Main Range: How Generalists Triumph in a Specialized World

Range: How Generalists Triumph in a Specialized World

A powerful argument for how to succeed in any field: develop broad interests and skills while everyone around you is rushing to specialize.



Plenty of experts argue that anyone who wants to develop a skill, play an instrument, or lead their field should start early, focus intensely, and rack up as many hours of deliberate practice as possible. If you dabble or delay, you'll never catch up to the people who got a head start. But a closer look at research on the world's top performers, from professional athletes to Nobel laureates, shows that early specialization is the exception, not the rule.



David Epstein examined the world's most successful athletes, artists, musicians, inventors, forecasters and scientists. He discovered that in most fields—especially those that are complex and unpredictable—generalists, not specialists, are primed to excel. Generalists often find their path late, and they juggle many interests rather than focusing on one. They're also more creative, more agile, and able to make connections their more specialized peers can't see.



Provocative, rigorous, and engrossing, Range makes a compelling case for actively cultivating inefficiency. Failing a test is the best way to learn. Frequent quitters end up with the most fulfilling careers. The most impactful inventors cross domains rather than deepening their knowledge in a single area. As experts silo themselves further while computers master more of the skills once reserved for highly focused humans, people who think broadly and embrace diverse experiences and perspectives will increasingly thrive.
Categories: Psychology
Year: 2019
Edition: Original retail
Publisher: Penguin Publishing Group
Language: english
Pages: 352
ISBN 10: 1509843493
ISBN 13: 978-1509843497
File: EPUB, 1.14 MB
Download (epub, 1.14 MB)

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Conversion to Modernities

Year: 1995
Language: english
File: PDF, 6.71 MB
ALSO BY DAVID EPSTEIN


			 				The Sports Gene





			RIVERHEAD BOOKS

			An imprint of Penguin Random House LLC

			penguinrandomhouse.com



			Copyright © 2019 by David Epstein

			Penguin supports copyright. Copyright fuels creativity, encourages diverse voices, promotes free speech, and creates a vibrant culture. Thank you for buying an authorized edition of this book and for complying with copyright laws by not reproducing, scanning, or distributing any part of it in any form without permission. You are supporting writers and allowing Penguin to continue to publish books for every reader.

			Library of Congress Cataloging-in-Publication Data

			Names: Epstein, David J., author.

			Title: Range : why generalists triumph in a specialized world / David Epstein.

			Description: New York : Riverhead Books, [2019] | Includes bibliographical references and index.

			Identifiers: LCCN 2018051571 (print) | LCCN 2018053769 (ebook) | ISBN 9780735214491 (ebook) | ISBN 9780735214484

			Subjects: LCSH: Expertise. | Ability.

			Classification: LCC BF378.E94 (ebook) | LCC BF378.E94 E67 2019 (print) | DDC 153.9—dc23

			LC record available at https://lccn.loc.gov/2018051571

			Cover image: Image Source / Getty Images




Version_2





For Elizabeth,

this one and any other one





Contents


			ALSO BY DAVID EPSTEIN

			TITLE PAGE

			COPYRIGHT

			DEDICATION

			EPIGRAPH

			INTRODUCTION: Roger vs. Tiger

			CHAPTER 1: The Cult of the Head Start

			CHAPTER 2: How the Wicked World Was Made

			CHAPTER 3: When Less of the Same Is More

			CHAPTER 4: Learning, Fast and Slow

			CHAPTER 5: Thinking Outside Experience

			CHAPTER 6: The Trouble with Too Much Grit

			CHAPTER 7: Flirting with Your Possible Selves

			CHAPTER 8: The Outsider Advantage

			CHAPTER 9: Lateral Thinking with Withered Technology

			CHAPTER 10: Fooled by Expertise

			CHAPTER 11: Learning to Drop Your Familiar Tools

			CHAPTER 12: Deliberate Amateurs

			CONCLUSION: Expanding Your Range

			ACKNOWLEDGMENTS

			NOTES

			INDEX

			ABOUT THE AUTHOR





And he refused to specialize in anything, preferring to keep an eye on the overall estate rather than any of its parts. . . . And Nikolay’s management produced the most brilliant results.

				—Leo Tolstoy, War and Peace



No tool is omnicompetent. There is no such thing as a master-key that will unlock all doors.

				—Arnold Toynbee, A Study of History





INTRODUCTION





Roger vs. Tiger





LET’S START WITH a couple of stories from the world of sports. This first one, you probably know.

			The boy’s father could tell something was different. At six months old, the boy could balance on his father’s palm as he walked through their home. At seven months, his father gave him a putter to fool around with, and the boy dragged it everywhere he went in his little circular baby walker. At ten months, he climbed down from his high chair, trundled over to a golf club that had been cut down to size for him, and imitated the swing he’d been watching in the garage. Because the father couldn’t yet talk with his son, he drew pictures to show the boy how to place his hands on the club. “It is very difficult to communicate how to putt when the child is too young to talk,” he would later note.

			At two—an age when the Centers for Disease Control and Prevention list physical developmental milestones like “kicks a ball” and “stands on tiptoe”—he went on national television and used a club tall enough to reach his shoulder to drive a ball past an admiring Bob Hope. That same year, he entered his first tournament, and won the ten-and-under division.

			There was no time to waste. By three, the boy was learning how to play out of a “sand twap,” and his father was mapping out his destiny. He knew his son had been chosen for this, and that it was his duty to guide him. Think about it: if you felt that certain about the path ahead, maybe you too would start prepping your three-year-old to handle the inevitable and insatiable media that would come. He quizzed the boy, playing reporter, teaching him how to give curt answers, never to offer more than precisely what was asked. That year, the boy shot 48, eleven over par, for nine holes at a course in California.

			When the boy was four, his father could drop him off at a golf course at nine in the morning and pick him up eight hours later, sometimes with the money he’d won from those foolish enough to doubt.

			At eight, the son beat his father for the first time. The father didn’t mind, because he was convinced that his boy was singularly talented, and that he was uniquely equipped to help him. He had been an outstanding athlete himself, and against enormous odds. He played baseball in college when he was the only black player in the entire conference. He understood people, and discipline; a sociology major, he served in Vietnam as a member of the Army’s elite Green Berets, and later taught psychological warfare to future officers. He knew he hadn’t done his best with three kids from a previous marriage, but now he could see that he’d been given a second chance to do the right thing with number four. And it was all going according to plan.

			The boy was already famous by the time he reached Stanford, and soon his father opened up about his importance. His son would have a larger impact than Nelson Mandela, than Gandhi, than Buddha, he insisted. “He has a larger forum than any of them,” he said. “He’s the bridge between the East and the West. There is no limit because he has the guidance. I don’t know yet exactly what form this will take. But he is the Chosen One.”



* * *



			—

			This second story, you also probably know. You might not recognize it at first.

			His mom was a coach, but she never coached him. He would kick a ball around with her when he learned to walk. As a boy, he played squash with his father on Sundays. He dabbled in skiing, wrestling, swimming, and skateboarding. He played basketball, handball, tennis, table tennis, badminton over his neighbor’s fence, and soccer at school. He would later give credit to the wide range of sports he played for helping him develop his athleticism and hand-eye coordination.

			He found that the sport really didn’t matter much, so long as it included a ball. “I was always very much more interested if a ball was involved,” he would remember. He was a kid who loved to play. His parents had no particular athletic aspirations for him. “We had no plan A, no plan B,” his mother would later say. She and the boy’s father encouraged him to sample a wide array of sports. In fact, it was essential. The boy “became unbearable,” his mother said, if he had to stay still for too long.

			Though his mother taught tennis, she decided against working with him. “He would have just upset me anyway,” she said. “He tried out every strange stroke and certainly never returned a ball normally. That is simply no fun for a mother.” Rather than pushy, a Sports Illustrated writer would observe that his parents were, if anything, “pully.” Nearing his teens, the boy began to gravitate more toward tennis, and “if they nudged him at all, it was to stop taking tennis so seriously.” When he played matches, his mother often wandered away to chat with friends. His father had only one rule: “Just don’t cheat.” He didn’t, and he started getting really good.

			As a teenager, he was good enough to warrant an interview with the local newspaper. His mother was appalled to read that, when asked what he would buy with a hypothetical first paycheck from playing tennis, her son answered, “a Mercedes.” She was relieved when the reporter let her listen to a recording of the interview and they realized there’d been a mistake: the boy had said “Mehr CDs,” in Swiss German. He simply wanted “more CDs.”

			The boy was competitive, no doubt. But when his tennis instructors decided to move him up to a group with older players, he asked to move back so he could stay with his friends. After all, part of the fun was hanging around after his lessons to gab about music, or pro wrestling, or soccer.

			By the time he finally gave up other sports—soccer, most notably—to focus on tennis, other kids had long since been working with strength coaches, sports psychologists, and nutritionists. But it didn’t seem to hamper his development in the long run. In his midthirties, an age by which even legendary tennis players are typically retired, he would still be ranked number one in the world.



