Today’s episode of Hidden Forces is made possible
by listeners like you. For more information about this week’s episode,
or for easy access to related programming, visit our website at hiddenforces.io, and
subscribe to our free email list. If you listen to the show on your Apple podcast
app, remember you can give us a review. Each review helps more people find the show
and join our amazing community. With that, please enjoy this week’s episode. What’s up everybody? My guest today is David Epstein. Probably the most wide ranging thinker I’ve
ever had on this podcast. David is what you would call a renaissance
man. He’s worked as an ecology researcher in the
Arctic, studied geology and astronomy while residing in the Sonoran Desert, and during
his time working as a journalist for Sports Illustrated, co-wrote the bombshell story
that revealed that Yankees third baseman and three time league MVP Alex Rodriguez was using
performance enhancing steroids as early as 2003. As if those accomplishments and experiences
weren’t enough, David is also the author of two wildly popular books, the Sports Gene,
which examines the science behind extraordinary athletic performance, and Range, a book that
attempts to explain why generalists triumph in today’s specialized world.
Anyone who knows me or who listens to this podcast won’t be surprised to learn that I
agree with David on pretty much everything insofar as learning is concerned. In fact, I’d go a step further, and say that
this podcast is or at least attempts to be the embodiment of the ideas that he writes
about in his most recent book. I am a full on believer in the power of wide
ranging, interdisciplinary thinking. I’ve often made the case for it on this program. It’s why I bring on guests and cover topics
from so many different domains and disciplines. Because I believe that in order to be successful
in today’s rapidly changing world, you can’t just rely on the narrow set of skills for
which you’ve received your diploma, or confine yourself to the sources and methods of a given
discipline or occupation. Regardless of how well they have served you
in the past. What I love most about this episode and my
conversation with David is that it makes the case empirically for the type of thinking
and learning that I think this show strives to instill. It’s from the bedrock of my own personal and
career development, and I’m excited to share that formula with all of you today. With that, please enjoy my invaluable conversation
with author and journalist David Epstein. David Epstein, welcome to Hidden Forces.
Thank you very much for having me. It’s great to have you here, man.
It’s great to be here, especially because I’m admiring that you’re an over preparer
for interviewing the way that I am; you’ve got your diagrams and highlighted notes and
all these things, so- Did you see this picture here? I use these pictures to remind me; they help
me navigate the most important content on the page, and this one I think is because
I wanted to talk about, are we becoming more similar to pre-industrial man? That’s the scene where the ape in 2001 realizes
that he can use the femur to hit people. By the way, this is great, you know, not to
get off track here. But you’re building your semantic network
by doing this, right? Which is when you learn something new, you
should try to relate it to a bunch of other stuff: visual, auditory experiences, whatever,
and that’s how you get it to stick more firmly in your mind and be more capable of retrieval,
right? If you connect things in your semantic network,
so that’s clearly you’re doing, you know, you intuited your way to that process.
Yeah. So totally. I mean, we could talk about that also. I have a whole process for that. You hear that, listeners? If you haven’t subscribed to the super nerd
tier yet, David just gave us an implicit endorsements of the rundowns.
Yeah, I actually have all sorts of methods for doing this. One of the things that I did early on, even
before I started interviewing people, was that I would, if there’s a book I really liked,
I would read it while I was also listening to it on eBook. A lot of times I would be reading passages
that I heard on the audio book, and I would remember the street that I was on when I heard
them. Oh, that’s so interesting. I don’t want to get you off track here-
No, it’s fine. This is great.
One of the researchers in the book named Ogi Ogas, who I talk about his work on the Dark
Horse Project, which is basically about how people find work that fits them well. But he’s also done a bunch of memory research. I mean, he’s a computational neuroscientist. He won half a million dollars in who wants
to be a millionaire, and he got the million dollar question right, but he had decided
not to risk it, and when he told me one of his strategies was because you want to build
a semantic network and relate things to each other, is when he learned stuff he’d stop
and try to relate it to all these other facts. Then in the show, he said they cut out most
of the time, so you can talk to the host as much as you want. So he would just blather and try to bring
up tons of random stuff, hoping that something would cue the right string in his semantic
network, and the answer would come to mind. So that was his strategy, and he got all the
questions right. That’s so interesting. Are you familiar with memory palaces?
Yeah, I use some memory techniques myself. I haven’t used memory palaces much, a little
bit. But yeah, I think it’s, yeah, it’s basically
building a semantic network. Yeah, there was also someone who wrote a book
called Moonwalking with Einstein. Yeah, yeah. Foer.
Fantastic. He actually won the pneumonic prize, like
the best memory prize or something like that. Got a great book deal out of it.
Yeah, and he had no background. He learned how to do that.
Yeah. It’s amazing.
Even for my first book I started reading memory research, and that’s one of the areas where
you realize it is tremendously improvable beyond what most people… Like, we don’t normally do almost any of the
stuff that supplements memory unless we’re having some really intense experience, basically.
That’s fascinating, man. So we were talking a little bit about my experience
with dementia before we started. The one thing we didn’t talk about was the
incredible memory improvement that I experienced right after my surgery. Because I got my acumen back immediately. Because there was some sort of block between
short term and long-term storage. When I got it back, I don’t think it was just
my perception, I think that my memory was actually sufficiently improved because I had
developed all these techniques to build redundancy into experience, and afterwards I had a photographic
memory. I’m not sure what it was, but you know, that
faded over time. But I had the best memory of my life was after
my surgery. Gosh. That’s in a category of, you know, I write
about these desirable difficulties, these challenges that make learning better in the
long run. I think having a brain tumor is a desirable
difficulty for memory improvement beyond even what I would have conceived, but that’s very
impressive. You know, it’s super interesting. You’d like it, man. Because I could tell from your work. So, I have so many questions for you. It’s hard to know where to start. I do want to maybe ask about your progress,
but maybe we can talk about that later. There were two things that stuck out to me. One was this idea of abstract thinking, and
the other one was the learning process itself. Some of it had to do with improvisational
learning, some of that just had to do with the fact that the way we learn is sometimes
more important than what we learn. What was this book about for you? How do you describe it to people?
That’s a good question because the question I start with is sort of amorphous. Like, how broad or specialized to be and why
or how? What’s sort of the conclusion, and what would
make a horrible unmarketable subtitle, but to me is one of the underlying themes of the
book, is that sometimes the things you can do that cause the most rapid apparent short
term progress, whether that’s in learning new material, choosing a career, developing
a skill at work, whatever it is. Sometimes that short efficient path can actually
undermine your long-term development. It’s these slower, often winding, more frustrating
early on paths that sometimes actually give you the advantage in the long-term, even if
it makes you behind in the short term. So to me that’s kind of the largest concept
in the book, although that would have been a subtitle that nobody would have bought the
book, so- That’s interesting. I mean, you talk about that, that actually,
I think one of the studies is the rhesus monkeys, where you see that differently in learning
actually creates stickiness. That’s right.
So I want to explore a little bit of why that is.
