The Reign of Specialists is Over

with David Epstein, Bestselling Author of Range

David Epstein
Author

David is the author of the #1 New York Times best sellers Range: Why Generalists Triumph in a Specialized World, and The Sports Gene, both of which have been translated to more than 20 languages. His TED talks (on athletes and specializing early) have been viewed more than 11 million times.

Satyen Sangani
Co-founder & CEO of Alation

As the Co-founder and CEO of Alation, Satyen lives his passion of empowering a curious and rational world by fundamentally improving the way data consumers, creators, and stewards find, understand, and trust data. Industry insiders call him a visionary entrepreneur. Those who meet him call him warm and down-to-earth. His kids call him “Dad.”

David Epstein
Author

 

David is the author of the #1 New York Times best sellers Range: Why Generalists Triumph in a Specialized World, and The Sports Gene, both of which have been translated to more than 20 languages. His TED talks (on athletes and specializing early) have been viewed more than 11 million times.

Satyen Sangani
Co-founder & CEO of Alation

 

As the Co-founder and CEO of Alation, Satyen lives his passion of empowering a curious and rational world by fundamentally improving the way data consumers, creators, and stewards find, understand, and trust data. Industry insiders call him a visionary entrepreneur. Those who meet him call him warm and down-to-earth. His kids call him “Dad.”

David Epstein
Author

 

David is the author of the #1 New York Times best sellers Range: Why Generalists Triumph in a Specialized World, and The Sports Gene, both of which have been translated to more than 20 languages. His TED talks (on athletes and specializing early) have been viewed more than 11 million times.

Satyen Sangani
Co-founder & CEO of Alation

 

As the Co-founder and CEO of Alation, Satyen lives his passion of empowering a curious and rational world by fundamentally improving the way data consumers, creators, and stewards find, understand, and trust data. Industry insiders call him a visionary entrepreneur. Those who meet him call him warm and down-to-earth. His kids call him “Dad.”

For a long time, I wanted to be an expert.

I got my degree in economics. I saw a long career further studying this topic in more and more detail.

But that’s not the path I chose.

After starting in academia, I felt constrained.

I didn’t want to focus all my energy on research and writing papers. Instead, I returned to the world of business — not because I knew it was the right path, but because I knew that economics was not.

And instead of focusing on one area of specialization, I dabbled.

I tried out a little bit of everything.

I considered multiple different business ideas, trying to find my place.

It wasn’t always easy.

Many days, I felt listless and unfocused.

But ultimately, my long sampling period prepared me for something else: entrepreneurship.

Launching a company has required me to pull from a broad range of skills and knowledge.

I never quite understood that connection between dabbling and entrepreneurship until I read the work of today’s guest, David Epstein.

Today, he’s the New York Times-bestselling author of two books: The Sports Gene and Range: Why Generalists Triumph in a Specialized World.

He’ll help us understand the relationship between insight and experience.


Producer Read: Welcome to Data Radicals, a show about the people who use data to see things that nobody else can. This episode features an interview with David Epstein, author of The Sports Gene and Range: Why Generalists Triumph in a Specialized World.

On this episode, David and Satyen discuss the power of generalization, the importance of exploration, and how businesses can create structures that encourage breakthroughs at their organizations.

Data Radicals is brought to you by the generous support of Alation, the data catalog and data governance platform that combines data intelligence with human brilliance. Learn more at Alation.com.

 

Why Is Generalization Important in Today’s Modern World?

Satyen Sangani: One of the reasons generalization is so important is that the modern world is more complex.

You would think this means that we have to get really specific about our knowledge, but there’s a huge premium to connecting seemingly unrelated dots.

There are huge rewards to thinking differently and to building relationships between things that seem totally different.

Abstract thinking has become commonplace over the last two centuries.

It’s something scientist James Flynn noticed when he realized that IQ tests were producing unexpected results.

David Epstein: James Flynn (1934–2020) was a political scientist who studied IQ test scores. IQ tests are normed to 100, so that a score of 100 is always the average, but the actual number of questions people were answering correctly was going up over time. This meant if the tests weren’t renormed, it appeared as if humanity was gaining about three IQ points per decade over the course of the 20th century, meaning someone at the end of the 20th century being tested looks like like they’re as a different species from someone who would have been tested early in the 20th century.

It wasn’t just that we are natively smarter than our grandparents and great grandparents. Rather, as work in society has gotten more complex, it has required more abstract thinking that has changed the way people think. So the IQ test that had the biggest changes was one called Raven’s Progressive Matrices (RPM). It was actually designed to be the one that would not require changes. So supposedly as, as one paper wrote, this would be the test that if Martians landed on Earth, we could give them this and find out how intelligent they are, because culture doesn’t matter at all. It’s basically a series of abstract patterns. There’s one missing and just from looking at the patterns, you have to figure out what the rules are and fill in the missing pattern.

There was supposed to be no change on this. It was made to be what’s called a “culturally reduced test,” meaning nothing you learn in life should matter.

That’s where the biggest gains were on this abstract stuff. Long story short, we’re living in a world now where we have to do a lot of what’s called knowledge transfer. We have to take things we learn and not be as our ancestors did (and as some native populations still do). We used to have very specific knowledge and did the same things over and over.

It was very important to have that kind of knowledge. But now, we’re constantly in the place of having to take our knowledge and apply it to problems. Relative to our forebears, we’re constantly asked to apply our knowledge to slightly new problems and to new types of people, which forces the shift of thinking that causes us to build more general models.

So to categorize things, we may not know how to do a lot of the things on a subsistence farm, and yet even toddlers start to categorize the types of things that are on the farm in an abstract way, such that they would recognize another thing of the same category, even if they’d never encountered it before. This kind of categorical thinking — what Flynn called scientific goggles — has fundamentally changed human perception so we are sort of more capable of generalizing and transferring our knowledge to new problems and novel problem solving.

 

The Two Types of Learning Environments

Satyen Sangani: Our learning environments can play a big role in how we perceive things as well.

Apparently, there are two types of learning environments: kind and wicked. I’ll let David explain the difference.

David Epstein: The kind and wicked learning environment are terms coined by a psychologist named Robin Hogarth (b. 1942).

  • Kind learning environments are domains where the next steps and goals are clear. The rules are clear and they never change. Patterns repeat, so you can bank on new repetitive patterns. Feedback is quick and accurate, usually not a lot of human behavior involved: Work next year will look like work last year.
  • On the other end of the spectrum — and it is a spectrum — is the wicked learning environment. Next steps and goals may not just be handed to you: You may have to figure them out. If there are rules at all, they may not be enumerated or clearly, or they may change without notice. Patterns don’t just repeat. Feedback could be delayed or inaccurate. Human behavior is often involved and work next year won’t necessarily look like work last year.

