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  • Transcriber: Leslie Gauthier Reviewer: Camille Martínez

  • So, I'd like to talk about the development of human potential,

  • and I'd like to start with maybe the most impactful modern story of development.

  • Many of you here have probably heard of the 10,000 hours rule.

  • Maybe you even model your own life after it.

  • Basically, it's the idea that to become great in anything,

  • it takes 10,000 hours of focused practice,

  • so you'd better get started as early as possible.

  • The poster child for this story is Tiger Woods.

  • His father famously gave him a putter when he was seven months old.

  • At 10 months, he started imitating his father's swing.

  • At two, you can go on YouTube and see him on national television.

  • Fast-forward to the age of 21,

  • he's the greatest golfer in the world.

  • Quintessential 10,000 hours story.

  • Another that features in a number of bestselling books

  • is that of the three Polgar sisters,

  • whose father decided to teach them chess in a very technical manner

  • from a very early age.

  • And, really, he wanted to show

  • that with a head start in focused practice,

  • any child could become a genius in anything.

  • And in fact,

  • two of his daughters went on to become Grandmaster chess players.

  • So when I became the science writer at "Sports Illustrated" magazine,

  • I got curious.

  • If this 10,000 hours rule is correct,

  • then we should see that elite athletes get a head start

  • in so-called "deliberate practice."

  • This is coached, error-correction-focused practice,

  • not just playing around.

  • And in fact, when scientists study elite athletes,

  • they see that they spend more time in deliberate practice --

  • not a big surprise.

  • When they actually track athletes over the course of their development,

  • the pattern looks like this:

  • the future elites actually spend less time early on

  • in deliberate practice in their eventual sport.

  • They tend to have what scientists call a "sampling period,"

  • where they try a variety of physical activities,

  • they gain broad, general skills,

  • they learn about their interests and abilities

  • and delay specializing until later than peers who plateau at lower levels.

  • And so when I saw that, I said,

  • "Gosh, that doesn't really comport with the 10,000 hours rule, does it?"

  • So I started to wonder about other domains

  • that we associate with obligatory, early specialization,

  • like music.

  • Turns out the pattern's often similar.

  • This is research from a world-class music academy,

  • and what I want to draw your attention to is this:

  • the exceptional musicians didn't start spending more time in deliberate practice

  • than the average musicians

  • until their third instrument.

  • They, too, tended to have a sampling period,

  • even musicians we think of as famously precocious,

  • like Yo-Yo Ma.

  • He had a sampling period,

  • he just went through it more rapidly than most musicians do.

  • Nonetheless, this research is almost entirely ignored,

  • and much more impactful

  • is the first page of the book "Battle Hymn of the Tiger Mother,"

  • where the author recounts assigning her daughter violin.

  • Nobody seems to remember the part later in the book

  • where her daughter turns to her and says, "You picked it, not me,"

  • and largely quits.

  • So having seen this sort of surprising pattern in sports and music,

  • I started to wonder about domains that affect even more people,

  • like education.

  • An economist found a natural experiment

  • in the higher-ed systems of England and Scotland.

  • In the period he studied, the systems were very similar,

  • except in England, students had to specialize in their mid-teen years

  • to pick a specific course of study to apply to,

  • whereas in Scotland, they could keep trying things in the university

  • if they wanted to.

  • And his question was:

  • Who wins the trade-off, the early or the late specializers?

  • And what he saw was that the early specializers jump out to an income lead

  • because they have more domain-specific skills.

  • The late specializers get to try more different things,

  • and when they do pick, they have better fit,

  • or what economists call "match quality."

  • And so their growth rates are faster.

  • By six years out,

  • they erase that income gap.

  • Meanwhile, the early specializers start quitting their career tracks

  • in much higher numbers,

  • essentially because they were made to choose so early

  • that they more often made poor choices.

  • So the late specializers lose in the short term

  • and win in the long run.

  • I think if we thought about career choice like dating,

  • we might not pressure people to settle down quite so quickly.

  • So this got me interested, seeing this pattern again,

  • in exploring the developmental backgrounds of people whose work I had long admired,

  • like Duke Ellington, who shunned music lessons as a kid

  • to focus on baseball and painting and drawing.

  • Or Maryam Mirzakhani, who wasn't interested in math as a girl --

  • dreamed of becoming a novelist --

  • and went on to become the first and so far only woman

  • to win the Fields Medal,

  • the most prestigious prize in the world in math.

  • Or Vincent Van Gogh had five different careers,

  • each of which he deemed his true calling before flaming out spectacularly,

  • and in his late 20s, picked up a book called "The Guide to the ABCs of Drawing."

  • That worked out OK.

  • Claude Shannon was an electrical engineer at the University of Michigan

  • who took a philosophy course just to fulfill a requirement,

  • and in it, he learned about a near-century-old system of logic

  • by which true and false statements could be coded as ones and zeros

  • and solved like math problems.

  • This led to the development of binary code,

  • which underlies all of our digital computers today.

  • Finally, my own sort of role model, Frances Hesselbein --

  • this is me with her --

  • she took her first professional job at the age of 54

  • and went on to become the CEO of the Girl Scouts,

  • which she saved.

