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.
“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.
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.
Moravec’s paradox: machines and humans frequently have opposite strengths and weaknesses.
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.
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.”
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.
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.
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.
The less skilled students tended to spend their time on the first instrument they picked up, as if they could not give up a perceived head start. The exceptional students developed more like the figlie del coro. “The modest investment in a third instrument paid off handsomely for the exceptional children,” the scientists concluded. The psychologists highlighted the variety of paths to excellence, but the most common was a sampling period, often lightly structured with some lessons and a breadth of instruments and activities, followed only later by a narrowing of focus, increased structure, and an explosion of practice volume. Sound familiar?
Improv masters learn like babies: dive in and imitate and improvise first, learn the formal rules later. “At the beginning, your mom didn’t give you a book and say, ‘This is a noun, this is a pronoun, this is a dangling participle,’” Cecchini told me. “You acquired the sound first. And then you acquire the grammar later.”
In totality, the picture is in line with a classic research finding that is not specific to music: breadth of training predicts breadth of transfer. That is, the more contexts in which something is learned, the more the learner creates abstract models, and the less they rely on any particular example. Learners become better at applying their knowledge to a situation they’ve never seen before, which is the essence of creativity.
Struggling to retrieve information primes the brain for subsequent learning, even when the retrieval itself is unsuccessful. The struggle is real, and really useful.
In the face of the unexpected, the range of available analogies helped determine who learned something new. In the lone lab that did not make any new findings during Dunbar’s project, everyone had similar and highly specialized backgrounds, and analogies were almost never used. “When all the members of the laboratory have the same knowledge at their disposal, then when a problem arises, a group of similar minded individuals will not provide more information to make analogies than a single individual,” Dunbar concluded.
the sunk cost mindset is so deeply entrenched that conmen know to begin by asking their marks for several small favors or investments before progressing to large asks. Once a mark has invested energy or money, rather than walking away from sunk costs he will continue investing, more than he ever wanted to, even as, to any rational observer, disaster becomes imminent. “The more we have invested and even lost,” Konnikova wrote, “the longer we will persist in insisting it will all work out.”
Dark horses were on the hunt for match quality. “They never look around and say, ‘Oh, I’m going to fall behind, these people started earlier and have more than me at a younger age,’” Ogas told me. “They focused on, ‘Here’s who I am at the moment, here are my motivations, here’s what I’ve found I like to do, here’s what I’d like to learn, and here are the opportunities. Which of these is the best match right now? And maybe a year from now I’ll switch because I’ll find something better.’” Each dark horse had a novel journey, but a common strategy. “Short-term planning,” Ogas told me. “They all practice it, not long-term planning.” Even people who look like consummate long-term visionaries from afar usually looked like short-term planners up close.
The precise person you are now is fleeting, just like all the other people you’ve been. That feels like the most unexpected result, but it is also the most well documented.
He and another psychologist aggregated the results of ninety-two studies and revealed that some personality traits change over time in fairly predictable ways. Adults tend to become more agreeable, more conscientious, more emotionally stable, and less neurotic with age, but less open to experience. In middle age, adults grow more consistent and cautious and less curious, open-minded, and inventive.* The changes have well-known impacts, like the fact that adults generally become less likely to commit violent crimes with age, and more able to create stable relationships. The most momentous personality changes occur between age eighteen and one’s late twenties, so specializing early is a task of predicting match quality for a person who does not yet exist. It could work, but it makes for worse odds. Plus, while personality change slows, it does not stop at any age. Sometimes it can actually happen instantly.
we learn who we are only by living, and not before. Ibarra concluded that we maximize match quality throughout life by sampling activities, social groups, contexts, jobs, careers, and then reflecting and adjusting our personal narratives. And repeat.
Themes emerged in the transitions. The protagonists had begun to feel unfulfilled by their work, and then a chance encounter with some world previously invisible to them led to a series of short-term explorations. At first, all career changers fell prey to the cult of the head start and figured it couldn’t possibly make sense to dispense with their long-term plans in favor of rapidly evolving short-term experiments. Sometimes they tried to talk themselves out of it. Their confidants advised them not to do anything rash; don’t change now, they said, just keep the new interest or talent as a hobby. But the more they dabbled, the more certain they were that it was time for a change. A new work identity did not manifest overnight, but began with trying something temporary, Hesselbein style, or finding a new role model, then reflecting on the experience and moving to the next short-term plan. Some career changers got richer, others poorer; all felt temporarily behind, but as in the Freakonomics coin-flip study, they were happier with a change.
