I don't need to, like, really dive deep on that. That being said, that's not a general principle.

Like there's other subjects where I find that like the tendency is to not do enough depth, like to not really go into it and understand things. And so in my mind, that's just because the specific constraints of learning a language tend to suggest this is the right strategy, that that's the optimal strategy. Whereas, you know, learning physics, for instance, I think that the problem is just that most people won't get nearly enough depth, right? Like that they just understand the concepts too superficially. They're not seeing the pullies.

They're not seeing the conservation of energy. And so when you're asking about what's the right way to approach a subject, I think it's really hard to do that in a goal-free setting. It's really hard to say, well, I want to learn physics the best way possible. I don't think there's really an answer to that. I think there's an answer to, well, I want to be able to pass these types of exams in my university class as the best way possible. Or I want to create groundbreaking physics research. Or I want to be able to discuss physics intelligently to other physicists. Or I want to like, there's all these different kinds of goals where it would be like, okay, this suggests.

a different way of like building around this. And there are more robust strategies. So like a strategy that works better if you have multiple goals, so if if you not only want to pass your class, but you also want to be able to invent things and like you have this sort of wider set of goals that you're considering, I do think that there are strategies that are more robust. So like if I'm learning Chinese for instance, and my only goal is to look really good for 10 minutes of like an audio thing that I can prepare, then obviously the strategy is to scripted. and to rigorously practice and have someone to actually not learn Chinese at all. But that's not a very robust plan.

So like if I'm doing like I would never do a project that way because to me it's like, well, that's not really what you want to do, even if that was one of the outcomes that you wanted to have. But I think that, you know, if you're learning Chinese and your goal is, well, I want to be able to talk to people versus I want to be able to read Confucius. Well, now you're talking about completely different strategies about going about it in my mind. Like I don't think the, you know, even if there's some overlap, like the thing that you do to be able to read ancient Chinese text and the thing that you do to be able to like, you know, chat with people on WeChat or something are totally different goals. And so one of my main kind of points that I like to make is that like you can't even really begin to think about optimizing unless you're say like optimizing for what. And learning tends to, I think learners tend to kind of discount that just because they're sort of like, well, I want to know X or I want to learn this topic. And. sometimes those assumptions about that tend to be the problem.

Out of curiosity, take the example you gave of trying to be a ground-bearing researcher. Say you're Cal Newport and you're trying to make a new proof. What is a strategy you used to learn at the cutting edge? I'm just curious. Well, again, I think that one of the key things there would be to specialize because, and you see this at like top-level researchers. Why they specialize is because it's much easier to like your benefit of,

getting a breakthrough is going to be, well, there's going to be some ideas that are closely related to what I'm doing. And I need to have them at like an extremely high level of fluency. And there's a drop off curve of like the value of ideas, the further away it goes. And so if you have a really, really high level of specialization in a field, then you probably are kind of at the cutting edge for techniques and stuff. And so you can find problems to work on. Now, that's a little bit of a different issue from, okay, I want to be an Albert Einstein or something like this. That I think is, is part of it, I think is just luck. You have to be in a fertile territory. But I think you also are kind of looking for, well, because everyone else is specializing, then I have to do something

competitively unique. So, I mean, this is also another consideration as well. Like you have the whole kind of, we're talking about this like everyone else in the world doesn't exist. But the fact that everyone in the world is also kind of pursuing some sort of strategy can sometimes mean that doing something that's sort of objectively suboptimal but is kind of, nobody does that, I think, gives an advantage. I think even my own life is an example of that, like, you know, obviously it's better if you can actually go to MIT and get an MIT degree and get the alumni network and all this kind of stuff. But the fact that nobody was doing the MIT challenge gave me kind of a claim to fame. Like if only one person is doing it,

that makes me kind of more unique. So the value to my career has probably been higher than getting an MIT degree. But I mean, if every single person was doing the MIT challenge, there's hundreds of thousands of people doing it. Well, then, okay, maybe the difference changes. So I think there is a sort of strategic rule in how you learn things. But again, I think one of the things I talk about in the book is sort of, all right, given the kind of goal that I'm optimizing for how do successful people do it.

And so again, go to academia and you say, how do I make things that inch forward the field? Well, you have to specialize. You have to figure out, okay, what's a problem that I will understand the best in the world? And then I can find ways that I can make improvements over it. And that's how our whole academic system is organized. And there's sort of no surprise for that. That may not be the thing that we want as a society. Maybe we want these sort of weird cross-pollinating insights that no one saw coming out of left field.

but I think as just a pure strategy on your own of just doing it, you're definitely going to get into situations where, okay, well, I learn these two things and there's no obvious overlap. And so I've mastered two, like, it's been kind of useless, right? So it's hard to pick those two things. It's hard to figure out, okay, what are the like insights that when I, when I breed these two ideas together, I get like a super idea and not just some like garbage idea that's like actually

much worse than what people are currently doing. The MIT challenge, is that an example of the failed simulation effect? I don't let you explain what that is. But is it that because it's a rare thing to do, that it just seems much more impressive? It's hard to like simulating your mind? Yeah. So the failed simulation effect comes from a good friend, Cal Newport. And the failed simulation effect was from his book, How to Become a High School superstar.

And the idea was that the impressiveness to which we assign things is, not based on how much work that thing requires, but on how hard it is for us to imagine doing that. So his example is that like if you're in like 14 different clubs and you're like the president of the debate team and you're on athletes and you did a bunch of AP courses and this kind of stuff, that's objectively a ton of work. Like it's like to be, you know, the high school valedictorian and do those things, it's a lot of work. But we can imagine ourselves just being a grind doing all of that, whereas when we see someone, let's say, like, you know, one of the examples he gives is a mutual friend of or is Manich SETI who published a book for programming for teens, like through an actual publisher that sold

fairly well when he was in high school. And like that's the kind of thing, like, I can't imagine doing that. Like I don't know how does a, how does a, you know, grade 11 kid do that, right? And so that makes him seem much more impressive, even if, you know, the amount of work is like strictly less than the amount of work that it takes for someone else to do. Now, I think the MIT challenge is a little bit of that example because it's clearly something that like the reason people found it impressive is that when they imagine doing it, they can't imagine doing it themselves and maybe I get the benefit of that. I don't know whether there's the technique that Cal talks about where you're kind of like, he sort of talks about how you kind of make these sort of inroads into a field and then you find