* * *



			—

			In 2006, Tiger Woods and Roger Federer met for the first time, when both were at the apex of their powers. Tiger flew in on his private jet to watch the final of the U.S. Open. It made Federer especially nervous, but he still won, for the third year in a row. Woods joined him in the locker room for a champagne celebration. They connected as only they could. “I’ve never spoken with anybody who was so familiar with the feeling of being invincible,” Federer would later describe it. They quickly became friends, as well as focal points of a debate over who was the most dominant athlete in the world.

			Still, the contrast was not lost on Federer. “His story is completely different from mine,” he told a biographer in 2006. “Even as a kid his goal was to break the record for winning the most majors. I was just dreaming of just once meeting Boris Becker or being able to play at Wimbledon some time.”

			It seems pretty unusual for a child with “pully” parents, and who first took his sport lightly, to grow into a man who dominates it like no one before him. Unlike Tiger, thousands of kids, at least, had a head start on Roger. Tiger’s incredible upbringing has been at the heart of a batch of bestselling books on the development of expertise, one of which was a parenting manual written by Tiger’s father, Earl. Tiger was not merely playing golf. He was engaging in “deliberate practice,” the only kind that counts in the now-ubiquitous ten-thousand-hours rule to expertise. The “rule” represents the idea that the number of accumulated hours of highly specialized training is the sole factor in skill development, no matter the domain. 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:



			Tiger has come to symbolize the idea that the quantity of deliberate practice determines success—and its corollary, that the practice must start as early as possible.

			The push to focus early and narrowly extends well beyond sports. We are often taught that the more competitive and complicated the world gets, the more specialized we all must become (and the earlier we must start) to navigate it. Our best-known icons of success are elevated for their precocity and their head starts—Mozart at the keyboard, Facebook CEO Mark Zuckerberg at the other kind of keyboard. The response, in every field, to a ballooning library of human knowledge and an interconnected world has been to exalt increasingly narrow focus. Oncologists no longer specialize in cancer, but rather in cancer related to a single organ, and the trend advances each year. Surgeon and writer Atul Gawande pointed out that when doctors joke about left ear surgeons, “we have to check to be sure they don’t exist.”

			In the ten-thousand-hours-themed bestseller Bounce, British journalist Matthew Syed suggested that the British government was failing for a lack of following the Tiger Woods path of unwavering specialization. Moving high-ranking government officials between departments, he wrote, “is no less absurd than rotating Tiger Woods from golf to baseball to football to hockey.”

			Except that Great Britain’s massive success at recent Summer Olympics, after decades of middling performances, was bolstered by programs set up specifically to recruit adults to try new sports and to create a pipeline for late developers—“slow bakers,” as one of the officials behind the program described them to me. Apparently the idea of an athlete, even one who wants to become elite, following a Roger path and trying different sports is not so absurd. 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.”

			When I began to write about these studies, I was met with thoughtful criticism, but also denial. “Maybe in some other sport,” fans often said, “but that’s not true of our sport.” The community of the world’s most popular sport, soccer, was the loudest. And then, as if on cue, in late 2014 a team of German scientists published a study showing that members of their national team, which had just won the World Cup, were typically late specializers who didn’t play more organized soccer than amateur-league players until age twenty-two or later. They spent more of their childhood and adolescence playing nonorganized soccer and other sports. Another soccer study published two years later matched players for skill at age eleven and tracked them for two years. Those who participated in more sports and nonorganized soccer, “but not more organized soccer practice/training,” improved more by age thirteen. Findings like these have now been echoed in a huge array of sports, from hockey to volleyball.

			The professed necessity of hyperspecialization forms the core of a vast, successful, and sometimes well-meaning marketing machine, in sports and beyond. In reality, the Roger path to sports stardom is far more prevalent than the Tiger path, but those athletes’ stories are much more quietly told, if they are told at all. Some of their names you know, but their backgrounds you probably don’t.

			I started writing this introduction right after the 2018 Super Bowl, in which a quarterback who had been drafted into professional baseball before football (Tom Brady), faced off against one who participated in football, basketball, baseball, and karate and had chosen between college basketball and football (Nick Foles). Later that very same month, Czech athlete Ester Ledecká became the first woman ever to win gold in two different sports (skiing and snowboarding) at the same Winter Olympics. When she was younger, Ledecká participated in multiple sports (she still plays beach volleyball and windsurfs), focused on school, and never rushed to be number one in teenage competition categories. The Washington Post article the day after her second gold proclaimed, “In an era of sports specialization, Ledecká has been an evangelist for maintaining variety.” Just after her feat, Ukrainian boxer Vasyl Lomachenko set a record for the fewest fights needed to win world titles in three different weight classes. Lomachenko, who took four years off boxing as a kid to learn traditional Ukrainian dance, reflected, “I was doing so many different sports as a young boy—gymnastics, basketball, football, tennis—and I think, ultimately, everything came together with all those different kinds of sports to enhance my footwork.”

			Prominent sports scientist Ross Tucker summed up research in the field simply: “We know that early sampling is key, as is diversity.”



* * *



			• • •

			In 2014, I included some of the findings about late specialization in sports in the afterword of my first book, The Sports Gene. The following year, I got an invitation to talk about that research from an unlikely audience—not athletes or coaches, but military veterans. In preparation, I perused scientific journals for work on specialization and career-swerving outside of the sports world. I was struck by what I found. 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.

			I also began to realize that some of the people whose work I deeply admired from afar—from Duke Ellington (who shunned music lessons to focus on drawing and baseball as a kid) to Maryam Mirzakhani (who dreamed of becoming a novelist and instead became the first woman to win math’s most famous prize, the Fields Medal)—seemed to have more Roger than Tiger in their development stories. I delved further and encountered remarkable individuals who succeeded not in spite of their range of experiences and interests, but because of it: a CEO who took her first job around the time her peers were getting ready to retire; an artist who cycled through five careers before he discovered his vocation and changed the world; 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 had only dipped my toe into research on specialization in the wider world of work, so in my talk to the small group of military veterans I mostly stuck to sports. I touched on the other findings only briefly, but the audience seized on it. All were late specializers or career changers, and as they filed up one after another to introduce themselves after the talk, I could tell that all were at least moderately concerned, and some were borderline ashamed of it.

			They had been brought together by the Pat Tillman Foundation, which, in the spirit of the late NFL player who left a professional football career to become an Army Ranger, provides scholarships to veterans, active-duty military, and military spouses who are undergoing career changes or going back to school. They were all scholarship recipients, former paratroopers and translators who were becoming teachers, scientists, engineers, and entrepreneurs. They brimmed with enthusiasm, but rippled with an undercurrent of fear. Their LinkedIn profiles didn’t show the linear progression toward a particular career they had been told employers wanted. They were anxious starting grad school alongside younger (sometimes much younger) students, or changing lanes later than their peers, all because they had been busy accumulating inimitable life and leadership experiences. Somehow, a unique advantage had morphed in their heads into a liability.

			A few days after I spoke to the Tillman Foundation group, a former Navy SEAL who came up after the talk emailed me: “We are all transitioning from one career to another. Several of us got together after you had left and discussed how relieved we were to have heard you speak.” I was slightly bemused to find that a former Navy SEAL with an undergraduate degree in history and geophysics pursuing graduate degrees in business and public administration from Dartmouth and Harvard needed me to affirm his life choices. But like the others in the room, he had been told, both implicitly and explicitly, that changing directions was dangerous.

			The talk was greeted with so much enthusiasm that the foundation invited me to give a keynote speech at the annual conference in 2016, and then to small group gatherings in different cities. Before each occasion, I read more studies and spoke with more researchers and found more evidence that it takes time—and often forgoing a head start—to develop personal and professional range, but it is worth it.

			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.

			Starting something new in middle age might look that way too. Mark Zuckerberg famously noted that “young people are just smarter.” And yet a tech founder who is fifty years old is nearly twice as likely to start a blockbuster company as one who is thirty, and the thirty-year-old has a better shot than a twenty-year-old. 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.

			Zuckerberg was twenty-two when he said that. It was in his interest to broadcast that message, just as it is in the interest of people who run youth sports leagues to claim that year-round devotion to one activity is necessary for success, never mind evidence to the contrary. But the drive to specialize goes beyond that. It infects not just individuals, but entire systems, as each specialized group sees a smaller and smaller part of a large puzzle.

			One revelation in the aftermath of the 2008 global financial crisis was the degree of segregation within big banks. Legions of specialized groups optimizing risk for their own tiny pieces of the big picture created a catastrophic whole. To make matters worse, responses to the crisis betrayed a dizzying degree of specialization-induced perversity. A federal program launched in 2009 incentivized banks to lower monthly mortgage payments for homeowners who were struggling but still able to make partial payments. A nice idea, but here’s how it worked out in practice: a bank arm that specialized in mortgage lending started the homeowner on lower payments; an arm of the same bank that specialized in foreclosures then noticed that the homeowner was suddenly paying less, declared them in default, and seized the home. “No one imagined silos like that inside banks,” a government adviser said later. Overspecialization can lead to collective tragedy even when every individual separately takes the most reasonable course of action.

