WEBVTT

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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
in 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.

NOTE Paragraph

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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,
error-correction-focused practice,

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not just playing around.

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And in fact, 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

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in deliberate practice
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.

NOTE Paragraph

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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.

NOTE Paragraph

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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 affect 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, 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, they could
keep trying things in the university

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if they wanted to.

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And his question was:

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Who wins the trade-off,
the early or 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,
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 in 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.

NOTE Paragraph

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So this got me interested,
seeing this pattern again,

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

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like Duke Ellington, who shunned
music lessons as a kid

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to focus on baseball
and painting and drawing.

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Or Maryam Mirzakhani, 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
had five different careers,

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each of which he deemed his true calling
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,
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.

NOTE Paragraph

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(Laughter)

NOTE Paragraph

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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.

NOTE Paragraph

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

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

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He tried some tennis,
some skiing, 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

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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.

NOTE Paragraph

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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
that we want to be good at

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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.

NOTE Paragraph

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(Laughter)

NOTE Paragraph

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Golf is the epitome of

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what the psychologist Robin Hogarth
called 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: also a kind learning environment.

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The grand master's advantage

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is largely 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, it may be inaccurate,

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

NOTE Paragraph

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So which one of these sounds like
the world 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

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the relationship
of the parts 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.

NOTE Paragraph

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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, 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.

NOTE Paragraph

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Now in some domains, 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, 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.

NOTE Paragraph

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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, it shows that increasingly,

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the most impactful patents
are not authored by individuals

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

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as classified by the US Patent Office,

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but rather by teams
that include individuals

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who have worked across a large number
of different technology classes

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and often merge things
from different domains.

NOTE Paragraph

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

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is a Japanese man named Gunpei Yokoi.

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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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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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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;
it's called Nintendo.

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Yokoi's creative philosophy

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translated to "lateral thinking
with withered technology,"

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

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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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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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because Yokoi knew
what his customers cared about

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wasn't color.

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It was durability, portability,
affordability, battery life,

00:10:50.812 --> 00:10:52.112
game selection.

00:10:52.136 --> 00:10:54.589
This is mine that I found
in my parents' basement.

NOTE Paragraph

00:10:54.613 --> 00:10:55.773
(Laughter)

NOTE Paragraph

00:10:55.797 --> 00:10:57.351
It's seen better days.

00:10:57.375 --> 00:10:59.120
But you can see the red light is on.

00:10:59.144 --> 00:11:01.035
I flipped it on and played some Tetris,

00:11:01.059 --> 00:11:03.045
which I thought was especially impressive

00:11:03.069 --> 00:11:05.533
because the batteries had expired
in 2007 and 2013.

NOTE Paragraph

00:11:05.557 --> 00:11:06.901
(Laughter)

NOTE Paragraph

00:11:07.489 --> 00:11:11.078
So this breadth advantage holds
in more subjective realms as well.

00:11:11.102 --> 00:11:14.574
In a fascinating study of what leads
some comic book creators

00:11:14.598 --> 00:11:17.438
to be more likely to make
blockbuster comics,

00:11:17.462 --> 00:11:18.771
a pair of researchers found

00:11:18.795 --> 00:11:22.327
that it was neither the number of years
of experience in the field

00:11:22.351 --> 00:11:25.217
nor the resources of the publisher

00:11:25.241 --> 00:11:27.457
nor the number of previous comics made.

00:11:27.481 --> 00:11:31.949
It was the number of different genres
that a creator had worked across.

00:11:31.973 --> 00:11:33.295
And interestingly,

00:11:33.319 --> 00:11:36.968
a broad individual
could not be entirely replaced

00:11:36.992 --> 00:11:38.786
by a team of specialists.

NOTE Paragraph

00:11:39.154 --> 00:11:42.124
We probably don't make as many
of those people as we could

00:11:42.148 --> 00:11:44.957
because early on,
they just look like they're behind

00:11:44.981 --> 00:11:48.677
and we don't tend to incentivize anything
that doesn't look like a head start

00:11:48.701 --> 00:11:49.878
or specialization.