new opportunities. And so the issue is just that there's all this kind of serendipity that pushes you forward to an outcome that people wouldn't have expected. I'm not sure whether that's true with the MIT challenge. I mean, I can explain how I did it. And it's not like someone else could attempt it. There isn't a lot of like, well, I just happened to find the right guy at MIT who gave me access to it. It wasn't like that. I just did it. But I think that, at least in the sort of idea of like, why do people find it interesting? Certainly because of that. I also benefit from the fact that if I had done like just some middle of the road university's computer science curriculum, people would be like whole hum about it. But I mean, most of the work

is just doing the curriculum. It's not the fact that it's MIT. Like I only picked MIT because they post their material online for free. Like if, you know, the University of, I don't know, like Wisconsin's computer science program uploaded their material. I could have used theirs too. And people would have been less impressed. I think I'm really kind of sneakily leveraging the fact that people associate MIT with being super, super smart. And that's because of how strangeer their application process is. And that's the very thing I didn't do when I did this project. So I definitely, you know, looking back on it, like I definitely am proud of the project.

But to me, it's kind of funny that like that's the thing that people fixate on. Because, you know, I also really like the language learning project. But it definitely, I think the MIT challenge is the one that captivates people for some reason. Right. Yeah, I'm studying computer science at UT. And our curriculum is, there's like classes where the programming assignments, they're hard to code up. So they literally just copy them off of the Stanford programming assignments. Like if you're the kind of student, and of course I'm not, if you're the kind of student who wants to copy answers off the internet, you would just look up like Stanford, CS, whatever, whatever.

And that's, you'd find it there. Yeah, so it's interesting how that works. Oh, that's another thing, too, that came in my favor is that MIT's computer science program is very massive. math and theory base, which means that it's intellectually more difficult. So it's like harder to understand the MIT curriculum. Like, you know, when they're doing analyses, they just assume everyone knows calculus, whereas in university, sometimes they'll go easy on you. Like, they won't make you do the calculus. Whereas they make you do the calculus in every MIT class. Like you take the intro microeconomic class. Like when I did it in my school, they never made you do the calculus. They

were always like, well, you find the intersection on this graph or something like this. Whereas in MIT is like, okay, so now we take this integral of this. And, and find the, you know, the Laplacian of the, like they just do that in MIT because they assume everyone has a very strong kind of math background. However, the way that works into your advantage is that like in a lot of other universities CS programs, they have these really long tedious programming assignments, whereas MIT like they really hit the sweet spot of like this is difficult but not a long amount of work. So when I did the project initially, it was, well, I'm only going to do the final exams. Like that was the idea. Just final exams, nothing else. And that was sort of

my justification for like, could you do it in a year? Because, well, you have to learn it. And you have to do a hard final exam, but you don't have to do all this other like busy work that you get at school. You don't have to do every single problem set or essays or, you know, group projects and stuff. And I was getting some flack in the beginning, like it was like the first week or two about like, well, why not the programming assignments? But it just turns out that you can add the programming assignments to that challenge. And it doesn't materially change the amount of work. Like I probably only added like an extra week or two to my schedule to also do the programming assignments, just because, you know, you do the programming assignments and they're

very tight at MIT, whereas, you know, people were questioning me because they went to different CS programs and they're like, well, I spend months working on this program assignments. There's no way you could do it. And it's sort of like, well, that's not how MIT does their course. They're like, it's all like proof by induction and you're doing things. Like, it's all like drawing graphs on pencil and paper. So I mean, from a practical point of view, do you want to do the heavy programming assignments to become a real programmer? Yeah, probably. But I think if your goal, was could I get the kind of high theory sort of conceptual education of MIT, then I think it works. And to be honest, because I'm not a practicing program, right, I just program for fun.

The conceptual stuff is really where all the value was for me. Like knowing how to design websites is not super valuable for me, but understanding how, like, information works in general actually translates really well when you're understanding, like cognitive science and stuff. So I'm actually glad that I did it that way, but I mean, my preferences are probably different from people who are like, well, you study CS so you could get a computer science job so you could be a programmer so you can, you know, do that. And that was just a different situation for me. Can you explain that? How does the knowledge about computation, you know, how to encode and transmit

computational information, how does that relate to the research you do or the research you read in cognitive science? Oh, I mean, like cognitive science is like explicitly overlaps with computer science. I mean, cognitive science as a discipline is usually philosophy, psychology, narrow science, and computer science, right? Like artificial intelligence and stuff. And so when you're reading papers that are talking about, like, I remember one paper I was reading, which don't ask me to cite it because I've lost the link now.

But he was a guy talking about a computational model for how chunking works. And I mean, if I didn't have a background in understanding computer science, this would be a very difficult paper to read because he's talking about how, well, you make these nodes and then you make these links. And that's how you can represent a data structure of conceptual information. I mean, if you've only done like, you know, site classes, this is a kind of a hard paper to understand. But if you, if you spent classes doing algorithms where you're trying to design systems that do just that, then it makes sense to you. It's

a natural language. You're like, oh, yeah, that's a linked list or all that. That's a tree structure. Oh, yeah, that's, you know, that's doing this thing, right? Uh-huh. That's a graph or this, like, you understand that. And I think, um, similarly, I think that like, you know, I was just doing a lot of research on motivation, for instance, and there's all this stuff in neuroscience about, like, the motor loop, which is this sort of basic kind of circuit motif that runs through the basal ganglia, and it's kind of how you're, like, you have all this stuff going on in your brain at the same time. It says, like, how does your brain decide to do one and only one thing? Because you've got like a billion neurons, and they're all firing all at the same time. And so it's this pattern that

basically just don't lock everything except the one thing that you want to do. And so, yes, this is a kind of neuroscience idea, but you can really visualize it in terms of like, oh, like, if you had to design a circuit to do this, this is very similar to electrical engineering where it's sort of like, we need to amplify one pattern and like, you know, we have to run it through a filter and there's like this center surround pattern. Anyway, there's lots of stuff like that. But to me, that's not the how do I program stuff in JavaScript. That is a understanding this kind of abstract theory level, which I mean, we were talking about low ability for transfer, but it's more just that you have to kind of get these abstract ideas in both domains for it to work.

