English subtitles

← Why specializing early doesn't always mean career success

Get Embed Code
37 Languages

Showing Revision 9 created 09/01/2020 by Erin Gregory.

  1. So, I'd like to talk about
    the development of human potential,
  2. and I'd like to start with maybe the most
    impactful modern story of development.
  3. Many of you here have probably heard
    of the 10,000 hours rule.
  4. Maybe you even model
    your own life after it.
  5. Basically, it's the idea
    that to become great in anything,
  6. it takes 10,000 hours
    of focused practice,
  7. so you'd better get started
    as early as possible.
  8. The poster child for this story
    is Tiger Woods.

  9. His father famously gave him a putter
    when he was seven months old.
  10. At 10 months, he started imitating
    his father's swing.
  11. At two, you can go on YouTube
    and see him on national television.
  12. Fast-forward to the age of 21,
  13. he's the greatest golfer in the world.
  14. Quintessential 10,000 hours story.
  15. Another that features
    in a number of bestselling books

  16. is that of the three Polgar sisters,
  17. whose father decided to teach them chess
    in a very technical manner
  18. from a very early age.
  19. And, really, he wanted to show
  20. that with a head start
    in focused practice,
  21. any child could become
    a genius in anything.
  22. And in fact,
  23. two of his daughters went on to become
    Grandmaster chess players.
  24. So when I became the science writer
    at "Sports Illustrated" magazine,

  25. I got curious.
  26. If this 10,000 hours rule is correct,
  27. then we should see
    that elite athletes get a head start
  28. in so-called "deliberate practice."
  29. This is coached,
    error-correction-focused practice,
  30. not just playing around.
  31. And in fact, when scientists
    study elite athletes,
  32. they see that they spend more time
    in deliberate practice --
  33. not a big surprise.
  34. When they actually track athletes
    over the course of their development,
  35. the pattern looks like this:
  36. the future elites actually spend
    less time early on
  37. in deliberate practice
    in their eventual sport.
  38. They tend to have what scientists
    call a "sampling period,"
  39. where they try a variety
    of physical activities,
  40. they gain broad, general skills,
  41. they learn about
    their interests and abilities
  42. and delay specializing until later
    than peers who plateau at lower levels.
  43. And so when I saw that, I said,

  44. "Gosh, that doesn't really comport
    with the 10,000 hours rule, does it?"
  45. So I started to wonder about other domains
  46. that we associate with obligatory,
    early specialization,
  47. like music.
  48. Turns out the pattern's often similar.
  49. This is research
    from a world-class music academy,

  50. and what I want to draw
    your attention to is this:
  51. the exceptional musicians didn't start
    spending more time in deliberate practice
  52. than the average musicians
  53. until their third instrument.
  54. They, too, tended to have
    a sampling period,
  55. even musicians we think of
    as famously precocious,
  56. like Yo-Yo Ma.
  57. He had a sampling period,
  58. he just went through it more rapidly
    than most musicians do.
  59. Nonetheless, this research
    is almost entirely ignored,

  60. and much more impactful
  61. is the first page of the book
    "Battle Hymn of the Tiger Mother,"
  62. where the author recounts
    assigning her daughter violin.
  63. Nobody seems to remember
    the part later in the book
  64. where her daughter turns to her
    and says, "You picked it, not me,"
  65. and largely quits.
  66. So having seen this sort of surprising
    pattern in sports and music,

  67. I started to wonder about domains
    that affect even more people,
  68. like education.
  69. An economist found a natural experiment
  70. in the higher-ed systems
    of England and Scotland.
  71. In the period he studied,
    the systems were very similar,
  72. except in England, students had
    to specialize in their mid-teen years
  73. to pick a specific course
    of study to apply to,
  74. whereas in Scotland, they could
    keep trying things in the university
  75. if they wanted to.
  76. And his question was:
  77. Who wins the trade-off,
    the early or the late specializers?
  78. And what he saw was that the early
    specializers jump out to an income lead
  79. because they have more
    domain-specific skills.
  80. The late specializers get to try
    more different things,
  81. and when they do pick,
    they have better fit,
  82. or what economists call "match quality."
  83. And so their growth rates are faster.
  84. By six years out,
  85. they erase that income gap.
  86. Meanwhile, the early specializers
    start quitting their career tracks
  87. in much higher numbers,
  88. essentially because they were
    made to choose so early
  89. that they more often made poor choices.
  90. So the late specializers
    lose in the short term
  91. and win in the long run.
  92. I think if we thought about
    career choice like dating,
  93. we might not pressure people
    to settle down quite so quickly.
  94. So this got me interested,
    seeing this pattern again,

