0:00:01.690,0:00:04.609
So, I'd like to talk about[br]the development of human potential,

0:00:04.633,0:00:09.723
and I'd like to start with maybe the most[br]impactful modern story of development.

0:00:09.747,0:00:13.527
Many of you here have probably heard[br]of the 10,000 hours rule.

0:00:13.551,0:00:15.661
Maybe you even model[br]your own life after it.

0:00:15.685,0:00:18.434
Basically, it's the idea[br]that to become great in anything,

0:00:18.458,0:00:21.394
it takes 10,000 hours[br]of focused practice,

0:00:21.418,0:00:23.772
so you'd better get started[br]as early as possible.

0:00:23.796,0:00:27.711
The poster child for this story[br]is Tiger Woods.

0:00:27.735,0:00:30.997
His father famously gave him a putter[br]when he was seven months old.

0:00:31.408,0:00:34.512
At 10 months, he started imitating[br]his father's swing.

0:00:34.973,0:00:38.443
At two, you can go on YouTube[br]and see him on national television.

0:00:38.467,0:00:40.125
Fast-forward to the age of 21,

0:00:40.149,0:00:42.008
he's the greatest golfer in the world.

0:00:42.032,0:00:43.684
Quintessential 10,000 hours story.

0:00:43.708,0:00:46.259
Another that features[br]in a number of bestselling books

0:00:46.283,0:00:48.060
is that of the three Polgar sisters,

0:00:48.084,0:00:51.290
whose father decided to teach them chess[br]in a very technical manner

0:00:51.314,0:00:52.470
from a very early age.

0:00:52.494,0:00:53.953
And, really, he wanted to show

0:00:53.977,0:00:55.996
that with a head start[br]in focused practice,

0:00:56.020,0:00:58.438
any child could become[br]a genius in anything.

0:00:58.462,0:00:59.638
And in fact,

0:00:59.662,0:01:02.814
two of his daughters went on to become[br]Grandmaster chess players.

0:01:02.838,0:01:06.087
So when I became the science writer[br]at "Sports Illustrated" magazine,

0:01:06.111,0:01:07.267
I got curious.

0:01:07.291,0:01:09.237
If this 10,000 hours rule is correct,

0:01:09.261,0:01:11.859
then we should see[br]that elite athletes get a head start

0:01:11.883,0:01:13.632
in so-called "deliberate practice."

0:01:13.656,0:01:16.395
This is coached,[br]error-correction-focused practice,

0:01:16.419,0:01:17.909
not just playing around.

0:01:17.933,0:01:20.289
And in fact, when scientists[br]study elite athletes,

0:01:20.313,0:01:23.159
they see that they spend more time[br]in deliberate practice --

0:01:23.183,0:01:24.339
not a big surprise.

0:01:24.363,0:01:27.757
When they actually track athletes[br]over the course of their development,

0:01:27.781,0:01:29.119
the pattern looks like this:

0:01:29.143,0:01:31.778
the future elites actually spend[br]less time early on

0:01:31.802,0:01:34.563
in deliberate practice[br]in their eventual sport.

0:01:34.587,0:01:37.883
They tend to have what scientists[br]call a "sampling period,"

0:01:37.907,0:01:40.162
where they try a variety[br]of physical activities,

0:01:40.186,0:01:42.020
they gain broad, general skills,

0:01:42.044,0:01:44.197
they learn about[br]their interests and abilities

0:01:44.221,0:01:48.184
and delay specializing until later[br]than peers who plateau at lower levels.

0:01:48.847,0:01:50.977
And so when I saw that, I said,

0:01:51.001,0:01:54.438
"Gosh, that doesn't really comport[br]with the 10,000 hours rule, does it?"

0:01:54.462,0:01:56.472
So I started to wonder about other domains

0:01:56.496,0:01:59.627
that we associate with obligatory,[br]early specialization,

0:01:59.651,0:02:00.965
like music.