And most people don't do that. Right, right. It also sounds like OS and time scheduling. Yeah. So going back to transfer, because you did mention that, but it does seem that your career, in terms of the learning you're doing, you're doing it explicitly because you see a high potential transfer

between the research you're now doing and the computer science you did before. Why are you pessimistic about transatlantic? transfer in general when it seems well i mean we have tons of studies showing that uh that like people who attend classes don't transfer a lot of what they learn to the outside world so again it goes back to my einstein point where i was making it like if you see data which says that x doesn't work for almost all people you should be skeptical of that as a general method for being successful at x right like i mean it just definitely raises the question of whether or this is successful and it's not just that this

only impacts kind of like, well, universities are full of like dull uninspired students, but if like you're the bright student and you'll do well. Like, I mean, one of the studies that I quote in there was that people who were honors level physics students could not solve problems that differed superficially from the ones that they studied. And so again, this specificity with the brain kind of comes down to this idea that like, yes, there is this kind of ability for transfer for you to be able to do this. But that's kind of like the magical bonus that happens sometimes. when you're lucky, but the base reality is, is that when you learn most skills, they adhere

quite specifically to the context you learn them in and when you're doing it. So if you're budgeting for a learning project where you're trying to accomplish a particular goal and your, you're sort of like flow chart of how you're going to do it is do this thing that's unrelated to what I want to be good at, then the magic transfer happens. And then at the end, I'm able to perform this skill. That's like a really bad like flow chart for doing this. What you should do is be like, okay, how do I line these up? And so I think learning broadly, learning for the ability to have these sort of mental models or concepts or deeper understandings and things, like I'm a big supporter of that. I do a lot of that. Like I learn lots of different subjects in the off chance that I'll be able to get one of those

deep insights. Now, I think if we're learning for this sort of concrete purpose, though, it's really useful to think of it in that way. It's very useful to think about it, okay, what is it that allows me to perform in the way that I want to be able to perform? And so as soon as I'm learning for a task, I adjust how I'm approaching things for that task, right? So if I'm doing a research paper, I know what I'm trying to get out of things and how I'm trying to read stuff and I'm not doing something that's totally unrelated. And so I think the directness idea just sort of goes to the fact that, yes, it is good to learn lots of things. And I think that if you do learn lots of things, just through sheer volume, you're going to get some of this nice transfer effects. And I'm not even really against learning broadly.

Like I think you could kind of, a close reading of ultra learning could be seen as like a kind of anti-David Epstein range kind of thesis. But that's not really my point. My point isn't that like, you should only learn really narrow, specialized things and like that's the only thing that matters. But simply that once you decide on what a goal is, it always makes more sense to approach it directly. You know, the kind of having this broad base to build off of is also good in general, but that's a little bit different from I'm trying to accomplish X. So do the thing that leads to X as opposed to something that like, well, I don't want to do that. That's too hard. I'm going to do this other thing that's kind of this, you know, nonsense thing. Right. Two questions.

So first, did you, when you were doing the MIT challenge, did you, did you have the goal to, were you going in with the goal having these transfer effects or were you just trying to learn about programming and computer science directly? I feel like a lot of the assumptions that I went and going to the MIT challenge did not bear out. So there is a weird sort of bittersweet

quality to that like the thing that people fixate on that like I did it is the part that I think was pretty successful. I mean there you could look through my exams and deem that my my sort of scores weren't high enough for your standards or something but like in the general sense I think I accomplished that.

but my idea going into the project was that I just finished university and the kind of, you know, like yourself, I was a student up until that point and school has been my life, right? Like when you're in university, all you know is school. And it seemed like this was a huge part of my life. And it seemed like this was a huge opportunity that like, why aren't more people doing this? Why aren't more people, you know, this was pre, you know, reading about like kind of all the signaling stories and kind of my general level of cynicism about university in general, I was just kind of like, this is, you know, instead of spending hundreds of thousand dollars, you can do it for

free. Like, they're just giving this away. And you could get this knowledge and you could learn it. And I was interested in learning computer science. I had already learned some programming before. So I did know some programming, but I didn't know like academic CS. And I was thinking about going back to school to study it. Like I was thinking about doing like going back as a mature student. doing another undergrad in computer science. And so this just seemed like the perfect opportunity. And because I had a blog and because I was kind of commenting on students and educational issues, it just felt like, you know, is this, is just, am I crazy?

Why has no one done this before? Like, why am I like, and if I'm going to be the first person doing this, like, presumably there's going to be, you know, millions to follow, right? So it seemed very exciting. It's kind of like, this is going to be the road to how everyone's going to do or how a substantial amount of people are going to do education in the future. Now, nothing has changed. I know of very few people who have kind of done anything like the MIT challenge to that scope. I know a handful of people have done their own similar-ish kind of projects. And I'm sure there's

people I don't know of that did things that were similar, but certainly not the massive waves that I had predicted. And so in that sense, it's a little bit like, okay, it didn't quite work out how I was wanting it to work out. But I think at the kind of the more object level, like not my kind of implications of the project, but like what the project actually was, I think it was pretty successful, you know, and I did learn a lot of programming. I did do programming after.

I don't get me wrong, but I think if my goal had been, I want to get the like the entry level computer programming job that is like, you know, would be the path to a career there. I would have done it a little differently. Like I mean, the MIT education kind of primes you more for working on,

not the routine stuff most programmers are working on. it would be more like priming you for working on kind of harder programming problems. So it would have been like a good jumping off point if I wanted to get into, you know, a certain branches of programming where it's sort of like, okay, this is harder to do. Like the average programmer just doesn't know how to do it like because it's, it's kind of complicated and the math is hard and things like that. But I mean, I think it would have not been a bad way to get a foothold into a job, the same way that getting an actual CS degree is a good foothold into a job,

even though the skills are not exactly the same. I think one of the benefits of having done it, though, is just that after you see the mental models and the ideas, you kind of have, oh, these are these abstract patterns that I can apply. But I mean, economics also has a lot of those patterns, and I didn't do a degree in that, but I feel like I get a lot of value out of those two. Yeah.