Hogarth was trying to help with this debate that was going on in literature that studied how people develop expertise.

There was this conflict where some people who studied expertise found that individuals would get better with narrow experience, kind of just doing the same thing over and over. Others found that not only would they not get better, they would get more confident, but not better, which produced these really bad results. It turns out these researchers were seeing different things about what happens with experience because in kind learning environments, this more specialized experience does tend to make people better. Yet people are often mistaking wicked environments for kind ones, and thinking if they do the same thing over and over and over, they’ll just get better. That turns out not to be the case in these more dynamic situations.

Satyen Sangani: That’s true for individuals, but interestingly also true for companies, right? Because companies grow and scale and get better by doing the same thing over and over again, just slightly more efficiently. Once you find a differentiated product and service, the best thing you can do is to figure out how to drive down the cost of delivery. On the other hand, a lot of those businesses are quickly put out of business when some new revolutionary thing comes along. There’s this duality of trying to hyper specialize, but at the same time needing to generalize in order to stay alive. So you don’t miss the next turn.

 

The Explore/Exploit Tradeoff

David Epstein: There’s deep literature on exactly that called the explore/exploit tradeoff:

  • “Explore” means looking for new ideas, new skills, new things to try, etc.
  • “Exploit” means digging down and improving on the things that you already know.

There’s actually a great study pertaining to individuals that just came out in Nature, one of the most prestigious journals in the world. They looked at 26,500 scientists, artists, and film directors, and found that their most impactful works in their life usually come clustered in a hot streak. Most people only have one, but sometimes lucky people have two and those hot streaks always followed a period of broader exploration, where they were dabbling in a bunch of different things, gaining a little bit of skills here and there, and then putting it together and exploiting just like you said. It’s a period of exploration, then you figure out what works and then you drill into that. This study was about individuals, but it was the best of its kind and followed about over 26,000 practitioners. It sort of echoes what you just said.

Satyen Sangani: What’s also interesting though is that people often confuse the two for each other. They think they’re in an exploit environment when they actually should be in an exploratory environment. Is there any heuristic for how you know when you should be doing one or the other, or how to even think about being aware or more aware of that?

David Epstein: Great question. That’s the reason why there’s still so much literature on explore/exploit is that it’s difficult to tell. There’s this wealth of literature on what’s called self-regulatory learning that is kind of about a constellation

I don’t think we can say, “You should spend X percent of the time doing explore,” but people who are good self-regulatory learners — and I think this goes for adaptable organizations as well — have systematic reflection where they will be doing things and say, “Here are my abilities and interests, here are advantages right now. Here’s what we’re going to do now. And then maybe a year from now, we’ll pivot because we will have learned something: about ourselves and our opportunities and our chances.”

They don’t just leave this up to intuition, but have systematic methods of reflecting constantly to ask, “Did that work how we thought, did we find something unexpected? Is that something we can use or something that means we need to pivot in another direction?” You keep doing that successively so you move faster. I don’t think we can say, “We know the exploration phase will be done by X, Y, and Z,” unless it’s an issue where you have no money left, in which case that period is over.

These reflective practices should be made systematic, because the self-regulatory learning literature shows we simply don’t do it well enough or systematically enough where we’re answering the same set of questions over and over after each thing we try. So we need to treat our personal and work experiments more scientifically. We say, “Here are the questions I’m setting out. I’m going to come back and answer these same ones and then pivot based on what I learned.”

You can imagine all of the data they can record. Then a prioritized list is given to the inspectors to investigate. Rather than visiting 30 restaurants that didn’t need an inspection, they now have a list of priorities where a violation is much more likely.

The inspectors feel they’re doing their job better — it’s easier to do their job. Showing them how data and these new techniques are going to make their job much easier is also part of data literacy.

That’s the kind of habit of mind that gives a successful approach. To add to what you said before this, you said an organization will go into exploit mode and that works, but then they also get disrupted because they get so much tunnel vision.

 

How Can Exploit-Mode Companies Continue to Explore?

Satyen Sangani: Even if your company is in exploit mode, you can still continue to explore — by bringing in outside voices.

This is one of the fundamental and underappreciated benefits of Diversity and Inclusion efforts.

David Epstein: InnoCentive was a spinoff from Eli Lilly where their director of research realized that even as big a company as they were, they were so in exploit mode they had ceased to do the kind of exploration and were not solving problems you would have thought one of the best-funded companies in the world would be able to solve.

He proposed coming up with problems that wouldn’t give away trade secrets, then posting them online for random people to solve. And all his colleagues thought, “That’s ridiculous. We’re organic chemists. As if someone who doesn’t work here is going to solve our problems?” And he replied, “What’s the harm?”

It turned out that of a third of those problems that had stumped Lilly researchers started getting solved by people from other domains: lawyers, engineers, all these different people. It worked so well, that he — the guy’s name is Alf Bingham — spun it off into a separate company called InnoCentive that helps other companies post problems for outside solvers.

One of the best predictors of whether a problem will get solved is the diversity of people who start sending in answers. So they try to frame the question to attract a very diverse group of people.

I think if you’re clever about it — and with things like data catalogs, even within your own organization — you can take advantage of some of that “explore” by just getting different eyeballs on the same kind of stuff. It’s an important thing to do, especially when you’re doing well in exploit mode, because that’s probably when you’re least likely to be thinking about the kind of exploring that ensures the future.


Satyen Sangani: There’s this question of optimization versus discovery and kind versus wicked learning environments. A lot of the time people get frustrated. When you’re exploiting, there’s clear and obvious progress. When you’re in this exploration mode, you could go for long periods of time. In my career I spent long periods of time reading books like, what do I want to do with my life? Because I just had no idea what I wanted to do with my life and I dabbled all the time. How do you get people to sort of be comfortable with success that maybe may not happen very often?

David Epstein: One of the very true (but much less marketable) subtitles for Range is that sometimes the things you can do that give you the fastest apparent kind of head start can actually undermine your long-term development. That’s true whether it comes to developing a skill, whether it’s choosing a career or developing a work project. Sometimes you actually need to do things that don’t optimize the short term in order for the success of the long term.

I think everyone realizes that to some extent. Do you want every company you’re invested in optimizing only for the short-term? No. Do you want a national research agenda optimizing only for the short term? No, but it’s harder on a personal level because we’re so attracted to headstarts.

I write in Range about Herminia Ibarra, a London business school professor who studies work transitions, and one of the things she wants to kind of debunk is this idea that you run into a phone booth as Clark Kent, and rip off your suit and come out as Superman, because work is part of identity — a huge part of identity — and identity doesn’t change overnight. It changes one little piece at a time.