0:02:00.989,0:02:02.841
Turns out the pattern's often similar.

0:02:02.865,0:02:05.281
This is research[br]from a world-class music academy,

0:02:05.305,0:02:07.666
and what I want to draw[br]your attention to is this:

0:02:07.690,0:02:11.508
the exceptional musicians didn't start[br]spending more time in deliberate practice

0:02:11.532,0:02:12.771
than the average musicians

0:02:12.795,0:02:14.186
until their third instrument.

0:02:14.210,0:02:16.289
They, too, tended to have[br]a sampling period,

0:02:16.313,0:02:18.728
even musicians we think of[br]as famously precocious,

0:02:18.752,0:02:19.958
like Yo-Yo Ma.

0:02:19.982,0:02:21.227
He had a sampling period,

0:02:21.251,0:02:24.083
he just went through it more rapidly[br]than most musicians do.

0:02:24.107,0:02:27.295
Nonetheless, this research[br]is almost entirely ignored,

0:02:27.319,0:02:28.646
and much more impactful

0:02:28.670,0:02:31.718
is the first page of the book[br]"Battle Hymn of the Tiger Mother,"

0:02:31.742,0:02:34.840
where the author recounts[br]assigning her daughter violin.

0:02:34.864,0:02:37.320
Nobody seems to remember[br]the part later in the book

0:02:37.344,0:02:40.467
where her daughter turns to her[br]and says, "You picked it, not me,"

0:02:40.491,0:02:41.641
and largely quits.

0:02:41.665,0:02:44.819
So having seen this sort of surprising[br]pattern in sports and music,

0:02:44.843,0:02:47.831
I started to wonder about domains[br]that affect even more people,

0:02:47.855,0:02:49.011
like education.

0:02:49.035,0:02:50.919
An economist found a natural experiment

0:02:50.943,0:02:53.238
in the higher-ed systems[br]of England and Scotland.

0:02:53.262,0:02:55.916
In the period he studied,[br]the systems were very similar,

0:02:55.940,0:02:59.193
except in England, students had[br]to specialize in their mid-teen years

0:02:59.217,0:03:01.422
to pick a specific course[br]of study to apply to,

0:03:01.446,0:03:04.676
whereas in Scotland, they could[br]keep trying things in the university

0:03:04.700,0:03:05.851
if they wanted to.

0:03:05.875,0:03:07.026
And his question was:

0:03:07.050,0:03:09.833
Who wins the trade-off,[br]the early or the late specializers?

0:03:09.857,0:03:13.352
And what he saw was that the early[br]specializers jump out to an income lead

0:03:13.376,0:03:15.538
because they have more[br]domain-specific skills.

0:03:15.562,0:03:18.164
The late specializers get to try[br]more different things,

0:03:18.188,0:03:20.254
and when they do pick,[br]they have better fit,

0:03:20.278,0:03:22.227
or what economists call "match quality."

0:03:22.251,0:03:24.909
And so their growth rates are faster.

0:03:24.933,0:03:26.090
By six years out,

0:03:26.114,0:03:27.749
they erase that income gap.

0:03:27.773,0:03:30.973
Meanwhile, the early specializers[br]start quitting their career tracks

0:03:30.997,0:03:32.159
in much higher numbers,

0:03:32.183,0:03:34.692
essentially because they were[br]made to choose so early

0:03:34.716,0:03:36.605
that they more often made poor choices.

0:03:36.629,0:03:38.835
So the late specializers[br]lose in the short term

0:03:38.859,0:03:40.015
and win in the long run.

0:03:40.039,0:03:42.619
I think if we thought about[br]career choice like dating,

0:03:42.643,0:03:45.514
we might not pressure people[br]to settle down quite so quickly.

0:03:45.538,0:03:48.027
So this got me interested,[br]seeing this pattern again,

0:03:48.051,0:03:52.004
in exploring the developmental backgrounds[br]of people whose work I had long admired,

0:03:52.028,0:03:54.615
like Duke Ellington, who shunned[br]music lessons as a kid

0:03:54.639,0:03:56.833
to focus on baseball[br]and painting and drawing.