When you were learning, was there specific way you geared your education so that you would maximize the potential of transfer learning? Well, I think the more you understand things, the better you get transfer. So this kind of goes back to this intuition chunking idea, but like the way transfer works, and I mean, I'm using a model. Like I'm not saying that this is like a scientifically proven thing,

but this is just sort of how I visualize it abstractly based on what I've read. But the idea is if you think of this kind of hierarchical model where you have kind of like at the very low level, there's sort of very superficial features of the things that you're encountering. And then there's increasingly abstract features about the problem. chunking is this kind of finding abstract and more abstract patterns, right? So you're, oh, this is a problem of this type is an example of like, that's not obvious unless you have this, all these base patterns to notice that that's what's going on. And so the idea here is that

understanding is this process. Understanding is this process of kind of building hierarchies of, of information that go deeper and deeper into understanding. And so those tend to be the things that transfer better because the more you understand, kind of quite abstract feature of a problem, the more you can spot it in other domains that you've understood enough to get to a place where you could see that that's the pattern that follows. And so if I were to contrast a different way of learning, a different way that was just focused on, well, I want to have high exam performance. So I'm just going to like memorize the most common solution types and just memorize how to do them. I don't understand why it works, but I'm going to

just do it that way. I think in MIT classes, that's probably a bad strategy just because there's too much diversity of problems. Like, I think that in some schools, they do actually teach that way because the students can't perform, like the kind of novel problems on exam solution sets. And I mean, there are exceptions. MIT did use some, like, oh, this was seen in a previous assignment says. So if I had done that problem, I had a leg up. But I mean, most of the exams you do, they give you kind of novel problems, so you have to have like a minimum level of understanding. But I mean, my general approach to learning is to focus on understanding over memorization. I mean, I'm using some examples as exceptions.

I mean, there are some things that like in language learning, for instance, that I focus on performance over understanding. But I mean, generally, if you're focusing on understanding, that's probably going to lead to better transfer than if you're focusing on memorization. And that had long been a bias in how I approached learning things. So I think, you know, there is probably a case to be made that if you were strictly concerned with performance on an immediately subsequent exam, you could sometimes get better performance with just memorizing. But I mean, even with like a long curriculum, like, you know, they assume you understand the Fourier transform in later classes. So if you just kind of like, well, I'm just going to memorize the equation.

You don't really understand what it does. Then you get to something later and it just doesn't make any sense to you. And you're like, crap, I have to go back down. understand this. So I do think the more that you like really understand ideas and really understand the principles behind them. And if you if you have that as a kind of an aim, then you're learning for better transfer, I think. And I think it just happens to be sort of probably the optimal approach for doing like complex exams like MIT, but certainly not for all schools. But I generally recommend it anyways because, you know, what's the point of just getting a good mark on an exam

if you don't know anything after. It's like useful for anything else, right? Yeah, yeah. Let me ask you about focus. You, you say in the book, shoot, hold on one second. You say in the book that I'm agnostic about whether focus can be trained as an ability in general. Now, unless I'm misunderstanding this out of context, this seems to contradict Cal Newport's book on Deep Work. Am I, am I missing something? Well, I mean, and I mean, Cal and I are good friends, and we, co-instruct a course on focus. So there's a certain sense where we are in agreement on the trainability of focus. Where I'm maybe, maybe I would disagree with Cal somewhat is that I think Cal

views it from a capacity point of view. So it's like he's using the kind of muscle metaphor for focus. And, you know, all the research I've done on transfer really suggests that that's not the right way of thinking about the brain is this muscle metaphor. Like, for instance, doing brain training games that improve working memory, don't help your working memory for things that aren't brain training games. So it's kind of one of those things that if you did a muscle analogy and said, well, I'm going to improve my working memory. You're kidding yourself because that's not how it works.

Now, you can improve your working memory for probably pretty specific things. Like chunking is an example of that or by, you know, developing quite specific strategy. So the question when we're talking about focus is what kind of thing are we talking about here? So if we're talking about the capacity for you to focus, like a kind of a cognitive ability, I think it's doubtful that you could robustly improve that particular faculty just by doing specific training. You could probably improve it in specific ways, but they're going to be, again, specific. So you could improve your ability to like focus in on maybe like certain types of problems because you've built up a kind of like sort of quite specific cognitive strategies

for doing that. Now that being said, well, why even talk about focus at all if I don't think it can be improved? Because I think that when we talk about focus casually, we're not just talking about a kind of cognitive faculty. We're also talking about, well, what are all the habits and routines and affective processes that influence focus? So the reason most people can have focus is it because their brain is incapable of focusing is because their phone's buzzing or because they're like, ah, this is boring and I'm getting distracted and I don't like enjoy what I'm learning or I don't find it interesting. So people lump a lot of things together.

So if I were to be writing like a scientific paper and I was trying to like, you know, making a bet on whether a scientific paper would find that there's like focus training programs, improve the ability to focus in the way that cognitive psychologists typically measure, I'd be a little bit pessimistic about it just because they're probably measuring in this tightly controlled experimental setting. If I were talking about,

are there ways that we could robustly reduce procrastination, reduce distraction, reduce the ability to like get frustrated and give up or like want to, you know, waste your time on something else, then yeah, I think that's totally trainable. I think that that's something that you probably could improve on. And so again, Cal Newport tends to take more of a facultative approach with some of these things. And I'm more skeptical of that. But I think for the average person, what is the message? And I think the message is you ought to focus more. And I think there's probably aspects that a normal person probably ascribes to being the ability to focus that you could improve. Oh, this is very interesting. But when Cal Newport says in Deep Work,

that, you know, the other things you do in your day after you're done with work, you know, your phone, your TV, that those diminish your faculty to focus on your actual work. Is this model wrong then? Is it, am I free to do whatever I want? I think the way it's stated is probably too crude to like, well, what's the model that that he has in mind there? So, and again, I think, like, as a practical consequence, yeah, maybe. I don't know the research so much on that. I think it is potentially possible at least to have, like, tons and tons of focus in your working hours and then just be, like, in this buzz on Twitter all day. I'm a little bit less pessimistic than Cal is that, like, well, that's going to destroy your brain in a kind of way. I don't

know that that's true. But what I will say is that what I think is going on is that from a motivational perspective, Twitter is like the variable reinforcement schedule. It's the Skinner Box that like gives you the rewards intermittently. And so you're willing to press it like constantly and perpetually all the time. And so if you're choosing between Twitter and reading this hard book, then like the motivational gradient is going to be to go on Twitter all the time. Right. And so when we're talking about focus there, is that a cognitive capacity or is that just like your affective ability to like choose this harder thing? And so if Cal's talking about focus in that way, which I think he is, then I kind of agree with him that if you don't, if you're not on Twitter, if you don't have