Mm-hmm (affirmative). But also that brings up a paradox, or a difficulty,
which is how do you know when something is just hard, and how do you know when it’s just
hard for you, in which case that brings us to sampling and how you should use that as
information to make decisions about what not to do.
Yeah, it’s a really tricky question. There are a lot of things embedded in there. In the one hand, we know there are things
like when someone’s learning new material, if they’re not getting at least 15% of the
stuff wrong, then it’s not difficult enough, and they’re not basically learning well. But this broader question of if something’s
difficult, maybe that means it’s not right for you. Right? I think that’s in part why the book doesn’t
really offer easy answers, right? In fact, I took a last section to make this
conclusion and say, people are always asking me for one sentence of advice, and I wouldn’t
have written this book if I could, right? This book is the anti-life hack book.
Right, exactly. So, I think part of the art of learning this
stuff… So, let me go back to, there’s this group
of studies called the Groningen talent studies that I learned about in my first book. One of the women who runs it in the Netherlands
is named Marije Elferink-Gemser, and what she would do was find kids, soccer players,
students, whatever. Some of these soccer players went on to the
pros, from age 12 and track them, and see what in general led some people to continue
getting off of plateaus. Like, to continue getting better when things
got hard. Probably the single most important characteristic
was what’s called self-regulatory learning, which as Marije, if she had to sum it up in
one word, it would be reflection. Like, those learners spend more time after
they do something, saying what did this work on? Why didn’t this work for me? I’m going to try something different. They keep reappraising and adapting that knowledge,
and they start to understand their own weaknesses, so they get a better understanding of, is
this something I’m just bad at or don’t like? Or do I just need to try different approaches
her? They sort of do that zigzagging over time,
and find places where they fit and strategies that work for them.
So I think whether you do that with a coach or a mentor, or you just do that yourself,
as we do things we need to be very reflective about them. Like, constantly reappraising what we learned
and asking how does this fit with me and my prior knowledge? On a larger scale, that I think relates to,
I mentioned Ogi Ogas, I mentioned his memory research, which is not in my book at all,
but he’s one of the researchers on Harvard’s dark horse project. That research is about how people find work
with high match quality. Match quality meaning the degree of fit between
their interest and abilities and the work that they do, which has incredibly important
for your performance and sense of fulfillment. What they found, they called it the dark horse
project because people would come in who were fulfilled in their work, and they would all
come in and say, well, don’t tell people…They were just interviewing them for informational
interview. Don’t tell people to do what I did, because
I started in this one thing, and realized it wasn’t for me, and did this other thing,
and this other. So they all viewed themselves, not all. The large majority viewed themselves as having
come out of nowhere. So that’s why the researchers called it the
dark horse project, because most people who found their way to success and fulfillment
were actually doing this zigzagging by constantly appraising how things sort of fit their interests
and abilities. So they weren’t the types to look around and
say, this person is younger than me and has more than me. They were like, here’s where I am. Here are my current skills and interests. Here are the opportunities in front of me. I’m going to try this one, and maybe a year
from now I’ll change because I will have learned things about myself. They do that over the course of their career
until they find a space that fits them. So that actually raises a few questions for
me. One is questions themselves. Is there a difference between the type of
intelligence… I mean, this came up for me when I was reading
the section with Fermi problems. Sometimes a problem is so wicked that in order
to get to an answer you have to approximate it by coming up by better and better questions
to kind of hit it from different angles, right? That sort of came up to me now when you were
talking, thinking about when you go through an experience, coming up with the right questions
to ask to dig deeper to understand really what was happening, refine, and retest. Talk to me a little bit about that. How does this type of learning that you described
in the book, this kind of range learning, how does that help with formulating better
questions or more insightful questions? Yeah, I mean I think it depends if we’re thinking
about you know, sort of navigating our own career, asking questions related to a specific
problem we’re working on, right? So I think most of us when we do things, if
we’re thinking about our own development, we don’t ask a lot of questions, period. Naturally you just decide either I’m good
at that or I’m bad at that, and then you just go on. You don’t really stop and reflect about your
experience. But I think if you’re tackling a specific
problem, one of the ways that you can use what I call range to help is to draw analogies
from other areas. So there’s a chapter about analogical learning,
where when you’re facing, you mentioned wicked problems. So to define that, that’s not my term. It’s a psychologist, Robin Hogarth. Wicked problems and wicked learning environments
are those where you’re not given rules. You may not even be given goals. The problem may change over time. You may or may not get feedback. It may be inaccurate. So you’re really in unknown territory, and
you have to try to come up with new solutions. It turns out one of the best ways for doing
that is being able to draw analogies to structurally similar problems from a variety of domains. So if you’re facing a medical problem, that
doesn’t mean just coming up with analogies from medicine, which are what’s called similar
on the surface, similar surface features. But finding structurally similar problems
in other domains, and using those to help you formulate the right questions in the one
you’re looking at. What accounts for that?
That’s a great question. I mean, at the base brain physiology level,
I don’t know. I know as Dedre Genter, who’s one of the world’s
foremast experts in analogical problem solving, I quote her in the book saying she thinks
this is empirically one of the things that sets us apart from other organisms in the
planet. Like, the ability to essentially learn without
experience in a specific task by drawing on structurally similar knowledge from other
domains. What exactly makes that work? I don’t know. I mean, the empirical results show that it
works. What the underlying why it works, I’m not
really sure. Because I was thinking about it, again. It’s so easy for me to get distracted because
there’s so many threads that tie into many of the different concepts that you write about
in the book. But in this case, I was thinking, is this
because learning about all these other disciplines actually helps when you’re looking at something
else? Or is it that learning about all those other
disciplines causes structural changes, and of course, there are always structural changes
happening in your brain, but that somehow your brain changes so that it’s better able
to adapt to new problems so that if you were to be on a Martian planet, where not correlates
to anything that you learned on Earth, you’d still be better.
Yeah. Oh, I think so. In fact, when Hogarth talked about being in
these wicked learning environments, he likened it at one point to playing what he called
Martian tennis. Where he said, “It’s like a sport, and you
just see people doing something, but you have no idea what the rules are. Nobody will tell you, and they change at any
moment.” Right? So he was using that in the way that you’re
using being on Mars, which is being thrown into a situation where you have no relevant
experience. In fact, when people are doing creative things,
that’s kind of to one degree or another the situation they’re always in. They’re trying to do something they haven’t
done that someone else hasn’t done, and that’s where drawing on these other areas seems to
be like the wellspring of creativity. So I was invited to this very small presentation,
and we’re not supposed to share stuff from it, but I think I can say this. What they were basically working on, it was
using natural language processing. What they were endeavoring to create, and
it seem they’ve come very far along, was a system that would effectively allow you to
translate a language with no intermediary language. With no Rosetta Stone. What they discovered was that concepts mapped
to the same place in geometrical space. That a cat in one language maps the same sort
of general area in another one. Very fascinating. It speaks to this point about analogies and
metaphors. Now we’re kind of skipping back to this point
about metaphors. In fact, so Alexander Lourie’s work was familiar
to me before coming across your book, and that particular example where he talks about
talking to non-modern, rural- He calls them pre-modern. Yeah.