0:03:56.857,0:03:59.914
Or Maryam Mirzakhani, who wasn't[br]interested in math as a girl --

0:03:59.938,0:04:01.523
dreamed of becoming a novelist --

0:04:01.547,0:04:04.059
and went on to become[br]the first and so far only woman

0:04:04.083,0:04:05.239
to win the Fields Medal,

0:04:05.263,0:04:07.535
the most prestigious prize[br]in the world in math.

0:04:07.559,0:04:09.774
Or Vincent Van Gogh[br]had five different careers,

0:04:09.798,0:04:13.314
each of which he deemed his true calling[br]before flaming out spectacularly,

0:04:13.338,0:04:17.541
and in his late 20s, picked up a book[br]called "The Guide to the ABCs of Drawing."

0:04:18.068,0:04:19.392
That worked out OK.

0:04:19.874,0:04:23.282
Claude Shannon was an electrical engineer[br]at the University of Michigan

0:04:23.306,0:04:26.286
who took a philosophy course[br]just to fulfill a requirement,

0:04:26.310,0:04:29.504
and in it, he learned about[br]a near-century-old system of logic

0:04:29.528,0:04:32.735
by which true and false statements[br]could be coded as ones and zeros

0:04:32.759,0:04:34.689
and solved like math problems.

0:04:34.713,0:04:37.040
This led to the development[br]of binary code,

0:04:37.064,0:04:40.137
which underlies all[br]of our digital computers today.

0:04:40.161,0:04:42.869
Finally, my own sort of role model,[br]Frances Hesselbein --

0:04:42.893,0:04:44.141
this is me with her --

0:04:44.165,0:04:47.316
she took her first professional[br]job at the age of 54

0:04:47.340,0:04:49.646
and went on to become[br]the CEO of the Girl Scouts,

0:04:49.670,0:04:50.846
which she saved.

0:04:50.870,0:04:52.612
She tripled minority membership,

0:04:52.636,0:04:55.398
added 130,000 volunteers,

0:04:55.422,0:04:58.835
and this is one of the proficiency badges[br]that came out of her tenure --

0:04:58.859,0:05:01.534
it's binary code for girls[br]learning about computers.

0:05:01.558,0:05:03.627
Today, Frances runs a leadership institute

0:05:03.651,0:05:05.858
where she works[br]every weekday, in Manhattan.

0:05:05.882,0:05:07.396
And she's only 104,

0:05:07.420,0:05:08.939
so who knows what's next.

0:05:08.963,0:05:10.113
(Laughter)

0:05:10.740,0:05:13.592
We never really hear developmental[br]stories like this, do we?

0:05:13.616,0:05:15.167
We don't hear about the research

0:05:15.191,0:05:18.314
that found that Nobel laureate scientists[br]are 22 times more likely

0:05:18.338,0:05:19.820
to have a hobby outside of work

0:05:19.844,0:05:21.087
as are typical scientists.

0:05:21.111,0:05:22.269
We never hear that.

0:05:22.293,0:05:24.738
Even when the performers[br]or the work is very famous,

0:05:24.762,0:05:26.724
we don't hear these[br]developmental stories.

0:05:26.748,0:05:28.879
For example, here's[br]an athlete I've followed.

0:05:28.903,0:05:31.363
Here he is at age six,[br]wearing a Scottish rugby kit.

0:05:31.387,0:05:33.591
He tried some tennis,[br]some skiing, wrestling.

0:05:33.615,0:05:36.831
His mother was actually a tennis coach[br]but she declined to coach him

0:05:36.855,0:05:39.051
because he wouldn't return balls normally.

0:05:39.075,0:05:41.304
He did some basketball,[br]table tennis, swimming.