First, people should think about setting up low-stakes experiments. You don’t have to totally jump out of your job. What can you do that gives you a little insight into something different, into doing a project a little differently? Maybe it’s just talking to someone with a different perspective. Maybe it’s shadowing someone.

I have the liberty to interview people in all these different things, just because of my self-appointed job title of “journalist.” I think setting up low-stakes practice is important. Having a mindset where you’re not where you’re also trying to improve your own match quality — that’s the term economists use to describe the degree of fit between what you’re doing and your interests and abilities.

I think we should always be thinking about how we can triangulate better match quality: Here’s who I am. Here are my interests and skills. Here are my opportunities. You try one opportunity now and then pivot based on that, so you’re increasingly moving toward better match quality, because it turns out that has a huge effect on a sense of fulfillment and your performance.

 

Why Managers Should Encourage Exploration

Satyen Sangani: It’s also incumbent upon managers to encourage exploration — and to shoulder the risks when progress is slow or non-existent.

David Epstein: It’s really on managers to underwrite some of that risk if they want people to explore, because people will respond to those kinds of incentives. This is what coaches do for great athletes who, at a certain level, need to start experimenting with their training to figure out how to get better, because no one can tell you exactly at those high levels. You need to experiment.

And so a coach underwrites that risk and says, “We’re going to walk this path together.” Managers can do that for employees where they’re saying, “I’m in this with you. I’m condoning this risk and I’ll accept some of the bureaucratic blowback if there is some. That’s a huge point because there’s actually evidence of some places that claim to want to give people exploration time — their 10 or 20 percent time that if there’s not other stuff — other cultural signals — and it’s just sort of mixed in with the normal time, They actually won’t take it, a lot of the time.

That’s why I wrote about these two Nobel laureates in the last chapter in Range. I talk about how they had these times set aside for their lab just once a week where they’d say, “Everyone do something that’s not funded! It can just be for pure curiosity, you can use other people’s equipment. You won’t be held accountable. This is the time where we do that.” Their Nobel prizes came out of what they called these Friday night experiments or Saturday morning experiments.

Another great example that can serve as an analogy is the sport of skeleton, which is one of the Winter Olympics events where people push a sled and they do the disco move “the worm” and they jump on it and go face first down the ice, basically. I spent time with a Canadian coach who was practicing at a track with his guys one day, and the Americans came with all this fancy equipment. He said, “Oh my gosh, we can’t compete with them. They have so much better stuff, accelerometers and everything.” He told his guys, “Go into the start house” — where you practice dry land starts — “and don’t come out until you’ve come up with something new because we need some other competitive advantage.” These guys have been training for years in this event and they use two-handed starts. Two hours later, they come out of the start house and say, “Coach, can we use a one-handed start, like a sprinter?”

It turns out you can. And suddenly they rewrite the record book — it was a short-lived competitive advantage because once they showed it, everyone had to do it. It was guys who have been training for years, but were so stuck doing things a certain way that it took their coach saying, “Go in that other place for the next few hours and don’t come out until you’ve done something different,” and they completely changed the sport overnight.

So that’s akin to this idea that you do need to designate that space for people to really believe in it and use it effectively.

 

Generalists and Specialists: Birds and Frogs

Satyen Sangani: I want to go a little bit backward. You and I both went to Columbia University and we both experienced this thing called the “core curriculum.” The theory is that you’re going to learn from all of these different philosophers and writers and schools of thought in music and art to develop this range that will allow you to approach the world. There are some people who are like, “That was a total waste of time, and I wish I never did it.”

Is everybody able to generalize or is that something that is different for different folks?

David Epstein: When we talked about the Flynn effect, professor Flynn meant everyone for this kind of information deluge age that we live in. We want everyone to have certain problem solving abilities and information-sifting abilities that transfer across domains. We do want for everyone that kind of scientific mindset and data literacy. One ability I preach is Fermi estimation, which is when you’re getting numbers — whether it’s in the news or at work — the ability to break down a question into a lot of small pieces to make estimates, to understand whether it’s even sensible at all or not. So there are certain skills we do want everyone to have, that they can apply no matter where they are, but I don’t think we want everyone to stay generalist.

I think we need a mix. I mean, the way I think about it is the way Freeman Dyson (1923–2020) put it. He’s an intellectual hero of mine, an eminent physicist, mathematician, and a wonderful writer. And as he said, if we want to help the ecosystem, we need both birds and frogs:

  • The frogs are down in the mud, looking at all these granular details.
  • The birds are soaring up above — not seeing those details — but integrating the knowledge of the different frogs.

He said we need both. The problem was that we’re increasingly telling everyone to be frogs and therefore we’re not going to have these integrators. And I agree with that: we definitely need both. It’s just, then, our advice tends to be, “I agree with Dyson,” and it tends to be for people to go be frogs. So I think we’ve overweighted it in one direction.

I do think that everyone, just to be an informed citizen, should have a certain degree of numeracy and problem solving skills. Because everyone specializes to one degree or another at some point or another. When I left science and ended up at a sports magazine, the scientist viewed me as the zigzag generalist, whereas the sports magazine people viewed me as a specialist because of my science background. So, to some degree, it’s semantic. I think we want this mix, but we’ve just gone a little overboard on one side of it. That’s all.

 


Satyen Sangani: It does seem to me that in a world where change is inevitable and people talk about capitalism having creative destruction in that world, you want to get good at playing certain games. But you also want to know when the game has changed. Reid Hoffman, who’s this famous entrepreneur who founded LinkedIn basically always says some entrepreneurs do really well in a particular phase of a startup, but then they forget that the game has changed and then they just don’t know how to play. And so some of that baseline thinking is required for adaptability in a world where the concepts are constantly different and the tools are different.

David Epstein: I recently added an afterward to Range that included some research in a dozen countries that actually matched people for their parents’ years of education, their own years of education, their national test scores when they were available. The difference was some got more career-focused education and some got broader education, or at least some of these people had tertiary education, some didn’t. The pattern in 11 of the 12 countries was that those who got the broader education were a little slower to be hired right out of education. They did sometimes start at lower salary, but they ended up sort of losing the short term and winning in the long run because they were so much more adaptable that first of all, their growth rates became faster.

But also when there were changes to their industry, they would often grow off of those as opposed to being really set back by them. That made a strong argument that this broad base, this broader toolkit, can really come in handy in faster-changing environments. In that study, the more rapidly changing the economy of a country was, the greater the lifetime advantage that accrued to the people who had that broader background.

