In this episode we discuss powerful thinking tools and strategies you can use to break through tough problems and give yourself confidence and clarity when you’re dealing with uncertain situations. We share the breakthrough strategy that was used to invent astrophysics, explore how you can make tough life and career choices, and show you how you can use quick experiments to test, learn, and get results quickly. We share all of this and much more in with our guest David Epstein.
David Epstein is the author of Range: Why Generalists Triumph in a Specialized World, and of the New York Times bestseller The Sports Gene. He has master's degrees in environmental science and journalism and has worked as an investigative reporter for ProPublica and as a senior writer for Sports Illustrated, writing some of their most high-profile investigative stories.
We don’t teach the skill of actually THINKING in today’s world.
There’s a HUGE advantage in connecting ideas and learning how to think broadly, especially as people specialize more. The more and more people specialize the more powerful range and broad thinking becomes.
For much of the 20th century most of progress was driven by specialization, but beginning in the 1980s, most breakthroughs started coming from multidisciplinary combinations and breadth, not depth.
The “cult of the head start” - the drive to specialize as narrowly and as early as possible.
What can we learn from the story of Tiger Woods?
In almost every sport, athletes start out with a “sampling period” and “systematically delay specialization."
Traditional chess is an activity where early specialization is really important.
Grandmaster’s advantage in chess stems primarily from deep pattern recognition.
Moraveck’s paradox - humans and machines have opposite strengths and weaknesses.
In freestyle chess, you outsource the pattern study to the computer, and you focus on the higher level strategy - it becomes a completely different game. That’s what has happened to success in today’s world.
You see the same pattern play out in ATMs and Bank Tellers, and across a wide swath of industries and domains.
“A broader set of integrative skills” is where humans can add the most value.
How “wicked learning environments” like business, investing, medicine, and human interaction are much trickier to navigate, and what that means for how you learn and improve
How do we play “Martian Tennis?” Where there are murky feedback loops and things constantly change.
Learning and improvement in “kind domains” vs “wicked domains”
Using “Fermi Problems” to navigate tough situations and learning environments
Why you should ask yourself “How many piano tuners are there in New York City?"
The Importance of “broadly applicable reasoning tools” over highly specific knowledge
The powerful thinking tool from the inventor of astrophysics that you can use to understand confusing and difficult phenomenon
Analogies are one of the most important tools for creative problem solving
Successful problem solvers are more able to determine the deep structure of a problem before they proceed to match a strategy to it.
Come up with an enormous number of analogies, as many analogies as you can, from different domains, with a structural similarity
“Switchers are winners” - why changing your job or changing what you study can end up being a huge win for you.
The economics concept of “match quality” and how it can impact the direction of your life
Who wins the tradeoff between early and late specializers?
Early specializers jump out to an initial lead, but then eventually lag behind and get left behind by the late specializers
Grit is great, but strategic quitting can be a great thing. Even the researcher of Grit, Angela Duckworth, supports changing directions.
It’s important to try things, and quit things, to find the true “match quality” for what can make you happier and make you a better performer
“We learn who we are in practice, not in theory.” - Herminia Ibarra
Which among my various possible selves should I start to explore now? How can I do that?
Start with quick experiments, test and learn, don’t begin with grand plans
Create a “book of small experiments” and start testing the things you might want to do or learn
Taking a beginning fiction writing class helped David become a better nonfiction writer.
Replacing the quotes with his own narration to make it more clear
How you can use the Japanese concept of “Bansho” to improve your thinking and become a more effective learner
“Making connections” knowledge vs “Using procedures” knowledge. Drawing broad and deep connections instead of learning routines.
Sometimes rapid development can undermine your longer term develop.
You want to develop deeper more flexible knowledge.
The power of using “Interleaving” as a learning method.
Forcing learners into "conceptual thinking" improves deep and longer lasting learning.
Homework: Create a “book of small experiments” and start testing the things you might want to do or learn. Do something new once a quarter. Create a hypothesis of why you want to explore that interest and test the hypothesis.
Homework: Whenever you’re thinking about a project you’re going to take on, you will make predictions about how that project will go, use the ‘outside view’ instead of the ‘inside view.’
Thank you so much for listening!
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This week's episode of The Science of Success is presented by Dr. Aziz Gazipura's Confidence University!
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Want To Dig In More?! - Here’s The Show Notes, Links, & Research
[Scholarly Article] APS - “The Two Settings of Kind and Wicked Learning Environments” by Robin M. Hogarth, Tomás Lejarraga, and Emre Soyer
[Article] Newsweek - “MAN VS. MACHINE“ by Steven Levy
[Article] The Guardian - “'Calling bullshit': the college class on how not to be duped by the news“ by James McWilliams
[Article] Scientific American - “The Interleaving Effect: Mixing It Up Boosts Learning” By Steven C. Pan
[Wiki Article] Fermi problem
[Wiki Article] Moravec's paradox
[Article] tdaxp - DUNCKER’S RADIATION PROBLEM
[Book Review] The New York Times - “Remember the ‘10,000 Hours’ Rule for Success? Forget About It” by Jim Holt
[Article] Morning Brew - “A Conversation With "Range" Author David Epstein” by Alex Hickey
[Article] NPR - “'Range' Argues That Specialization Should Not Be The Goal For Most” by Bradley Babendir
[Profile] TED Speaker Profile - David Epstein
[Article] Medium - “Lessons from “Range” by David Epstein” by Kyle Nielson
[Article] The Verge - “Why specialization can be a downside in our ever-changing world” by Angela Chen
[Article] ProPublica - “When Evidence Says No, But Doctors Say Yes” by David Epstein
[Article] CBS News - “"Range" author David Epstein explains why generalization beats specialization” by David Morgan
[Podcast] Finding Mastery - AUTHOR DAVID EPSTEIN: SPORTS GENE, CURIOSITY, SELF-DISCOVERY
[Podcast] EconTalk - David Epstein on Mastery, Specialization, and Range
[Podcast] The Learning Leader - Episode #310: David Epstein – Why Generalists Will Rule The World
[Podcast] Good Life Project - WHY GENERALISTS BEAT SPECIALISTS | DAVID EPSTEIN
[Podcast] Art of Manliness - Podcast #127: The Sports Gene With David Epstein
[Podcast] Lewis Howes - EP. 817 You Don’t Have to be the Best
[Podcast] Way of Champions - #116 Why Generalists Triumph in a Specialized World with David Epstein
APB Speakers - Epstein and Gladwell discuss “Range” at MIT - David Epstein
Politics and Prose - David Epstein with Daniel Pink
Next Big Idea Club - An Introduction to "Range" by David Epstein
Mike Matthews - David Epstein on the truth of genetics and physical abilities
Range: Why Generalists Triumph in a Specialized World by David Epstein
[SoS Episode] Self Help For Smart People - How You Can Spot Bad Science & Decode Scientific Studies with Dr. Brian Nosek
[Website] Herminia Ibarra
[Study] Harvard University - The Dark Horse Project
[00:00:04.4] ANNOUNCER: Welcome to The Science of Success. Introducing your host, Matt Bodnar.
