Range: Why Generalists Triumph in a Specialized World

by David Epstein

Deliberate practice, according to the study of thirty violinists that spawned the rule, occurs when learners are “given explicit instructions about the best method,” individually supervised by an instructor, supplied with “immediate informative feedback and knowledge of the results of their performance,” and “repeatedly perform the same or similar tasks.”

Reams of work on expertise development shows that elite athletes spend more time in highly technical, deliberate practice each week than those who plateau at lower levels:

The response, in every field, to a ballooning library of human knowledge and an interconnected world has been to exalt increasingly narrow focus.

Elite athletes at the peak of their abilities do spend more time on focused, deliberate practice than their near-elite peers. But when scientists examine the entire developmental path of athletes, from early childhood, it looks like this: Eventual elites typically devote less time early on to deliberate practice in the activity in which they will eventually become experts. Instead, they undergo what researchers call a “sampling period.” They play a variety of sports, usually in an unstructured or lightly structured environment; they gain a range of physical proficiencies from which they can draw; they learn about their own abilities and proclivities; and only later do they focus in and ramp up technical practice in one area.

The title of one study of athletes in individual sports proclaimed “Late Specialization” as “the Key to Success”; another, “Making It to the Top in Team Sports: Start Later, Intensify, and Be Determined.”

Czech athlete Ester Ledecká became the first woman ever to win gold in two different sports (skiing and snowboarding) at the same Winter Olympics.

One study showed that early career specializers jumped out to an earnings lead after college, but that later specializers made up for the head start by finding work that better fit their skills and personalities.

I found a raft of studies that showed how technological inventors increased their creative impact by accumulating experience in different domains, compared to peers who drilled more deeply into one; they actually benefited by proactively sacrificing a modicum of depth for breadth as their careers progressed. There was a nearly identical finding in a study of artistic creators.

an inventor who stuck to a self-made antispecialization philosophy and turned a small company founded in the nineteenth century into one of the most widely resonant names in the world today.

I dove into work showing that highly credentialed experts can become so narrow-minded that they actually get worse with experience, even while becoming more confident—a dangerous combination. And I was stunned when cognitive psychologists I spoke with led me to an enormous and too often ignored body of work demonstrating that learning itself is best done slowly to accumulate lasting knowledge, even when that means performing poorly on tests of immediate progress. That is, the most effective learning looks inefficient; it looks like falling behind.

Researchers at Northwestern, MIT, and the U.S. Census Bureau studied new tech companies and showed that among the fastest-growing start-ups, the average age of a founder was forty-five when the company was launched.

Overspecialization can lead to collective tragedy even when every individual separately takes the most reasonable course of action.

An internationally renowned scientist (whom you will meet toward the end of this book) told me that increasing specialization has created a “system of parallel trenches” in the quest for innovation. Everyone is digging deeper into their own trench and rarely standing up to look in the next trench over, even though the solution to their problem happens to reside there. The scientist is taking it upon himself to attempt to despecialize the training of future researchers; he hopes that eventually it will spread to training in every field. He profited immensely from cultivating range in his own life, even as he was pushed to specialize. And now he is broadening his purview again, designing a training program in an attempt to give others a chance to deviate from the Tiger path. “This may be the most important thing I will ever do in my life,” he told me.

Klein has shown that experts in an array of fields are remarkably similar to chess masters in that they instinctively recognize familiar patterns.

When I asked Garry Kasparov, perhaps the greatest chess player in history, to explain his decision process for a move, he told me, “I see a move, a combination, almost instantly,” based on patterns he has seen before.

One of Klein’s colleagues, psychologist Daniel Kahneman, studied human decision making from the “heuristics and biases” model of human judgment. His findings could hardly have been more different from Klein’s. When Kahneman probed the judgments of highly trained experts, he often found that experience had not helped at all. Even worse, it frequently bred confidence but not skill.

it showed experience simply did not create skill in a wide range of real-world scenarios, from college administrators assessing student potential to psychiatrists predicting patient performance to human resources professionals deciding who will succeed in job training. In those domains, which involved human behavior and where patterns did not clearly repeat, repetition did not cause learning. Chess, golf, and firefighting are exceptions, not the rule.

Narrow experience made for better chess and poker players and firefighters, but not for better predictors of financial or political trends, or of how employees or patients would perform.

The domains Klein studied, in which instinctive pattern recognition worked powerfully, are what psychologist Robin Hogarth termed “kind” learning environments. Patterns repeat over and over, and feedback is extremely accurate and usually very rapid. In golf or chess, a ball or piece is moved according to rules and within defined boundaries, a consequence is quickly apparent, and similar challenges occur repeatedly.