 

The Failure of Overspecialization

Satyen Sangani: You have a counterexample, which I think is just as interesting, about the failure of overspecialization. You talk a lot about experts who forecast and some of the work that came off of what happens when experts hyper-specialize over time and whether their prediction accuracy is better or worse. Take us through that research because it would be interesting for people to hear what the downside scenarios look like.

David Epstein: This was the most famous research ever done in forecasting and it involved predictions being made over 20 years. It was about 82,000 specific probability predictions with specific deadlines and hard questions. It turned out that the worst forecasters turned out to be the most specialized experts — people who’d spent their entire careers studying one or two problems — who would see the whole world through one lens or mental model. The most specialized ones actually got worse at forecasting as they accumulated experience, which obviously is not what you want, and when their forecast would go horribly wrong, they would — for listeners who might have their Bayesian thinking [the idea that more may be known about a physical situation than is contained in the data from a single experiment] hat on — correct in the wrong direction to say, “I’d have this perfectly, if only this one other thing had gone on.” They would really get wrapped up in one worldview.

That’s not to say these people aren’t useful because the people who were good predictors would go to these kinds of specialists who help create, unearth, and make available new knowledge. They would go to them for facts, not necessarily opinions. And they would integrate from all these different people. They were perspective-collectors or, as the researcher who led the work described them, they have “dragonfly eyes.” Dragonflies’ eyes are made of thousands of different lenses, each of which takes a different picture, which are then integrated in the dragonfly’s brain. He said that describes the people who were good forecasters:

  • Sometimes they had an area of specialty.
  • Sometimes they didn’t, but more important than what they thought was how they thought: they collected perspectives and integrated them.

Those people did fantastically well. Then, when they were put on teams of 12, those kinds of dragonfly-eyed — so-called superforecasters — made each other 50 percent better in their individual predictions because they would collect information from one another and view their ideas as hypotheses in need of testing.

They were good teammates, even if there was sometimes “polite antagonism.” They argued, but in a civil way, and they outperformed — without access to classified data — a prediction market of intelligence analysts in the U.S. intelligence community who did have access to classified data by about 30 percent. It went so well, they spun it off into a separate prediction-making business.

Satyen Sangani: I love this idea of “many sightedness” or the imagery of a dragonfly eye. It gets to the idea of empathy. You’re never going to see the world in its fullness without looking at it through other people’s perspectives. So much of your work speaks to me because a lot of my friends and family would have called me a dabbler and they would have said, “You’re just like doing this and you’re doing that and you can’t keep your brain focused on a single idea at a single point in time.” And I’d say for the first 20 years of my career, I sort of struggled with that. Just hearing these stories for so many people who kind of go through that struggle of dabbling, it’s great to hear there could be a phenomenal outcome at the end of that story,

David Epstein: I referenced that nature study that just came out with the 26,000-plus individuals and how their periods of exploration preceded their sort of hot streaks at work. It turned out that they could have those hot streaks at any age. If you’re a curious person, there can be dividends to that exploration phase, even at the same time as it’s harrowing in terms of career progression,


 

Why Exploration Can Be Frustrating

Satyen Sangani: Exploration can be frustrating.

It can feel tedious and you can burn out easily because there’s a constant sense you’re not making progress.

But amazing things can come out of it.

For me, the most powerful example of this is the life story of one of our greatest artists: Vincent Van Gogh.

David Epstein: His father was a minister and his most famous sermon was one where he would preach about the sower of fields. He would say, think of all the short-sighted people who have turned down fields that could have been planted if they just had the patience to do the work so they could reap the rewards later.

Vincent as a child was sort of besotted by that image and had this incredible work ethic. At first he was a very good student and he went away to a fancy boarding school and he did quite well, but he didn’t really like being far away from home and living with strangers. So he left that. He wasn’t sure what to do. Eventually, his uncle Lou is starting this growing art dealership, gives him a job and Vincent would repeat this pattern that happened in every sort of professional path, and his life, where he for a while where he would dive in with both feet, super-enthusiastically, work until he dropped, would do really well, would get promoted and all these sorts of things.

Then he would start to burn out, or have some personal conflicts, or something didn’t work. That happened with the art dealership. So he left that. He becomes a teacher; that doesn’t totally work. He’s a tutor; the same thing happens. He’s a bookkeeper and he loves books and he actually saves the store one day by moving, just with physical endurance, armload after armload of books during a flood.

But he has a higher ambition. So he trains to be a preacher and wears a hat with candles on it so he can study even at night. He does well in some subjects, but Latin and Greek, he’s just floundering. He leaves that, goes to another training program to be a pastor, but he’s no good at the TED talk kind of short sermon, and eventually decides he’s just going to be an itinerant kind of preacher. He goes to the coal country where services are needed most, and it gets off to this great start. But again, he basically flames out, and he finds himself in his late 20s with no possessions and no achievements to his name.

He writes this beautiful letter to his brother saying, “It seems like I’m being lazy because I don’t know what to do, but it’s actually that I just haven’t found the thing.” He likens himself to a bird in a cage in spring, seeing other birds fly by and knowing that there’s something he’s supposed to do, but not knowing what it is and banging his head against the bars of the cage and people looking and saying, “You’ve got everything you need there!” And he’s saying, “I don’t! I need freedom! I need to find who I’m supposed to be!”

So he writes that letter and he picks up a book called The Guide to the ABCs of Drawing, written for children, and starts drawing the life he sees around him. And his very next letter to his brother is short. He says, basically, “I’m being brief. I’m drawing. I’d like to get back to it.”

And that’s the start. That’s where he finds, in sort of his moment of deepest despair, the beginning of his life’s path, which over the next decade leads toward all these artistic experiments where at one point he says, “There’s no such thing as color. Everything’s a shade of black,” and he’ll only paint with black. And then he says, “There’s no such thing as black. I won’t even use black for the night sky.” And he pinballs between all these experiments and the last two years of his life arrives at this completely unique style that builds the bridge from classical to modern art. And so probably every van Gogh you’ve ever seen, anywhere on the screen saver or whatever, is from just that last two-year period where he sort of stopped his explorer and started his exploit. And so both personally with finding a domain for himself, and then with his unique style, he went through these sampling periods emblematic of some of the things I was writing about.


 

The Power of Exploration

Satyen Sangani:

“. . . If you could somehow see in me something other than some sort of idler.
Because there are idlers and idlers, who form a contrast.
There’s the one who’s an idler through laziness and weakness of character, through the baseness of his nature; you may, if you think fit, take me for such a one.

Then there’s the other idler, the idler truly despite himself, who is gnawed inwardly by a great desire for action, who does nothing because he’s imprisoned in something.