[00:00:11] MB: Welcome to the Science of Success, the number one evidence-based growth podcast on the internet with more than 4 million downloads and listeners in over a hundred countries.
In this episode, we discuss powerful thinking tools and strategies you can use to break through tough problems and give yourself confidence and clarity when you're dealing with uncertain situations.
We share the breakthrough strategy that was used to invent astrophysics. Explore how you can make tough life and career choices, and show you how you can use quick experiments to test, learn and get results rapidly. We share all these and much more with our guest this episode, David Epstein
Are you a fan of the show and have you been enjoying the content that we put together for you? If you have, I would love it if you signed up for our email list. We have some amazing content on their along with the really great free course that we put a ton of time into called How to Create Time for What Matters Most in Your Life. If that sounds exciting and interesting and you want a bunch of other free goodies and giveaways along with that, just go to successpodcast.com. You can sign up right on the homepage. That’s successpodcast.com. Or if you're on your phone right now, all you have to do is text the word “smarter”. That's S-M-A-R-T-E-R to the number 44222.
On our previous episode, we discussed what creates great performance at work. We uncovered how you can do better work in fewer hours. How you can get rid of wasted meetings with hacks that you can use to make your meetings radically more productive to finally remove the things that are distracting you and learn the recipes you need to say no to your boss the right way so that you can focus on the biggest things that will create the most value in your work. We shared all of that and many more lessons with our previous guest, Dr. Morten Hansen. If you want to do better work in less time, listen to our previous interview.
Now, for our interview with David.
Please note, this episode contains profanity.
[00:02:20] MB: Today, we have another awesome guest on the show, David Epstein. David is the author of Range: Why Generalists Triumph in a Specialized World, and of the New York Times bestseller, The Sports Gene. He has a master’s degree in environmental science and journalism and has worked as an investigative reporter for ProPublica and is a senior writer for Sports Illustrated writing some of their most high-profile investigative stories.
David, welcome to The Science of Success.
[00:02:44] DE: Hey, Matt. Pleasure to be here.
[00:02:46] MB: Well, I'm really excited to have you on the show today. As I was selling in the preshow, I’m a huge fan of Range and I really enjoyed the book. I recommended it to many different people, and it just touches on such an important theme and idea, this notion that in today's world we have all this information at our fingertips, and the real skill, the real challenge is in a world of deep specialization of tons and tons of infinite information. The real skill that becomes more and more valuable is how do we step back and start to combine things and how do we really teach the skill of actually thinking.
[00:03:17] DE: Yeah, and unfortunately we often kind of don't, right? And I think that's really unfortunate, because people – Some of the work that I marshaled in the book, I wanted to show how big an advantage there is to connecting ideas and learning how to think broadly, particularly as people specialize more, right?
So it's like them more and more specialized people or push to get, the greater the advantages are for people who can kind of look across domains and integrate knowledge. This is showing up in pretty interesting ways in research. Like some of the research in technological innovation that I looked at showed that for a lot of the 20th century, the people making the biggest impacts were those who drilled really deeply down into one area of technology as classified by the U.S. Patent Office.
But then, starting in about the mid-1980s, where we have the explosion of information technology and suddenly huge amounts of information are quickly available and widely disseminated, suddenly you start seeing the biggest and most valuable impacts coming from people who had actually spread their work across a large number of different technological domains, often bringing something from one area where it was kind of normal and putting it into another area where it was rare and more valuable. And that trend has only accelerated, but it seems like our notion of how to be successful really hasn't kind of kept pace with that.
[00:04:33] MB: Yeah, it’s a really interesting problem, because in today's world there is almost a scream or cry or drive to specialize as early as you possibly can to really dig in and focus on one particular thing, and yet that skill set of broad thinking is so powerful.
[00:04:51] DE: Yeah, and I think you mentioned the drive to specialize as sort of early and narrowly as possible, right? Or what I call raise the cult of the head start, basically. And I think it might be useful to sort of share the jumping off point that I start the book with, which comes from the sports world. The introduction I called Roger versus Tiger, basically. I started with Tiger Woods, because I think Tiger Woods is probably the most powerful modern development story. It's been at the core of at least a half dozen best-selling books. And even if you don't really know the details of the Tiger story, you probably kind of absorbed the gist.
His father gave him a putter when he was 7-months-old. 10 months, he started imitating a swing. He was physically precocious. Two-years-old, you can go on YouTube and see him on national television demonstrating a swing. Three-years-old he’s saying, “I’m going to be the next Jack Nicholas.” You fast-forward at age 21, he’s the best golfer in the world. And that has been I think probably the most written about and most disseminated story of development of expertise.
So, against that kind, on the other side of the teeter totter, I put the story of Roger Federer, who played some basketball, some tennis, some swimming when he was a kid. His mother was a tennis coach, but declined to coach him because he wouldn’t balls normally. He went on to play handball, volleyball, skateboarding, rugby, a number of other sports.
When his coaches tried to bump him up a level to play against older boys, he declined, because he just want to talk about pro wrestling after practice. He did wrestling after that, and he just kept trying one sport after another, and was not focused on being great early.
In fact, when he got good enough to warrant an interview with a local newspaper, the reporter asked him what he'd buy if he ever became a pro with his first hypothetical paycheck, and he said a Mercedes. And his mother, who like did not want him focused on sports to that degree was appalled and asked the reporter if she could listen to the interview recording. And the reporter obliged and it turned out Roger had actually said more CDs in Swiss German. He just wanted more CDs, not a Mercedes. So she was okay with that.