Expert firefighters, when faced with a new situation, like a fire in a skyscraper, can find themselves suddenly deprived of the intuition formed in years of house fires, and prone to poor decisions. With a change of the status quo, chess masters too can find that the skill they took years to build is suddenly obsolete.

In 1998, he helped organize the first “advanced chess” tournament, in which each human player, including Kasparov himself, paired with a computer. Years of pattern study were obviated. The machine partner could handle tactics so the human could focus on strategy. It was like Tiger Woods facing off in a golf video game against the best gamers. His years of repetition would be neutralized, and the contest would shift to one of strategy rather than tactical execution. In chess, it changed the pecking order instantly. “Human creativity was even more paramount under these conditions, not less,” according to Kasparov. Kasparov settled for a 3–3 draw with a player he had trounced four games to zero just a month earlier in a traditional match.

The grandmasters never had photographic memories after all. Through repetitive study of game patterns, they had learned to do what Chase and Simon called “chunking.” Rather than struggling to remember the location of every individual pawn, bishop, and rook, the brains of elite players grouped pieces into a smaller number of meaningful chunks based on familiar patterns. Those patterns allow expert players to immediately assess the situation based on experience, which is why Garry Kasparov told me that grandmasters usually know their move within seconds.

the game’s strategic complexity provides a lesson: the bigger the picture, the more unique the potential human contribution. Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly.

The subtitle of Schwartz’s paper: “How Not to Teach People to Discover Rules”—that is, by providing rewards for repetitive short-term success with a narrow range of solutions.

insist on “having one foot outside your world.”

Steve Jobs gave, in which he famously recounted the importance of a calligraphy class to his design aesthetics. “When we were designing the first Macintosh computer, it all came back to me,” he said. “If I had never dropped in on that single course in college, the Mac would have never had multiple typefaces or proportionally spaced fonts.”

Claude Shannon, who launched the Information Age thanks to a philosophy course he took to fulfill a requirement at the University of Michigan. In it, he was exposed to the work of self-taught nineteenth-century English logician George Boole, who assigned a value of 1 to true statements and 0 to false statements and showed that logic problems could be solved like math equations.

Shannon did a summer internship at AT&T’s Bell Labs research facility. There he recognized that he could combine telephone call-routing technology with Boole’s logic system to encode and transmit any type of information electronically. It was the fundamental insight on which computers rely. “It just happened that no one else was familiar with both those fields at the same time,” Shannon said.

The gains around the world on Raven’s Progressive Matrices—where change was least expected—were the biggest of all. “The huge Raven’s gains show that today’s children are far better at solving problems on the spot without a previously learned method for doing so,” Flynn concluded. They are more able to extract rules and patterns where none are given.

Even in countries that have recently had a decrease in verbal and math IQ test scores, Raven’s scores went up. The cause, it seemed, was some ineffable thing in modern air. Not only that, but the mystery air additive somehow supercharged modern brains specifically for the most abstract tests.

Check this in Slovakia?

premodern people miss the forest for the trees; modern people miss the trees for the forest.

In Flynn’s terms, we now see the world through “scientific spectacles.” He means that rather than relying on our own direct experiences, we make sense of reality through classification schemes, using layers of abstract concepts to understand how pieces of information relate to one another.

a city dweller traveling through the desert will be completely dependent on a nomad to keep him alive. So long as they remain in the desert, the nomad is a genius.

Flynn was bemused to find that the correlation between the test of broad conceptual thinking and GPA was about zero. In Flynn’s words, “the traits that earn good grades at [the university] do not include critical ability of any broad significance.”

The goal is not just to transfer knowledge, but to raise fundamental questions and to become familiar with the powerful ideas that shape our society.”

Here and there, professors have begun to pick up the challenge. A class at the University of Washington titled “Calling Bullshit” (in staid coursebook language: INFO 198/BIOL 106B), focused on broad principles fundamental to understanding the interdisciplinary world and critically evaluating the daily firehose of information. When the class was first posted in 2017, registration filled up in the first minute.

Fortunately, as an undergrad, I did have a chemistry professor who embodied Flynn’s ideal. On every exam, amid typical chemistry questions, was something like this: “How many piano tuners are there in New York City?” Students had to estimate, just by reasoning, and try to get the right order of magnitude. The professor later explained that these were “Fermi problems,” because Enrico Fermi—who created the first nuclear reactor beneath the University of Chicago football field—constantly made back-of-the-envelope estimates to help him approach problems.* The ultimate lesson of the question was that detailed prior knowledge was less important than a way of thinking. On the first exam, I went with gut instinct (“I have no clue, maybe ten thousand?”)—way too high. By the end of the class, I had a new tool in my conceptual Swiss Army knife, a way of using what little I did know to make a guess at what I didn’t. I knew the population of New York City; most single people in studio apartments probably don’t have pianos that get tuned, and most of my friends’ parents had one to three children, so how many households are in New York? What portion might have pianos? How often are pianos tuned? How long might it take to tune a piano? How many homes can one tuner reach in a day? How many days a year does a tuner work? None of the individual estimates has to be particularly accurate in order to get a reasonable overall answer.