He doesn’t have what he would need to be productive.
Such a person doesn’t always know himself what he could do, but he feels by instinct, I’m good for something!
I feel I have a reason for being!
I know that I could be a quite different man!
There’s something within me, so what is it!
That’s an entirely different idler; you may, if you think fit, take me for such a one.”

This was a letter written by Vincent Van Gogh to his brother Theo in 1880.

He was 27 years old at the time and it would be another 7 years before he would produce most of the master work that we all recognize.

I can’t think of a more inspiring testament to the power of exploration.

So let’s make sure we keep exploring.

Let’s make sure we keep dabbling.

If you’d like to read more about Vincent Van Gogh’s letter, David writes about it in an excellent piece in his newsletter Range Widely at DavidEpstein.Bulletin.Com.

Thank you again to David for joining us for this special episode of Data Radicals.

This is Satyen Sangani, Co-Founder and CEO of Alation. Thank you for listening.

For a long time, I wanted to be an expert.

I got my degree in economics. I saw a long career further studying this topic in more and more detail.

But that’s not the path I chose.

After starting in academia, I felt constrained.

I didn’t want to focus all my energy on research and writing papers. Instead, I returned to the world of business — not because I knew it was the right path, but because I knew that economics was not.

And instead of focusing on one area of specialization, I dabbled.

I tried out a little bit of everything.

I considered multiple different business ideas, trying to find my place.

It wasn’t always easy.

Many days, I felt listless and unfocused.

But ultimately, my long sampling period prepared me for something else: entrepreneurship.

Launching a company has required me to pull from a broad range of skills and knowledge.

I never quite understood that connection between dabbling and entrepreneurship until I read the work of today’s guest, David Epstein.

Today, he’s the New York Times-bestselling author of two books: The Sports Gene and Range: Why Generalists Triumph in a Specialized World.

He’ll help us understand the relationship between insight and experience.


Producer Read: Welcome to Data Radicals, a show about the people who use data to see things that nobody else can. This episode features an interview with David Epstein, author of The Sports Gene and Range: Why Generalists Triumph in a Specialized World.

On this episode, David and Satyen discuss the power of generalization, the importance of exploration, and how businesses can create structures that encourage breakthroughs at their organizations.

Data Radicals is brought to you by the generous support of Alation, the data catalog and data governance platform that combines data intelligence with human brilliance. Learn more at Alation.com.

 

Why Is Generalization Important in Today’s Modern World?

Satyen Sangani: One of the reasons generalization is so important is that the modern world is more complex.

You would think this means that we have to get really specific about our knowledge, but there’s a huge premium to connecting seemingly unrelated dots.

There are huge rewards to thinking differently and to building relationships between things that seem totally different.

Abstract thinking has become commonplace over the last two centuries.

It’s something scientist James Flynn noticed when he realized that IQ tests were producing unexpected results.

David Epstein: James Flynn (1934–2020) was a political scientist who studied IQ test scores. IQ tests are normed to 100, so that a score of 100 is always the average, but the actual number of questions people were answering correctly was going up over time. This meant if the tests weren’t renormed, it appeared as if humanity was gaining about three IQ points per decade over the course of the 20th century, meaning someone at the end of the 20th century being tested looks like like they’re as a different species from someone who would have been tested early in the 20th century.

It wasn’t just that we are natively smarter than our grandparents and great grandparents. Rather, as work in society has gotten more complex, it has required more abstract thinking that has changed the way people think. So the IQ test that had the biggest changes was one called Raven’s Progressive Matrices (RPM). It was actually designed to be the one that would not require changes. So supposedly as, as one paper wrote, this would be the test that if Martians landed on Earth, we could give them this and find out how intelligent they are, because culture doesn’t matter at all. It’s basically a series of abstract patterns. There’s one missing and just from looking at the patterns, you have to figure out what the rules are and fill in the missing pattern.

There was supposed to be no change on this. It was made to be what’s called a “culturally reduced test,” meaning nothing you learn in life should matter.

That’s where the biggest gains were on this abstract stuff. Long story short, we’re living in a world now where we have to do a lot of what’s called knowledge transfer. We have to take things we learn and not be as our ancestors did (and as some native populations still do). We used to have very specific knowledge and did the same things over and over.

It was very important to have that kind of knowledge. But now, we’re constantly in the place of having to take our knowledge and apply it to problems. Relative to our forebears, we’re constantly asked to apply our knowledge to slightly new problems and to new types of people, which forces the shift of thinking that causes us to build more general models.

So to categorize things, we may not know how to do a lot of the things on a subsistence farm, and yet even toddlers start to categorize the types of things that are on the farm in an abstract way, such that they would recognize another thing of the same category, even if they’d never encountered it before. This kind of categorical thinking — what Flynn called scientific goggles — has fundamentally changed human perception so we are sort of more capable of generalizing and transferring our knowledge to new problems and novel problem solving.

 

The Two Types of Learning Environments

Satyen Sangani: Our learning environments can play a big role in how we perceive things as well.

Apparently, there are two types of learning environments: kind and wicked. I’ll let David explain the difference.

David Epstein: The kind and wicked learning environment are terms coined by a psychologist named Robin Hogarth (b. 1942).

  • Kind learning environments are domains where the next steps and goals are clear. The rules are clear and they never change. Patterns repeat, so you can bank on new repetitive patterns. Feedback is quick and accurate, usually not a lot of human behavior involved: Work next year will look like work last year.
  • On the other end of the spectrum — and it is a spectrum — is the wicked learning environment. Next steps and goals may not just be handed to you: You may have to figure them out. If there are rules at all, they may not be enumerated or clearly, or they may change without notice. Patterns don’t just repeat. Feedback could be delayed or inaccurate. Human behavior is often involved and work next year won’t necessarily look like work last year.

Hogarth was trying to help with this debate that was going on in literature that studied how people develop expertise.

There was this conflict where some people who studied expertise found that individuals would get better with narrow experience, kind of just doing the same thing over and over. Others found that not only would they not get better, they would get more confident, but not better, which produced these really bad results. It turns out these researchers were seeing different things about what happens with experience because in kind learning environments, this more specialized experience does tend to make people better. Yet people are often mistaking wicked environments for kind ones, and thinking if they do the same thing over and over and over, they’ll just get better. That turns out not to be the case in these more dynamic situations.

Satyen Sangani: That’s true for individuals, but interestingly also true for companies, right? Because companies grow and scale and get better by doing the same thing over and over again, just slightly more efficiently. Once you find a differentiated product and service, the best thing you can do is to figure out how to drive down the cost of delivery. On the other hand, a lot of those businesses are quickly put out of business when some new revolutionary thing comes along. There’s this duality of trying to hyper specialize, but at the same time needing to generalize in order to stay alive. So you don’t miss the next turn.