0:05:41.328,0:05:43.497
When his coaches wanted[br]to move him up a level

0:05:43.521,0:05:44.672
to play with older boys,

0:05:44.696,0:05:47.671
he declined, because he just wanted[br]to talk about pro wrestling

0:05:47.695,0:05:49.231
after practice with his friends.

0:05:49.255,0:05:50.751
And he kept trying more sports:

0:05:50.775,0:05:53.765
handball, volleyball, soccer,[br]badminton, skateboarding ...

0:05:53.789,0:05:56.011
So, who is this dabbler?

0:05:56.674,0:05:58.530
This is Roger Federer.

0:05:58.554,0:06:01.754
Every bit as famous[br]as an adult as Tiger Woods,

0:06:01.778,0:06:05.061
and yet even tennis enthusiasts[br]don't usually know anything

0:06:05.085,0:06:06.597
about his developmental story.

0:06:06.621,0:06:09.221
Why is that, even though it's the norm?

0:06:09.245,0:06:12.424
I think it's partly because[br]the Tiger story is very dramatic,

0:06:12.448,0:06:14.835
but also because it seems like[br]this tidy narrative

0:06:14.859,0:06:17.855
that we can extrapolate to anything[br]that we want to be good at

0:06:17.879,0:06:19.241
in our own lives.

0:06:19.265,0:06:20.861
But that, I think, is a problem,

0:06:20.885,0:06:24.416
because it turns out that in many ways,[br]golf is a uniquely horrible model

0:06:24.440,0:06:26.696
of almost everything[br]that humans want to learn.

0:06:26.720,0:06:28.050
(Laughter)

0:06:28.074,0:06:29.237
Golf is the epitome of

0:06:29.261,0:06:32.732
what the psychologist Robin Hogarth[br]called a "kind learning environment."

0:06:32.756,0:06:35.965
Kind learning environments[br]have next steps and goals that are clear,

0:06:35.989,0:06:37.839
rules that are clear and never change,

0:06:37.863,0:06:41.115
when you do something, you get feedback[br]that is quick and accurate,

0:06:41.139,0:06:43.339
work next year will look like[br]work last year.

0:06:43.363,0:06:45.799
Chess: also a kind learning environment.

0:06:45.823,0:06:47.205
The grand master's advantage

0:06:47.229,0:06:49.692
is largely based on[br]knowledge of recurring patterns,

0:06:49.716,0:06:51.765
which is also why[br]it's so easy to automate.

0:06:51.789,0:06:55.032
On the other end of the spectrum[br]are "wicked learning environments,"

0:06:55.056,0:06:57.276
where next steps and goals[br]may not be clear.

0:06:57.300,0:06:58.881
Rules may change.

0:06:58.905,0:07:01.449
You may or may not get feedback[br]when you do something.

0:07:01.473,0:07:03.414
It may be delayed, it may be inaccurate,

0:07:03.438,0:07:06.116
and work next year[br]may not look like work last year.

0:07:06.140,0:07:10.352
So which one of these sounds like[br]the world we're increasingly living in?

0:07:10.376,0:07:12.844
In fact, our need to think[br]in an adaptable manner

0:07:12.868,0:07:14.979
and to keep track of interconnecting parts

0:07:15.003,0:07:17.340
has fundamentally changed our perception,

0:07:17.364,0:07:19.197
so that when you look at this diagram,

0:07:19.221,0:07:22.551
the central circle on the right[br]probably looks larger to you

0:07:22.575,0:07:24.011
because your brain is drawn to

0:07:24.035,0:07:26.170
the relationship[br]of the parts in the whole,

0:07:26.194,0:07:28.856
whereas someone who hasn't been[br]exposed to modern work

0:07:28.880,0:07:31.505
with its requirement for adaptable,[br]conceptual thought,

0:07:31.529,0:07:34.605
will see correctly that[br]the central circles are the same size.

0:07:35.073,0:07:38.145
So here we are in the wicked work world,

0:07:38.169,0:07:41.680
and there, sometimes[br]hyperspecialization can backfire badly.