			Highly specialized health care professionals have developed their own versions of the “if all you have is a hammer, everything looks like a nail” problem. Interventional cardiologists have gotten so used to treating chest pain with stents—metal tubes that pry open blood vessels—that they do so reflexively even in cases where voluminous research has proven that they are inappropriate or dangerous. A recent study found that cardiac patients were actually less likely to die if they were admitted during a national cardiology meeting, when thousands of cardiologists were away; the researchers suggested it could be because common treatments of dubious effect were less likely to be performed.

			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.

			I hope this book helps you understand why.



* * *



			• • •

			When the Tillman Scholars spoke of feeling unmoored, and worried they were making a mistake, I understood better than I let on. I was working on a scientific research vessel in the Pacific Ocean after college when I decided for sure that I wanted to be a writer, not a scientist. I never expected that my path from science into writing would go through work as the overnight crime reporter at a New York City tabloid, nor that I would shortly thereafter be a senior writer at Sports Illustrated, a job that, to my own surprise, I would soon leave. I began worrying that I was a job-commitment-phobic drifter who must be doing this whole career thing wrong. Learning about the advantages of breadth and delayed specialization has changed the way I see myself and the world. The research pertains to every stage of life, from the development of children in math, music, and sports, to students fresh out of college trying to find their way, to midcareer professionals in need of a change and would-be retirees looking for a new vocation after moving on from their previous one.

			The challenge we all face is how to maintain the benefits of breadth, diverse experience, interdisciplinary thinking, and delayed concentration in a world that increasingly incentivizes, even demands, hyperspecialization. While it is undoubtedly true that there are areas that require individuals with Tiger’s precocity and clarity of purpose, as complexity increases—as technology spins the world into vaster webs of interconnected systems in which each individual only sees a small part—we also need more Rogers: people who start broad and embrace diverse experiences and perspectives while they progress. People with range.





CHAPTER 1





The Cult of the Head Start





ONE YEAR AND FOUR DAYS after World War II in Europe ended in unconditional surrender, Laszlo Polgar was born in a small town in Hungary—the seed of a new family. He had no grandmothers, no grandfathers, and no cousins; all had been wiped out in the Holocaust, along with his father’s first wife and five children. Laszlo grew up determined to have a family, and a special one.

			He prepped for fatherhood in college by poring over biographies of legendary thinkers, from Socrates to Einstein. He decided that traditional education was broken, and that he could make his own children into geniuses, if he just gave them the right head start. By doing so, he would prove something far greater: that any child can be molded for eminence in any discipline. He just needed a wife who would go along with the plan.

			Laszlo’s mother had a friend, and the friend had a daughter, Klara. In 1965, Klara traveled to Budapest, where she met Laszlo in person. Laszlo didn’t play hard to get; he spent the first visit telling Klara that he planned to have six children and that he would nurture them to brilliance. Klara returned home to her parents with a lukewarm review: she had “met a very interesting person,” but could not imagine marrying him.

			They continued to exchange letters. They were both teachers and agreed that the school system was frustratingly one-size-fits-all, made for producing “the gray average mass,” as Laszlo put it. A year and a half of letters later, Klara realized she had a very special pen pal. Laszlo finally wrote a love letter, and proposed at the end. They married, moved to Budapest, and got to work. Susan was born in early 1969, and the experiment was on.

			For his first genius, Laszlo picked chess. In 1972, the year before Susan started training, American Bobby Fischer defeated Russian Boris Spassky in the “Match of the Century.” It was considered a Cold War proxy in both hemispheres, and chess was suddenly pop culture. Plus, according to Klara, the game had a distinct benefit: “Chess is very objective and easy to measure.” Win, lose, or draw, and a point system measures skill against the rest of the chess world. His daughter, Laszlo decided, would become a chess champion.

			Laszlo was patient, and meticulous. He started Susan with “pawn wars.” Pawns only, and the first person to advance to the back row wins. Soon, Susan was studying endgames and opening traps. She enjoyed the game and caught on quickly. After eight months of study, Laszlo took her to a smoky chess club in Budapest and challenged grown men to play his four-year-old daughter, whose legs dangled from her chair. Susan won her first game, and the man she beat stormed off. She entered the Budapest girls’ championship and won the under-eleven title. At age four she had not lost a game.

			By six, Susan could read and write and was years ahead of her grade peers in math. Laszlo and Klara decided they would educate her at home and keep the day open for chess. The Hungarian police threatened to throw Laszlo in jail if he did not send his daughter to the compulsory school system. It took him months of lobbying the Ministry of Education to gain permission. Susan’s new little sister, Sofia, would be homeschooled too, as would Judit, who was coming soon, and whom Laszlo and Klara almost named Zseni, Hungarian for “genius.” All three became part of the grand experiment.

			On a normal day, the girls were at the gym by 7 a.m. playing table tennis with trainers, and then back home at 10:00 for breakfast, before a long day of chess. When Laszlo reached the limit of his expertise, he hired coaches for his three geniuses in training. He spent his extra time cutting two hundred thousand records of game sequences from chess journals—many offering a preview of potential opponents—and filing them in a custom card catalog, the “cartotech.” Before computer chess programs, it gave the Polgars the largest chess database in the world to study outside of—maybe—the Soviet Union’s secret archives.

			When she was seventeen, Susan became the first woman to qualify for the men’s world championship, although the world chess federation did not allow her to participate. (A rule that would soon be changed, thanks to her accomplishments.) Two years later, in 1988, when Sofia was fourteen and Judit twelve, the girls comprised three of the four Hungarian team members for the women’s Chess Olympiad. They won, and beat the Soviet Union, which had won eleven of the twelve Olympiads since the event began. The Polgar sisters became “national treasures,” as Susan put it. The following year, communism fell, and the girls could compete all over the world. In January 1991, at the age of twenty-one, Susan became the first woman to achieve grandmaster status through tournament play against men. In December, Judit, at fifteen years and five months, became the youngest grandmaster ever, male or female. When Susan was asked on television if she wanted to win the world championship in the men’s or women’s category, she cleverly responded that she wanted to win the “absolute category.”

			None of the sisters ultimately reached Laszlo’s highest goal of becoming the overall world champion, but all were outstanding. In 1996, Susan participated in the women’s world championship, and won. Sofia peaked at the rank of international master, a level down from grandmaster. Judit went furthest, climbing up to eighth in the overall world ranking in 2004.

			Laszlo’s experiment had worked. It worked so well that in the early 1990s he suggested that if his early specialization approach were applied to a thousand children, humanity could tackle problems like cancer and AIDS. After all, chess was just an arbitrary medium for his universal point. Like the Tiger Woods story, the Polgar story entered an endless pop culture loop in articles, books, TV shows, and talks as an example of the life-hacking power of an early start. An online course called “Bring Up Genius!” advertises lessons in the Polgar method to “build up your own Genius Life Plan.” The bestseller Talent Is Overrated used the Polgar sisters and Tiger Woods as proof that a head start in deliberate practice is the key to success in “virtually any activity that matters to you.”

			The powerful lesson is that anything in the world can be conquered in the same way. It relies on one very important, and very unspoken, assumption: that chess and golf are representative examples of all the activities that matter to you.



* * *



			• • •

			Just how much of the world, and how many of the things humans want to learn and do, are really like chess and golf?

			Psychologist Gary Klein is a pioneer of the “naturalistic decision making” (NDM) model of expertise; NDM researchers observe expert performers in their natural course of work to learn how they make high-stakes decisions under time pressure. 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. Kasparov said he would bet that grandmasters usually make the move that springs to mind in the first few seconds of thought. Klein studied firefighting commanders and estimated that around 80 percent of their decisions are also made instinctively and in seconds. After years of firefighting, they recognize repeating patterns in the behavior of flames and of burning buildings on the verge of collapse. When he studied nonwartime naval commanders who were trying to avoid disasters, like mistaking a commercial flight for an enemy and shooting it down, he saw that they very quickly discerned potential threats. Ninety-five percent of the time, the commanders recognized a common pattern and chose a common course of action that was the first to come to mind.

			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.

			Kahneman included himself in that critique. He first began to doubt the link between experience and expertise in 1955, as a young lieutenant in the psychology unit of the Israel Defense Forces. One of his duties was to assess officer candidates through tests adapted from the British army. In one exercise, teams of eight had to get themselves and a length of telephone pole over a six-foot wall without letting the pole touch the ground, and without any of the soldiers or the pole touching the wall.* The difference in individuals’ performances were so stark, with clear leaders, followers, braggarts, and wimps naturally emerging under the stress of the task, that Kahneman and his fellow evaluators grew confident they could analyze the candidates’ leadership qualities and identify how they would perform in officer training and in combat. They were completely mistaken. Every few months, they had a “statistics day” where they got feedback on how accurate their predictions had been. Every time, they learned they had done barely better than blind guessing. Every time, they gained experience and gave confident judgments. And every time, they did not improve. Kahneman marveled at the “complete lack of connection between the statistical information and the compelling experience of insight.” Around that same time, an influential book on expert judgment was published that Kahneman told me impressed him “enormously.” It was a wide-ranging review of research that rocked psychology because 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.