In the graduation-speech approach, you decide where you want to be in twenty years, and then ask: what should I do now to get there? I propose instead that you don’t commit to anything in the future, but just look at the options available now, and choose those that will give you the most promising range of options afterward.
“Knowledge is a double-edged sword. It allows you to do some things, but it also makes you blind to other things that you could do.”
Swanson wanted to show that areas of specialist literature that never normally overlapped were rife with hidden interdisciplinary treasures waiting to be connected. He created a computer system, Arrowsmith, that helped other users do what he did—devise searches that might turn up distant but relevant sets of scientific articles, and ignited a field of information science that grapples with connecting diverse areas of knowledge, as specialties that can inform one another drift apart.
Yokoi had no desire (or capability) to compete with electronics companies that were racing one another to invent some entirely new sliver of dazzling technology. Nor could Nintendo compete with Japan’s titans of traditional toys—Bandai, Epoch, and Takara—on their familiar turf. With that, and Drive Game, in mind, Yokoi embarked on an approach he called “lateral thinking with withered technology.” Lateral thinking is a term coined in the 1960s for the reimagining of information in new contexts, including the drawing together of seemingly disparate concepts or domains that can give old ideas new uses. By “withered technology,” Yokoi meant tech that was old enough to be extremely well understood and easily available, so it didn’t require a specialist’s knowledge. The heart of his philosophy was putting cheap, simple technology to use in ways no one else considered. If he could not think more deeply about new technologies, he decided, he would think more broadly about old ones. He intentionally retreated from the cutting edge, and set to monozukuri.
Yokoi was the first to admit it. “I don’t have any particular specialist skills,” he once said. “I have a sort of vague knowledge of everything.” He advised young employees not just to play with technology for its own sake, but to play with ideas. Do not be an engineer, he said, be a producer. “The producer knows that there’s such a thing as a semiconductor, but doesn’t need to know its inner workings. . . . That can be left to the experts.” He argued, “Everyone takes the approach of learning detailed, complex skills. If no one did this then there wouldn’t be people who shine as engineers. . . . Looking at me, from the engineer’s perspective, it’s like, ‘Look at this idiot,’ but once you’ve got a couple hit products under your belt, this word ‘idiot’ seems to slip away somewhere.”
Freeman Dyson styled it this way: we need both focused frogs and visionary birds. “Birds fly high in the air and survey broad vistas of mathematics out to the far horizon,” Dyson wrote in 2009. “They delight in concepts that unify our thinking and bring together diverse problems from different parts of the landscape. Frogs live in the mud below and see only the flowers that grow nearby. They delight in the details of particular objects, and they solve problems one at a time.” As a mathematician, Dyson labeled himself a frog, but contended, “It is stupid to claim that birds are better than frogs because they see farther, or that frogs are better than birds because they see deeper.” The world, he wrote, is both broad and deep. “We need birds and frogs working together to explore it.”
In the early twentieth century, for example, the state of Iowa alone had more than a thousand opera houses, one for every fifteen hundred residents. They were theaters, not just music venues, and they provided full-time employment for hundreds of local acting troupes and thousands of actors. Fast forward to Netflix and Hulu. Every customer can have Meryl Streep on demand, and the Iowa opera houses are extinct. So much for thousands of fully employed stage actors in Iowa.
“What I mean,” she said, “is I’m not qualified fundamentally to do what I do.” She described her approach to innovation almost like investigative journalism, except her version of shoe-leather reporting is going door-to-door among her peers. She is a “T-shaped person,” she said, one who has breadth, compared to an “I-shaped person,” who only goes deep, an analog to Dyson’s birds and frogs. “T-people like myself can happily go to the I-people with questions to create the trunk for the T,” she told me. “My inclination is to attack a problem by building a narrative. I figure out the fundamental questions to ask, and if you ask those questions of the people who actually do know their stuff, you are still exactly where you would be if you had all this other knowledge inherently. It’s mosaic building. I just keep putting those tiles together. Imagine me in a network where I didn’t have the ability to access all these people. That really wouldn’t work well.”
In low-uncertainty domains, teams of specialists were more likely to author useful patents. In high-uncertainty domains—where the fruitful questions themselves were less obvious—teams that included individuals who had worked on a wide variety of technologies were more likely to make a splash. The higher the domain uncertainty, the more important it was to have a high-breadth team member. As with the molecular biology groups Kevin Dunbar studied that used analogical thinking to solve problems, when the going got uncertain, breadth made the difference.
