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

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

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

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Maybe you even model
your own life after it.

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

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it takes 10,000 hours
of focused practice,

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

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The poster child
for this story is Tiger Woods.

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

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

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

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Fast-forward to the age of 21,

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he's the greatest golfer in the world.

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Quintessential 10,000 hours story.

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Another that features a number
of bestselling books

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is that of the three Polgar sisters,

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

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from a very early age.

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And really he wanted to show

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that with a head start
in focused practice,

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any child could become
a genius in anything.

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And in fact,

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two of his daughters went on
to become grandmaster chess players.

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

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I got curious.

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If this 10,000 hours rule is correct,

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then we should see that elite
athletes get a head start

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in so-called "deliberate practice."

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This is coached,

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error-correction focused practice,

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not just playing around.

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And in fact,

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when scientists study elite athletes,

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

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not a big surprise.

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

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the pattern looks like this:

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the future elites actually spend
less time early on in deliberate practice

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in their eventual sport.

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

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where they try a variety
of physical activities,

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they gain broad, general skills,

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they learn about
their interests and abilities

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

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And so when I saw that I said,

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

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So I started to wonder about other domains

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that we associate with obligatory,
early specialization,

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like music.

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Turns out the pattern's often similar.

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This is research from
a world-class music academy,

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

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

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than the average musicians

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until their third instrument.

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They, too, tended to have
a sampling period.

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Even musicians we think of
as famously precocious,

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like Yo-Yo Ma.

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He had a sampling period,

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

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Nonetheless, this research
is almost entirely ignored,

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and much more impactful

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

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where the author recounts 
assigning her daughter violin.

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Nobody seems to remember
the part later in the book

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

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and largely quits.

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

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I started to wonder about domains
that effect even more people,

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like education.

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An economist found a natural experiment

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in the higher-ed systems
of England and Scotland.

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In the period he studied
the systems were very similar

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except in England,

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students had to specialize
in their mid-teen years

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to pick a specific course
of study to apply to,

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whereas in Scotland,

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they could keep trying things
in the university if they wanted to.

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And his question was:

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who wins the trade-off,

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the early of the late specializers?

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

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because they have more
domain-specific skills.

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The late specializers
get to try more different things,

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and when they do pick,

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they have better fit,

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or what economists call "match quality."

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And so their growth rates are faster.

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By six years out,

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they erase that income gap.

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Meanwhile, the early specializers
start quitting their career tracks

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in much higher numbers,

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essentially because they were
made to choose so early

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that they more often made poor choices.

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So the late specializers
lose in the short term

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and win the long run.

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I think if we thought about
career choice like dating,

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

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So this got me interested --

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seeing this pattern again --

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in exploring the developmental backgrounds

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of people whose work I had long admired.

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Like Duke Ellington,

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who shunned music lessons
as a kid to focus on baseball

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and painting and drawing.

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Or Maryam Mirzakhani,

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who wasn't interested
in math as a girl --

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dreamed of becoming a novelist --

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

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to win the Fields Medal,

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the most prestigious prize
in the world in math.

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Or Vincent Van Gogh --

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had five different careers,

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each of which he deemed
his true calling

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before flaming out spectacularly.

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

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That worked out OK.

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Claude Shannon was an electrical engineer
at the University of Michigan

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who took a philosophy course
just to fulfill a requirement,

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

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

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and solved like math problems.

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This led to the development
of binary code,

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which underlies all of our
digital computers today.

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Finally, my own sort of role model,

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Frances Hesselbein --

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this is me with her --

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she took her first professional
job at the age of 54

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

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which she saved.

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She tripled minority membership,

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added 130,000 volunteers,

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

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it's binary code for girls
learning about computers.

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Today, Frances runs a leadership institute

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where she works every
weekday in Manhattan,

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and she's only 104,

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so who knows what's next.

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(Laughter)

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

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We don't hear about the research

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that found that Nobel laureate scientists
are 22 times more likely

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to have a hobby outside of work

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as are typical scientists.