And kind of the question I had was which one of these is the norm? Because we've heard of both of these people as adults obviously, but we only hear about one of their development, and what I found was that the research shows that in fact in basically every sport, almost every sport, athletes who go on to become elite start with what scientists call sampling period. They play a wide variety of sports. They learn broad general skills that they later integrate. They learn about their interests. They learn about their abilities and they systematically delay specializing until later than their peers. So I thought that was kind of a good symbolic jumping off point for what I’ve then found in a lot of other domains.
[00:07:27] MB: It's so fascinating. One of my favorite examples from an early part of the book was this notion of chess, and it ties back to what you said a second ago, that in the early part or for most of human history really and the early part of the 20th century, the skill of winning it chess was all about this deep specialization or the memorization of tactics. But then you talked about this new form of chess that’s emerged called free chess or freestyle chess. Can you explain what that is and how the skillset of being successful at that game is completely different and really relates to the theme that we’ve just explored?
[00:07:59] DE: Yes. So to give a little background on that, chess in fact is an activity, traditional chess, is an activity where early specialization is really important. I definitely don't claim in the book that everything benefits from this breadth or this range. So a lot of things do. But chess, the grandmasters advantages in chess is based on recognition of recurring patterns, essentially.
So if you haven't started studying patterns by age 12, your chances of reaching international master status, which is one down from grand master, drops by one in four to about one in 55. So you got to be studying those patterns early because, again, that is the advantage. But computers are much better at recognizing patterns than humans are.
So once we had computers with sufficient power, they blew humans away, most notably in 1997 when Deep Blue beat Gary Kasparov, the best chess player in the world. But Kasparov recognized in the way the computer played something called Moravec's paradox, which is this idea that humans and computers or machines often have opposite strengths and weaknesses. So, Kasparov said, “You know what? I wonder what would happen if we teamed up humans and computers.” And so he helped launch what's called freestyle chess tournaments. In freestyle chess, a computer can play on its own. A human can play on his or her own. But also humans can play with other humans and with other computers, whatever you want. You can play however you want.
To his surprise, the winner of the first big tournament was neither a grand master nor a supercomputer, nor a grand master with a supercomputer, but a pair of amateur chess players with three normal laptops. Somehow, this combination, it turned out that freestyle chess required a totally different skill set than traditional chess. Basically, the computers, you could outsource all of the years of pattern study to the computer, and then the job became thinking about much higher level strategy. Instead of sort of these tactical patterns, thinking about how do you manage the little battles to try to win the war and how do you process streaming information from multiple computers and direct them to search whatever you think is valuable.
So, Kasparov's conclusion was that these amateurs were actually better at coaching the computers than the grandmasters who were sort of used to a very certain type of play were. So those amateurs, they beat the best. They were playing the highest level of chess ever seen, and this is sort of a theme in what is most automatable. So the fact that chess – Chess is amenable to early specialization, because it's based on recurring patterns, and that is exactly what makes it relatively easy to automate.
So we’re kind of in this work era where the things that are most based on repetitive actions and repetitive thinking and repetitive solutions are the quickest to be automated, and the uniquely human skills are this sort of much more big picture, broader sets of skills that require integrating knowledge. So I thought that was a good symbol of kind of where the work world is going, and we’ve seen that in other industries as well, where the years of specialized repetitive experience can be outsourced in a flash, and it makes the entire challenge completely different.
[00:11:09] MB: I just thought that was a really succinct and great analogy of what's happened in today's world and the skills that are required to be successful in the 21st century.
[00:11:18] DE: And this isn't just for sort of chess used to be viewed as kind of the epitome of human cognition, right? But that's not really the case, because obviously it can be passed to computers pretty quickly. But we’ve seen this in all sorts of other places too. When I was going back and looking at – So, in Newsweek's cover of the Kasparov Deep Blue back when Newsweek was one of the largest magazines in the world, the cover was the brain's last stand, right? This was viewed as this sort of showdown. Would a human still have anything to add?
And when I was going back and looking at coverage of technological innovation and disruption, you'd see this constantly. Like there was his big TV series about ATMs when they first debuted, and tons of articles that were all about others, a few hundred thousand bank tellers in the United States and they’re all going to go out of business overnight, because now we have ATMs.
In fact, what happen instead is that there are more ATMs in the US. There was a rise in bank tellers, and that happened because, first of all, the ATMs made each bank branch cheaper to operate, and that meant banks could open more branches. So fewer tellers per branch, but more branches overall. But it completely changed the job from one of repetitive cash transactions, to one where that's all handled by the ATM. And the bank teller is essentially a customer service representative, and a marketing professional, or a financial advisor. They turned into these individuals with a much broader and often including soft skillset instead of someone who specialized in these repetitive transactions.
So those kind of freestyle chess transitions, they really abound all over the place and in domains that actually have had more AI and more computers, job growth has been greater, but it's changed the job to one that requires these broader sets of more integrative skills. If you can do that, then you’re well-positioned for the future.
[00:13:14] MBI love that phrase, broader sets of more integrative skills. But one of the things I really enjoyed about Range is that – A question I've pondered for years is how to apply the method or the lessons of deliberate practice to skills or fields, like business, where the feedback loops are often long, or murky, or even counterintuitive. And you had a great discussion in the book that really brought a new level of clarity for me about understanding this, which was around this distinction between what you call wicked domains and what you call kind domains. I’d love you to explain that.
[00:13:50] DE: Yes. So those are terms coined by the psychologist, Robin Hogarth. And to explain kind domains, again, we can go back to something we've talked about already. Chess is a good one, and golf is a really good one. So a kind domain or a kind learning environment is one in which all the necessary information is clear. The next steps and goals of what you should be aiming at are very clear. Nothing is hidden. Every time you do something, you get automatic feedback that is both immediate and accurate.
So, again, you can think of something like golf where you hit the ball and you see exactly what happens. The feedback is immediate and accurate, and you essentially try to do the same thing over and over with as little deviation as possible. Some of the people that study it actually kind of classify golf as almost like an industrial task. And chess, it's a huge store of previous data. Information is clear. You’re seeing recurring patterns and you see what happens right away, basically, and kind of being accurate.
But again – So those domains, kind learning environments or kind domains are amenable to specialization, because the challenge doesn't change. Work next year will look like work last year. The problem is we've extrapolated those to other areas of work, like the business world, and golf and chess happened to be really poor models of most things that people want to learn, because most of the things we want and need to learn now in this much more dynamic work world are what Hogarth would call wicked learning environments.