			The difference between what Klein and Kahneman documented in experienced professionals comprised a profound conundrum: Do specialists get better with experience, or not?

			In 2009, Kahneman and Klein took the unusual step of coauthoring a paper in which they laid out their views and sought common ground. And they found it. Whether or not experience inevitably led to expertise, they agreed, depended entirely on the domain in question. 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. Drive a golf ball, and it either goes too far or not far enough; it slices, hooks, or flies straight. The player observes what happened, attempts to correct the error, tries again, and repeats for years. That is the very definition of deliberate practice, the type identified with both the ten-thousand-hours rule and the rush to early specialization in technical training. The learning environment is kind because a learner improves simply by engaging in the activity and trying to do better. Kahneman was focused on the flip side of kind learning environments; Hogarth called them “wicked.”

			In wicked domains, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both.

			In the most devilishly wicked learning environments, experience will reinforce the exact wrong lessons. Hogarth noted a famous New York City physician renowned for his skill as a diagnostician. The man’s particular specialty was typhoid fever, and he examined patients for it by feeling around their tongues with his hands. Again and again, his testing yielded a positive diagnosis before the patient displayed a single symptom. And over and over, his diagnosis turned out to be correct. As another physician later pointed out, “He was a more productive carrier, using only his hands, than Typhoid Mary.” Repetitive success, it turned out, taught him the worst possible lesson. Few learning environments are that wicked, but it doesn’t take much to throw experienced pros off course. 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 a 1997 showdown billed as the final battle for supremacy between natural and artificial intelligence, IBM supercomputer Deep Blue defeated Garry Kasparov. Deep Blue evaluated two hundred million positions per second. That is a tiny fraction of possible chess positions—the number of possible game sequences is more than atoms in the observable universe—but plenty enough to beat the best human. According to Kasparov, “Today the free chess app on your mobile phone is stronger than me.” He is not being rhetorical.

			“Anything we can do, and we know how to do it, machines will do it better,” he said at a recent lecture. “If we can codify it, and pass it to computers, they will do it better.” Still, losing to Deep Blue gave him an idea. In playing computers, he recognized what artificial intelligence scholars call Moravec’s paradox: machines and humans frequently have opposite strengths and weaknesses.

			There is a saying that “chess is 99 percent tactics.” Tactics are short combinations of moves that players use to get an immediate advantage on the board. When players study all those patterns, they are mastering tactics. Bigger-picture planning in chess—how to manage the little battles to win the war—is called strategy. As Susan Polgar has written, “you can get a lot further by being very good in tactics”—that is, knowing a lot of patterns—“and have only a basic understanding of strategy.”

			Thanks to their calculation power, computers are tactically flawless compared to humans. Grandmasters predict the near future, but computers do it better. What if, Kasparov wondered, computer tactical prowess were combined with human big-picture, strategic thinking?

			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. “My advantage in calculating tactics had been nullified by the machine.” The primary benefit of years of experience with specialized training was outsourced, and in a contest where humans focused on strategy, he suddenly had peers.

			A few years later, the first “freestyle chess” tournament was held. Teams could be made up of multiple humans and computers. The lifetime-of-specialized-practice advantage that had been diluted in advanced chess was obliterated in freestyle. A duo of amateur players with three normal computers not only destroyed Hydra, the best chess supercomputer, they also crushed teams of grandmasters using computers. Kasparov concluded that the humans on the winning team were the best at “coaching” multiple computers on what to examine, and then synthesizing that information for an overall strategy. Human/Computer combo teams—known as “centaurs”—were playing the highest level of chess ever seen. If Deep Blue’s victory over Kasparov signaled the transfer of chess power from humans to computers, the victory of centaurs over Hydra symbolized something more interesting still: humans empowered to do what they do best without the prerequisite of years of specialized pattern recognition.

			In 2014, an Abu Dhabi–based chess site put up $20,000 in prize money for freestyle players to compete in a tournament that also included games in which chess programs played without human intervention. The winning team comprised four people and several computers. The captain and primary decision maker was Anson Williams, a British engineer with no official chess rating. His teammate, Nelson Hernandez, told me, “What people don’t understand is that freestyle involves an integrated set of skills that in some cases have nothing to do with playing chess.” In traditional chess, Williams was probably at the level of a decent amateur. But he was well versed in computers and adept at integrating streaming information for strategy decisions. As a teenager, he had been outstanding at the video game Command & Conquer, known as a “real time strategy” game because players move simultaneously. In freestyle chess, he had to consider advice from teammates and various chess programs and then very quickly direct the computers to examine particular possibilities in more depth. He was like an executive with a team of mega-grandmaster tactical advisers, deciding whose advice to probe more deeply and ultimately whose to heed. He played each game cautiously, expecting a draw, but trying to set up situations that could lull an opponent into a mistake.

			In the end, Kasparov did figure out a way to beat the computer: by outsourcing tactics, the part of human expertise that is most easily replaced, the part that he and the Polgar prodigies spent years honing.



* * *



			• • •

			 			In 2007, National Geographic TV gave Susan Polgar a test. They sat her at a sidewalk table in the middle of a leafy block of Manhattan’s Greenwich Village, in front of a cleared chessboard. New Yorkers in jeans and fall jackets went about their jaywalking business as a white truck bearing a large diagram of a chessboard with twenty-eight pieces in midgame play took a left turn onto Thompson Street, past the deli, and past Susan Polgar. She glanced at the diagram as the truck drove by, and then perfectly re-created it on the board in front of her. The show was reprising a series of famous chess experiments that pulled back the curtain on kind-learning-environment skills.

			The first took place in the 1940s, when Dutch chess master and psychologist Adriaan de Groot flashed midgame chessboards in front of players of different ability levels, and then asked them to re-create the boards as well as they could. A grandmaster repeatedly re-created the entire board after seeing it for only three seconds. A master-level player managed that half as often as the grandmaster. A lesser, city champion player and an average club player were never able to re-create the board accurately. Just like Susan Polgar, grandmasters seemed to have photographic memories.

			After Susan succeeded in her first test, National Geographic TV turned the truck around to show the other side, which had a diagram with pieces placed at random. When Susan saw that side, even though there were fewer pieces, she could barely re-create anything at all.

			That test reenacted an experiment from 1973, in which two Carnegie Mellon University psychologists, William G. Chase and soon-to-be Nobel laureate Herbert A. Simon, repeated the De Groot exercise, but added a wrinkle. This time, the chess players were also given boards with the pieces in an arrangement that would never actually occur in a game. Suddenly, the experts performed just like the lesser players. 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. For Susan Polgar, when the van drove by the first time, the diagram was not twenty-eight items, but five different meaningful chunks that indicated how the game was progressing.

			Chunking helps explain instances of apparently miraculous, domain-specific memory, from musicians playing long pieces by heart to quarterbacks recognizing patterns of players in a split second and making a decision to throw. The reason that elite athletes seem to have superhuman reflexes is that they recognize patterns of ball or body movements that tell them what’s coming before it happens. When tested outside of their sport context, their superhuman reactions disappear.

			We all rely on chunking every day in skills in which we are expert. Take ten seconds and try to memorize as many of these twenty words as you can:


Because groups twenty patterns

				meaningful are words easier into chunk remember

				really sentence familiar can to you much in a.



			Okay, now try again:


Twenty words are really much easier to

				remember in a meaningful sentence because

				you can chunk familiar patterns into groups.



			Those are the same twenty pieces of information, but over the course of your life, you’ve learned patterns of words that allow you to instantly make sense of the second arrangement, and to remember it much more easily. Your restaurant server doesn’t just happen to have a miraculous memory; like musicians and quarterbacks, they’ve learned to group recurring information into chunks.

			Studying an enormous number of repetitive patterns is so important in chess that early specialization in technical practice is critical. Psychologists Fernand Gobet (an international master) and Guillermo Campitelli (coach to future grandmasters) found that the chances of a competitive chess player reaching international master status (a level down from grandmaster) dropped from one in four to one in fifty-five if rigorous training had not begun by age twelve. Chunking can seem like magic, but it comes from extensive, repetitive practice. Laszlo Polgar was right to believe in it. His daughters don’t even constitute the most extreme evidence.