In kind environments, where the goal is to re-create prior performance with as little deviation as possible, teams of specialists work superbly. Surgical teams work faster and make fewer mistakes as they repeat specific procedures, and specialized surgeons get better outcomes even independent of repetitions. If you need to have surgery, you want a doctor who specializes in the procedure and has done it many times, preferably with the same team, just as you would want Tiger Woods to step in if your life was on the line for a ten-foot putt. They’ve been there, many times, and now have to re-create a well-understood process that they have executed successfully before. The same goes for airline crews. Teams that have experience working together become exceedingly efficient at delegating all of the well-understood tasks required to ensure a smooth flight. When the National Transportation Safety Board analyzed its database of major flight accidents, it found that 73 percent occurred on a flight crew’s first day working together. Like surgeries and putts, the best flight is one in which everything goes according to routines long understood and optimized by everyone involved, with no surprises.
University of Utah professor Abbie Griffin has made it her work to study modern Thomas Edisons—“ serial innovators,” she and two colleagues termed them. Their findings about who these people are should sound familiar by now: “high tolerance for ambiguity”; “systems thinkers”; “additional technical knowledge from peripheral domains”; “repurposing what is already available”; “adept at using analogous domains for finding inputs to the invention process”; “ability to connect disparate pieces of information in new ways”; “synthesizing information from many different sources”; “they appear to flit among ideas”; “broad range of interests”; “they read more (and more broadly) than other technologists and have a wider range of outside interests”; “need to learn significantly across multiple domains”; “Serial innovators also need to communicate with various individuals with technical expertise outside of their own domain.”
Research on aviation accidents, for example, found that “a common pattern was the crew’s decision to continue with their original plan” even when conditions changed dramatically. When Weick spoke with hotshot Paul Gleason, one of the best wildland firefighters in the world, Gleason told him that he preferred to view his crew leadership not as decision making, but as sensemaking. “If I make a decision, it is a possession, I take pride in it, I tend to defend it and not listen to those who question it,” Gleason explained. “If I make sense, then this is more dynamic and I listen and I can change it.” He employed what Weick called “hunches held lightly.” Gleason gave decisive directions to his crew, but with transparent rationale and the addendum that the plan was ripe for revision as the team collectively made sense of a fire.
Von Braun started “Monday Notes”: every week engineers submitted a single page of notes on their salient issues. Von Braun handwrote comments in the margins, and then circulated the entire compilation. Everyone saw what other divisions were up to, and how easily problems could be raised. Monday Notes were rigorous, but informal.
“I told them I expect disagreement with my decisions at the time we’re trying to make decisions, and that’s a sign of organizational health,” he told me. “After the decisions are made, we want compliance and support, but we have permission to fight a little bit about those things in a professional way.” He emphasized that there is a difference between the chain of command and the chain of communication, and that the difference represents a healthy cross-pressure. “I warned them, I’m going to communicate with all levels of the organization down to the shop floor, and you can’t feel suspicious or paranoid about that,” he said. “I told them I will not intercept your decisions that belong in your chain of command, but I will give and receive information anywhere in the organization, at any time. I just can’t get enough understanding of the organization from listening to the voices at the top.”
He turned on a dime and switched to studying chemistry. He never even thought to feel behind. On the contrary, “that was really very valuable, because at the end I had a good background in biology and wasn’t frightened of biology, and then I wasn’t frightened of chemistry. That gave me a great deal of power in the early days of molecular biology.” What sounds like hyperspecialization today was actually a bold hybrid at the time.
To the end of his life, he encouraged students to think laterally, broaden their experience, and forge their own path in search of match quality. “I try to teach people, ‘Don’t end up a clone of your thesis adviser,’” he told me. “Take your skills to a place that’s not doing the same sort of thing. Take your skills and apply them to a new problem, or take your problem and try completely new skills.”
His Friday evenings are like Smithies’s Saturday mornings; they balance the rest of the week’s standard practice with wide-roaming exploration. They embrace what Max Delbrück, a Nobel laureate who studied the intersection of physics and biology, called “the principle of limited sloppiness.” Be careful not to be too careful, Delbrück warned, or you will unconsciously limit your exploration.
Casadevall declared that the pace of progress had slowed, while the rate of retractions in scientific literature had accelerated, proportionally outpacing the publication of new studies. “If this continues unabated,” he said, “the entire literature will be retracted in a few years.” It was science gallows humor, but grounded in data. Part of the problem, he argued, is that young scientists are rushed to specialize before they learn how to think; they end up unable to produce good work themselves and unequipped to spot bad (or fraudulent) work by their colleagues.
New collaborations allow creators “to take ideas that are conventions in one area and bring them into a new area, where they’re suddenly seen as invention,” said sociologist Brian Uzzi, Amaral’s collaborator. Human creativity, he said, is basically an “import/ export business of ideas.”