0:07:41.704,0:07:44.037
For example, in research[br]in a dozen countries

0:07:44.061,0:07:46.879
that matched people[br]for their parents' years of education,

0:07:46.903,0:07:48.067
their test scores,

0:07:48.091,0:07:49.498
their own years of education,

0:07:49.522,0:07:52.226
the difference was[br]some got career-focused education

0:07:52.250,0:07:54.411
and some got broader, general education.

0:07:54.435,0:07:57.206
The pattern was those who got[br]the career-focused education

0:07:57.230,0:07:59.614
are more likely to be hired[br]right out of training,

0:07:59.638,0:08:01.670
more likely to make more money right away,

0:08:01.694,0:08:04.099
but so much less adaptable[br]in a changing work world

0:08:04.123,0:08:06.860
that they spend so much less time[br]in the workforce overall

0:08:06.884,0:08:09.786
that they win in the short term[br]and lose in the long run.

0:08:09.810,0:08:13.189
Or consider a famous,[br]20-year study of experts

0:08:13.213,0:08:16.013
making geopolitical[br]and economic predictions.

0:08:16.037,0:08:20.115
The worst forecasters[br]were the most specialized experts,

0:08:20.139,0:08:23.327
those who'd spent their entire careers[br]studying one or two problems

0:08:23.351,0:08:26.461
and came to see the whole world[br]through one lens or mental model.

0:08:26.485,0:08:27.994
Some of them actually got worse

0:08:28.018,0:08:30.446
as they accumulated[br]experience and credentials.

0:08:30.470,0:08:35.289
The best forecasters were simply[br]bright people with wide-ranging interests.

0:08:35.789,0:08:37.764
Now in some domains, like medicine,

0:08:37.788,0:08:40.975
increasing specialization has been[br]both inevitable and beneficial,

0:08:40.999,0:08:42.159
no question about it.

0:08:42.183,0:08:44.112
And yet, it's been a double-edged sword.

0:08:44.136,0:08:47.776
A few years ago, one of the most popular[br]surgeries in the world for knee pain

0:08:47.800,0:08:49.787
was tested in a placebo-controlled trial.

0:08:49.811,0:08:51.727
Some of the patients got "sham surgery."

0:08:51.751,0:08:53.714
That means the surgeons make an incision,

0:08:53.738,0:08:55.919
they bang around like[br]they're doing something,

0:08:55.943,0:08:57.612
then they sew the patient back up.

0:08:57.636,0:08:59.121
That performed just as a well.

0:08:59.145,0:09:02.298
And yet surgeons who specialize[br]in the procedure continue to do it

0:09:02.322,0:09:03.472
by the millions.

0:09:04.043,0:09:08.260
So if hyperspecialization isn't always[br]the trick in a wicked world, what is?

0:09:08.284,0:09:10.045
That can be difficult to talk about,

0:09:10.069,0:09:12.283
because it doesn't always[br]look like this path.

0:09:12.307,0:09:14.623
Sometimes it looks like[br]meandering or zigzagging

0:09:14.647,0:09:15.940
or keeping a broader view.

0:09:15.964,0:09:17.535
It can look like getting behind.

0:09:17.559,0:09:20.389
But I want to talk about what[br]some of those tricks might be.

0:09:20.413,0:09:24.160
If we look at research on technological[br]innovation, it shows that increasingly,

0:09:24.184,0:09:26.949
the most impactful patents[br]are not authored by individuals

0:09:26.973,0:09:29.845
who drill deeper, deeper, deeper[br]into one area of technology

0:09:29.869,0:09:31.706
as classified by the US Patent Office,

0:09:31.730,0:09:34.702
but rather by teams[br]that include individuals

0:09:34.726,0:09:37.992
who have worked across a large number[br]of different technology classes

0:09:38.016,0:09:40.208
and often merge things[br]from different domains.