00:11:49.902 --> 00:11:52.749
In fact, I think in the well-meaning
drive for a head start,

00:11:52.773 --> 00:11:55.625
we often even counterproductively
short-circuit even the way

00:11:55.649 --> 00:11:56.946
we learn new material,

00:11:56.970 --> 00:11:58.555
at a fundamental level.

00:11:58.579 --> 00:12:02.425
In a study last year,
seventh-grade math classrooms in the US

00:12:02.449 --> 00:12:05.325
were randomly assigned
to different types of learning.

00:12:05.349 --> 00:12:07.925
Some got what's called "blocked practice."

00:12:07.949 --> 00:12:09.691
That's like, you get problem type A,

00:12:09.715 --> 00:12:12.624
AAAAA, BBBBB, and so on.

00:12:12.648 --> 00:12:14.073
Progress is fast,

00:12:14.097 --> 00:12:15.248
kids are happy,

00:12:15.272 --> 00:12:16.436
everything's great.

00:12:16.460 --> 00:12:20.498
Other classrooms got assigned
to what's called "interleaved practice."

00:12:20.522 --> 00:12:23.778
That's like if you took all the problem
types and threw them in a hat

00:12:23.802 --> 00:12:25.146
and drew them out at random.

00:12:25.170 --> 00:12:28.097
Progress is slower,
kids are more frustrated.

00:12:28.121 --> 00:12:30.784
But instead of learning
how to execute procedures,

00:12:30.808 --> 00:12:34.621
they're learning how to match
a strategy to a type of problem.

00:12:34.645 --> 00:12:36.240
And when the test comes around,

00:12:36.264 --> 00:12:39.698
the interleaved group blew
the block practice group away.

00:12:39.722 --> 00:12:41.129
It wasn't even close.

NOTE Paragraph

00:12:41.825 --> 00:12:45.445
Now, I found a lot of this research
deeply counterintuitive,

00:12:45.469 --> 00:12:46.818
the idea that a head start,

00:12:46.842 --> 00:12:49.121
whether in picking a career
or a course of study

00:12:49.145 --> 00:12:50.764
or just in learning new material,

00:12:50.788 --> 00:12:53.481
can sometimes undermine
long-term development.

00:12:53.505 --> 00:12:56.288
And naturally, I think there are
as many ways to succeed

00:12:56.312 --> 00:12:57.680
as there are people.

00:12:57.704 --> 00:13:01.925
But I think we tend only to incentivize
and encourage the Tiger path,

00:13:01.949 --> 00:13:03.736
when increasingly, in a wicked world,

00:13:03.760 --> 00:13:06.689
we need people who travel
the Roger path as well.

00:13:06.713 --> 00:13:09.271
Or as the eminent physicist
and mathematician

00:13:09.295 --> 00:13:12.719
and wonderful writer,
Freeman Dyson, put it --

00:13:12.743 --> 00:13:15.638
and Dyson passed away yesterday,

00:13:15.662 --> 00:13:17.937
so I hope I'm doing
his words honor here --

00:13:17.961 --> 00:13:22.854
as he said: for a healthy ecosystem,
we need both birds and frogs.

00:13:22.878 --> 00:13:24.181
Frogs are down in the mud,

00:13:24.205 --> 00:13:26.303
seeing all the granular details.

00:13:26.327 --> 00:13:29.084
The birds are soaring up above
not seeing those details

00:13:29.108 --> 00:13:31.156
but integrating
the knowledge of the frogs.

00:13:31.180 --> 00:13:32.491
And we need both.

00:13:32.515 --> 00:13:34.210
The problem, Dyson said,

00:13:34.234 --> 00:13:36.909
is that we're telling everyone
to become frogs.

00:13:36.933 --> 00:13:38.135
And I think,

00:13:38.159 --> 00:13:39.611
in a wicked world,

00:13:39.635 --> 00:13:41.786
that's increasingly shortsighted.

NOTE Paragraph

00:13:41.810 --> 00:13:43.102
Thank you very much.

NOTE Paragraph

00:13:43.126 --> 00:13:46.086
(Applause)