			For more than fifty years, psychiatrist Darold Treffert studied savants, individuals with an insatiable drive to practice in one domain, and ability in that area that far outstrips their abilities in other areas. “Islands of genius,” Treffert calls it.* Treffert documented the almost unbelievable feats of savants like pianist Leslie Lemke, who can play thousands of songs from memory. Because Lemke and other savants have seemingly limitless retrieval capacity, Treffert initially attributed their abilities to perfect memories; they are human tape recorders. Except, when they are tested after hearing a piece of music for the first time, musical savants reproduce “tonal” music—the genre of nearly all pop and most classical music—more easily than “atonal” music, in which successive notes do not follow familiar harmonic structures. If savants were human tape recorders playing notes back, it would make no difference whether they were asked to re-create music that follows popular rules of composition or not. But in practice, it makes an enormous difference. In one study of a savant pianist, the researcher, who had heard the man play hundreds of songs flawlessly, was dumbstruck when the savant could not re-create an atonal piece even after a practice session with it. “What I heard seemed so unlikely that I felt obliged to check that the keyboard had not somehow slipped into transposing mode,” the researcher recorded. “But he really had made a mistake, and the errors continued.” Patterns and familiar structures were critical to the savant’s extraordinary recall ability. Similarly, when artistic savants are briefly shown pictures and asked to reproduce them, they do much better with images of real-life objects than with more abstract depictions.

			It took Treffert decades to realize he had been wrong, and that savants have more in common with prodigies like the Polgar sisters than he thought. They do not merely regurgitate. Their brilliance, just like the Polgar brilliance, relies on repetitive structures, which is precisely what made the Polgars’ skill so easy to automate.



* * *



			• • •

			With the advances made by the AlphaZero chess program (owned by an AI arm of Google’s parent company), perhaps even the top centaurs would be vanquished in a freestyle tournament. Unlike previous chess programs, which used brute processing force to calculate an enormous number of possible moves and rate them according to criteria set by programmers, AlphaZero actually taught itself to play. It needed only the rules, and then to play itself a gargantuan number of times, keeping track of what tends to work and what doesn’t, and using that to improve. In short order, it beat the best chess programs. It did the same with the game of Go, which has many more possible positions. But the centaur lesson remains: the more a task shifts to an open world of big-picture strategy, the more humans have to add.

			AlphaZero programmers touted their impressive feat by declaring that their creation had gone from “tabula rasa” (blank slate) to master on its own. But starting with a game is anything but a blank slate. The program is still operating in a constrained, rule-bound world. Even in video games that are less bound by tactical patterns, computers have faced a greater challenge.

			The latest video game challenge for artificial intelligence is StarCraft, a franchise of real-time strategy games in which fictional species go to war for supremacy in some distant reach of the Milky Way. It requires much more complex decision making than chess. There are battles to manage, infrastructure to plan, spying to do, geography to explore, and resources to collect, all of which inform one another. Computers struggled to win at StarCraft, Julian Togelius, an NYU professor who studies gaming AI, told me in 2017. Even when they did beat humans in individual games, human players adjusted with “long-term adaptive strategy” and started winning. “There are so many layers of thinking,” he said. “We humans sort of suck at all of them individually, but we have some kind of very approximate idea about each of them and can combine them and be somewhat adaptive. That seems to be what the trick is.”

			In 2019, in a limited version of StarCraft, AI beat a pro for the first time. (The pro adapted and earned a win after a string of losses.) But 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. According to Gary Marcus, a psychology and neural science professor who sold his machine learning company to Uber, “In narrow enough worlds, humans may not have much to contribute much longer. In more open-ended games, I think they certainly will. Not just games, in open ended real-world problems we’re still crushing the machines.”

			The progress of AI in the closed and orderly world of chess, with instant feedback and bottomless data, has been exponential. In the rule-bound but messier world of driving, AI has made tremendous progress, but challenges remain. In a truly open-world problem devoid of rigid rules and reams of perfect historical data, AI has been disastrous. IBM’s Watson destroyed at Jeopardy! and was subsequently pitched as a revolution in cancer care, where it flopped so spectacularly that several AI experts told me they worried its reputation would taint AI research in health-related fields. As one oncologist put it, “The difference between winning at Jeopardy! and curing all cancer is that we know the answer to Jeopardy! questions.” With cancer, we’re still working on posing the right questions in the first place.

			In 2009, a report in the esteemed journal Nature announced that Google Flu Trends could use search query patterns to predict the winter spread of flu more rapidly than and just as accurately as the Centers for Disease Control and Prevention. But Google Flu Trends soon got shakier, and in the winter of 2013 it predicted more than double the prevalence of flu that actually occurred in the United States. Today, Google Flu Trends is no longer publishing estimates, and just has a holding page saying that “it is still early days” for this kind of forecasting. Tellingly, Marcus gave me this analogy for the current limits of expert machines: “AI systems are like savants.” They need stable structures and narrow worlds.

			When we know the rules and answers, and they don’t change over time—chess, golf, playing classical music—an argument can be made for savant-like hyperspecialized practice from day one. But those are poor models of most things humans want to learn.

			When narrow specialization is combined with an unkind domain, the human tendency to rely on experience of familiar patterns can backfire horribly—like the expert firefighters who suddenly make poor choices when faced with a fire in an unfamiliar structure. Chris Argyris, who helped create the Yale School of Management, noted the danger of treating the wicked world as if it is kind. He studied high-powered consultants from top business schools for fifteen years, and saw that they did really well on business school problems that were well defined and quickly assessed. But they employed what Argyris called single-loop learning, the kind that favors the first familiar solution that comes to mind. Whenever those solutions went wrong, the consultant usually got defensive. Argyris found their “brittle personalities” particularly surprising given that “the essence of their job is to teach others how to do things differently.”

			Psychologist Barry Schwartz demonstrated a similar, learned inflexibility among experienced practitioners when he gave college students a logic puzzle that involved hitting switches to turn light bulbs on and off in sequence, and that they could play over and over. It could be solved in seventy different ways, with a tiny money reward for each success. The students were not given any rules, and so had to proceed by trial and error.* If a student found a solution, they repeated it over and over to get more money, even if they had no idea why it worked. Later on, new students were added, and all were now asked to discover the general rule of all solutions. Incredibly, every student who was brand-new to the puzzle discovered the rule for all seventy solutions, while only one of the students who had been getting rewarded for a single solution did. 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.

			All this is bad news for some of the business world’s favorite successful-learning analogies—the Polgars, Tiger, and to some degree analogies based in any sport or game. Compared to golf, a sport like tennis is much more dynamic, with players adjusting to opponents every second, to surfaces, and sometimes to their own teammates. (Federer was a 2008 Olympic gold medalist in doubles.) But tennis is still very much on the kind end of the spectrum compared to, say, a hospital emergency room, where doctors and nurses do not automatically find out what happens to a patient after their encounter. They have to find ways to learn beyond practice, and to assimilate lessons that might even contradict their direct experience.

			The world is not golf, and most of it isn’t even tennis. As Robin Hogarth put it, much of the world is “Martian tennis.” You can see the players on a court with balls and rackets, but nobody has shared the rules. It is up to you to derive them, and they are subject to change without notice.



* * *



			• • •

			 			We have been using the wrong stories. Tiger’s story and the Polgar story give the false impression that human skill is always developed in an extremely kind learning environment. If that were the case, specialization that is both narrow and technical and that begins as soon as possible would usually work. But it doesn’t even work in most sports.

			If the amount of early, specialized practice in a narrow area were the key to innovative performance, savants would dominate every domain they touched, and child prodigies would always go on to adult eminence. As psychologist Ellen Winner, one of the foremost authorities on gifted children, noted, no savant has ever been known to become a “Big-C creator,” who changed their field.

			There are domains beyond chess in which massive amounts of narrow practice make for grandmaster-like intuition. Like golfers, surgeons improve with repetition of the same procedure. Accountants and bridge and poker players develop accurate intuition through repetitive experience. Kahneman pointed to those domains’ “robust statistical regularities.” But when the rules are altered just slightly, it makes experts appear to have traded flexibility for narrow skill. In research in the game of bridge where the order of play was altered, experts had a more difficult time adapting to new rules than did nonexperts. When experienced accountants were asked in a study to use a new tax law for deductions that replaced a previous one, they did worse than novices. Erik Dane, a Rice University professor who studies organizational behavior, calls this phenomenon “cognitive entrenchment.” His suggestions for avoiding it are about the polar opposite of the strict version of the ten-thousand-hours school of thought: vary challenges within a domain drastically, and, as a fellow researcher put it, insist on “having one foot outside your world.”

