Why specializing early doesnt always mean career success David Epstein

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 playing card company in Kyoto.

He realized he wasn’t equipped
to work on the cutting edge,

but that there was so much
information easily available

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

in ways that specialists
were too narrow to see.

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

with some well-known technology
from the credit card industry

and made handheld games.

And they were a hit.

And it turned this playing card company,

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

into a toy and game operation.

You may have heard of it;
it’s called Nintendo.

Yokoi’s creative philosophy

translated to “lateral thinking
with withered technology,”

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

And his magnum opus was this:

the Game Boy.

Technological joke in every way.

And it came out at the same time
as color competitors from Saga and Atari,

and it blew them away,

because Yokoi knew
what his customers cared about

wasn’t color.

It was durability, portability,
affordability, battery life,

game selection.

This is mine that I found
in my parents' basement.

(Laughter)

It’s seen better days.

But you can see the red light is on.

I flipped it on and played some Tetris,

which I thought was especially impressive

because the batteries had expired
in 2007 and 2013.

(Laughter)

So this breadth advantage holds
in more subjective realms as well.

In a fascinating study of what leads
some comic book creators

to be more likely to make
blockbuster comics,

a pair of researchers found

that it was neither the number of years
of experience in the field

nor the resources of the publisher

nor the number of previous comics made.

It was the number of different genres
that a creator had worked across.

And interestingly,

a broad individual
could not be entirely replaced

by a team of specialists.

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

because early on,
they just look like they’re behind

and we don’t tend to incentivize anything
that doesn’t look like a head start

or specialization.

In fact, I think in the well-meaning
drive for a head start,

we often even counterproductively
short-circuit even the way

we learn new material,

at a fundamental level.

In a study last year,
seventh-grade math classrooms in the US

were randomly assigned
to different types of learning.

Some got what’s called “blocked practice.”

That’s like, you get problem type A,

AAAAA, BBBBB, and so on.

Progress is fast,

kids are happy,

everything’s great.

Other classrooms got assigned
to what’s called “interleaved practice.”

That’s like if you took all the problem
types and threw them in a hat

and drew them out at random.

Progress is slower,
kids are more frustrated.

But instead of learning
how to execute procedures,

they’re learning how to match
a strategy to a type of problem.

And when the test comes around,

the interleaved group blew
the block practice group away.

It wasn’t even close.

Now, I found a lot of this research
deeply counterintuitive,

the idea that a head start,

whether in picking a career
or a course of study

or just in learning new material,

can sometimes undermine
long-term development.

And naturally, I think there are
as many ways to succeed

as there are people.

But I think we tend only to incentivize
and encourage the Tiger path,

when increasingly, in a wicked world,

we need people who travel
the Roger path as well.

Or as the eminent physicist
and mathematician

and wonderful writer,
Freeman Dyson, put it –

and Dyson passed away yesterday,

so I hope I’m doing
his words honor here –

as he said: for a healthy ecosystem,
we need both birds and frogs.

Frogs are down in the mud,

seeing all the granular details.

The birds are soaring up above
not seeing those details

but integrating
the knowledge of the frogs.

And we need both.

The problem, Dyson said,

is that we’re telling everyone
to become frogs.

And I think,

in a wicked world,

that’s increasingly shortsighted.

Thank you very much.

(Applause)