Where you aren’t just given all the information that you need, and some will remain hidden, human behaviors involved. The next steps in the goals you're aiming at aren’t just clearly laid out for you. When you do something, you may not get feedback at all, or you may get feedback that's delayed, or you may get feedback that's completely inaccurate.
Matt turns out to be kind of the norm for most of the things that most of us want to do. In fact, in these wicked learning environments, doing the same thing over and over can often have really perverse unintended consequences. One of the examples that Hogarth liked to use was of this – He used a lot of examples from medicine, and one of the examples was this doctor in New York City who got famous because he would predict over and over correctly that patients would develop typhoid, and he could do that just from feeling around their tongues or palpating their tongues with his hands. And over and over, he got it right before they showed a single symptom. So he became rich and famous for doing this over and over.
But years later, one of his colleagues observed that using only his hands, he had been a more productive carrier of typhoid than even typhoid Mary. It turned out that he was actually spreading the typhoid by touching patients tongues with his hands. The feedback he was getting was telling him that he was right over and over again. So he did it more and more and more and more.
Most of us might not be in that wicked of a learning environment, where the feedback actually enforces the exact wrong lesson. But most of us are not in a situation like chess or golf. Most of us are playing what Hogarth would call Martian tennis, where we know things are happening. We see people playing. We can try to interpret what's going on, but nobody just hands us the rules, and they can change at any moment. So work next year might not look like work last year. My guess is, for most people listening, the domain they’re in, they can count on work next year looking like work last year until the end of their career.
[00:17:10] MB: One of the great tools or strategies that you talked about to be more effective at playing Martian tennis or navigating these wicked learning environments were, as you call them, Fermi problems. Tell me a little bit more about how we can use that tool and other tools to become more effective at operating and learning and improving in these wicked environments.
[00:17:30] DE: Yeah. So they’re Fermi problems. Named after Enrico Fermi, the great physicist who led the creation of the first sustainable fission reaction, and they’re called Fermi problems because Fermi found it really useful. First, he would sort of screen some of the people he’d work with, but he also himself found it really useful to make large-scale estimates really quickly so that he could tell if he was kind of going on the right track. So, Fermi problem is one in which you aren't given a lot of information. But you have to kind of use things that you’re already familiar with to try to break down a problem and get a sense of where you should even start to think, essentially.
So one of the well-known ones that I actually got on a college chemistry exam, at time I had never heard of it, but is to ask how many piano tuners there are in New York City. So I literally had this question on a on a college chemistry exam. And the thing is it sounds difficult and kind of obscure. And if first, most people's instinct is just to say like, “Gosh! I don't know. A thousand? 10,000?”
But the point, what you really want to do, is break the question down into all of these tiny chunks that you can actually deal with, with things that you know. So you say like, “Okay, how many people live in New York City? It’s about 9 million. Obviously, everyone doesn't own a piano, and it's probably only families that own pianos. And how large is a typical family? I don’t know. Four, five people.
So, how many families do I think there are in New York City? I guess that would mean – I’m trying to do this in my head. Like, 1 to 2 million. How many families do you think own pianos? I don't know, between one and five and between one and 10. So that would leave you with like something like 50 to 150,000 pianos. And then you ask, “How often do they need to be tuned, and how many pianos can a one tuner tune I a day?” So you go through these estimates, and the thing is none of them has to be particularly accurate for you actually to come out with a pretty good estimate at the end.
So I think based on what I was saying, it would be something in the hundreds of the number piano tuners who can serve all the pianos in New York City. And Fermi found this incredibly useful when he was starting out with a problem of trying to think through where should he start. What direction should he head? Is something that he's trying to do feasible or not feasible? And he used that a lot in development of the first controlled nuclear explosions.
When you're dealing with these problems, these more wicked problems where you don't – You can't just go look it up or you don't have previous experience where you know the answer. Someone can just tell you the perfect answer. Using these sort of broad estimation skills can really sort of help you kind of define the Martian tennis playing field so you sort of know where to start. It's also incredibly valuable for – Like once you start getting used to Fermi estimation. I’d refer anyone who's interested. Go online.
There's a college course called Calling Bullshit that the University of Washington had put up its syllabus online, and one of the classes is about using Fermi estimation to understand really quickly that certain stats you’re being fed on cable news are maybe technically accurate or being completely miss portrayed essentially.
So it turns out to be a really useful skill for the wicked world. Just taking these problems instead of reacting with intuition, trying to break them down into constituent parts and get a sense of what you're dealing with, since no one can really tell you. I hope that makes some sense. Nobody's asked me about that before actually interestingly, I mean, in all of the interviews of done about this book.
[00:20:58] MB: That's fascinating to me, because to me that was maybe one of, if not the most important concepts and chapters in the entire book. And there are some amazing themes and ideas in there. But just this notion that it's something that I think the whole project of this podcast is all about the same quest to teach people these broadly applicable reasoning tools and the way to actually think about the world and how to interpret in today's world. There're so much misinformation and data out, and “data out there” that can be interpreted a bunch of different ways. It's such an important problem to think about how do we shape our minds and think more effectively in these wicked environments.
[00:21:37] DE: I think it's such a great tool. And you're totally right. And it’s such a broad tool, right? So, when I was at Sports Illustrated or ProPublica. When I was doing investigative work and when I'm evaluating scientific papers, like I’ve kind of practiced Fermi estimation when I can.
So, if I get curious about something I see in a newspaper and suddenly I want to know – I don't know. Like how many – I mean, one I was doing the other day was – I was talking to somebody. I was trying to guess how many NCAA track and field athletes throughout in the United States. So it doesn't matter what I was trying to guess. But when I do that, instead of trying to Google it right away, I'll try to do Fermi estimation, and I’ve noticed that once you try to do it, it starts coming naturally to you.
So, instead of just using your intuition, you start doing it whenever you see numbers. And it's been so useful to me when, say, if I'm working on an investigative piece when someone is giving me stats that are misleading. And maybe I only have the one interview to be talking to someone, and so I have to kind of make some estimates in my head while it's going. And you can pretty quickly figure out if they’re really misleading you or if a scientific paper is kind of maybe not portraying it's data very well, or if a business is pitching its data in a way that isn't really representative of what's going on.
I’ve found in readily useful, but it took some sort of practice, where instead of Googling something right away that I’m interested in. I try to actually go to that process of breaking it down into these things that I do know and see if I can get the right order of magnitude.