0:09:40.232,0:09:43.663
Someone whose work I've admired[br]who was sort of on the forefront of this

0:09:43.687,0:09:45.580
is a Japanese man named Gunpei Yokoi.

0:09:45.604,0:09:48.381
Yokoi didn't score well[br]on his electronics exams at school,

0:09:48.405,0:09:51.701
so he had to settle for a low-tier job[br]as a machine maintenance worker

0:09:51.725,0:09:53.546
at a playing card company in Kyoto.

0:09:53.570,0:09:56.616
He realized he wasn't equipped[br]to work on the cutting edge,

0:09:56.640,0:09:59.564
but that there was so much[br]information easily available

0:09:59.588,0:10:02.562
that maybe he could combine things[br]that were already well-known

0:10:02.586,0:10:05.155
in ways that specialists[br]were too narrow to see.

0:10:05.179,0:10:08.679
So he combined some well-known technology[br]from the calculator industry

0:10:08.703,0:10:11.619
with some well-known technology[br]from the credit card industry

0:10:11.643,0:10:13.084
and made handheld games.

0:10:13.108,0:10:14.462
And they were a hit.

0:10:14.486,0:10:16.708
And it turned this playing card company,

0:10:16.732,0:10:20.303
which was founded in a wooden[br]storefront in the 19th century,

0:10:20.327,0:10:22.084
into a toy and game operation.

0:10:22.108,0:10:24.334
You may have heard of it;[br]it's called Nintendo.

0:10:24.358,0:10:25.655
Yokoi's creative philosophy

0:10:25.679,0:10:28.860
translated to "lateral thinking[br]with withered technology,"

0:10:28.884,0:10:31.812
taking well-known technology[br]and using it in new ways.

0:10:31.836,0:10:33.775
And his magnum opus was this:

0:10:33.799,0:10:34.979
the Game Boy.

0:10:35.003,0:10:37.440
Technological joke in every way.

0:10:37.464,0:10:41.279
And it came out at the same time[br]as color competitors from Saga and Atari,

0:10:41.303,0:10:42.965
and it blew them away,

0:10:42.989,0:10:45.629
because Yokoi knew[br]what his customers cared about

0:10:45.653,0:10:46.803
wasn't color.

0:10:46.827,0:10:50.788
It was durability, portability,[br]affordability, battery life,

0:10:50.812,0:10:52.112
game selection.

0:10:52.136,0:10:54.589
This is mine that I found[br]in my parents' basement.

0:10:54.613,0:10:55.773
(Laughter)

0:10:55.797,0:10:57.351
It's seen better days.

0:10:57.375,0:10:59.120
But you can see the red light is on.

0:10:59.144,0:11:01.035
I flipped it on and played some Tetris,

0:11:01.059,0:11:03.045
which I thought was especially impressive

0:11:03.069,0:11:05.533
because the batteries had expired[br]in 2007 and 2013.

0:11:05.557,0:11:06.901
(Laughter)

0:11:07.489,0:11:11.078
So this breadth advantage holds[br]in more subjective realms as well.

0:11:11.102,0:11:14.574
In a fascinating study of what leads[br]some comic book creators

0:11:14.598,0:11:17.438
to be more likely to make[br]blockbuster comics,

0:11:17.462,0:11:18.771
a pair of researchers found

0:11:18.795,0:11:22.327
that it was neither the number of years[br]of experience in the field

0:11:22.351,0:11:25.217
nor the resources of the publisher

0:11:25.241,0:11:27.457
nor the number of previous comics made.

0:11:27.481,0:11:31.949
It was the number of different genres[br]that a creator had worked across.

0:11:31.973,0:11:33.295
And interestingly,

0:11:33.319,0:11:36.968
a broad individual[br]could not be entirely replaced

0:11:36.992,0:11:38.786
by a team of specialists.