Pre-modern people, where he gives them basically a logical sentence, and says in the far north
where there is snow, all bears are white. Now you are in the far north, what color are
the bears? They’re like, how would I know? I’ve never seen, I’ve never been to the north. How would I know what color they are? That also comes up in the chapter with the
students in math class, which is an amazing chapter, when you see how they’re trying to
process and understand. Yeah, I’m glad you enjoyed that one. I almost cut it.
I love that chapter. I love that chapter. I love when she’s like, math is twice as long
as social studies. They’re like, wait, I thought recess what
the longest. Right, right. She’s trying to make a conceptual point, ask
a question, right? They are stuck taking it like really, really
seriously, right? So that’s a [crosstalk 00:17:55].
And we take this for granted. Because we learn how to think this way. I don’t remember I had read this, it might
have been in Ong’s book, but I think actually, it was James Miller who said that language
is like legislation for parliament. It’s what the brain does. Again, it brings back this point about concept
and metaphor. Oh, so you bring up the Flynn effect and the
fact that IQs have been rising three points every decade.
Yeah. This is accounted for almost entirely based
on the more abstract parts of the test. Yeah, yeah. So the places where improvement was expected
the least, right? Like, the Raven’s progressive matrices, where
you just have to fill in abstract patterns that are missing. That was designed to like, if aliens land. It would be able to assess them because you
don’t need to learn anything, like turns out that’s where the most improvement has been.
Yes. So the thing that comes up in reading your
book is that this might be because we’re living in a period of increased change that every
decade is not just the world is changing more, but the rate of change is changing, and that
the stuff that we learned 10 years ago, or that our parents knew, becomes increasingly
less relevant to us, and so that in order to traverse this landscape, we need to have
more abstract ways of approaching the world. Is that correct?
Yeah, totally. This is… You know, by the way, even in countries where
scores on other IQ tests that are about information have gone down, abstract scores still go up. What Lourie saw in his work, and what others
have seen in work since then is sort of the greater dose of modern work that someone got,
the better they did in these abstract sorts of tasks. Which didn’t seem to make sense. But Flynn’s work, so this is called the Flynn
effect, this rising of IQ scores. Flynn’s work suggests that it is because of
like a fundamental change in the way we have to think in order to… Because as the work world sort of changes
rapidly, we have to increasingly learn from other people’s experiences, right?
Like, you mentioned that example where Lourie asked what people have called the pre-modern
villagers, where it’s cold and there’s snow. All bears are white. Then he says, “So in Novaya Zemlya which is
in the far north, and it’s cold, and there’s snow, what color are the bears? They say, well, you’d have to ask somebody
who’s been there, right? For us, we completely take for granted the
fact that we can make a logical deduction based on what other people have learned, and
then we have that knowledge forever. We do that all the time. We understand stuff we’ve had no direct experience
with. We take knowledge we learn in one area and
apply it to something totally different. These individuals who have not engaged in
modern world cannot do that. That’s totally, totally foreign to them. That’s basically how we get by in work now. Like, even having this conversation, you’re
having to do something that you have not exactly done before, and take previous skills and
previous learnings and apply them to a totally new situation. That’s how we get by. That’s so essential now. We have to constantly use knowledge to deduce
how to do something. Like, if you ever see, you know, a younger
person, because with each generation, this effect has gotten stronger through the 20th
century. Like, kids can pick up a video game; they
don’t have to get the rules. Right? You’ll often see someone who’s older who didn’t
kind of grow up with that same way, they want to know the rules. Like, they might want to read the instructions,
or something like that. Increasingly now, do you even read directions
for an app when you download it? No, you just start fiddling, and based on
the knowledge you’ve accrued by fiddling with other things and apps, you just start to kind
of deduce it. That would be like, totally foreign to people
who didn’t grow up in a culture where they have to learn abstraction to learn things
from experiences they didn’t have, but that other people had.
That’s also interesting because they have now developed an abstract set of behaviors
that we engage in when we deal with new applications, we expect things to behave a certain way when
we swipe. When we scroll.
Right. That’s sort of universal across applications. Those sort of get embedded in there.
Yeah, so learn those fundamental skills, right, and then you can transfer them to other places. If they don’t work well, then you’re kind
of confused, but you know to try something else. But we’re constantly, like I don’t know when
you first even started swiping or got, but I didn’t like necessarily grow up with that
stuff, right? I didn’t get a cellphone until I [crosstalk]
No. No. We got that…the iPhone was the first-
Right, and so we adapt to that, right? But it’s very different than you know, you
mentioned work in sort of in industrial economy, essentially, which is not pre-modern, but
even some of our parents or grandparents could expect a life where they had a discrete period
of training and could then apply that forever in the work. That is absolutely not the case now, right? We’re having to relearn and reinvent over
and over and over and over and over. I want to go back to this thing with Lourie’s
work because even though the majority of your work is in all these different areas, and
dealing with again, interdisciplinary learning, the abstract stuff really resonates with me. The thing that we didn’t mention. We talked about the pre-modern people that
he interviewed, but the modern people, in one of these, he goes, “Well, to go by your
words, they should all be white.” Yeah.
I think Gleick talks about this in his book, The Information, where he says, in that moment,
a conceptual leap has occurred. Where those words have an identity, have a
meaning all their own. They’re detached from what they were.
That’s right. So that individual is engaging in… For him, an early abstraction, right? So the people who had not been exposed to… These were, just to give a little background,
Lourie found this natural experiment in these groups of people in rural, kind of isolated,
what’s now Uzbekistan. During the Russian revolution some of them
were quickly forced to engage in more modern work, and some were still kind of left in
subsistence farming communities. So again, the pre-modern people would not
have answered that white bear thing. This guy who had some exposure, who had started
to become exposed to the modern world, he realized that he was being asked to sort of
make a deduction or abstraction. He says, “Well, if I trust your words, you
know, then those bears are white.” Then if you get to people who’ve started going
to school, and they’re doing complex, interconnected work, they’re just like, oh, they’re white. Right? They totally take for granted that we can
learn things without experience, and they can be true.
That you can adopt a set of logical rules. Yeah.
This is also a conceptual leap. Totally, and what I thought was fascinating
about that work was that I totally take that kind of thinking for granted. Right? Because we’ve grown up in this world where
we live by transferring skills, by doing different things constantly. By classifying things so that we can learn
about things that we haven’t directly experienced. So it’s fascinating to learn that that is
not like a given of human nature, and in fact it’s a kind of thinking that’s sort of fundamental
to modern challenges. So what does that mean in a world, if we’re
able to think more abstractly, first of all, is that an adaptation? Is that like a fitness feature of this environment?
Yeah, it’s an adaptation. The only part of fitness feature I’d be cautious
about is usually when people use that term in sort of natural selection, evolution, it
means something that’s encoded in the genes over the generations and passed down. But this is something that literally happens
to people during their own lifetime in many cases. So I think it’s absolutely an adaptation.