 

The Explore/Exploit Tradeoff

David Epstein: There’s deep literature on exactly that called the explore/exploit tradeoff:

  • “Explore” means looking for new ideas, new skills, new things to try, etc.
  • “Exploit” means digging down and improving on the things that you already know.

There’s actually a great study pertaining to individuals that just came out in Nature, one of the most prestigious journals in the world. They looked at 26,500 scientists, artists, and film directors, and found that their most impactful works in their life usually come clustered in a hot streak. Most people only have one, but sometimes lucky people have two and those hot streaks always followed a period of broader exploration, where they were dabbling in a bunch of different things, gaining a little bit of skills here and there, and then putting it together and exploiting just like you said. It’s a period of exploration, then you figure out what works and then you drill into that. This study was about individuals, but it was the best of its kind and followed about over 26,000 practitioners. It sort of echoes what you just said.

Satyen Sangani: What’s also interesting though is that people often confuse the two for each other. They think they’re in an exploit environment when they actually should be in an exploratory environment. Is there any heuristic for how you know when you should be doing one or the other, or how to even think about being aware or more aware of that?

David Epstein: Great question. That’s the reason why there’s still so much literature on explore/exploit is that it’s difficult to tell. There’s this wealth of literature on what’s called self-regulatory learning that is kind of about a constellation

I don’t think we can say, “You should spend X percent of the time doing explore,” but people who are good self-regulatory learners — and I think this goes for adaptable organizations as well — have systematic reflection where they will be doing things and say, “Here are my abilities and interests, here are advantages right now. Here’s what we’re going to do now. And then maybe a year from now, we’ll pivot because we will have learned something: about ourselves and our opportunities and our chances.”

They don’t just leave this up to intuition, but have systematic methods of reflecting constantly to ask, “Did that work how we thought, did we find something unexpected? Is that something we can use or something that means we need to pivot in another direction?” You keep doing that successively so you move faster. I don’t think we can say, “We know the exploration phase will be done by X, Y, and Z,” unless it’s an issue where you have no money left, in which case that period is over.

These reflective practices should be made systematic, because the self-regulatory learning literature shows we simply don’t do it well enough or systematically enough where we’re answering the same set of questions over and over after each thing we try. So we need to treat our personal and work experiments more scientifically. We say, “Here are the questions I’m setting out. I’m going to come back and answer these same ones and then pivot based on what I learned.”

You can imagine all of the data they can record. Then a prioritized list is given to the inspectors to investigate. Rather than visiting 30 restaurants that didn’t need an inspection, they now have a list of priorities where a violation is much more likely.

The inspectors feel they’re doing their job better — it’s easier to do their job. Showing them how data and these new techniques are going to make their job much easier is also part of data literacy.

That’s the kind of habit of mind that gives a successful approach. To add to what you said before this, you said an organization will go into exploit mode and that works, but then they also get disrupted because they get so much tunnel vision.

 

How Can Exploit-Mode Companies Continue to Explore?

Satyen Sangani: Even if your company is in exploit mode, you can still continue to explore — by bringing in outside voices.

This is one of the fundamental and underappreciated benefits of Diversity and Inclusion efforts.

David Epstein: InnoCentive was a spinoff from Eli Lilly where their director of research realized that even as big a company as they were, they were so in exploit mode they had ceased to do the kind of exploration and were not solving problems you would have thought one of the best-funded companies in the world would be able to solve.

He proposed coming up with problems that wouldn’t give away trade secrets, then posting them online for random people to solve. And all his colleagues thought, “That’s ridiculous. We’re organic chemists. As if someone who doesn’t work here is going to solve our problems?” And he replied, “What’s the harm?”

It turned out that of a third of those problems that had stumped Lilly researchers started getting solved by people from other domains: lawyers, engineers, all these different people. It worked so well, that he — the guy’s name is Alf Bingham — spun it off into a separate company called InnoCentive that helps other companies post problems for outside solvers.

One of the best predictors of whether a problem will get solved is the diversity of people who start sending in answers. So they try to frame the question to attract a very diverse group of people.

I think if you’re clever about it — and with things like data catalogs, even within your own organization — you can take advantage of some of that “explore” by just getting different eyeballs on the same kind of stuff. It’s an important thing to do, especially when you’re doing well in exploit mode, because that’s probably when you’re least likely to be thinking about the kind of exploring that ensures the future.


Satyen Sangani: There’s this question of optimization versus discovery and kind versus wicked learning environments. A lot of the time people get frustrated. When you’re exploiting, there’s clear and obvious progress. When you’re in this exploration mode, you could go for long periods of time. In my career I spent long periods of time reading books like, what do I want to do with my life? Because I just had no idea what I wanted to do with my life and I dabbled all the time. How do you get people to sort of be comfortable with success that maybe may not happen very often?

David Epstein: One of the very true (but much less marketable) subtitles for Range is that sometimes the things you can do that give you the fastest apparent kind of head start can actually undermine your long-term development. That’s true whether it comes to developing a skill, whether it’s choosing a career or developing a work project. Sometimes you actually need to do things that don’t optimize the short term in order for the success of the long term.

I think everyone realizes that to some extent. Do you want every company you’re invested in optimizing only for the short-term? No. Do you want a national research agenda optimizing only for the short term? No, but it’s harder on a personal level because we’re so attracted to headstarts.

I write in Range about Herminia Ibarra, a London business school professor who studies work transitions, and one of the things she wants to kind of debunk is this idea that you run into a phone booth as Clark Kent, and rip off your suit and come out as Superman, because work is part of identity — a huge part of identity — and identity doesn’t change overnight. It changes one little piece at a time.

First, people should think about setting up low-stakes experiments. You don’t have to totally jump out of your job. What can you do that gives you a little insight into something different, into doing a project a little differently? Maybe it’s just talking to someone with a different perspective. Maybe it’s shadowing someone.

I have the liberty to interview people in all these different things, just because of my self-appointed job title of “journalist.” I think setting up low-stakes practice is important. Having a mindset where you’re not where you’re also trying to improve your own match quality — that’s the term economists use to describe the degree of fit between what you’re doing and your interests and abilities.

I think we should always be thinking about how we can triangulate better match quality: Here’s who I am. Here are my interests and skills. Here are my opportunities. You try one opportunity now and then pivot based on that, so you’re increasingly moving toward better match quality, because it turns out that has a huge effect on a sense of fulfillment and your performance.