[00:23:03] MB: It's so funny, and I don't want to keep harping on this topic, but it is really important. And we have a couple great previous interviews that talk about how to decode scientific studies and see through some of these things. So we’ll make sure to include those and the information around the calling Bullshit course in the show notes for listeners to be able to dig into that even more.
Another topic that I thought was almost parallel to this, and there's many recurrent and related themes in the book, obviously, but the story of Kepler and the toolset of thinking by analogy, to me, really mirrored Fermi thinking in many ways and I thought was a great skillset to solve complicated and confusing challenges in today's world and these wicked learning environments.
[00:23:44] DE: Yeah, and I think – So, the story, just in a nutshell, the story of Kepler. And I studied astronomy in college, and so I'm prone to use stories of astronomers. But, essentially, Kepler kind of invented astrophysics in the sense that when he started his astronomical investigations in the 16th century, astronomers thought that the heavens were – Like all heavenly bodies were riding on these invisible crystalline spheres and you just couldn't see them, but everything did the same thing for eternity, and that there were these souls inside of the planets that caused them to move how they did and all these sorts of things. He started to see things that didn't comport with that.
Like he saw a comet go across the sky in Europe and said, “Wait. Why haven't –” Like really close to the earth, and he said, “Okay, why didn’t that break the crystalline spheres?”
He kept having questions about things that didn't fit. Like he saw a supernova, which is the light from the death of an exploding star, basically, and said, “Wait, but nothing is supposed to change in the heavens. So something seems wrong.”
He pretty soon realized that you for 2,000 years, before him, essentially, these were the beliefs about the universe. And all the sudden he realized that some of them are probably wrong. But he didn't have anything to go on, because he was so far outside of traditional knowledge that he didn't really have much to work with. So he turned to analogies saying, “Okay.” He noticed that the planets had different motion based on their relation to the sun. He said, “Gosh! Is there something about the sun that is causing the planets to move in these patterns?” Of course, it is. It's the Sun's gravity.
But there wasn't even a concept of gravity as a force at the time. There wasn't a concept of any forces that work throughout the universe at the time. So he would say things like, “All right. Well, maybe it's not the sun, because the sun can't be touching all of the planets, right? The planets were supposed to have their own souls that move them around.”
But then he'd start to think and say like, “Well, is it possible to affect something without touching it?” And he just read about magnetism and he said, “Magnets affect things without touching them. So maybe it is possible.” He said, “Maybe, in fact, it's the sun's light, because there are some force that would have to show up at the planet to cause it to move, but you couldn't detect anywhere between the source and the planet, and light is like that, and you shine it from a source, and you can't detect it until it hit something. So, maybe that was proof of concept.”
Basically, I don’t want to draw the story out too much, but he just started going from one analogy to another of trying to decide what was possible in the universe, essentially. And by the end, he essentially figured out that there were laws according to which the planets moved. He even laid down kind of a precursor to gravity and figured out that the moons affect the tides and things like that, which even Galileo made fun of him for thinking, but he was correct.
So he was the first person who sort of took the heavens out of the realm of kind of mythology. And so the day two work based on physical laws, and because he was doing this novel problem-solving, right? This wicked problem solving where he couldn't just look at past patterns. He had to try to draw analogies from other areas of the world. And that turns out that analogies are basically one of the most important tools for creative problem-solving.
So, one of the researchers I write about in Range, a guy named Kevin Dunbar, spent a huge amount of time in scientific labs figuring out why some do and some don't make breakthroughs. Essentially, what predicted breakthroughs – Breakthroughs usually came when a lab – Something happened that wasn't expected. At first they would think it was wrong or a mistake or some equipment was broken or something like that. If it kept showing up, they would then say, “All right. There’s something real here. What we do with it?”
What predicted whether a lab would make a breakthrough or not was essentially the number and breadth of analogies that they could draw on to try to start thinking about how to attack a problem. So, in labs that had only experts in sort of one field. One of the labs he studied was all E. coli experts. They didn't have baton of range to bring different analogies to the problem.
In others, there'd be like a med student and a physicist and a chemist and an undergrad and all these sorts of things, and those labs were much more likely to have breakthroughs, because they would start tossing out all of these analogies for thinking and something would resonate with the structure of the problem they were facing. And that would give them kind of an approach to take. This shows up all over the place.
So, the problem is I think our structures work against people developing these thinking skills. So, when I went to spend time with a woman named Deidre Gentner, who’s probably at Northwestern University. Probably the world's expert in using analogies for problem-solving. She came up with this test, a test site how well people can solve problems outside their sort of area of specialization, basically. Problems they haven't seen before, essentially.
And she tested on Northwestern students, and what she found was plenty of them were pretty good or quite good at solving problems that they had already seen in whatever their major was. But when it got out of something they'd seen, the students who did the best for these ones who didn't have a major, they were in this program called the integrated science program, where they just had lots of little minors that taught them how different disciplines approach problems. So they did the best. Then when I went around and talk to her colleagues, they would say, “Yeah, we don’t really like that program, because those kids are falling behind, because they don't have a real major.”
So here you have the world's expert in this kind of very important problem-solving saying, ‘Here are the kids we’re doing the best with,” and her own colleagues saying, “Yeah, but they're getting behind.” So that to me was sort of one of the kind of perverse outcomes of our drive toward specialization, where we can look at the people who are actually doing the best problem-solving and say, “Yeah, but they’re behind.” That seems crazy to me.
But anyway, we don't – Normally, when people think in analogies, we think in the first one that comes to mind. It’s like Kahneman's availability heuristic, whatever dramatic analogy comes to mind. And actually the science is pretty clear that if you want to be a more creative problem solver, what you should do is come up with an enormous number of analogies. Like come up with as many as you can from as many different domains as you can that seem to have a structural relation to the problem you're working on, and it has an enormous impact on people's ability to successfully creatively solve problems.
[00:29:55] MB: That was such a great chapter. And another example that I thought was really interesting was the – I think thing is called Dunkner’s or Duncker’s radiation problem.
[00:30:05] DE: Yeah. Yeah, do you want me to – I can give Duncker’s radiation problem, but I feel like I've already like rambled too much on analogies.
[00:30:11] MB: No. No. No. It's such a great toolset and such an important thinking tool that I think it's worth sharing Duncker’s radiation problem really quickly so that people and get a sense of how you can – Because you can have the realization in real time as you explore that to see how simple they can be.