So if we’re heading into a world more and more automation and this continues to happen,
what does that mean for the type of work that people should be… How should people train themselves, think
about education? What are the practical implications of that?
Yeah, so our education system, and I mentioned this. A lot of our parents and certainly our grandparents
could expect like a training period, followed by a doing period. Not, like, retraining and relearning and all
those things so much. So our education system was built for that. It came out of Taylorism or basically science
of management efficiency. Now a lot of those things that were most well
suited to the Taylorism approach are also the ones that are most easily automated, where
there’s a training period, and then someone can sort of do the same thing over and over
again. Our education system is changing, but there’s
a ton of momentum, and it still has a lot of anchor in its lineage, which is in Taylorism
and preparing people for industrial society. The more we get away from that, the more important
it is for people to learn ways of thinking and ways of learning, because they’re going
to have to keep reinventing, because their training period is going to be their whole
life instead. So I think we need to kind of realize that
teaching people how to think is more important than what to think, especially early on.
So it’s kind of like the industrial environment was more predictable.
Yeah. There was a certain amount of data, and it
was kind of like chess, right? I mean, what we learn about chess, which is
there are a finite set of moves, and it isn’t that IBM was good at playing chess the ways
humans were. Well, maybe. The point is, it was really good because it
could run through all the different possible positions and all the different possible games. Now this type of intelligence that we’re talking
about now allows you to create new solutions for problems you’ve never encountered before. In other words, if you don’t have a protocol,
you can come up with something new. Right?
Right. In chess, and I should say, even no computer
can compute all the possible games. There’s like more than atoms in the universe. But it can do a lot more than humans can. Chess is what Hogarth would call a quintessential
kind learning environment. Clear rules, clear goals, clear next steps. Essentially infinite store of previous data
that you can study. Work next year will look like work last year
in chess, and you get feedback that is very clear, unequivocal, and fairly rapid. So that kind of makes sense for why computers
would be so good at that, right? Because the grandmaster’s advantage is based
on pattern recognition. In fact, chess and other kind learning environments,
reward early specialization. If you haven’t started studying patterns by
age 12, your chance of reaching international master status, which is one down from grandmaster,
drops from 1 in 4 to 1 in 55. But computers are much better at that kind
of pattern recognition. So in the late 90s when Gary Kasparov lost
to deep blue, he noticed an example of what’s called Moravec’s paradox, that humans and
computers often have opposite strengths and weaknesses. He noticed that the computer was so much better
at tactics, which are like the patterns, the small combinations of moves, that it could
beat him. But that he felt it wasn’t as good at strategy,
sort of this more amorphous, how do you arrange the little battles to win the war? So, he helped start these freestyle chess
tournaments where anyone can play in any way they want. So computers play alone, grandmasters play
alone, people play with computers. The winners were not grandmasters, not super
computers, not grandmasters with super computers, but two amateur chess players with three normal
laptops. So all of a sudden, when you could outsource
all that pattern learning that Gary Kasparov had spent his life doing, and suddenly the
best people had this totally different skillset where they knew something about computer algorithms,
they knew something about chess, they could handle lots of streaming information and sort
of coach the chess computers on where to play next. It was really interesting just seeing some
of the winners of these tournaments, they would say they couldn’t even really analyze
the game, they just knew how to sort of coach the computers better.
So I think increasingly, these are called centaurs, these human/computer partnerships,
and I think increasingly that’s like a model we should think of preparing for in work. So to use another example that’s not in Range,
I looked back at news coverage in the early 1970s when ATMs first came online, and it
all says, okay, bank tellers are going out of business. You know, overnight. In fact, what’s happened is that as there
have been more ATMs there have been more bank tellers because ATMs have made each branch
cheaper to operate, and so banks have opened more branches. Fewer tellers per branch, but more tellers
overall. But it has totally changed the job from one
of these repetitive cash transactions to one where the tellers are like, customer service
representatives and marketing professionals, and advisors, like this much more difficult,
broader, harder to automate sorts of skills. So I think those are sort of models we should
keep in mind as we think about how work is going to change.
That Kasparov story is funny, when he first lost to Deep Blue, we did an episode where
we talked about that with Hannah Frye, and I didn’t know this, but apparently Kasparov
thinks he could have beaten Deep Blue were it not for the fact that Deep Blue, it would
pause. Yeah. He thinks it cheated.
Threw him off. Yes. It’s like, behavior algorithm.
Yeah, that’s interesting. He’s critical of other things they did. Like, he thinks there was sort of maybe some
rule bending or breaking with mid game programming and all this stuff. He said that today the free test app on your
cell phone would beat him now. Yeah, exactly. Yeah, yeah. So you talk about the Challenger, space shuttle
Challenger disaster in the book, and you make a very small mention about either Apollo 11
or Gene Krantz. Have you read Gene’s biography?
I have, yeah. Okay, so I thought about Apollo 13 when I
was reading this book before I got to the part with Challenger. I wonder, did you compare those two in your
mind? The case of the 1970 disaster in space, where
they had to figure out how to take the lunar module and bring it back. I think the lunar module was only equipped
to keep two astronauts on the moon for like two days. They had to figure out how to make it last
for four days with three astronauts in it. They had to totally improvise. I mean, think completely out of the box, right? So I guess two questions, is first, how does
that compare to Challenger or some of these other environments? Also, what does that say about NASA and other
organizations when they’re young, and then when they get older and they become more process
oriented? That’s interesting question. Interesting we’re talking about stuff I’ve
done a lot of podcasts, but some of these stuff I haven’t talked about at all before. Yeah, you can’t help but think… I minored in astronomy in college, and I love
space, and I so I can’t help but think of Apollo 13, and that improvisation was amazing,
right? It reminds me of… I only make short mention of this in late
in the book, but Bill Gore, who founded the company that created Gore-Tex, he’s an engineer;
he founded the company based on his observation that companies tended to do their most innovative
work in times of crisis because disciplinary boundaries would go out the window, and people
would start figuring out what their colleagues could do, and what they could borrow from
them, and how they could collaborate, and all these things. Not protecting their turf.
This is relevant to how organizations change. So in times of crisis, people have a stake
in survival of the organization or in this case of the astronauts. I mean, so that’s what they did, right? They got everyone on board, and they just
started improvising. But I think often times, and you see this
in startups, and why can start ups disrupt larger companies. In crisis or in a start-up, everyone has a
stake in succeeding, right, and not going out of business or having some cataclysm. Then as an organization gets larger and more
mature and has more momentum, it goes through what Safi Bahcall this writer [crosstalk 00:32:37].
I was just thinking about Safi. We’re going to talk about this.
He calls this a phase transition. Yeah, phase transition.