 

Why Managers Should Encourage Exploration

Satyen Sangani: It’s also incumbent upon managers to encourage exploration — and to shoulder the risks when progress is slow or non-existent.

David Epstein: It’s really on managers to underwrite some of that risk if they want people to explore, because people will respond to those kinds of incentives. This is what coaches do for great athletes who, at a certain level, need to start experimenting with their training to figure out how to get better, because no one can tell you exactly at those high levels. You need to experiment.

And so a coach underwrites that risk and says, “We’re going to walk this path together.” Managers can do that for employees where they’re saying, “I’m in this with you. I’m condoning this risk and I’ll accept some of the bureaucratic blowback if there is some. That’s a huge point because there’s actually evidence of some places that claim to want to give people exploration time — their 10 or 20 percent time that if there’s not other stuff — other cultural signals — and it’s just sort of mixed in with the normal time, They actually won’t take it, a lot of the time.

That’s why I wrote about these two Nobel laureates in the last chapter in Range. I talk about how they had these times set aside for their lab just once a week where they’d say, “Everyone do something that’s not funded! It can just be for pure curiosity, you can use other people’s equipment. You won’t be held accountable. This is the time where we do that.” Their Nobel prizes came out of what they called these Friday night experiments or Saturday morning experiments.

Another great example that can serve as an analogy is the sport of skeleton, which is one of the Winter Olympics events where people push a sled and they do the disco move “the worm” and they jump on it and go face first down the ice, basically. I spent time with a Canadian coach who was practicing at a track with his guys one day, and the Americans came with all this fancy equipment. He said, “Oh my gosh, we can’t compete with them. They have so much better stuff, accelerometers and everything.” He told his guys, “Go into the start house” — where you practice dry land starts — “and don’t come out until you’ve come up with something new because we need some other competitive advantage.” These guys have been training for years in this event and they use two-handed starts. Two hours later, they come out of the start house and say, “Coach, can we use a one-handed start, like a sprinter?”

It turns out you can. And suddenly they rewrite the record book — it was a short-lived competitive advantage because once they showed it, everyone had to do it. It was guys who have been training for years, but were so stuck doing things a certain way that it took their coach saying, “Go in that other place for the next few hours and don’t come out until you’ve done something different,” and they completely changed the sport overnight.

So that’s akin to this idea that you do need to designate that space for people to really believe in it and use it effectively.

 

How to Build Data ACES Within Organizations

Satyen Sangani: I want to go a little bit backward. You and I both went to Columbia University and we both experienced this thing called the “core curriculum.” The theory is that you’re going to learn from all of these different philosophers and writers and schools of thought in music and art to develop this range that will allow you to approach the world. There are some people who are like, “That was a total waste of time, and I wish I never did it.”

Is everybody able to generalize or is that something that is different for different folks?

David Epstein: When we talked about the Flynn effect, professor Flynn meant everyone for this kind of information deluge age that we live in. We want everyone to have certain problem solving abilities and information-sifting abilities that transfer across domains. We do want for everyone that kind of scientific mindset and data literacy. One ability I preach is Fermi estimation, which is when you’re getting numbers — whether it’s in the news or at work — the ability to break down a question into a lot of small pieces to make estimates, to understand whether it’s even sensible at all or not. So there are certain skills we do want everyone to have, that they can apply no matter where they are, but I don’t think we want everyone to stay generalist.

I think we need a mix. I mean, the way I think about it is the way Freeman Dyson (1923–2020) put it. He’s an intellectual hero of mine, an eminent physicist, mathematician, and a wonderful writer. And as he said, if we want to help the ecosystem, we need both birds and frogs:

  • The frogs are down in the mud, looking at all these granular details.
  • The birds are soaring up above — not seeing those details — but integrating the knowledge of the different frogs.

He said we need both. The problem was that we’re increasingly telling everyone to be frogs and therefore we’re not going to have these integrators. And I agree with that: we definitely need both. It’s just, then, our advice tends to be, “I agree with Dyson,” and it tends to be for people to go be frogs. So I think we’ve overweighted it in one direction.

I do think that everyone, just to be an informed citizen, should have a certain degree of numeracy and problem solving skills. Because everyone specializes to one degree or another at some point or another. When I left science and ended up at a sports magazine, the scientist viewed me as the zigzag generalist, whereas the sports magazine people viewed me as a specialist because of my science background. So, to some degree, it’s semantic. I think we want this mix, but we’ve just gone a little overboard on one side of it. That’s all.

 


Satyen Sangani: It does seem to me that in a world where change is inevitable and people talk about capitalism having creative destruction in that world, you want to get good at playing certain games. But you also want to know when the game has changed. Reid Hoffman, who’s this famous entrepreneur who founded LinkedIn basically always says some entrepreneurs do really well in a particular phase of a startup, but then they forget that the game has changed and then they just don’t know how to play. And so some of that baseline thinking is required for adaptability in a world where the concepts are constantly different and the tools are different.

David Epstein: I recently added an afterward to Range that included some research in a dozen countries that actually matched people for their parents’ years of education, their own years of education, their national test scores when they were available. The difference was some got more career-focused education and some got broader education, or at least some of these people had tertiary education, some didn’t. The pattern in 11 of the 12 countries was that those who got the broader education were a little slower to be hired right out of education. They did sometimes start at lower salary, but they ended up sort of losing the short term and winning in the long run because they were so much more adaptable that first of all, their growth rates became faster.

But also when there were changes to their industry, they would often grow off of those as opposed to being really set back by them. That made a strong argument that this broad base, this broader toolkit, can really come in handy in faster-changing environments. In that study, the more rapidly changing the economy of a country was, the greater the lifetime advantage that accrued to the people who had that broader background.

 

The Failure of Overspecialization

Satyen Sangani: You have a counterexample, which I think is just as interesting, about the failure of overspecialization. You talk a lot about experts who forecast and some of the work that came off of what happens when experts hyper-specialize over time and whether their prediction accuracy is better or worse. Take us through that research because it would be interesting for people to hear what the downside scenarios look like.

David Epstein: This was the most famous research ever done in forecasting and it involved predictions being made over 20 years. It was about 82,000 specific probability predictions with specific deadlines and hard questions. It turned out that the worst forecasters turned out to be the most specialized experts — people who’d spent their entire careers studying one or two problems — who would see the whole world through one lens or mental model. The most specialized ones actually got worse at forecasting as they accumulated experience, which obviously is not what you want, and when their forecast would go horribly wrong, they would — for listeners who might have their Bayesian thinking [the idea that more may be known about a physical situation than is contained in the data from a single experiment] hat on — correct in the wrong direction to say, “I’d have this perfectly, if only this one other thing had gone on.” They would really get wrapped up in one worldview.