			Scientists and members of the general public are about equally likely to have artistic hobbies, but scientists inducted into the highest national academies are much more likely to have avocations outside of their vocation. And those who have won the Nobel Prize are more likely still. Compared to other scientists, Nobel laureates are at least twenty-two times more likely to partake as an amateur actor, dancer, magician, or other type of performer. Nationally recognized scientists are much more likely than other scientists to be musicians, sculptors, painters, printmakers, woodworkers, mechanics, electronics tinkerers, glassblowers, poets, or writers, of both fiction and nonfiction. And, again, Nobel laureates are far more likely still. The most successful experts also belong to the wider world. “To him who observes them from afar,” said Spanish Nobel laureate Santiago Ramón y Cajal, the father of modern neuroscience, “it appears as though they are scattering and dissipating their energies, while in reality they are channeling and strengthening them.” The main conclusion of work that took years of studying scientists and engineers, all of whom were regarded by peers as true technical experts, was that those who did not make a creative contribution to their field lacked aesthetic interests outside their narrow area. As psychologist and prominent creativity researcher Dean Keith Simonton observed, “rather than obsessively focus[ing] on a narrow topic,” creative achievers tend to have broad interests. “This breadth often supports insights that cannot be attributed to domain-specific expertise alone.”

			Those findings are reminiscent of a speech 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.” Or electrical engineer 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. It resulted in absolutely nothing of practical importance until seventy years after Boole passed away, when 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.

			In 1979, Christopher Connolly cofounded a psychology consultancy in the United Kingdom to help high achievers (initially athletes, but then others) perform at their best. Over the years, Connolly became curious about why some professionals floundered outside a narrow expertise, while others were remarkably adept at expanding their careers—moving from playing in a world-class orchestra, for example, to running one. Thirty years after he started, Connolly returned to school to do a PhD investigating that very question, under Fernand Gobet, the psychologist and chess international master. Connolly’s primary finding was that early in their careers, those who later made successful transitions had broader training and kept multiple “career streams” open even as they pursued a primary specialty. They “traveled on an eight-lane highway,” he wrote, rather than down a single-lane one-way street. They had range. The successful adapters were excellent at taking knowledge from one pursuit and applying it creatively to another, and at avoiding cognitive entrenchment. They employed what Hogarth called a “circuit breaker.” They drew on outside experiences and analogies to interrupt their inclination toward a previous solution that may no longer work. Their skill was in avoiding the same old patterns. In the wicked world, with ill-defined challenges and few rigid rules, range can be a life hack.

			Pretending the world is like golf and chess is comforting. It makes for a tidy kind-world message, and some very compelling books. The rest of this one will begin where those end—in a place where the popular sport is Martian tennis, with a view into how the modern world became so wicked in the first place.





CHAPTER 2





How the Wicked World Was Made





THE TOWN OF DUNEDIN sits at the base of a hilly peninsula that juts off of New Zealand’s South Island into the South Pacific. The peninsula is famous for yellow-eyed penguins, and Dunedin boasts, demurely, the world’s steepest residential street. It also features the University of Otago, the oldest university in New Zealand, and home to James Flynn, a professor of political studies who changed how psychologists think about thinking.

			He started in 1981, intrigued by a thirty-year-old paper that reported IQ test scores of American soldiers in World Wars I and II. The World War II soldiers had performed better, by a lot. A World War I soldier who scored smack in the middle of his peers—the 50th percentile—would have made only the 22nd percentile compared to soldiers in World War II. Flynn wondered if perhaps civilians had experienced a similar improvement. “I thought, if IQ gains had occurred anywhere,” he told me, “maybe they had occurred everywhere.” If he was right, psychologists had been missing something big right before their eyes.

			Flynn wrote to researchers in other countries asking for data, and on a dull November Saturday in 1984, he found a letter in his university mailbox. It was from a Dutch researcher, and it contained years of raw data from IQ tests given to young men in the Netherlands. The data were from a test known as Raven’s Progressive Matrices, designed to gauge the test taker’s ability to make sense of complexity. Each question of the test shows a set of abstract designs with one design missing. The test taker must try to fill in the missing design to complete a pattern. Raven’s was conceived to be the epitome of a “culturally reduced” test; performance should be unaffected by material learned in life, inside or outside of school. Should Martians alight on Earth, Raven’s should be the test capable of determining how bright they are. And yet Flynn could immediately see that young Dutchmen had made enormous gains from one generation to the next.

			Flynn found more clues in test reference manuals. IQ tests are all standardized so that the average score is always 100 points. (They are graded based on a curve, with 100 in the middle.) Flynn noticed that the tests had to be restandardized from time to time to keep the average at 100, because test takers were giving more correct answers than they had in the past. In the twelve months after he received the Dutch letter, Flynn collected data from fourteen countries. Every single one showed huge gains for both children and adults. “Our advantage over our ancestors,” as he put it, is “from the cradle to the grave.”

			Flynn had asked the right question. Score gains had occurred everywhere. Other academics had stumbled upon pieces of the same data earlier, but none had investigated whether it was part of a global pattern, even those who were having to tweak the test scoring system to keep the average at 100. “As an outsider,” Flynn told me, “things strike me as surprising that I think people trained in psychometrics just accepted.”



* * *



			• • •

			The Flynn effect—the increase in correct IQ test answers with each new generation in the twentieth century—has now been documented in more than thirty countries. The gains are startling: three points every ten years. To put that in perspective, if an adult who scored average today were compared to adults a century ago, she would be in the 98th percentile.

			When Flynn published his revelation in 1987, it hit the community of researchers who study cognitive ability like a firebomb. The American Psychological Association convened an entire meeting on the issue, and psychologists invested in the immutable nature of IQ test scores offered an array of explanations to usher the effect away, from more education and better nutrition—which presumably contributed—to test-taking experience, but none fit the unusual pattern of score improvements. On tests that gauged material picked up in school or with independent reading or study—general knowledge, arithmetic, vocabulary—scores hardly budged. Meanwhile, performance on more abstract tasks that are never formally taught, like the Raven’s matrices, or “similarities” tests, which require a description of how two things are alike, skyrocketed.

			A young person today asked to give similarities between “dusk” and “dawn” might immediately realize that both connote times of day. But they would be far more likely than their grandmothers to produce a higher-level similarity: both separate day from night. A child today who scores average on similarities would be in the 94th percentile of her grandparents’ generation. When a group of Estonian researchers used national test scores to compare word understandings of schoolkids in the 1930s to those in 2006, they saw that improvement came very specifically on the most abstract words. The more abstract the word, the bigger the improvement. The kids barely bested their grandparents on words for directly observable objects or phenomena (“hen,” “eating,” “illness”), but they improved massively on imperceptible concepts (“law,” “pledge,” “citizen”).

			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. What manner of change, Flynn wondered, could be at once so large and yet so particular?



* * *



			• • •

			Through the late 1920s and early 1930s, remote reaches of the Soviet Union were forced through social and economic changes that would normally take generations. Individual farmers in isolated areas of what is now Uzbekistan had long survived by cultivating small gardens for food, and cotton for everything else. Nearby in the mountain pasturelands of present-day Kyrgyzstan, herders kept animals. The population was entirely illiterate, and a hierarchical social structure was enforced by strict religious rules. The socialist revolution dismantled that way of life almost overnight.

			The Soviet government forced all that agricultural land to become large collective farms and began industrial development. The economy quickly became interconnected and complex. Farmers had to form collective work strategies, plan ahead for production, divvy up functions, and assess work along the way. Remote villages began communicating with distant cities. A network of schools opened in regions with 100 percent illiteracy, and adults began learning a system of matching symbols to sounds. Villagers had used numbers before, but only in practical transactions. Now they were taught the concept of a number as an abstraction that existed even without reference to counting animals or apportioning food. Some village women remained fully illiterate but took short courses on how to teach kindergartners. Other women were admitted for longer study at a teachers’ school. Classes in preschool education and the science and technology of agriculture were offered to students who had no formal education of any kind. Secondary schools and technical institutes soon followed. In 1931, amid that incredible transformation, a brilliant young Russian psychologist named Alexander Luria recognized a fleeting “natural experiment,” unique in the history of the world. He wondered if changing citizens’ work might also change their minds.

			When Luria arrived, the most remote villages had not yet been touched by the warp-speed restructuring of traditional society. Those villages gave him a control group. He learned the local language and brought fellow psychologists to engage villagers in relaxed social situations—teahouses or pastures—and discuss questions or tasks designed to discern their habits of mind.

			Some were very simple: present skeins of wool or silk in an array of hues and ask participants to describe them. The collective farmers and farm leaders, as well as the female students, easily picked out blue, red, and yellow, sometimes with variations, like dark blue or light yellow. The most remote villagers, who were still “premodern,” gave more diversified descriptions: cotton in bloom, decayed teeth, a lot of water, sky, pistachio. Then they were asked to sort the skeins into groups. The collective farmers, and young people with even a little formal education, did so easily, naturally forming color groups. Even when they did not know the name of a particular color, they had little trouble putting together darker and lighter shades of the same one. The remote villagers, on the other hand, refused, even those whose work was embroidery. “It can’t be done,” they said, or, “None of them are the same, you can’t put them together.” When prodded vigorously, and only if they were allowed to make many small groups, some relented and created sets that were apparently random. A few others appeared to sort the skeins according to color saturation, without regard to the color.