Where suddenly people go away from what Bill Gore had identified, so they’re just doing
the best things to keep things alive, to where they’re just protecting their rank, and their
little turf, and often times that goes in the opposite direction as the organization
as a whole, because it causes people to try to maintain their sort of silos, because they
don’t want their turf invaded. I think that that happened as NASA, as post-moon
landing, as funding becomes more challenged, and so there’s sort of a harsher Congressional
eye on NASA, what happened, and the same thing that happens with a lot of companies. They get mature and then face some trouble,
is people retreated into their disciplinary silos, and started very rigidly following
previous processes. In the Challenger case, you know, as I described,
they did that even to the point where they found themselves in a situation the process
was not made for. Where they demanded data in order to make
a decision, but the engineers that were saying, I don’t think we should launch, did not have
the data. Nobody had the data. All they had was hunches, and so the ultimate
ruling was, well, if you don’t have the data, it’s not an engineering call. Like, you’re just going on superstition, and
so they followed their process, which was sort of a CYA thing. Obviously they had a disaster. Years later, when they had their only other… Because NASA’s amazing. But when they had their other major disaster-
Columbia. Columbia. The Columbia investigation board said it was
such a cultural carbon copy that they deemed NASA not a learning organization. They hadn’t learned from Challenger. What they saw there was engineers who wanted
a photograph of what they thought was a damaged part of the shuttle, ask DOD for some high
res photographs, and when their managers found out, they apologized to DOD, they stopped
the request, and said, “Sorry for going outside of normal channels. This will never happen again.” Right? When normally in time of crisis you want those
disciplinary boundaries going out the window, and I think they just became so rigid in their
process that the process became an end unto itself, instead of an ends for a mean.
It’s ironic because they were taking more risk by being risk averse, by following process.
Yeah. Yeah, and when things were going according
to previous experience, the process was great. So I by no means think they should just freelancing
out of the blue, right? It’s just that, and Richard Feynman noted
this in the Challenger investigation hearings. Like, I read all the transcripts there, and
it was actually interesting. He caught on to this, even though it didn’t
sort of end up in the final report, that they needed to recognize when they were outside
of previous experience, and that in that case, the previous procedure needed to be amended
or dropped and they needed other ways to think about things.
So we had Safi on the show some episodes back. I think it was 80 something, and of course
we talked about Vannevar Bush. We also did an episode on Claude Shannon,
so this kind of intersected, and Vannevar played the role of the team leader that brought
people from disparate domains into one place, fighting for a common cause. Like, we talked about the story about microwave
radar, its deployment over the Atlantic, and how that led to the turning of the tide, and
the sinking of all these- Loved his book, by the way. I should just say Loonshots. I’m a big fan.
It is a great book. Yeah, no, it’s a very good book, actually. It’s done very well, like your books. Both your books are best sellers I think,
So I mean, these types of stories fascinate me, and one of the stories actually that’s
in your book that could have easily been in Safi’s book is the story of Nintendo, and
what’s the name of the founder? Or not the founder, the guy that was working…
Gunpei Yokoi. Gunpei Yokoi. So who coined that term-
His philosophy, lateral thinking with withered technology.
Yeah. He did, and of course that’s a translation
from Japanese. One of the helpful things, in both my books
I’ve had some foreign writing that never appeared in English translated which is always a good
thing– Yeah, which is brilliant. Another thing I’m super jealous of, you have
a statistician on contract. Yeah, you know, and he-
That is amazing. He’s a friend, and he would do it for free,
I guarantee you, because you know, I’ve done it… He would do it for fun, I just don’t want
to take advantage of him, and I want him to feel obligated to take my phone calls. So when I’m this period in the first year
of research, where, and I don’t want to distract from lateral thinking with withered technology,
we can get back to that. I love this.
In the first year of research, and this relates to some things I’ve been thinking about, and
we’ve been talking about, but I kind of try to read 10 scientific articles a day every
day for the year, and most of that goes nowhere. Right? So I have to tolerate this inefficiency that
I used to beat myself up about, but now I increasingly realize like, oh, well, that’s
why I find a bunch of things that are not already all over the place, because I allow
that inefficiency. But when I’m in that period, I’m constantly
calling him, and being… You know, I was in grad school for science. Like, I’ve taken statistics courses. But he’s doing this stuff every day, and sometimes
I’m looking at studies and saying, is this conclusion right? This study’s making a strong conclusion, but
I don’t know if their method can support it. I end up learning a ton just from doing that
back and forth, so it’s really helpful. So we’ll get to lateral thinking with wither
technology, but I want to say a couple things. One, I think it’s invaluable having someone
that you can geek out with. Yeah, yeah.
I don’t have someone right now to do that. I’ve gone through various stages where I have. I kind of burn people out.
Yeah. A lot of times, because I’ll call them, and
I’ll just be like right into it. For other people, it’s not their thing. It’s not what they’re doing on a regular basis,
and sometimes they’re exhausted from a long day at work, or they had some issue coming
at them. Just want to geek the hell out, just try to
exercise my brain on this issue. But you said something about the first year
or so is reading. You put a goal of 10, an arbitrary goal of
10 research papers a day. But you don’t have an overall goal.
Right. Right? This is something I really want to explore
with you. Because I found this to be true in my own
life. Actually, I should say, this entire book was
a self-reinforcing bias book for me in so many ways. So I tried to focus where I could on what
sort of didn’t confirm my bias because it was just so heavily biased towards the way
I think. Yay, confirmation bias.
100%. But this thing about not having a goal. The other thing I was telling you before we
turned on the microphone was that when I started preparing for this, I realized that fine,
the first year I was really learning a lot for the amount of work I was putting in, but
I feel like lately the last year or so, I’m not learning enough, and I think that’s by
design because I was working so much and doing so much work, and I wasn’t leaving enough
space for my life, and I was trying to make room for my life, and by doing that, I systemized. By doing that, I wasn’t learning as much as
well. So when I wasn’t doing the show or earlier
on, if I didn’t have, let’s say, an episode to record, I would start reading books without
any particular idea in mind of what I was going to talk about or anything else or where
it was going to lead me, and that kind of free thinking I found to be the most valuable.
I agree. I don’t really start, like in my two experiences,
I won’t take a book contract until I’ve actually done a lot of research because I would have
no idea what I’m going to write about, and I actually instead of saying, here’s my idea,
and then researching. I actually just want to cast a really broad
net about some questions that I’m interested in, and try to approach them from all these
different angles. The problem is it’s so inefficient, right? Is you have to accept that a ton of your time
is going to be quote, unquote “wasted.” I don’t think it’s wasted, but it’s not going
to explicitly use it anywhere. But that’s the only way you find the stuff
you are going to use and roam to the ideas that are unique to you. The problem is, from the outside, that often
looks like just wasting time. You know?
I think, you know, it makes sense if you think about it even geospatially. If you have a goal in mind, you’ve already
limited yourself. Now, by limiting yourself, you’ll get to where
you want to go faster, but where you go is not going to have as much convexity. I think when you open yourself up to more
possibilities simultaneous, you’re not closing things down. You discover things that end up being the
most valuable. Yeah.