[00:30:27] DE: Yeah. So, Duncker’s radiation problem is, “Okay. Everyone try to solve it ready.” It's basically you’re a doctor and you have a patient who has a deadly tumor in his stomach and there's a kind of array, a medical array, like a radiation, that can be pointed at the tumor and can destroy the tumor.
The problem is at low intensity, the ray will arrive at the tumor and not destroy it. But at high enough intensity, to destroy the tumor, the Ray will also destroy all the healthy tissue that it passes through on the way to the tumor. So, how can you save the patient by destroying the tumor without damaging any healthy tissue in the process? So that's the question. If you are in the actual study, you'd get more time to think about it.
But while you're thinking about that, here’s another story. Many years ago, a general wanted to capture a country back from a brutal dictator, and to do that he had to capture a fortress in the center of the country. And he had plenty of troops to be able to do that, and there were roads that lead to the fortress radiating out like spokes on a wheel from that fortress. The problem was they were narrow and they were strewn with mines.
So, he walked all – The general walked all his troops down one of those roads. A lot of them would be killed by the mines and they might not be able take the fortress when they got there. So, the general said, “You know what? I'm going to split up my troops into smaller groups so they can walk down the road without setting off the mines, and then we’ll spread them around the various roads and synchronize our watches and we’ll arrive at the fortress at the same time.” So that's what they did and they overtook the fortress.
So in some famous problem-solving studies, almost nobody gets the first radiation problem initially, but then about a third of people get the radiation problem after they've also been told that story that I just told and they have some time to think about it. And now here comes a final story after which most people eventually solved the first problem. So, in this final story, there’s was once a small town. There's a fire in a small town in a barn and it was in danger of spreading to houses nearby, but it was near a lake. So neighbors came out and started getting buckets and throwing water on the fire while it was still smaller. But they couldn't get it to go out.
Eventually, the fire chief showed up and said, “Okay. Everybody, stop what you're doing. Go fill your buckets with water and then come back here,” and he arranged them in a circle around the fire and said, “1, 2, 3, we’ll all throw once,” and they did that and dampened the fire and soon it was out and the fire chief got a raise.
So after people get that story, actually, the majority of people solved the initial story. So, again, you're not getting as much time as a person would in an actual study. But the answer is that you can arrange the medical raise in a circle, essentially, around with the center being the patient's tumor, and you can have each individual ray pass through healthy tissue at low intensity, but they all converge at the tumor in high enough intensity to destroy the tumor.
So, the point of this study was to test how much giving analogies structurally similar to structurally similar problems improve people's problem-solving. And it turned out to be that it took the groups – It took people from almost no one solving the initial problem to most people solving the initial problem, and this is kind of a theme in studies of creative problem-solving where if people can come up with relevant analogies, they are vastly more likely to come up with a successful solution to a problem.
[00:33:59] MB: Hey, I'm here real quick with confidence expert, Dr. Aziz Gazipura to share another lightning round insight with you. Aziz, how can our listeners use science to get more dates with people they really want?
[00:34:13] AG: I love that question, and the answer is the science of confidence. So whenever we’re struggling, we want to date. We’re afraid to put ourselves out there. We’re worried on some level that we’re going to get a negative response. If you didn't have that worry, if you knew that this person you’re going to ask out was going to say yes and be excited to go out with, we’ll all be doing it without hesitation.
So the thing that stops us is anxiety, is fear, is self-doubt, and that is a confidence issue. So if we build our confidence, all of a sudden we’ll have way more opportunities to put ourselves out there and to date. So sometimes we think, “What's the pickup line? What’s the thing I should say? How do I approach the person?” We get so focused on the how, and what we want to do is we want to take a step back and say, “How do I actually change what’s going on inside of me to feel more confident?”
There are so many ways we could do, and I have a course called Confidence University where I have a whole course on dating mastery. But one major tidbit out of that one is right now you have a story in your mind about why you're not attractive. Why someone wouldn’t be over the moon to go on a date with you? You want to find that story and take it out, uproot it.
So right now think about why you not attractive and how can you change that story to see yourself as someone who’s actually highly desirable? What are your qualities? What do you bring to a date or a relationship that would make someone love spending time with you? If you get more clear on that, all of a sudden a lot of your anxiety and fear are going to evaporate.
[00:35:40] MB: Do you want to be more confident and get more dates? Visit successpodcast.com/confidence. That’s successpodcast.com/confidence to sign up for Confidence University and finally master dating.
[00:35:58] MB: I want to switch gears and discuss another theme or idea from the from that I thought was so important, which is this notion of switchers being winners and how changing direction sometimes, which we are doing now in the conversation, can be really beneficial.
[00:36:16] DE: Yeah. So, there's a lot of evidence that when people switch, and particularly we’re talking about jobs or what they study, that they are doing so in response to information about what economists call match quality, which is a term for the degree of fit between an individual’s abilities and their interests and the work that they do. That turns out to be incredibly important for their sense of fulfillment, for their performance, and this importance of sort of doing some quitting in search of match quality shows up in a whole bunch of different areas of research.
From higher ed, so one of the studies I enjoyed, was an economist who saw a natural experiment in the higher ed systems of England and Scotland. In England, in the period he studied, students had to specialize in their mid-teen years to decide what program of study to apply to. And in Scotland, they could continue sampling different programs of study all the way through the end of university, and his question was, “Who wins the tradeoff? The early or late specializers?” The people who have to pick early or those who can kind of try different things and do some quitting or what scientists call sampling, since it’s less derogatory.
It turns out that the early specializers do jump out to an income lead, because they have more domain-specific skills. But the later specializers get to try multiple different things, and in doing that they get better sense of what opportunities are out there and also or their own abilities and interests. So when they do pick, when they do settle on something, they have faster growth rates.
So by six years out of university, they fly past the early specializers in income. And then the early specializers quitting their career tracks in much higher numbers, basically because they were made to choose so early and not allowed to quit that they chose poorly more often. I should say when they did quit anyway, even though they had huge disincentive from doing so, they then had faster growth rates.
So, quitting, there's a lot of evidence that it is in response to this information that there's actually something better for you to do. The so-called Freakonomics economist, Steve Levitt, who I’m sure a lot of people know, he actually ran this really interesting experiment where people agreed to make major life choices based on the results of a coin flip.
What the most common question that people asked in this study was should they change their job. So the people who got the flip, who flipped the coin and the coin indicated they should change their job, and they did change their job. Those people ended up better off than those who simply followed the coin flip who were already at the point of questioning whether they should make a change. So, that is something they came in with.