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We never hear that.

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Even when the performers
or the work is very famous,

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we don't hear these
developmental stories.

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

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Here he is at age six,

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wearing a Scottish rugby kit.

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He tried some tennis,

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some skiing,

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wrestling.

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

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because he wouldn't return balls normally.

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He did some basketball,
table tennis, swimming.

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When his coaches wanted to move
him up a level to play with older boys,

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he declined because he just wanted
to talk about pro wrestling

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after practice with his friends.

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And he kept trying more sports:

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handball, volleyball, soccer,
badminton, skateboarding --

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so who is this dabbler?

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This is Roger Federer.

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Every bit as famous
as an adult as Tiger Woods,

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and yet even tennis enthusiasts
don't usually know anything

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about his developmental story.

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Why is that even though it's the norm?

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

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But also because it seems
like this tidy narrative

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that we can extrapolate to anything

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that we want to be good
at in our own lives.

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But that I think is a problem,

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

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of almost everything
that humans want to learn.

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(Laughter)

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Golf is the epitome of what
the psychologist Robin Hogarth called

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a "kind" learning environment.

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

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rules that are clear and never change --

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when you do something you get feedback
that is quick and accurate;

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work next year will look
like work last year.

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Chess:

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also a kind learning environment.

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The grandmasters' advantage is based
on knowledge of recurring patterns,

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

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

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where next steps and goals
may not be clear.

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Rules may change.

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

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It may be delayed,

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it may be inaccurate

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

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So which one of these
sounds like the world

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we're increasingly living in?

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In fact, our need to think
in an adaptable manner

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and to keep track of interconnecting parts

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has fundamentally changed our perception

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so that when you look at this diagram,

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the central circle on the right
probably looks larger to you

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because your brain is drawn
to the relationship of the parts

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in the whole,

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whereas someone who hasn't been
exposed to modern work

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with its requirement for adaptable,
conceptual thought,

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

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So here we are in the wicked work world,

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and there, sometimes
hyperspecialization can backfire badly.

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For example,

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in research in a dozen countries

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that matched people
for their parents' years of education,

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their test scores,

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their own years of education,

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the difference was some got
career-focused education

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and some got broader, general education.

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The pattern was those who got
the career-focused education

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are more likely to be hired
right out of training,

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more likely to make more money right away,

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but so much less adaptable
in a changing work world

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that they spend so much less time
in the workforce overall

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

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Or consider a famous,
20-year study of experts

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making geopolitical
and economic predictions.

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The worst forecasters
were the most specialized experts.

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

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

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Some of them actually got worse

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as they accumulated
experience and credentials.

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

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Now in some domains,

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like medicine,

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increasing specialization has been
both inevitable and beneficial.

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No question about it.

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

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A few years ago,

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one of the most popular surgeries
in the world for knee pain

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was tested in a placebo-controlled trial.

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Some of the patients got "sham surgery."

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That means the surgeons make an incision,

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they bang around like
they're doing something,

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then they sew the patient back up.

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That performed just as a well.

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

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by the millions.

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

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That can be difficult to talk about

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because it doesn't
always look like this path.

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Sometimes it looks like
meandering or zigzagging

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or keeping a broader view.

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It can look like getting behind.

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

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If we look at research
on technological innovation,

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it shows that increasingly,

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the most impactful patents
are not authored by individuals

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00:09:26,610 --> 00:09:29,491
who drill deeper, deeper, deeper
into one area of technology,

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as classified by the US Patent Office,

255
00:09:31,532 --> 00:09:34,726
but rather by teams
that include individuals

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00:09:34,726 --> 00:09:38,016
who have worked across a large number
of different technology classes

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and often merge things
from different domains.

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Someone whose work I've admired

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00:09:41,653 --> 00:09:43,657
who was sort of
on the forefront of this --

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00:09:43,657 --> 00:09:45,605
a Japanese man named Gunpei Yokoi.