It’s fat tails. It makes me think of two things too. First of all, that I pinned two quotes from
a director and a writer that I love, Christopher Nolan and Eric Larsen, where they both said,
between projects I basically need to read widely with no apparent purpose. It’s like, the purpose is they are trying
to find something that they don’t know to look for until they find it. Right? So there is a purpose, but you just have to
go on that exploration. The other thing, you mentioned setting a goal
being limiting. I think it’s okay to set a goal as long as
you’re willing to amend it as you go forward. As I was doing this reporting, I was reading
a lot of Nobel acceptance speeches, and just I’m interested individual that anyway, and
one thing that I found really interesting was almost every year in recent years, someone
who is winning a Nobel prize in the sciences says, “Well, of course I wouldn’t really be
able to do my work today because now you have to basically say what you’re going to find
before you find it when you’re applying for a grant.” I’m like, maybe we should be paying attention
to the fact that they’re all saying it, and I’m sure they had some goal when they were
doing their research. They weren’t just like, randomly deciding
what to do. But clearly were willing to, or felt they
had the freedom to deviate from that goal when it made sense.
So what popped into my head when you were talking was an episode that we did with Bill
Janeway on the innovation economy, on the work he’s done. Are you familiar with it?
Mm-hmm (affirmative), mm-hmm (affirmative). So we’ve funded so much research without knowing
where it will go. Mm-hmm (affirmative).
I’ve come to the view that that’s important, and I think that we discount that, and we
discount the role of heavy investments at the early stage of the innovation cycle, where
risk is fraught and normal businesses are not going to invest. How does that relate to your research, and
what do you think about that? No, I think you’re exactly right. I sort of talk about toward the very end of
the book, where we actually need to allow for this inefficiency, and if you look at
the history of science, so many of the biggest breakthroughs have come from someone who is
pursuing a particular question that interests them, but that question does not tie to the
eventual application that makes the breakthrough, right? I think we talk about Vannevar Bush. When he wrote his report for the president,
he managed the US scientific efforts during World War II, and his report, Science: The
Endless Frontier, was essentially describing successful research culture. He would use these phrases like, it basically
comprises of bright, curious people pursuing questions of their own volition and interest. This was not a pie in the sky guy, right? He’s dealing with the Manhattan project, and
mass production of medications and things like that. But he realized that the best way to get to
those applications was actually to allow this inefficiency because we by definition could
not really often, in many cases, not know what we were looking for.
Well, Vannevar Bush had pushed Claude Shannon to go to Cold Spring Harbor to learn about
genetics and DNA. Yeah. Yeah. I mean, this is-
Remarkable. So when I first got into journalism, because
I was living in a tent in the Arctic, trying to become a scientist when I said I should
become a writer. Yea, that’s another crazy story.
But one of my early jobs, I was writing about this startup that was writing about higher
education policy, and I would sit in on hearings in Congress of the science and space subcommittee
in the Senate. At the time, the chair was Kay Bailey Hutchinson,
and she would often start meetings by taking this, like a stack of research proposals,
and taking one off the top and reading whatever the title was, and then tossing it, and saying,
if it didn’t directly say, like we’re going to discover some commercial technology, then
she would say, how is this going to help us get ahead of China and Indiana? Over and over and over.
I mean, and she was identifying things in geology, biology, anything that wasn’t like
directly tech engineering, right? Geology and biology and economics, these are
huge breakthroughs, and productive innovations that have come from these disciplines. It was sort of scary to hear this idea of
purifying selection, where you say, we only want to fund these narrow things that will
lead directly to some commercial application that is named in the proposal, because I think
that really defies everything we’ve learned about how innovation often works.
Yeah. That’s so interesting. So why do you think it is that people have
this predisposition towards thinking categorically? Is it because we have a structure that the
human brain by definition stereotypes, that’s how we learn. We put things into groupings. I mean, what accounts for this?
You mean, this idea that we should kind of go straight at something very clearly–
You know, Claude Shannon, who encompasses so many of these things, including the importance
of play and unstructured learning, which I’d like to talk about.
I think A Mind at Play is the title of his biography, but yeah.
Jimmy Soni’s book, we had Jimmy Soni on, that’s right. The Mind at Play, exactly. You know, he studied Boolean algebra at Michigan,
and then he studied electrical engineering with Vannever at MIT.
Yeah. That was integral for him to create the mathematical
theory of communication, which he wrote on the side while he was working at Bell Labs,
right? Right, in which he said, “Just so happened
nobody was familiar with these two different domains at the same time.”
Yeah, remarkable now. Which laid the foundation for all of our digital
computers. Exactly. Exactly, and this is the guy, by the way,
said unstructured learning and playing, he created all sorts of things. Of course there was a mechanical mouse, but
he also created a flame throwing trumpet for his son, reportedly.
Yeah. But there’s something that came up a number
of times when I was reading your book in my mind, which had to do with not just unstructured
learning, but self-teaching. Mm-hmm (affirmative).
What is it about being self-taught? There’s so many interesting aspects of that. First of all, what is it about that that separates
that type of learning from other types of learning, where you’re taught? Unsupervised versus supervised. Also, is there something more fundamentally
ancient about that type of learning? I mean, I don’t know. Kind of just throwing it out there because
that’s what I do every week, and I suck at being taught. You know, I’ve never been really great at
sitting in lectures. You know, I’m much more interactive. But also I’d much prefer having reading something,
finding it interesting, like your story about going to Columbia Library, and having all
the citations hyperlinked. Like, totally get it. You know what I mean? Like being able to just go-
Wow, you really did your homework. Being able to fly… Yeah, I always do. Being able to see one thing, and say, you
know what? Halt. I want to learn more about that. I want to learn enough where I no longer feel
that I need to, and then I’m going to continue. You know, that never was something I could
do in class because it was structured. There as a syllabus. This is what we’re going to read today, and
that’s it. That never worked for me.
Yeah, and I get it. I mean, sometimes I’m hesitant to rag on school
because it’s such a, it’s like the most complicated challenge.
It depends on the teacher too. In the world, of course. To prepare people, not to get off topic here,
but people say like, oh, school today. Public schools, like worse than in my day,
or whatever. Actually, there is no measure by which students
today don’t have a better grasp of basic concepts than their parents did. None. You can look at any achievement test. What you need to do to reach proficiency levels
in different grades is so much more difficult than what you had to do in the past. It’s just that the work world challenges have
been moving even faster. I find that fascinating. I remember reading that in the book, and that
didn’t make intuitive sense. Because I felt like everyone’s getting stupider.
So that’s why I printed, I took a question you needed for sixth grade math proficiency
from Massachusetts middle school in 1980, like sort of very beginning of the explosion
of the knowledge economy, and then in 2010, and one of them is like, the former you just
need to memorize an algorithm. The latter you need to turn words into variables,
and have multiple parts interact, and all these things. So it’s so much harder, and that’s the same
level of proficiency. So students have a harder challenge. Teachers are faced with having to teach in
a way that they themselves did not experience as students, so I think that’s really difficult.