			Geometric shapes followed suit. The greater the dose of modernity, the more likely an individual grasped the abstract concept of “shapes” and made groups of triangles, rectangles, and circles, even if they had no formal education and did not know the shapes’ names. The remote villagers, meanwhile, saw nothing alike in a square drawn with solid lines and the same exact square drawn with dotted lines. To Alieva, a twenty-six-year-old remote villager, the solid-line square was obviously a map, and the dotted-line square was a watch. “How can a map and a watch be put together?” she asked, incredulous. Khamid, a twenty-four-year-old remote villager, insisted that filled and unfilled circles could not go together because one was a coin and the other a moon.

			The pattern continued for every genre of question. Pressed to make conceptual groupings—akin to the similarities questions on IQ tests—remote villagers reverted to practical narratives based on their direct experience. When psychologists attempted to explain a “which one does not belong” grouping exercise to thirty-nine-year-old Rakmat, they gave him the example of three adults and one child, with the child obviously different from the others. Except Rakmat could not see it that way. “The boy must stay with the others!” he argued. The adults are working, “and if they have to keep running out to fetch things, they’ll never get the job done, but the boy can do the running for them.” Okay, then, how about a hammer, a saw, a hatchet, and a log—three of them are tools. They are not a group, Rakmat replied, because they are useless without the log, so why would they be together?

			Other villagers removed either the hammer or the hatchet, which they saw as less versatile for use with the log, unless they considered pounding the hatchet into the log with the hammer, in which case it could stay. Perhaps, then, bird/rifle/dagger/bullet? You can’t possibly remove one and have a group, a remote villager insisted. The bullet must be loaded in the rifle to kill the bird, and “then you have to cut the bird up with the dagger, since there’s no other way to do it.” These were just the introductions explaining the grouping task, not the actual questions. No amount of cajoling, explanation, or examples could get remote villagers to use reasoning based on any concept that was not a concrete part of their daily lives.

			The farmers and students who had begun to join the modern world were able to practice a kind of thinking called “eduction,” to work out guiding principles when given facts or materials, even in the absence of instructions, and even when they had never seen the material before. This, it turns out, is precisely what Raven’s Progressive Matrices tests. Imagine presenting the villagers living in premodern circumstances with abstract designs from the Raven’s test.

			Some of the changes wrought by modernity and collective culture seem almost magical. Luria found that most remote villagers were not subject to the same optical illusions as citizens of the industrialized world, like the Ebbinghaus illusion. Which middle circle below looks bigger?



			If you said the one on the right, you’re probably a citizen of the industrialized world. The remote villagers saw, correctly, that they are the same, while the collective farmers and women in teachers’ school picked the one on the right. Those findings have been repeated in other traditional societies, and scientists have suggested it may reflect the fact that premodern people are not as drawn to the holistic context—the relationship of the various circles to one another—so their perception is not changed by the presence of extra circles. To use a common metaphor, premodern people miss the forest for the trees; modern people miss the trees for the forest.

			Since Luria’s voyage to the interior, scientists have replicated his work in other cultures. The Kpelle people in Liberia were subsistence rice farmers, but in the 1970s roads began snaking toward them, connecting the Kpelle to cities. Given similarities tests, teenagers who were engaged with modern institutions grouped items by abstract categories (“All of these things can keep us warm”), while the traditional teens generated groups that were comparatively arbitrary, and changed frequently even when they were asked to repeat the exact same task. Because the touched-by-modernity teens had constructed meaningful thematic groups, they also had far superior recall when asked later to recount the items. The more they had moved toward modernity, the more powerful their abstract thinking, and the less they had to rely on their concrete experience of the world as a reference point.



* * *



			• • •

			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. We have grown up in a world of classification schemes totally foreign to the remote villagers; we classify some animals as mammals, and inside of that class make more detailed connections based on the similarity of their physiology and DNA.

			Words that represent concepts that were previously the domain of scholars became widely understood in a few generations. The word “percent” was almost absent from books in 1900. By 2000 it appeared about once every five thousand words. (This chapter is 5,500 words long.) Computer programmers pile layers of abstraction. (They do very well on Raven’s.) In the progress bar on your computer screen that fills up to indicate a download, abstractions are legion, from the fundamental—the programming language that created it is a representation of binary code, the raw 1s and 0s the computer uses—to the psychological: the bar is a visual projection of time that provides peace of mind by estimating the progress of an immense number of underlying activities.

			Lawyers might consider how results of one court case brought by an individual in Oklahoma could be relevant to a different one brought by a company in California. In order to prep, they might try out different hypothetical arguments while putting themselves in the shoes of an opposing attorney to predict how they will argue. Conceptual schemes are flexible, able to arrange information and ideas for a wide variety of uses, and to transfer knowledge between domains. Modern work demands knowledge transfer: the ability to apply knowledge to new situations and different domains. Our most fundamental thought processes have changed to accommodate increasing complexity and the need to derive new patterns rather than rely only on familiar ones. Our conceptual classification schemes provide a scaffolding for connecting knowledge, making it accessible and flexible.

			Research on thousands of adults in six industrializing nations found that exposure to modern work with self-directed problem solving and nonrepetitive challenges was correlated with being “cognitively flexible.” As Flynn makes sure to point out, this does not mean that brains now have more inherent potential than a generation ago, but rather that utilitarian spectacles have been swapped for spectacles through which the world is classified by concepts.* Even recently, within some very traditional or orthodox religious communities that have modernized but that still block women from engaging in modern work, the Flynn effect has proceeded more slowly for women than for men in the same community. Exposure to the modern world has made us better adapted for complexity, and that has manifested as flexibility, with profound implications for the breadth of our intellectual world.

			In every cognitive direction, the minds of premodern citizens were severely constrained by the concrete world before them. With cajoling, some solved the following logic sequence: “Cotton grows well where it is hot and dry. England is cold and damp. Can cotton grow there or not?” They had direct experience growing cotton, so some of them could answer (tentatively and when pushed) for a country they had never visited. The same exact puzzle with different details stumped them: “In the Far North, where there is snow, all bears are white. Novaya Zemlya is in the Far North and there is always snow there. What colors are the bears there?” That time, no amount of pushing could get the remote villagers to answer. They would respond only with principles. “Your words can be answered only by someone who was there,” one man said, even though he had never been to England but had just answered the cotton question. But even a faint taste of modern work began to change that. Given the white bear puzzle, Abdull, forty-five and barely literate but chairman of a collective farm, would not give an answer confidently, but he did exercise formal logic. “To go by your words,” he said, “they should all be white.”

			The transition completely transformed the villagers’ inner worlds. When the scientists from Moscow asked the villagers what they would like to know about them or the place they came from, the isolated farmers and herders generally could not come up with a single question. “I haven’t seen what people do in other cities,” one said, “so how can I ask?” Whereas those engaged in collective farming were readily curious. “Well, you just spoke about white bears,” said thirty-one-year-old Akhmetzhan, a collective farmer. “I don’t understand where they come from.” He stopped for a moment to ponder. “And then you mentioned America. Is it governed by us or by some other power?” Nineteen-year-old Siddakh, who worked on a collective farm and had studied in a school for two years, was brimming with imaginative questions that probed self-improvement, from the personal to the local and global: “Well, what could I do to make our kolkhozniks [collective farmers] better people? How can we obtain bigger plants, or plant ones which will grow to be like big trees? And then I’m interested in how the world exists, where things come from, how the rich became rich and why the poor are poor.”

			Where the very thoughts of premodern villagers were circumscribed by their direct experiences, modern minds are comparatively free. This is not to say that one way of life is uniformly better than another. As Arab historiographer Ibn Khaldun, considered a founder of sociology, pointed out centuries ago, 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.

			But it is certainly true that modern life requires range, making connections across far-flung domains and ideas. Luria addressed this kind of “categorical” thinking, which Flynn would later style as scientific spectacles. “[It] is usually quite flexible,” Luria wrote. “Subjects readily shift from one attribute to another and construct suitable categories. They classify objects by substance (animals, flowers, tools), materials (wood, metal, glass), size (large, small), and color (light, dark), or other property. The ability to move freely, to shift from one category to another, is one of the chief characteristics of ‘abstract thinking.’”



* * *



			• • •

			Flynn’s great disappointment is the degree to which society, and particularly higher education, has responded to the broadening of the mind by pushing specialization, rather than focusing early training on conceptual, transferable knowledge.

			Flynn conducted a study in which he compared the grade point averages of seniors at one of America’s top state universities, from neuroscience to English majors, to their performance on a test of critical thinking. The test gauged students’ ability to apply fundamental abstract concepts from economics, social and physical sciences, and logic to common, real-world scenarios. 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.”*

			Each of twenty test questions gauged a form of conceptual thinking that can be put to widespread use in the modern world. For test items that required the kind of conceptual reasoning that can be gleaned with no formal training—detecting circular logic, for example—the students did well. But in terms of frameworks that can best put their conceptual reasoning skills to use, they were horrible. Biology and English majors did poorly on everything that was not directly related to their field. None of the majors, including psychology, understood social science methods. Science students learned the facts of their specific field without understanding how science should work in order to draw true conclusions. Neuroscience majors did not do particularly well on anything. Business majors performed very poorly across the board, including in economics. Econ majors did the best overall. Economics is a broad field by nature, and econ professors have been shown to apply the reasoning principles they’ve learned to problems outside their area.* Chemists, on the other hand, are extraordinarily bright, but in several studies struggled to apply scientific reasoning to nonchemistry problems.