Unsurprisingly, Fermi problems were a topic in the “Calling Bullshit” course. It used a deceptive cable news report as a case study to demonstrate “how Fermi estimation can cut through bullshit like a hot knife through butter.”

Figlie took singing lessons, and learned to play every instrument their institution owned. It helped that they were paid for learning new skills.

John Sloboda is undoubtedly one of the most influential researchers in the psychology of music. His 1985 book The Musical Mind ranged from the origins of music to the acquisition of playing skill, and set a research agenda that the field is still carrying out today.

Just as it is in golf, procedure practice is important in math. But when it comprises the entire math training strategy, it’s a problem. “Students do not view mathematics as a system,” Richland and her colleagues wrote. They view it as just a set of procedures. Like when Patrick was asked how variable expressions connected to the world, and answered that they were good for answering questions in math class.

Bent Flyvbjerg, chair of Major Programme Management at Oxford University’s business school, has shown that around 90 percent of major infrastructure projects worldwide go over budget (by an average of 28 percent) in part because managers focus on the details of their project and become overly optimistic.

Psychologist Kevin Dunbar began documenting how productive labs work in the 1990s, and stumbled upon a modern version of Keplerian thinking. Faced with an unexpected finding, rather than assuming the current theory is correct and that an observation must be off, the unexpected became an opportunity to venture somewhere new—and analogies served as the wilderness guide. When Dunbar started, he simply set out to document the process of discovery in real time. He focused on molecular biology labs because they were blazing new trails, particularly in genetics and treatments for viruses, like HIV. He spent a year with four labs in the United States, playing a fly on the wall, visiting the labs every day for months, and later extended the work to more labs in the United States, Canada, and Italy. He became such a familiar presence that scientists called him to make sure he knew about impromptu meetings. The surface features of the labs were very different. One had dozens of members, others were small. A few were all men, one was all women. All had international reputations. The weekly lab meetings made the most interesting viewing. Once a week, the entire team came together—lab director, grad students, postdoctoral fellows, technicians—to discuss some challenge a lab member was facing. The meetings were nothing like the heads-down, solitary work in stereotypical portrayals of scientists, huddled over their test tubes. Dunbar saw free-flowing and spontaneous exchange. Ideas were b...

In the face of the unexpected, the range of available analogies helped determine who learned something new. In the lone lab that did not make any new findings during Dunbar’s project, everyone had similar and highly specialized backgrounds, and analogies were almost never used. “When all the members of the laboratory have the same knowledge at their disposal, then when a problem arises, a group of similar minded individuals will not provide more information to make analogies than a single individual,” Dunbar concluded.

Learning stuff was less important than learning about oneself. Exploration is not just a whimsical luxury of education; it is a central benefit.

According to Levitt, the study suggested that “admonitions such as ‘winners never quit and quitters never win,’ while well-meaning, may actually be extremely poor advice.” Levitt identified one of his own most important skills as “the willingness to jettison” a project or an entire area of study for a better fit.

Attempting to be a professional athlete or actor or to found a lucrative start-up is unlikely to succeed, but the potential reward is extremely high. Thanks to constant feedback and an unforgiving weed-out process, those who try will learn quickly if they might be a match, at least compared to jobs with less constant feedback. If they aren’t, they go test something else, and continue to gain information about their options and themselves.

Instead of asking whether someone is gritty, we should ask when they are. “If you get someone into a context that suits them,” Ogas said, “they’ll more likely work hard and it will look like grit from the outside.”

“Which among my various possible selves should I start to explore now? How can I do that?” Be a flirt with your possible selves.* Rather than a grand plan, find experiments that can be undertaken quickly. “Test-and-learn,” Ibarra told me, “not plan-and-implement.”

Michelangelo was actually a test-and-learn all-star. He constantly changed his mind and altered his sculptural plans as he worked. He left three-fifths of his sculptures unfinished, each time moving on to something more promising. The first line of Wallace’s analysis: “Michelangelo did not expound a theory of art.” He tried, then went from there. He was a sculptor, painter, master architect, and made engineering designs for fortifications in Florence. In his late twenties he even pushed visual art aside to spend time writing poems (including one about how much he grew to dislike painting), half of which he left unfinished.