So what I think the problem often is, and you mentioned early on the rhesus macaques,
the study that looked at, if you gave them hints frequently while they were learning,
they did not retain that knowledge. Even though they looked like they did. So once you took the hints away, like it was
a disaster. So I think forms of excessive hint giving
in classroom learning really hamper learning overall. Teachers don’t often know they’re doing it,
right? That’s sort of the opening scene of that chapter
in Range is a teacher who outwardly kind of seems to be doing a great job. But her students are, I don’t want to say
tricking, because they’re not doing it on purpose. They’re just trying to get to the answer as
quickly as possible. Asking questions in such a way that they will
eventually get her to give hints that kind of give away how you can just solve the problem
without really understanding what you’re doing. Yeah, yeah, yeah.
Only later in the class, even though they’ve gotten all this stuff right, does it become
really obviously that they actually have no idea what’s going on.
I thought that chapter was towards the end, the one with the math students?
No, that was chapter four. Okay.
There’s a later, yeah, that was chapter four. Okay. Because the one I’m thinking of as you’re
talking is the one that compared to the Japanese classes. But that’s not the one you’re-
Yeah, no, no. It’s in that chapter also.
Okay. Okay. So I had it, for some reason I had it later. You know, that stuff is fascinating, and that
goes back to the point of abstractions. You can see how they’re not going macro enough.
Yeah. They’re going as macro as they have to in
order to solve the problem. The minimum amount of information.
Which is smart, in a way. They’re being expedient.
They’re being efficient. Right, exactly. Exactly. The problem is that being efficient in your
learning is often not good. Right? So self-teaching, as frustrating as it may
be, it removes that hint giving crutch that very often leads to people not learning anything
at all. There’s also something just really cool about
it, that I appreciated more while reading this book and reflecting on it, which is,
you’re teaching yourself. It’s such a powerful thing, especially in
this economy today, to develop the tools to learn on your own, and I don’t know how common
that is, so maybe we can talk about that also. But another thing, this kind of, it’s not
exactly improvisation, but you talk about inside thinking and the dangers of inside
thinking. I’d love for you to maybe tell our audience
what that is. But I wondered, if you extrapolate that out,
could that not lead to an economy where systematically a society where people are taking less risk
overall? Because inside thinking is all about judging
your ideas objectively about the metrics of risk, and then if everyone’s doing that, isn’t
everyone sort of not taking as many risks, and innovation would drop?
I think that’s definitely possible. I mean, so the inside view, what psychologists
call the inside view, is essentially when someone focuses very narrowly on all the little
details of whatever task it is they have at hand. You know, whatever. They’re making an investment. They’re doing–
Like an entrepreneur. A classic case of an entrepreneur is you overestimate
your own chances of success. That’s right.
You’d never start a business if you actually looked at it objectively.
Yeah, maybe that part’s actually important, right? The problem with the inside view sometimes,
and it’s not that we shouldn’t do that, focus on those details, is that that’s intuitive
for us. Like, if we need to solve some kind of problem,
we focus on all this minutiae, and ignore the outside view, which is essentially kind
of like using analogies, which is looking at all these structurally similar cases, and
saying, well, what usually happens? What usually works? A better way to make judgments turns out to
be at least starting with that outside view, right? We do the inside view very intuitively.
Here’s an interesting example. It’s so persuasive, this inside view, that
if you ask someone to research, say, political or a horse in the Kentucky Derby, the more
facts they learn, whatever question they investigate, if you ask them why might this person win? As they learn more facts, they will raise
the probability that person or that horse will win. If you ask them, why will this person lose? They will raise their probability prediction
of why that person will lose. Sometimes to the point where if you ask people
to do this in sequence, they will end up with a greater than 100% probability of any horse
winning the Kentucky Derby. Because whatever question they’re focused
on, they’re not trying to disprove it, right? They are essentially gathering facts to fit
a certain narrative. So you want to start by zooming out and looking
at these larger external features of sort of what usually happens in situations like
this before you start focusing on the minutiae. But usually we only do the minutiae part.
Yeah, no, I guess I was sort of wondering if everyone took that advice, what would that
mean for entrepreneurship, in think, you know Josh Wolfe, right?
Yeah. Josh and I were talking about this on an episode
that we did together. By the way, your voice reminds me of Josh.
Oh, really, I take that as a complement. When I was listening to you on other episodes,
I was like, who does this remind me of? Then Josh came up, because you mentioned that
you know him, and you sound a lot like him. Not because we’re both digressive and talk
a little faster? You’re both a little nerdy. You both kind of talk a little fast, and I
think there’s something similar there. But I wonder because it is kind of, in order
to be a successful entrepreneur, in order to start something from scratch, you have
to have a ridiculously sort of almost na’efve, in most cases, sense of what you can accomplish. You’re going to completely overstate the odds
in your favor. Maybe. Or you could also, right, just think that
the odds are low but the payoff is really high. Which I think is what a lot of people… Like, people thing about, when they think
about betting, for example, they’re like, I want this side, not that side. Right, I want this boxer, not that boxer. Which makes no sense. How you should be thinking is, I would take
this boxer that I don’t initially want, for X odds, or X payoff, right? I assume lots of entrepreneurs know that they’re
facing uphill odds, right? Because that’s one area where the outside
view is pretty well known that most startups fail, but that the payoff can be really high,
so that’s a good gamble to take. Yeah. But there’s something about the optimism of
starting something, you know? Yeah.
I mean, I look back to when I started this podcast. I think it was ridiculous that I thought I
could get it to where it is today in terms of listenership and everything else. It’s ridiculous when I look back objectively
at it, and so many things that could have gone wrong, and I put in insane amounts of
work, and it still could have not worked out. Yeah, but also you’re obviously a very curious
person, and so my guess is that would failure have meant slower audience growth than you
currently have? I don’t know. But my guess is there’s a lot in this process
that you enjoy and find of value no matter what. So of course you hope for it to develop an
audience, but that there’s a lot more to it for you than that.
So this is really great because what you’re touching on is something that Steve Jobs,
many other people have talked about it. When he said in his Stanford speech, and I’m
sure other places that you should do what you love because even if it doesn’t work out
or if you don’t get rich or whatever, you’re still doing what you love. If you’re doing something that you don’t love,
in order to be successful you have to work so hard that you’d only have to be insane
to actually do it, if you didn’t love it. This speaks to this thing about sampling. We touched on it a little bit, but I really
do want to get into it because it’s one of the most tangible, clear reasons why this
makes sense. This approach. The Roger Federer approach, as opposed to
the Tiger Woods approach, which is that you don’t know. Right, and also just generally, life is uncertain. We change. The future’s unknown. The idea that we can have our lives planned
out. Not even necessarily from an early age, which
is just ludicrous. But even anywhere along the road, we close
ourselves off so much to learning about ourselves, and the things that we’re most aligned with,
and the things that we’re most aligned with are where we’re going to excel with the least
amount of effort. Right, right, and find the greatest sense
of fulfillment. That doesn’t mean there won’t be effort, but
we can get farther, right? So again, you’re getting at this issue of
match quality, the term economists use to describe the degree of fit between one’s abilities
and interests and the work that they do. It doesn’t mean that the work will be easy,
but it turns out as a researcher told me, that everyone’s familiar with the concept
of grit, right? You know, stick-to-itiveness.