			Students Flynn tested often mistook subtle value judgments for scientific conclusions, and in a question that presented a tricky scenario and required students not to mistake a correlation for evidence of causation, they performed worse than random. Almost none of the students in any major showed a consistent understanding of how to apply methods of evaluating truth they had learned in their own discipline to other areas. In that way, the students had something in common with Luria’s remote villagers—even the science majors were typically unable to generalize research methods from their own field to other fields. Flynn’s conclusion: “There is no sign that any department attempts to develop [anything] other than narrow critical competence.”



* * *



			• • •

			Flynn is now in his eighties. He has a full white beard, the wind-buffeted cheeks of a lifelong runner, and piles of white curls that tuft and billow like cumulus clouds around his head. His house on a hill in Dunedin looks out over a gently rolling green farmscape.

			When he recounts his own education at the University of Chicago, where he was captain of the cross-country team, he raises his voice. “Even the best universities aren’t developing critical intelligence,” he told me. “They aren’t giving students the tools to analyze the modern world, except in their area of specialization. Their education is too narrow.” He does not mean this in the simple sense that every computer science major needs an art history class, but rather that everyone needs habits of mind that allow them to dance across disciplines.

			Chicago has long prided itself on a core curriculum dedicated to interdisciplinary critical thinking. The two-year core, according to the university, “is intended as an introduction to the tools of inquiry used in every discipline—science, mathematics, humanities, and social sciences. 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.” But even at Chicago, Flynn argues, his education did not maximize the modern potential for applying conceptual thinking across domains.

			Professors, he told me, are just too eager to share their favorite facts gleaned from years of acceleratingly narrow study. He has taught for fifty years, from Cornell to Canterbury, and is quick to include himself in that criticism. When he taught intro to moral and political philosophy, he couldn’t resist the urge to impart his favorite minutiae from Plato, Aristotle, Hobbes, Marx, and Nietzsche.

			Flynn introduced broad concepts in class, but he is sure that he often buried them in a mountain of other information specific to that class alone—a bad habit he worked to overcome. The study he conducted at the state university convinced him that college departments rush to develop students in a narrow specialty area, while failing to sharpen the tools of thinking that can serve them in every area. This must change, he argues, if students are to capitalize on their unprecedented capacity for abstract thought. They must be taught to think before being taught what to think about. Students come prepared with scientific spectacles, but do not leave carrying a scientific-reasoning Swiss Army knife.

			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.

			Jeannette Wing, a computer science professor at Columbia University and former corporate vice president of Microsoft Research, has pushed broad “computational thinking” as the mental Swiss Army knife. She advocated that it become as fundamental as reading, even for those who will have nothing to do with computer science or programming. “Computational thinking is using abstraction and decomposition when attacking a large complex task,” she wrote. “It is choosing an appropriate representation for a problem.”

			Mostly, though, students get what economist Bryan Caplan called narrow vocational training for jobs few of them will ever have. Three-quarters of American college graduates go on to a career unrelated to their major—a trend that includes math and science majors—after having become competent only with the tools of a single discipline.

			One good tool is rarely enough in a complex, interconnected, rapidly changing world. As the historian and philosopher Arnold Toynbee said when he described analyzing the world in an age of technological and social change, “No tool is omnicompetent.”



* * *



			• • •

			Flynn’s passion resonated deeply with me. Before turning to journalism, I was in grad school, living in a tent in the Arctic, studying how changes in plant life might impact the subterranean permafrost. Classes consisted of stuffing my brain with the details of Arctic plant physiology. Only years later—as an investigative journalist writing about poor scientific research—did I realize that I had committed statistical malpractice in one section of the thesis that earned me a master’s degree from Columbia University. Like many a grad student, I had a big database and hit a computer button to run a common statistical analysis, never having been taught to think deeply (or at all) about how that statistical analysis even worked. The stat program spit out a number summarily deemed “statistically significant.” Unfortunately, it was almost certainly a false positive, because I did not understand the limitations of the statistical test in the context in which I applied it. Nor did the scientists who reviewed the work. As statistician Doug Altman put it, “Everyone is so busy doing research they don’t have time to stop and think about the way they’re doing it.” I rushed into extremely specialized scientific research without having learned scientific reasoning. (And then I was rewarded for it, with a master’s degree, which made for a very wicked learning environment.) As backward as it sounds, I only began to think broadly about how science should work years after I left it.

			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. Remote Uzbek villagers would not perform well on Fermi problems, but neither did I before taking that class. It was easy to learn, though. Having grown up in the twentieth century, I was already wearing the spectacles, I just needed help capitalizing on them. I remember nothing about stoichiometry, but I use Fermi thinking regularly, breaking down a problem so I can leverage what little I know to start investigating what I don’t, a “similarities” problem of sorts.

			Fortunately, several studies have found that a little training in broad thinking strategies, like Fermi-izing, can go a long way, and can be applied across domains. 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.” It gives anyone consuming numbers, from news articles to advertisements, the ability quickly to sniff out deceptive stats. That’s a pretty handy hot butter knife. I would have been a much better researcher in any domain, including Arctic plant physiology, had I learned broadly applicable reasoning tools rather than the finer details of Arctic plant physiology.



* * *



			• • •

			Like chess masters and firefighters, premodern villagers relied on things being the same tomorrow as they were yesterday. They were extremely well prepared for what they had experienced before, and extremely poorly equipped for everything else. Their very thinking was highly specialized in a manner that the modern world has been telling us is increasingly obsolete. They were perfectly capable of learning from experience, but failed at learning without experience. And that is what a rapidly changing, wicked world demands—conceptual reasoning skills that can connect new ideas and work across contexts. Faced with any problem they had not directly experienced before, the remote villagers were completely lost. That is not an option for us. The more constrained and repetitive a challenge, the more likely it will be automated, while great rewards will accrue to those who can take conceptual knowledge from one problem or domain and apply it in an entirely new one.

			The ability to apply knowledge broadly comes from broad training. A particular skilled group of performers in another place and time turned broad training into an art form. Their story is older, and yet a much better parable than chess prodigies for the modern age.





CHAPTER 3





When Less of the Same Is More





ANYWHERE A TRAVELER to seventeenth-century Venice turned an ear, they could hear music exploding from its traditional bounds. Even the name of the musical era, “Baroque,” is taken from a jewelers’ term to describe a pearl that was extravagantly large and unusually shaped.

			Instrumental music—music that did not depend on words—underwent a complete revolution. Some of the instruments were brand-new, like the piano; others were enhanced—violins made by Antonio Stradivari would sell centuries later for millions of dollars. The modern system of major and minor keys was created. Virtuosos, the original musical celebrities, were anointed. Composers seized on their skill and wrote elaborate solos to push the boundaries of the best players’ abilities. The concerto was born—in which a virtuoso soloist plays back and forth against an orchestra—and Venetian composer Antonio Vivaldi (known as il Prete Rosso, the Red Priest, for his flame-red hair) became the form’s undisputed champion. The Four Seasons is as close to a pop hit as three-hundred-year-old music gets. (A mashup with a song from Disney’s Frozen has ninety million YouTube plays.)

			Vivaldi’s creativity was facilitated by a particular group of musicians who could learn new music quickly on a staggering array of instruments. They drew emperors, kings, princes, cardinals, and countesses from across Europe to be regaled by the most innovative music of the time. They were the all-female cast known as the figlie del coro, literally, “daughters of the choir.” Leisure activities like horseback riding and field sports were scarce in the floating city, so music bore the full weight of entertainment for its citizens. The sounds of violins, flutes, horns, and voices spilled into the night from every bobbing barge and gondola. And in a time and place seething with music, the figlie dominated for a century.

			“Only in Venice,” a prominent visitor wrote, “can one see these musical prodigies.” They were both ground zero of a musical revolution and an oddity. Elsewhere, their instruments were reserved for men. “They sing like angels, play the violin, the flute, the organ, the oboe, the cello, and the bassoon,” an astonished French politician remarked. “In short, no instrument is large enough to frighten them.” Others were less diplomatic. Aristocratic British writer Hester Thrale complained, “The sight of girls handling the double bass, and blowing into the bassoon did not much please me.” After all, “suitable feminine instruments” were more along the lines of the harpsichord or musical glasses.

			The figlie left the king of Sweden in awe. Literary rogue Casanova marveled at the standing-room-only crowds. A dour French concert reviewer singled out a particular violinist: “She is the first of her sex to challenge the success of our great artists.” Even listeners not obviously disposed to support the arts were