  • She tripled minority membership,

  • added 130,000 volunteers,

  • and this is one of the proficiency badges that came out of her tenure --

  • it's binary code for girls learning about computers.

  • Today, Frances runs a leadership institute

  • where she works every weekday, in Manhattan.

  • And she's only 104,

  • so who knows what's next.

  • (Laughter)

  • We never really hear developmental stories like this, do we?

  • We don't hear about the research

  • that found that Nobel laureate scientists are 22 times more likely

  • to have a hobby outside of work

  • as are typical scientists.

  • We never hear that.

  • Even when the performers or the work is very famous,

  • we don't hear these developmental stories.

  • For example, here's an athlete I've followed.

  • Here he is at age six, wearing a Scottish rugby kit.

  • He tried some tennis, some skiing, wrestling.

  • His mother was actually a tennis coach but she declined to coach him

  • because he wouldn't return balls normally.

  • He did some basketball, table tennis, swimming.

  • When his coaches wanted to move him up a level

  • to play with older boys,

  • he declined, because he just wanted to talk about pro wrestling

  • after practice with his friends.

  • And he kept trying more sports:

  • handball, volleyball, soccer, badminton, skateboarding ...

  • So, who is this dabbler?

  • This is Roger Federer.

  • Every bit as famous as an adult as Tiger Woods,

  • and yet even tennis enthusiasts don't usually know anything

  • about his developmental story.

  • Why is that, even though it's the norm?

  • I think it's partly because the Tiger story is very dramatic,

  • but also because it seems like this tidy narrative

  • that we can extrapolate to anything that we want to be good at

  • in our own lives.

  • But that, I think, is a problem,

  • because it turns out that in many ways, golf is a uniquely horrible model

  • of almost everything that humans want to learn.

  • (Laughter)

  • Golf is the epitome of

  • what the psychologist Robin Hogarth called a "kind learning environment."

  • Kind learning environments have next steps and goals that are clear,

  • rules that are clear and never change,

  • when you do something, you get feedback that is quick and accurate,

  • work next year will look like work last year.

  • Chess: also a kind learning environment.

  • The grand master's advantage

  • is largely based on knowledge of recurring patterns,

  • which is also why it's so easy to automate.

  • On the other end of the spectrum are "wicked learning environments,"

  • where next steps and goals may not be clear.

  • Rules may change.

  • You may or may not get feedback when you do something.

  • It may be delayed, it may be inaccurate,

  • and work next year may not look like work last year.

  • So which one of these sounds like the world we're increasingly living in?

  • In fact, our need to think in an adaptable manner

  • and to keep track of interconnecting parts

  • has fundamentally changed our perception,

  • so that when you look at this diagram,

  • the central circle on the right probably looks larger to you

  • because your brain is drawn to

  • the relationship of the parts in the whole,

  • whereas someone who hasn't been exposed to modern work

  • with its requirement for adaptable, conceptual thought,

  • will see correctly that the central circles are the same size.

  • So here we are in the wicked work world,

  • and there, sometimes hyperspecialization can backfire badly.

  • For example, in research in a dozen countries

  • that matched people for their parents' years of education,

  • their test scores,

  • their own years of education,

  • the difference was some got career-focused education

  • and some got broader, general education.

  • The pattern was those who got the career-focused education

  • are more likely to be hired right out of training,

  • more likely to make more money right away,

  • but so much less adaptable in a changing work world

  • that they spend so much less time in the workforce overall

  • that they win in the short term and lose in the long run.

  • Or consider a famous, 20-year study of experts

  • making geopolitical and economic predictions.

  • The worst forecasters were the most specialized experts,

  • those who'd spent their entire careers studying one or two problems

  • and came to see the whole world through one lens or mental model.

  • Some of them actually got worse

  • as they accumulated experience and credentials.

  • The best forecasters were simply bright people with wide-ranging interests.

  • Now in some domains, like medicine,

  • increasing specialization has been both inevitable and beneficial,

  • no question about it.

  • And yet, it's been a double-edged sword.

  • A few years ago, one of the most popular surgeries in the world for knee pain

  • was tested in a placebo-controlled trial.

  • Some of the patients got "sham surgery."

  • That means the surgeons make an incision,

  • they bang around like they're doing something,

  • then they sew the patient back up.

  • That performed just as a well.

  • And yet surgeons who specialize in the procedure continue to do it

  • by the millions.

  • So if hyperspecialization isn't always the trick in a wicked world, what is?

  • That can be difficult to talk about,

  • because it doesn't always look like this path.

  • Sometimes it looks like meandering or zigzagging

  • or keeping a broader view.

  • It can look like getting behind.

  • But I want to talk about what some of those tricks might be.

  • If we look at research on technological innovation, it shows that increasingly,

  • the most impactful patents are not authored by individuals

  • who drill deeper, deeper, deeper into one area of technology

  • as classified by the US Patent Office,

  • but rather by teams that include individuals

  • who have worked across a large number of different technology classes

  • and often merge things from different domains.

  • Someone whose work I've admired who was sort of on the forefront of this

  • is a Japanese man named Gunpei Yokoi.

  • Yokoi didn't score well on his electronics exams at school,

  • so he had to settle for a low-tier job as a machine maintenance worker

  • at a