Angela Duckworth. Right. It has a very specific, actually, psychology
definition. But anyway, I think the general understanding
is good enough. What this other researcher in the area told
me is that when you get fit, it looks like grit. Meaning that grit isn’t necessarily something
that just is innate to a person no matter what they’re doing. But then when you get someone in a situation
that suits them well, they will display some of the characteristics of grit even if they
didn’t before. Turns out you can take someone who’s really
competent in one area, put them in other areas where they’re totally uncomfortable, and suddenly
they’ll look not very gritty. Right?
I was a college athlete, right? Some of the most competitive, hardworking
people I’ve ever seen on the track were like the biggest chickens I’ve ever seen in the
classroom. And vice versa. So was it, did they lose their grit at a certain
hour every day? No, it’s because they had better match quality
with some of these tasks than they did with others. So I think part of what happens through sampling
and why the book starts with sports is this counterintuitive pattern that most elite athletes
actually specialized later than peers who plateau at lower levels, because they’re going
through this sampling period where they’re learning this broad range of skills, but also
having a chance to learn about themselves, and their interests, and what their options
are. The more they can delay selection, to a certain
point, the better their odds of matching well. Now in the wider, non-sports world, I think
you brought up something about closing off learning about ourselves, and that gets to
an important concept that I mention in Range called the end of history illusion. This psychological finding that at every time
period in life, if someone asks you, have you changed a lot based on your previous experiences,
you’ll be like, well, yeah. Of course, I’ve learned a lot. I’ve changed. Then you’ll think, but I won’t change that
much in the future. We underestimate future change at every time
point in life. We’re always saying, yeah, I changed a lot
before, but now I’m pretty much done. That continues through our whole life. So the fastest time of personality change
is like 18 to your late 20s. Which is usually when we’re trying to force
people to specialize, right? So we’re trying to make them decide for a
person they don’t yet know, in a world that doesn’t yet exist, and pretending that’s something
static. Where I think we need to be cognizant of the
fact that there is substantial personality change over the course of your life and the
way you spend your time. What you think your strengths and weaknesses
are. You know, et cetera. What your skills are. So this is a lifelong process of fitting yourself
to the work that you’re doing. So I want to move us to the overtime, David,
but I want to mention before we do that something that I said to you before we started this
conversation, which is that there is a romantic quality to your findings. Or maybe a better way to put it is, this book
is very compatible with a romantic view of life.
This one’s interesting. I’ve never heard this one before. So I think you should, and I found what you
said very interesting, so I think you should hold forth a little bit here. [crosstalk 00:58:38].
I’ll see if I can communicate it. I’m struggling today. But you talked about it in terms of who was
this Girl Scouts leader? Frances Hesselbein.
Yeah. Frances, Herminia Ibarra also. What this book is saying, and you kind of
mentioned it a little bit before when you talked about don’t look at what other people
are doing. Focus on your own inclinations or where your
passions are. I think this is a more fulfilling life, and
I think a lot of times people feel like there’s got to be a hack to success. There’s got to be like the five step method. Like, this is what I got to do. I got to put my ducks in a row.
You know, what this is saying is quite the opposite. It’s saying that we each have our own path,
and that we have to explore that path. You know, I mentioned, I don’t know if I said
it during the episode, but we had Jerry Colonna on, and one of the things that he said in
that episode was that, or it was maybe in his book, that when we’re at that fork in
the road, we so desperately want to know which path will lead to success. But we can’t know, and even the sort of posing
it that way is almost an illusion, you know? Because we are the path. We are the accumulation of our experiences. Anyway, I feel like there was a strong element
of that in the book. I think you’re getting at, I’ll read some
of that five step life hack stuff as much as the next guy, just like I’ll read about
how other people achieve success, and know that I’m not going to do it the same way,
but then maybe there are things I can gather. But what I don’t like about the sort of concrete,
here’s how to succeed stuff, is it purports to imbue you with self-knowledge without you
having to have done the things that actually cause you accrue self-knowledge. That take time. Right?
We’re sort of obsessed with head starts, I think, because I think we’re wired to think
of what we see someone as today as a stable trajectory. So if you’re ahead of me right now, our development
lines will forever be parallel into the future. That turns out to almost never be the case.
Life’s a marathon, it’s not a sprint. That’s right, and similarly in a marathon,
if you see someone come off the line first, you can be pretty sure they’re not going to
win. It goes to your point, it’s a wicked world. These are non-linear systems.
That’s right. You know?
That’s right. There’s non-linear payoff.
Not only that, and this is sort of microcosm of that, but one of the studies that I found
earlier that I enjoyed was this economist who found a natural experiment in the higher
ed systems of England and Scotland. Systems are very similar. Except in England, students had to specialize
in their mid-teen years to apply to specific course of study. In Scotland they could keep sampling later
in university if they wanted to. His question was, who wins the trade off? The early specializers or the late?
It turns out the early specializers jump out to an income lead because they have more domain
specific skills. But the late specializers get more time to
sample and learn where they fit, and so when they do settle down, they have better match
quality. So their growth rates are higher. So by six years out, they fly past the early
specializers. Meanwhile, the early specializers start quitting
their career tracks in much higher numbers. So one of his conclusions was that in higher
ed, the return to learning about your personal match quality is higher than the return to
the specific skills that you learn. I think that’s sort of a lesson for life.
That’s powerful. Yeah, no, that’s super powerful. Also, you can’t see past the horizon. You can’t see past your face. You can’t see past your nose. So you don’t know what the future holds, so
you don’t know the stuff that you think today’s a waste of time, might actually be the most
essential thing you ever learned five years from now. It can catapult to a new place of success. David, stay put. We’re going to move it to the overtime.
For regular listeners, you know the drill. If you’re new to the program, head over to
Patreon.com/hiddenforces to access our audio file, autodidact or super nerd tiers, where
you can get access to the overtime of me and David talking, as well as to the transcript
of our conversation, as well as to this illustrious, very beautiful rundown full of pictures from
Jim Henson’s Labyrinth, Stanley Kubrick’s 2001, see, I got David Bowie here. Pictures of Gene Kranz at mission control. Alice in Wonderland, this way up, this way
left. Yonder that way.
So David, I appreciate it so much. Thank you for coming on the program and stick
around, we’ll be right back. My pleasure, thank you for having me.
Today’s episode of Hidden Forces was recorded at Creative Media Design Studio in New York
City. For more information about this week’s episode,
or if you want easy access to related programming, visit our website at hiddenforces.io, and
subscribe to our free email list. If you want access to overtime segments, episode
transcripts, and show rundowns full of links and detailed information related to each and
every episode, check out our premium subscription, available through the Hidden Forces website
or through our Patreon page at Patreon.com/hiddenforces. Today’s episode was produced by me and edited
by Stylianos Nicolaou. For more episodes, you can check out our website
at hiddenforces.io. Join the conversation at Facebook, Twitter,
and Instagram @hiddenforcespod, or send me an email. As always, thanks for listening. We’ll see you next week.