these variable ratio reinforcement schedules that are constantly like, ooh, it would be more fun to be on YouTube right now. Oh, it would be more fun to check my phone right now. Like if you don't have those things constantly as active primed habits in your, in your behavioral repertoire, it is easier to focus on reading a hard book. And I think that's the way you could get better at reading a book. But as the ability to like, like for instance, I'm, I'm skeptical of people who say, well, I meditate for an hour a day. And so therefore I'm going to have enhanced focus capacity. Because to me, that sounds like the brain training thing, which we know to be false. But if we're talking about, well, I'm removing a lot of these distracting options from my kind of like

list of habitual responses to things, will that allow me to. to sustain endurance and persistence on harder activities. I think that's probably true. So that's my take. Is it a way to think about the analogy here that, you know, you have certain forms of exercise like cross-training that don't seem to be highly correlated with other physical activities

or performance other physical activities. But then you have stuff or you take whatever example you want there. But there's some things like weightlifting, which seem to be strongly correlated with your ability to play football or soccer or whatever. Is meditation? if there's a distinction there, I don't know if there is, but if there is one, can an activity like meditation be in the productive sector? Well, I think meditation can be good, but I think it's important to know what meditation is for.

And I feel like a lot of the research on meditation is quite poor. I say this is a non-researcher, so I'm certainly going to get in shit for it because, like, people are, like, the problem of meditation is that the people who like meditation, and I mean, I've been on multiple meditation retreats. I've done meditation daily for periods of months. So I'm not like just some guy who didn't get it. And like I understand why people benefit. But the problem is a little bit that like it is very tied in with essentially religion.

It's Buddhism. And so I think that that can sometimes, you know, I don't like attacking anyone's spiritual beliefs. And there is a really strong philosophical component to meditation and to what the right way to live life is and stuff. And so I don't want to say like, oh, you shouldn't meditate. or like people who meditate that's bad. I think it's more sometimes you need to question the very specific claims as stated. And so one of the specific claims is that because I meditate for an hour every day, I'm going to be much more focused.

And I don't know whether that's true. Maybe it is. If it is, it's definitely not the mental model I have of how the mind works. But I think that when we're talking about, does Twitter destroy your ability to concentrate, I think that the mechanism for a statement like that to potentially be true is that Twitter is super appealing. It is super enticing. And if it is one of the kind of default habitual responses you have at any moment, you have to put in a lot of energy resisting that, right? Like if you think about like in an era before television, people could regularly like sit and listen to long radio programs.

You know what I mean? Like that was a regular like sit around the table and listen to radio. We don't do that anymore. and why, I think probably because visual media is maybe a little bit more compelling. And similarly, you know, when novels were the only kind of form of entertainment, people would like binge read novels who maybe now would be like binge watching reality TV or or on Facebook or something. And so as technology has developed, we've developed in increasingly enticing options. And so the more you engage in those habitually, I think the more it is hard to reduce that impulse.

Like if you eat junk food all the time, it's going to be hard in a particular moment to be like, I'm going to eat this kale salad rather than a hamburger because your kind of context that you're in is like, oh, you know what would be better right now, the hamburger. Whereas if you never eat junk food or you only eat it in like very limited contexts and it's just like, well, I just eat kale salads for lunch, right? Like it just makes sense for you to do that. It doesn't require willpower. And so I think of it more in this way.

But I mean, Cal has his own mental model of how this. this works and I'm not really a debating type. So if he disagrees with me, I just, this is just sort of my kind of layman interpretation of what I've read about cognitive seconds. Can we go back to the MIT challenge? Because I just remember that article you wrote about why people are getting so much better at Tetris. And you talked about how somebody, somebody did the four minute mile and then now you have hundreds of people who have been the four minute mile. People have been it in high school. Why has it not happened to the MIT challenge? They see somebody, they see somebody you can get a STEM degree at

MIT online. Now everybody's like, I can do it in six months. I have some regrets about that article. And the regret I have is that I, like, it was one of those things where I should have just focused on one explainer and I just threw in the banister thing. And now I regret it. Because actually someone pointed out to me that the banister thing apparently wasn't true. That like the, if you look at the, I don't have the link for it, but they were like, did some of my research. Like that's like a commonly quoted thing that like you broke the barrier. And if you look at like trend lines for performance and stuff. It wasn't the case that like, oh, they needed an example.

Like it was just that we were just generally innovating in kind of the mile long running and it was generally going down like this. And so I don't know. I don't know the full argument, but they were basically refuting that idea that this just sort of like needing an example to do something. There seems to be something to it at the very least that like a lot of innovation is the difficulty of just like coming up with an idea but like once you're aware an idea exists it's like oh that's one of the things i could do but i don't know whether it's the case as i was sort of

claiming in that article perhaps mistakenly that like a known barrier just knowing that it has been surmounted allows people to perform better like just knowing that someone has done x in this time without knowing how they did it or like having some kind of like detail of their method which may require some innovation to figure out um is enough um and so So like the kind of what I wish I had focused on in that article and only made about it because it's super interesting is the speed running community, which like I'm not like super into like I don't do any speed running and I don't really even play video games. But speed running as a kind of cultural phenomenon is just fascinating to me and it's extra fascinating to me because it's kind of seen as sort of low status. Like so this is a bunch of guys in their basement on Twitch playing games. but I just think like this is like people should be studying this like there should be academic papers written about this because the Tetris example like hey which is that Tetris was really really popular in like the late 80s early 90s you know the whole world was playing it and people were bad at it like they weren't that good and now there's this much smaller group of people playing who are insane they're now playing like a level which it like glitches out the game so you can't even see the blocks falling and you can like it's going so fast but also the entire levels glitched out so you can't even see what's happening and they're like

beating those levels and stuff. It's crazy. And I think what this shows is that this is just kind of a natural phenomenon where you're seeing, ah, this is how innovation happens. This is how we make kind of learning progress in society is through these sort of network structures that that a few key innovations unlocked this kind of explosion in performance growth for a field because all of a sudden, you know, the original way that speed running worked is it was through, these sort of magazines or like these kind of old school 1990s websites where people would just like have someone verify it and then they would publish it. So I don't know like I'm not an expert on the on the movement, but I think there was some kind of magazine that they would publish like sort of like some of the

best times and stuff. The problem is you can't learn from that. So what switched up and what changed I think the entire domain was that the proof for that you got a particular speed run time was to post live stream footage of you doing it. And this turns out to be a game breaker because now anyone who wants to see what you did can watch it. So it's one of those things where like not only, it's like the patent system a little bit, like not only to get the patent, you have to show how it works, right? And so you're allowing people to innovate on it. And so I'm very interested in this kind of sociological aspect of learning because I think, you know, I'm a big fan of a lot of work on like kind of progress studies and stuff, just the idea that we are not.

not innovating at the rate we should and at the rate that arguably we ought to be innovating at if we want to solve big problems like global warming and hunger and poverty and disease and pandemics and things like this that we ought to be innovating a lot more. And so understanding the kind of, you know, this was not something I talked about in ultra learning because it was all about like kind of psychology and cognizance, but this sociological aspects of innovation and learning are really fascinating to me. And, you know, another book I recommend is David Witten's, I think it's called the invention of science. It might be the history.