0:11:39.154,0:11:42.124
We probably don't make as many[br]of those people as we could

0:11:42.148,0:11:44.957
because early on,[br]they just look like they're behind

0:11:44.981,0:11:48.677
and we don't tend to incentivize anything[br]that doesn't look like a head start

0:11:48.701,0:11:49.878
or specialization.

0:11:49.902,0:11:52.749
In fact, I think in the well-meaning[br]drive for a head start,

0:11:52.773,0:11:55.625
we often even counterproductively[br]short-circuit even the way

0:11:55.649,0:11:56.946
we learn new material,

0:11:56.970,0:11:58.555
at a fundamental level.

0:11:58.579,0:12:02.425
In a study last year,[br]seventh-grade math classrooms in the US

0:12:02.449,0:12:05.325
were randomly assigned[br]to different types of learning.

0:12:05.349,0:12:07.925
Some got what's called "blocked practice."

0:12:07.949,0:12:09.691
That's like, you get problem type A,

0:12:09.715,0:12:12.624
AAAAA, BBBBB, and so on.

0:12:12.648,0:12:14.073
Progress is fast,

0:12:14.097,0:12:15.248
kids are happy,

0:12:15.272,0:12:16.436
everything's great.

0:12:16.460,0:12:20.498
Other classrooms got assigned[br]to what's called "interleaved practice."

0:12:20.522,0:12:23.778
That's like if you took all the problem[br]types and threw them in a hat

0:12:23.802,0:12:25.146
and drew them out at random.

0:12:25.170,0:12:28.097
Progress is slower,[br]kids are more frustrated.

0:12:28.121,0:12:30.784
But instead of learning[br]how to execute procedures,

0:12:30.808,0:12:34.621
they're learning how to match[br]a strategy to a type of problem.

0:12:34.645,0:12:36.240
And when the test comes around,

0:12:36.264,0:12:39.698
the interleaved group blew[br]the block practice group away.

0:12:39.722,0:12:41.129
It wasn't even close.

0:12:41.825,0:12:45.445
Now, I found a lot of this research[br]deeply counterintuitive,

0:12:45.469,0:12:46.818
the idea that a head start,

0:12:46.842,0:12:49.121
whether in picking a career[br]or a course of study

0:12:49.145,0:12:50.764
or just in learning new material,

0:12:50.788,0:12:53.481
can sometimes undermine[br]long-term development.

0:12:53.505,0:12:56.288
And naturally, I think there are[br]as many ways to succeed

0:12:56.312,0:12:57.680
as there are people.

0:12:57.704,0:13:01.925
But I think we tend only to incentivize[br]and encourage the Tiger path,

0:13:01.949,0:13:03.736
when increasingly, in a wicked world,

0:13:03.760,0:13:06.689
we need people who travel[br]the Roger path as well.

0:13:06.713,0:13:09.271
Or as the eminent physicist[br]and mathematician

0:13:09.295,0:13:12.719
and wonderful writer,[br]Freeman Dyson, put it --

0:13:12.743,0:13:15.638
and Dyson passed away yesterday,

0:13:15.662,0:13:17.937
so I hope I'm doing[br]his words honor here --

0:13:17.961,0:13:22.854
as he said: for a healthy ecosystem,[br]we need both birds and frogs.

0:13:22.878,0:13:24.181
Frogs are down in the mud,

0:13:24.205,0:13:26.303
seeing all the granular details.

0:13:26.327,0:13:29.084
The birds are soaring up above[br]not seeing those details

0:13:29.108,0:13:31.156
but integrating[br]the knowledge of the frogs.

0:13:31.180,0:13:32.491
And we need both.

0:13:32.515,0:13:34.210
The problem, Dyson said,

0:13:34.234,0:13:36.909
is that we're telling everyone[br]to become frogs.

0:13:36.933,0:13:38.135
And I think,

0:13:38.159,0:13:39.611
in a wicked world,

0:13:39.635,0:13:41.786
that's increasingly shortsighted.

0:13:41.810,0:13:43.102
Thank you very much.

0:13:43.126,0:13:46.086
(Applause)