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00:09:45,605 --> 00:09:48,405
Yokoi didn't score well
on his electronics exams at school,

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

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at a playing card company in Kyoto.

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00:09:53,676 --> 00:09:56,640
He realized he wasn't equipped
to work on the cutting edge,

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but that there was so much
information easily available

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that maybe he could combine things
that were already well-known

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in ways that specialists
were too narrow to see.

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So he combined some well-known technology
from the calculator industry

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00:10:08,856 --> 00:10:11,745
with some well-known technology
from the credit card industry

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and made handheld games.

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And they were a hit.

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And it turned this playing card company,

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which was founded in a wooden
storefront in the 19th century,

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into a toy and game operation.

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You may have heard of it;

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it's called Nintendo.

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00:10:24,445 --> 00:10:26,374
Yokoi's creative philosophy translated

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to "lateral thinking 
with withered technology;"

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taking well-known technology
and using it in new ways.

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00:10:31,836 --> 00:10:33,978
And his magnum opus was this:

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the Game Boy.

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Technological joke in every way.

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00:10:37,559 --> 00:10:41,441
And it came out at the same time
as color competitors from Saga and Atari

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and it blew them away

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00:10:43,130 --> 00:10:46,417
because Yokoi knew what his
customers cared about wasn't color.

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00:10:46,417 --> 00:10:47,813
It was durability,

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portability,

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00:10:48,942 --> 00:10:50,081
affordability,

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00:10:50,081 --> 00:10:51,078
battery life --

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00:10:51,078 --> 00:10:52,133
game selection.

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00:10:52,136 --> 00:10:54,613
This is mine that I found
in my parents' basement --

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00:10:54,613 --> 00:10:55,797
(Laughter)

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seen better days.

294
00:10:57,375 --> 00:10:59,088
But you can see the red light is on.

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00:10:59,088 --> 00:11:00,948
I flipped it on and played some Tetris,

296
00:11:00,948 --> 00:11:02,905
which I thought was especially impressive

297
00:11:02,905 --> 00:11:05,288
because the batteries had expired
in 2007 and 2013.

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00:11:05,288 --> 00:11:06,652
(Laughter)

299
00:11:07,601 --> 00:11:11,213
So this breadth advantage holds
in more subjective realms as well.

300
00:11:11,213 --> 00:11:14,598
In a fascinating study of what leads
some comic book creators

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to be more likely to make
blockbuster comics,

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00:11:17,462 --> 00:11:18,755
a pair of researchers found

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00:11:18,755 --> 00:11:22,434
that it was neither the number of years
of experience in the field

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00:11:22,434 --> 00:11:25,423
nor the resources of the publisher

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00:11:25,423 --> 00:11:27,526
nor the number of previous comics made.

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00:11:27,662 --> 00:11:31,973
It was the number of different genres
that a creator had worked across.

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00:11:31,973 --> 00:11:33,387
And interestingly,

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00:11:33,387 --> 00:11:37,108
a broad individual
could not be entirely replaced

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00:11:37,108 --> 00:11:38,786
by a team of specialists.

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We probably don't make as many
of those people as we could

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00:11:42,148 --> 00:11:43,409
because early on,

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they just look like they're behind

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00:11:45,057 --> 00:11:48,673
and we don't tend to incentivize anything
that doesn't look like a head start

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00:11:48,673 --> 00:11:49,816
or specialization.

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00:11:49,816 --> 00:11:52,807
In fact, I think in the well-meaning
drive for a head start,

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00:11:52,807 --> 00:11:56,158
we often even counterproductively
short circuit even the way we learn

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00:11:56,158 --> 00:11:58,364
new material at a fundamental level.

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00:11:58,579 --> 00:11:59,611
In a study last year,

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00:11:59,611 --> 00:12:02,514
seventh-grade math classrooms in the US

320
00:12:02,514 --> 00:12:05,413
were randomly assigned
to different types of learning.