Invention of science, I think. And he's a historian who goes through kind of the scientific revolution. And, you know, this is just one perspective. But like he kind of points out that there were cultural shifts triggered by very particular events happening that created just the right kind of mechanism that created science. So it's very weird to think of science as an invention. because a lot of it's just obvious to us right now, but he kind of goes through rigorously

how European languages didn't have a word for discovery until Christopher Columbus discovered the new world because culturally it was assumed that the ancients knew everything and there was nothing known that they didn't already know it hadn't already figured out. It was just about like, well, let's make sense of their theories. And so Christopher Columbus discovering the new world that there was something new to discover

was itself kind of an innovation. and that the printing press, for instance, changed the culture from a manuscript culture to a book culture. And being a book culture created facts because all of a sudden what was written in a book would be fixed and stay for all times. So you could point to so-and-so wrote this in this particular book in a way that, you know, the ancients were often dubious of experimental evidence because it was just this kind of like, well, you know, things changed. And you thought this one day and you think this the other day. and you need to have some indelible record of what someone said in one moment to even make that process possible. And so he talks about this and I think this is sort of goes into this idea that like, I think speed running, which maybe I'm wrong. But I think speed running is a really culturally

underrated force because it is, I think that's a real innovation that in order to claim credit for a particular accomplishment, you must post a video record of you doing it. which is not true for most fields. Like I just think that this is a real lost opportunity that like, why don't we have in a lot of like even within a company, like even within Google or something, why don't we have, well,

the best programmers who are competing for the top for being the best programmers are constantly on a live stream of what they're programming. You know? Yeah. I mean, it could be a little bit harder because, I mean, there are some certain factors about speed runs that make them kind of amenable to this.

So maybe there's additional innovations I'm missing. I'm sure that people, Google and stuff have thought about this, but my feeling is just that this sociological sort of pattern of like how we gain knowledge not as an individual, but as society, I think is fascinating. And I think it's, we haven't even scratched the surface of all the ways that technology could assist that. So that's a different problem for like how do we, you know, disseminate what is the college curriculum to a larger group of people. That's a totally different problem than how do we actually innovate and improve things in society?

That's fascinating. By the way, you get a 10 out of 10 on an interesting tangent there, going from speed running to the Enlightenment. Have you heard of George Hots, by the way? No, maybe you can tell me what. Okay, so he's a guy who does exactly this. He was, he was the first kid when he was 17 to jailbreak an iPhone, first person to jailbreak a PS2. And now he does live streams on YouTube all the time. He's like folding the COVID proteins. He's doing leak code problems. He's, you know, according to Pascal. There is a kind of nascent community of Twitch streamers and live streamers for certain things. Part of me suspects that there's additional ingredients that are necessary to make this

apply beyond normal problems. Like I even did a live stream project. I was so excited about this kind of thing that quantum mechanics. I did a live stream project for quantum mechanics. Admittedly, it was a little bit kind of lukewarm on the result. I think, you know, it would have been nice if live stream had been a thing when I was doing the MIT challenge. But I think the major problem of that is. that the, it's very difficult to parse what's happening there. Like a live stream for a speed run

could be like four minutes so everyone could easily watch it and study it. Whereas if I've got like 400 hours of me doing a task, like it's kind of impossible for you to watch it and really easily learn from it. And so I don't know, there's probably additional things that are necessary to make that broadly applicable. I think maybe just live streaming alone is not enough. And I think that's maybe why we haven't yet seen an explosion is that there's like one or two more innovations that we need to lock that in. But I'm bullish on the fact that we don't really understand innovation at a sociological level. And I think that this is an area where if we had a better theory of innovation and a better theory of how we could do that culturally and socially, you know, rather than just

like the typical way that I think is proceeded where we're trying to explain how we've innovated up to this point rather than like, well, what are the additional kind of social innovations that would themselves prime more innovations? I think there's lots of opportunities for technology that we haven't scratched yet. It's interesting how peer groups play into this. This podcast is named the Lunar Society after a group in England during the Industrial Revolution that was fantastically productive that included Charles Darwin's grandfather, that included James Watt, that included a bunch of the important industrialists and scientists at the time. And it's an interesting question why these kinds of congregations of people that

defined very productive cultures and time periods, like in Florence, like in England, why they haven't been congregating and happening across the internet where people are connected all the time, right? Like, where are the modern Michelangelo's and the other artists meeting up today on the internet too? I don't know. So yeah, like the whole reason of like why is, you know, and I've become persuaded by the thesis that progress and individuals, have stalled in in the world that like you know the the first half of the 20th century had more innovation than the second half and I mean we're only into the you know we're not even halfway through the first half of the 21st century but I mean I one of the arguments that was given is that like well

we see all this innovation in computers but in the first half of the 20th century or in the second half of the 19th century, there was huge innovations everywhere, like in every single aspect of life. And so it's the fact that innovations have been confined to a single kind of industry. And I mean, even like startups and stuff, they're kind of, let's apply the internet business model to industry X has become kind of a motif. So it's hard to even say that that Airbnb and Uber represent separate innovations, that they're both kind of like, what if we take the hotel industry and the taxi cab industry and run them like their software industries and it turns out that works. So it's kind of hard to even sort of think about computation and stuff as

being like how many of those are discrete innovations. And so I don't know. I think that like, you know, there's some arguments that we have a different culture than we did before, that like there's been an increase in in sort of barriers to innovation. It could be it could be related to just low-hanging fruit. Like, you know, making advances in physics in Einstein's day could happen by a guy and a patent clerk and now, well, theoretically at least is requiring, you know, to figure out that there's the Higgs boson required, you know, billions and billions of dollars at CERN to build like the largest machine on our.