But if they got the coin flipped that said don't change your job. Those people ended up worse off. Because, again, our moves are usually made in response to match quality information. So, I think some of the popular concepts we think about, like grit, which is one that’s really popular. We should not take those to mean that strategic quitting is a bad thing. In fact, Angela Duckworth, the researcher most associated with grit.
The same week my book came out, I subscribed to her newsletter. The title of her newsletter was Summer is for Sampling, and she said, “Young people during the summer should try a bunch of different things ,and you don't want to be gritty and not quit before you know what you should be doing.” And she actually said that it took her a decade of moving through various things to figure out where she should focus and put her energy in.
So, we actually need to try stuff and be allowed to quit stuff if we want to find match quality. And match quality has an incredible impact on your happiness, and your performance, and your persistence. So as one of the researchers told me, “When you get fit, it looks like grit.” Meaning if you get people in a situation with high match quality, they will display the characteristics of grit like work ethic and persistence even if they didn't before. So I think that's a pretty important way to think about some of these concepts.
[00:40:12] MB: Such a great concept from the book and something that, in today's world, so many young people feel the need to specialize rapidly and to not give up, and yet the opposite strategy can really be beneficial. Even the notion that you talked about later on was this idea of experimenting and exploring a myriad of possible selves that you might have in the future. Tell me a little bit more about that and the importance of running small experiments and tests as supposed to laying out grand plans for your future.
[00:40:47] DE: Yeah, that's interesting. That came from this section of the book focused on the work of a woman named Herminia Ibarra, who essentially studies how people find good career fits for themselves and how they transition between careers. Her work really resonated with me, because like I was living in a tent in the Arctic. I was training to be a scientist when I decided for sure to become a writer, and I still and now have no idea what I'm doing next.
So, she gave one of my favorite quotes in the book, which is, “We learn who we are in practice, not in theory.” And what she means by that is that there’s this huge industry of personality quizzes and career gurus who sort of want to deliver simple advice that's like, “Take this quiz and then just introspect into yourself and march confidently forward that you know what you should do.”
But what Herminia meant when she said we learn we are in practice, not in theory is that the actual research shows that we are not so good at introspecting into ourselves and understanding our abilities and interests and our opportunities without actually going out and trying stuff. So we learn who we are in practice by doing stuff. As she says, act and then think. You want to do things and then reflect on it and kind of go forward triangulating a fit for yourself that way.
And so the way that she found – She and a pair of Harvard researchers whose work I write about called the Dark Horse Project. This is, again, about – This about people who find fulfilling work. They both found that the way that people who do find fulfilling work proceed is via small personal experiments. So, we may think of career changing or finding careers is taking these big leaps or setting out a 10 or 20-year goal or something like that. But that's kind of the opposite of the norm.
So, in this this project at Harvard, the Dark Horse Project, the reason it's called the Dark Horse Project is because when the subject came in for informational interviews early on, they would all say like, “Well, don't tell people to do what I did, because I started in this one thing, and then I switched, or I dropped out of law school, whatever, and took me a while to figure out what I should do.” And some of them said, “It turned out the thing that I wanted to do wasn't actually available. So I had to become an entrepreneur. But I came out of nowhere and I was lucky. So don't tell people to follow my advice.”
And the large majority of them would say stuff like that. So they viewed themselves as having come out of nowhere. That's why it got the name the Dark Horse Project. But their common trait – And there were a few people who followed. There were some people who followed like a linear career path, I should say. It was just a small minority.
Most of them had this habit of mind where instead of saying, “Here's what I’m going to do in 10 or 20 years.” They'd say, “Here's who I am right now. Here are my skills and interests. Here are the options in front of me. I'm going to try this one right now, and then maybe a year from now I’ll change because I will have learned something about myself.” And they just keep viewing their opportunities as these little chances to experiment about their own skills and interests and their options in the world and they just keep going forward, bouncing from one to another until they triangulate a spot that sort of works for themselves.
That resonated with me so much that I decided to sort of proactively start doing it. So I actually started something I call a book of small experiments, where at least every other month I basically like I did when I was a science grad student. I put on a hypothesis about something I think I'll enjoy, or that I think will help my skills, and then I find some way to test that.
Whether that's taking a class, whether it's talking to somebody who knows things that I don't, or engaging in some kind of new project, and keeping that book kind of forces me to keep doing those experiments. I have to say, it’s been like one of the most valuable things I've done. Even for this book, Range, which I’ve written two books and I try to make those books projects that are kind of at the limit of my skill level at that time.
One of the things that really helped me with Range was for one of my experiments, I got stuck with – I was having trouble organizing the information in Range to make the whole thing coherent and not just seem like a bunch of magazine articles stapled together. So, I decided some fiction writers are incredible structural magicians. So I said, “All right. I’m going to take an online beginner’s fiction writing class and see if that will help me with my structure problems.” So, that was kind of my hypothesis that it would help.
So I go to this beginner’s class. Nobody cares what anybody's done. Most of the people have never published anything. So I'm out of my comfort zone. In fact, I didn't really get what I expected from that class, which was structural help. But in one of the exercises, we had to write a story with no dialogue whatsoever. And something about doing that exercise flipped a switch in my head where I said, “You know what? In my last two years of magazine writing, I've been sort of leaning on quotes,” and you want to do that in investigative writing, like I was doing. The lawyers especially want to do that. Put things in –Let people say things in their own words.
But I had taken it over to working on Range and I was often using quotes when I didn't totally understand something. So, sort of papering over it. If I don't understand it, the readers certainly not going to understand it. I was using quotes in lazy ways. I went back and realized what I had to understand better and took out a huge number of quotes from the book and replaced them with my own narration that I thought was more clear and more simple than the quotes.
It was kind of scary in a way that it didn't occur to me what I was doing. I was in such autopilot until I took this class, and it kind of knocked me out of my normal rut of competence and showed me something that I could do better. So, it’s these experiments like that that at least every other month I do something. It's not always as big as taking a class, but I'm totally committed to my book of small experiments, because I think it's kind of like whether you're looking for a new career or not and trying to find your interests.
Basically, if you’re going to the gym every day and lifting the same number of weights the same number times every day, you might not get worse, but you won't get better. I think that's the mode a lot of us get in when we become competent. We do the same thing over and over and over, and that's not the way to get better.