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00:12:05,413 --> 00:12:07,928
Some got what's called "blocked practice."

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00:12:08,051 --> 00:12:09,811
That's like, you get problem type A,

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00:12:09,811 --> 00:12:10,931
AAAAA,

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00:12:10,931 --> 00:12:11,932
BBBBB,

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00:12:11,835 --> 00:12:12,835
and so on.

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00:12:12,835 --> 00:12:14,232
Progress is fast;

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00:12:14,232 --> 00:12:15,427
kids are happy;

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00:12:15,427 --> 00:12:16,460
everything's great.

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00:12:16,460 --> 00:12:20,210
Other classrooms got assigned
to what's called "interleaved practice."

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That's like if you took
all the problem types

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00:12:22,642 --> 00:12:23,742
and threw them in a hat

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00:12:23,742 --> 00:12:25,074
and drew them out at random.

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00:12:25,074 --> 00:12:26,300
Progress is slower;

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00:12:26,300 --> 00:12:28,029
kids are more frustrated.

335
00:12:28,274 --> 00:12:30,975
But instead of learning
how to execute procedures,

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00:12:30,975 --> 00:12:34,401
they're learning how to match
a strategy to a type of problem.

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00:12:34,645 --> 00:12:36,369
And when the test comes around,

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00:12:36,369 --> 00:12:39,697
the interleaved group blew
the block practice group away.

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00:12:40,006 --> 00:12:41,233
It wasn't even close.

340
00:12:41,825 --> 00:12:45,070
Now I found a lot of this research
deeply counterintuitive.

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00:12:45,419 --> 00:12:46,778
The idea that a head start,

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00:12:46,778 --> 00:12:49,027
whether in picking a career
or a course of study

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00:12:49,027 --> 00:12:50,713
or just in learning new material,

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00:12:50,713 --> 00:12:53,579
can sometimes undermine
long-term development.

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00:12:53,579 --> 00:12:57,704
And naturally, I think there are as many
ways to succeed as there are people,

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00:12:57,704 --> 00:13:02,086
but I think we tend only to incentivize
and encourage the Tiger path,

347
00:13:02,086 --> 00:13:03,805
when increasingly in a wicked world,

348
00:13:03,805 --> 00:13:06,590
we need people who travel
the Roger path as well.

349
00:13:06,713 --> 00:13:09,295
Or as the eminent physicist
and mathematician

350
00:13:09,295 --> 00:13:12,842
and wonderful writer
Freeman Dyson put it --

351
00:13:12,842 --> 00:13:15,845
and Dyson passed away yesterday,

352
00:13:15,845 --> 00:13:18,240
so I hope I'm doing
his words honor here.

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00:13:18,247 --> 00:13:19,248
As he said,

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00:13:19,248 --> 00:13:20,574
"For a healthy ecosystem,

355
00:13:20,574 --> 00:13:22,565
we need both birds and frogs.

356
00:13:23,020 --> 00:13:24,256
Frogs are down in the mud,

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00:13:24,256 --> 00:13:26,175
seeing all the granular details.

358
00:13:26,399 --> 00:13:29,108
The birds are soaring up above
not seeing those details,

359
00:13:29,108 --> 00:13:31,157
but integrating
the knowledge of the frogs."

360
00:13:31,157 --> 00:13:32,461
And we need both.

361
00:13:32,515 --> 00:13:34,400
The problem, Dyson said,

362
00:13:34,400 --> 00:13:36,996
is that we're telling
everyone to become frogs.

363
00:13:36,996 --> 00:13:38,159
And I think,

364
00:13:38,159 --> 00:13:39,680
in a wicked world,

365
00:13:39,680 --> 00:13:41,753
that's increasingly shortsighted.

366
00:13:41,967 --> 00:13:43,126
Thank you very much.

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00:13:43,126 --> 00:13:46,086
(Applause)