earth to like to just figure that out. I mean, you know, we're a long way from when he figured out brownie in motion and just like kind of in back the envelope. This is how big atoms are by just looking at like dust in in a water glass. Like there may be the case that we've just found a lot of the easy innovations. Or it's possible that there is, you know, spaces that are unexplored, but we haven't found the kind of first frontier innovation there. So, you know, Alan Turing writing the kind of first paper on the computer, I mean, this opens up an entire explosion of fields for research and thinking and stuff, but it kind of hinges on someone sort of opening that crack and then you can have this kind of explosion of growth that kind of leads

off of that idea. So I don't know. I don't know what the reason is why things have seemed to have slowed, but I definitely think that if we had a better understanding some of the principles of innovation and of like how it works sociologically. And I say this not necessarily being an expert even on that literature. I'm sure people will point out all the things that we have discovered on that. But I think that this is something that if we understood better, it could lead to, you know, more experimentations in how do we think about group structures, how do we think about networks, how do we think about prizes, incentives, cultures even of innovation that might lead us there. because, you know, I think, I definitely think this is not a zero-sum game.

Innovation is the most positive some game there is. So I think that, like, you know, if there was some part of the world that became a new Silicon Valley for something new, that would be very, very good for the world. And yet, you know, what are the things preventing that? Yeah. For the people listening, I recommend that you listen to my podcast with Jason Crawford about the Roots of Progress, the Murray-Rights, and also Caleb Wadney. Jason Crawford as well, right? Yeah, yeah. I had him on the podcast a while ago.

Let's talk about Paul Graham's new essay that just released yesterday called Early Work, where he talks about how the products of your, when you're starting off a project are not going to look that impressive. And he gives various methods of just kind of getting over that hump and kind of ignoring how unimpressive it seems. What do you think of that? I mean, I think it's true. I've written about it before, perhaps less eloquently than Paul Graham or with less

authority probably, but I've written about this before. I think one of the things that, you know, and I feel this myself, is that there's this opportunity cost problem. And I think this might itself be one of the, like we're talking about like what are the things that deterrent in a nation is that once you get good at something and it starts producing rewards, it's very difficult to go kind of like climb down that gradient and do stuff that's like quite unrewarding. And so maybe this is also part of the problem. Like we're talking about. about how do you perform, how do you do things? And the issue is that like when you're a kid,

you're bad at everything, right? You're objectively, like kids are objectively terrible at all things. And I say this like having a son. And so like apologies if you listens to this one day. But they are. Like they're not good at anything. But we praise them for doing stuff and for trying things and for exploring their talents. And, you know,

I think there's probably some developmental parts of it. But like kids try and do lots of things, right? They like, it's normal as a kid. to, you know, do art and to do every single kind of sport and to, you know, do all these things. Like, we don't say, oh, yeah, you're not good at this. You better stop now, right? But as adults, we have this threshold for like, okay, well, you need to be at least this good to even be doing this, right? Yeah. And so I think that prevents us from doing a lot of things. Some of that's probably wise. I mean, you know, if you spend all your life doing things that you're not as talented for,

then maybe you won't spend time doing the things that are good. And I think, you think that they're in a limited lifespan, there probably is a kind of exploration training phase. And then like, all right, that was the best opportunity for you. Now you just work on it for 15 years to make your mark. I think there's probably some merit in that as well. Like, I think it was even Robin Hansen who kind of made the point that like, you know, your 20s and maybe even your 30s or for like training. And then like your 40s and early 50s are like make your mark. And then you're done. Right. So like you kind of have to think about your career life cycle in that way. And even for me, I feel like I'm transitioning.

more into the exploit versus explore mode of my career that when I was 23 and I was doing the MIT challenge, I was really kind of, well, there's just this huge vast terrain of areas I want to explore. I just had at university, so I'm learning languages, I'm doing all sorts of different things, whereas now I kind of feel like, oh, these are the sort of my strengths and weaknesses and what I've done well. And so like where can I really apply that to make an impact? So I do think that there is some benefit of that. But I do think that part of the challenge is just that, I don't know, I feel like people are just way too unambitious in general and not in like the ambition like I want to be better than other people way, but they just don't think of big projects. They don't work on them. They don't have like, you know, big dreams to do cool things. Or if they are, it's usually just something like, I don't know, you just boils down to something like social status. Like I want to be the, you know, the person that does this that's better than other people. And.

I don't know. I feel like, I don't know how you change that, but I do think that rewarding kind of a culture where you want to do kind of ambitious, original things that are kind of interesting and you don't know where they're going to lead. I think that that's having that in you is kind of rare. And I think that cultivating it is probably good for yourself and society. you inadvertently answered my next question which was what kind of advice would you give to a 20 year old and it's you sound it sounded like we're talking about being more ambitious is there anything else he would say what would I give so people always ask me what I would give myself as advice for a 20 year old and it's weird to say this it sounds like really arrogant to say this but when I go back there's not actually a lot that I would change and I think part of it is just that I actually have respect from my younger self because I think as you get older you get more complacent and soft in ways so there's was things that I did where I was like, ah, I was really balzy that I did that, that I don't know

whether I would have the guts to do now, which is a weird way of thinking about it, because we often think in terms of like just strictly increasing capacities. But I think, you know, and I think also doing things under uncertainty is different. Like it's very easy to look back with hindsight, know something worked out and be like, well, I wouldn't have worried so much about X, but like maybe because you were worried about it that actually happened. So I tend to avoid doing that. But I thought it'd be like generally speaking about what I would think about. My first thing would be that I think one of the tendencies I had when I was younger was a stronger compulsion to like monetize isn't the right word, but like prove things would be successful.

And I think the MIT challenge and the year that English were a little bit of a like kind of a dip in that. And I think they only happened because I'd already kind of got my kind of blogging business and this sort of thing underway. But I think where I sort of recognize more is just that if you do have that kind of personality where you have some prospects for things, you're a little bit clever, you're willing to work on interesting innovative projects. Just working on really interesting innovative projects and like pouring your whole heart into them, it's probably what you should be doing. whereas you see people like sub optimally trying to optimize for things like, you know, like here's how I can make a little bit of money doing X when it's sort of like, no, no, no, you're working on like the coolest thing and now you want to like find some way to like, you know, do something else.

So I'll give an example. Like I know someone who was planning on going into medicine. I mean, this isn't an innovation example. It was planning going into medicine, very smart. But then gets a waitressing job and is like earning good tips, like earning for, her at that time good money. And so that stealing away energy from studying, she doesn't go into medical school and that path closes.