So, for me, the book of small experiments is both about finding new interests in the world, but also about making sure I'm not doing the same thing over and over and over, because whether that's a motor skill or cognitive skill, for the most part, we know that you need what's called variability. Basically, variable practice, in order to get better at something, which means you need to be changing up what you're doing constantly.
[00:46:50] MB: Great example, and touching briefly on this notion of variable practice, you had a great discussion in the book around this notion of the Japanese concept of bansho, or the idea of using connections questions and making connections questions. Tell me briefly about that that topic or that idea.
[00:47:09] DE: This is interesting. You’re asking me about things that I've done a lot of interviews and very few people have asked me about some of things you're asking me about. So, kudos to you for latching on to some things that others aren't and reading carefully.
Bansho is a Japanese word that essentially describes – Well, not essentially. It does describe a form of writing on the blackboard that charts like the intellectual journey of a class across numerous ideas. So if you walk into a Japanese math classroom, you'll see – There’s not like the overhead projector. You'll see a blackboard that is like the size of an entire classroom wall, essentially, and each of the kids has a magnet with their name on it.
And the entire class period will often be one question that the class works on together, but they start out and the teacher will ask for a volunteer to come up and come up with an idea for approaching the question. The kid will come up to the blackboard and put their name magnet next to what they start writing and they'll show an idea. And maybe it will be right and maybe it will be wrong. Then someone else will be asked to come up with a different idea for approaching the problem.
So you'll have multiple streams of approaches to the problem going on at once, and students coming up one after another saying, “Well, what could be a next step? Okay. What could be a different next step?” By the end of class, you had multiple approaches, some right and some wrong to this one problem that draws in a number of different concepts for math. So this is called – This is an attempt to impart what researchers who study learning called making connections knowledge, where through a single problem, you're forced to draw together concepts from different areas of math, and that's stands in contrast to what’s called using procedures knowledge, which is essentially just learning how to execute algorithms or tricks. A lot of people call them over and over and over.
This gets at what I think is one of the important kind of sub themes of Range, which is that sometimes the things you can do to cause the fastest rapid improvement, which is doing this – Using procedures practice, causes improvement really rapidly can actually undermine your long-term development. So making connections knowledge comes slower, but it's much more flexible. And we can actually even impart it in some more simple ways than bansho.
So, here's a study that just came out that didn't come on time for the book, but is on a concept that I use in the same chapter you’re talking about, and this concept is called interleaving. This is a form of studying that all explain. So in this this study, seventh grade math classrooms were randomly assigned to different types of math learning. Some were assigned to what’s called blocked practice, where you get problem type A, A, A, A. Problem type B, B, B, B, C, C, C. So on, and you practice the procedure over and over and over. And the kids get really fast, really good at this really quickly. They make progress. They rate their own learning as being good. They rate their teacher as being really good.
Other classrooms got assigned to interleave practice, where instead of getting A, A, A, B, B, B and all that, you get like A, D, E, C, F. You get problems as if all problem types were thrown in a hat and randomly drawn out. In that situation, the kids at first are frustrated. Their progress is slow. They rate their teacher as worse. But instead of learning how to execute procedures, they are being forced how to learn how to match a strategy to a type of problem and to connect concepts to the type of problem as supposed to just like executing a procedure.
And come test time, all the classes took the same test. The group that had interleave training destroyed the block practice group. The effect size was like on the order of taking a kid from the 50th percentile and moving them to the 80th percentile, and that's all because the learning was structured to make it more difficult and to force the learners into conceptual thinking instead of using procedures thinking. So that’s just another way to accomplish what's going on in those Japanese classrooms.
But again, gets at this theme of the thing that you can do to make learning feel the fastest and easiest may actually be bad for your long-term developments. I highly recommend if people are trying to learn anything, they should interleave it essentially. Instead of trying the same thing over and over, mix it all up. You'll feel worse. You'll feel more frustrated. You'll do worse at first, and in the long run you'll do much, much, much better.
[00:51:22] MB: Such a great concept, and that's why I wanted to dig into it and explore it. So, we've covered a lot of different themes and ideas today. It's been a really interesting conversation. For listeners who want to start somewhere, who want to concretely implement something from our discussion, what is one piece of homework or action item that you would give them to begin implementing some of these themes and ideas?
[00:51:44] DE: I mean, I would tell them to start a book of small experiments personally, and you don't have to do every other month like I do. Maybe start like once a quarter, where you take time to assess something that you think you could get better at, or something that you might be interested in but you don't know, and make your hypothesis of how you could get better or how you could explore this interest. And then go test that. What's an experiment that I can do to go test that? I’d say try to stick to that and really do it. So I’ve found that to be incredibly fruitful.
The other thing I would say is – This relates to something we talked about. But we didn't touch exactly on it, but it kind of relates to analogies. Whenever you're thinking about a project that you’re going to take on, to some degree, whether explicitly or implicitly, you are going to make predictions about how that project is going to go.
One of the kind of errors that people make when they do this is they focus very tightly on the details of their own project and they try to make predictions. That’s called the inside view. What you actually want to do is look at the basic structure of the project you're thinking about and then depart from it and go try to find a bunch of other structurally similar projects and see how those ones went. That's what you should base your estimate on. That's called the outside view.
So it’s using analogies to other similar projects and not focusing on the internal details, and you'll be much, much more accurate. So I would highly recommend that kind of thinking, and that's explained in one of the chapters on using analogies for thinking, in chapter 5, as it relates to investors predicting return on investment, to prediction of revenues of movies and all these other things. So there're some good examples of how it can be applied to basically like whatever you want to apply it to.
[00:53:27] MB: The whole concept of the outside view versus the inside view and base rates and all of that probably could be an entire episode that we could dig into.
[00:53:35] DE: For sure.
[00:53:35] MB: But, unfortunately, I know we’re running out of time. For listeners who want to find you, find the book, find your work online, what is the best place for them to do that?
[00:53:43] DE: Davidepstein.com is my website, and @DavidEpstein on Twitter. And I just started an infrequent newsletter that kind of has a bunch of stuff that I learned in the reporting of the book, but they couldn't fit in there, but that people might be interested in. There's a signup on my webpage if so. Of course, it's free and usually short and pretty infrequent.
[00:54:02] MB: Well, David, thank you so much for coming on the show, for sharing all these wisdom. It's been a great conversation. I really enjoyed reading range. It was a fantastic book, and I hope people will go check it out.
[00:54:13] DE: Pleasure is all mine. Thanks for having me.
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