That’s not to say these people aren’t useful because the people who were good predictors would go to these kinds of specialists who help create, unearth, and make available new knowledge. They would go to them for facts, not necessarily opinions. And they would integrate from all these different people. They were perspective-collectors or, as the researcher who led the work described them, they have “dragonfly eyes.” Dragonflies’ eyes are made of thousands of different lenses, each of which takes a different picture, which are then integrated in the dragonfly’s brain. He said that describes the people who were good forecasters:

  • Sometimes they had an area of specialty.
  • Sometimes they didn’t, but more important than what they thought was how they thought: they collected perspectives and integrated them.

Those people did fantastically well. Then, when they were put on teams of 12, those kinds of dragonfly-eyed — so-called superforecasters — made each other 50 percent better in their individual predictions because they would collect information from one another and view their ideas as hypotheses in need of testing.

They were good teammates, even if there was sometimes “polite antagonism.” They argued, but in a civil way, and they outperformed — without access to classified data — a prediction market of intelligence analysts in the U.S. intelligence community who did have access to classified data by about 30 percent. It went so well, they spun it off into a separate prediction-making business.

Satyen Sangani: I love this idea of “many sightedness” or the imagery of a dragonfly eye. It gets to the idea of empathy. You’re never going to see the world in its fullness without looking at it through other people’s perspectives. So much of your work speaks to me because a lot of my friends and family would have called me a dabbler and they would have said, “You’re just like doing this and you’re doing that and you can’t keep your brain focused on a single idea at a single point in time.” And I’d say for the first 20 years of my career, I sort of struggled with that. Just hearing these stories for so many people who kind of go through that struggle of dabbling, it’s great to hear there could be a phenomenal outcome at the end of that story,

David Epstein: I referenced that nature study that just came out with the 26,000-plus individuals and how their periods of exploration preceded their sort of hot streaks at work. It turned out that they could have those hot streaks at any age. If you’re a curious person, there can be dividends to that exploration phase, even at the same time as it’s harrowing in terms of career progression,


 

Why Exploration Can Be Frustrating

Satyen Sangani: Exploration can be frustrating.

It can feel tedious and you can burn out easily because there’s a constant sense you’re not making progress.

But amazing things can come out of it.

For me, the most powerful example of this is the life story of one of our greatest artists: Vincent Van Gogh.

David Epstein: His father was a minister and his most famous sermon was one where he would preach about the sower of fields. He would say, think of all the short-sighted people who have turned down fields that could have been planted if they just had the patience to do the work so they could reap the rewards later.

Vincent as a child was sort of besotted by that image and had this incredible work ethic. At first he was a very good student and he went away to a fancy boarding school and he did quite well, but he didn’t really like being far away from home and living with strangers. So he left that. He wasn’t sure what to do. Eventually, his uncle Lou is starting this growing art dealership, gives him a job and Vincent would repeat this pattern that happened in every sort of professional path, and his life, where he for a while where he would dive in with both feet, super-enthusiastically, work until he dropped, would do really well, would get promoted and all these sorts of things.

Then he would start to burn out, or have some personal conflicts, or something didn’t work. That happened with the art dealership. So he left that. He becomes a teacher; that doesn’t totally work. He’s a tutor; the same thing happens. He’s a bookkeeper and he loves books and he actually saves the store one day by moving, just with physical endurance, armload after armload of books during a flood.

But he has a higher ambition. So he trains to be a preacher and wears a hat with candles on it so he can study even at night. He does well in some subjects, but Latin and Greek, he’s just floundering. He leaves that, goes to another training program to be a pastor, but he’s no good at the TED talk kind of short sermon, and eventually decides he’s just going to be an itinerant kind of preacher. He goes to the coal country where services are needed most, and it gets off to this great start. But again, he basically flames out, and he finds himself in his late 20s with no possessions and no achievements to his name.

He writes this beautiful letter to his brother saying, “It seems like I’m being lazy because I don’t know what to do, but it’s actually that I just haven’t found the thing.” He likens himself to a bird in a cage in spring, seeing other birds fly by and knowing that there’s something he’s supposed to do, but not knowing what it is and banging his head against the bars of the cage and people looking and saying, “You’ve got everything you need there!” And he’s saying, “I don’t! I need freedom! I need to find who I’m supposed to be!”

So he writes that letter and he picks up a book called The Guide to the ABCs of Drawing, written for children, and starts drawing the life he sees around him. And his very next letter to his brother is short. He says, basically, “I’m being brief. I’m drawing. I’d like to get back to it.”

And that’s the start. That’s where he finds, in sort of his moment of deepest despair, the beginning of his life’s path, which over the next decade leads toward all these artistic experiments where at one point he says, “There’s no such thing as color. Everything’s a shade of black,” and he’ll only paint with black. And then he says, “There’s no such thing as black. I won’t even use black for the night sky.” And he pinballs between all these experiments and the last two years of his life arrives at this completely unique style that builds the bridge from classical to modern art. And so probably every van Gogh you’ve ever seen, anywhere on the screen saver or whatever, is from just that last two-year period where he sort of stopped his explorer and started his exploit. And so both personally with finding a domain for himself, and then with his unique style, he went through these sampling periods emblematic of some of the things I was writing about.


 

The Power of Exploration

Satyen Sangani:

“. . . If you could somehow see in me something other than some sort of idler.
Because there are idlers and idlers, who form a contrast.
There’s the one who’s an idler through laziness and weakness of character, through the baseness of his nature; you may, if you think fit, take me for such a one.

Then there’s the other idler, the idler truly despite himself, who is gnawed inwardly by a great desire for action, who does nothing because he’s imprisoned in something.

He doesn’t have what he would need to be productive.
Such a person doesn’t always know himself what he could do, but he feels by instinct, I’m good for something!
I feel I have a reason for being!
I know that I could be a quite different man!
There’s something within me, so what is it!
That’s an entirely different idler; you may, if you think fit, take me for such a one.”

This was a letter written by Vincent Van Gogh to his brother Theo in 1880.

He was 27 years old at the time and it would be another 7 years before he would produce most of the master work that we all recognize.

I can’t think of a more inspiring testament to the power of exploration.

So let’s make sure we keep exploring.

Let’s make sure we keep dabbling.

If you’d like to read more about Vincent Van Gogh’s letter, David writes about it in an excellent piece in his newsletter Range Widely at DavidEpstein.Bulletin.Com.

Thank you again to David for joining us for this special episode of Data Radicals.

This is Satyen Sangani, Co-Founder and CEO of Alation. Thank you for listening.

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