And to me, it's sort of like optimizing for waitressing salaries is totally the wrong thing to do at that point in your life, especially if you're bright and you have some ambition. You should have totally been all in on the medicine path because it has way higher value in the long run. But I think it's one of those things that when you're 20 and you know, maybe your family's not super rich

and you see yourself making a lot of money, and you're like, I could have a house at 23 or something like this. It's very tempting to go down that route because you have this limited experience. And so I think what I would kind of change maybe about my own perspective is that if you have some ability and you have like,

and you know this about yourself, if you're listening to this, you know, you're reasonably clever and you're hardworking, you're listening to podcasts like this and you're not just like, okay, how do I get through the day? I have some ideas and stuff, then I would be trying to like, yeah, you need to earn enough money

to live because you can't always get in a situation where people will prop up your kind of ambitions or fantasies. And I do think it makes sense to do things that have some reward potential because if you do things that are just totally crazy and they have no basis in the real world, that also might be a waste of time. But I think prematurely trying to kind of, okay, this is what the opportunity is and I'm going to like get the value right now out of it seems to me to be like a kind of something that super talented people do maybe mistakenly. That's very good advice. That if you could just like, no, no, no, just keep going down and doing kind of crazy, interesting

kind of projects that build your skill and do this kind of thing. And like, because you're basically increasing the quality of problems you can work on in your 30s and 40s. And then if you're working on really high quality problems when you're, you know, 35 or 45, like that's where you want to be rather than I'm, you know, finding a way to make a little bit of money when I'm 22 or something. I mean, that's hard to do. It's hard to do, especially if, you know, like I also didn't come.

It's not like my parents were super rich or something like this. Like I paid my way through university and I scraped all the money when I was doing the MIT challenge and stuff. But I think there is a difference between I am paying the bills versus, ah, is like an okay immediate sort of making a bit of money opportunity or this is an okay immediate like get me a bit of status opportunity and i'm going to kind of run for that rather than investing in these longer-term ambitions when i ask for advice for 20-year-olds i'm always doing it under the pretense of i'm really asking for advice for me right but that that was one of the

that's my advice for you then is like you know work on stuff so that like no and i think it's true is that and I think there's like kind of a bit of status anxiety that would like when you're young you're kind of like you want to prove yourself and like you want people to respect you and you want to like earn that kind of social credit right now because and I think it depends on your reference group like I think if you have a high quality reference group they'll push you in those ambitious directions but um you know and especially I feel like my background like I didn't uh like I grew up in a small town and like small towns are kind of notorious for like it's like ambitious people leave small towns. there was very much a kind of like the crab pulling the other crabs down out of the pot kind of situation I felt like in high school. And then I went to a sort of middling university where again there was like, you know, there weren't a lot of people around me who had that kind of perspective on things. And so when that is the sort of surrounding culture, I feel like there is more anxiety and pressure to like, I need to prove myself right away that I'm doing the right thing.

And so maybe even the sort of way of doing it and maybe the value of. schools like MIT and stuff is if you surround yourself with people at MIT, then you're kind of like the idea of, okay, I'm going to do some world changing innovation and I need to build up the sort of knowledge and capital to reach that point feels more natural than it might be at some other university where it's like, well, why are you doing this? I'm making all this money doing this side gig great now and you should be more like me or something like that. Very good advice. I actually have a vivid example in my mind of how that's directly applicable to me. final question you said you now have a son i i you so the last chapter of your book is it was very

fascinating was my favorite chapter because it's like transitions like a malcolm gladwell book uh and i want to ask so i'll let you explain the example of judith oh i forget the last name you did have you did polgar yes have you been utilizing that the example of her teaching or your son you know there's no way that i would do that i think for me the the the the Polgar example. And I do have a little bit of regrets now because the Polgar's are kind of like, it's a little bit overused as a story. It's a little bit like the reason I didn't put Einstein in the book, wasn't because he wasn't a good example because he's too well-known. But the Polgar's have been kind of like, you read books in this genre and they're in a lot of books right now. So I only

really kind of realized that belatedly. But I found it very interesting. I definitely thought that it was a kind of interesting experiment. So in the same way that nowadays, I don't really think that like the average person should try to do the MIT challenge, but it's kind of a like, this is something that could be done and use it as a kind of guidepost of things that are interesting there. And so for me, and maybe this is my own cultural biases, but I definitely don't have the kind of attitude toward parenting that like, I'm going to engineer my son to be like something. I think what I want to do is to give him access to experiences so that the kind of person he wants to. He wants to, become is sort of influenced at least by, you know, what kinds of things did I show him were possibilities.

And so this is sort of the attitude I take with my readers and I take me my blog is that I don't like the kind of manipulative approach to, here's how I twist and prod you into the thing. Rather, I always feel like the best way to do things is to set an example, give people resources, show them how it's done. and allow them to kind of become their own person. And that sometimes means making their own mistakes too. Like you think that they should do X and they're not doing this. So I'm giving kind of like my younger self advice. But when my son is, you know, in his young 20s,

he may see things differently from me. And I'm certainly not going to be in a position to like, I don't know, no, no, no, you need to do this or that. But I think that the main kind of lesson in my mind of the kind of poker example is just how far you could go, like how far. far you could go in sort of changing someone's kind of like base nature. And it's definitely a provocative experiment. I think certainly a counterweight to a lot of the ideas that you get from psychology and certainly behavioral genetics that like most of us are fixed and we have no

changeable traits. And like it's all like this is just certainly definitely a, it kind of seems like a counter example. It seems definitely like a, hmm that's interesting you know that you just decided that you're going to have a chess prodigy and you had three like it's uh it's definitely interesting but i think as an example i feel like um to me the idea that we are authors of our own life is like super central to my value system that i would never want to rob my son of that feeling um that you know that whatever he chooses to do and excel and believe in life that this is something that, you know, he is the, he is the agent of as opposed to like, okay, I'm living my dad's life plan for me chapter, chapter three or something like that.

So that's my stance on it. I know people certainly differ, but that's how I think about it. Well, Scott Young, thank you so much for being on the podcast. This was the most fun podcast I've done and certainly the most personally interesting and useful. Oh, wow. It was a great pleasure to have you. You have a great guest list, so I'm very proud to have ranked so highly. Thank you.

Thank you.