  95. in exploring the developmental backgrounds
    of people whose work I had long admired,
  96. like Duke Ellington, who shunned
    music lessons as a kid
  97. to focus on baseball
    and painting and drawing.
  98. Or Maryam Mirzakhani, who wasn't
    interested in math as a girl --
  99. dreamed of becoming a novelist --
  100. and went on to become
    the first and so far only woman
  101. to win the Fields Medal,
  102. the most prestigious prize
    in the world in math.
  103. Or Vincent Van Gogh
    had five different careers,
  104. each of which he deemed his true calling
    before flaming out spectacularly,
  105. and in his late 20s, picked up a book
    called "The Guide to the ABCs of Drawing."
  106. That worked out OK.
  107. Claude Shannon was an electrical engineer
    at the University of Michigan
  108. who took a philosophy course
    just to fulfill a requirement,
  109. and in it, he learned about
    a near-century-old system of logic
  110. by which true and false statements
    could be coded as ones and zeros
  111. and solved like math problems.
  112. This led to the development
    of binary code,
  113. which underlies all
    of our digital computers today.
  114. Finally, my own sort of role model,
    Frances Hesselbein --

  115. this is me with her --
  116. she took her first professional
    job at the age of 54
  117. and went on to become
    the CEO of the Girl Scouts,
  118. which she saved.
  119. She tripled minority membership,
  120. added 130,000 volunteers,
  121. and this is one of the proficiency badges
    that came out of her tenure --
  122. it's binary code for girls
    learning about computers.
  123. Today, Frances runs a leadership institute
  124. where she works
    every weekday, in Manhattan.
  125. And she's only 104,
  126. so who knows what's next.
  127. (Laughter)

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

  129. We don't hear about the research
  130. that found that Nobel laureate scientists
    are 22 times more likely
  131. to have a hobby outside of work
  132. as are typical scientists.
  133. We never hear that.
  134. Even when the performers
    or the work is very famous,
  135. we don't hear these
    developmental stories.
  136. For example, here's
    an athlete I've followed.

  137. Here he is at age six,
    wearing a Scottish rugby kit.
  138. He tried some tennis,
    some skiing, wrestling.
  139. His mother was actually a tennis coach
    but she declined to coach him
  140. because he wouldn't return balls normally.
  141. He did some basketball,
    table tennis, swimming.
  142. When his coaches wanted
    to move him up a level
  143. to play with older boys,
  144. he declined, because he just wanted
    to talk about pro wrestling
  145. after practice with his friends.
  146. And he kept trying more sports:
  147. handball, volleyball, soccer,
    badminton, skateboarding ...
  148. So, who is this dabbler?
  149. This is Roger Federer.
  150. Every bit as famous
    as an adult as Tiger Woods,
  151. and yet even tennis enthusiasts
    don't usually know anything
  152. about his developmental story.
  153. Why is that, even though it's the norm?
  154. I think it's partly because
    the Tiger story is very dramatic,

  155. but also because it seems like
    this tidy narrative
  156. that we can extrapolate to anything
    that we want to be good at
  157. in our own lives.
  158. But that, I think, is a problem,
  159. because it turns out that in many ways,
    golf is a uniquely horrible model
  160. of almost everything
    that humans want to learn.
  161. (Laughter)

  162. Golf is the epitome of

  163. what the psychologist Robin Hogarth
    called a "kind learning environment."
  164. Kind learning environments
    have next steps and goals that are clear,
  165. rules that are clear and never change,
  166. when you do something, you get feedback
    that is quick and accurate,
  167. work next year will look like
    work last year.
  168. Chess: also a kind learning environment.
  169. The grand master's advantage
  170. is largely based on
    knowledge of recurring patterns,
  171. which is also why
    it's so easy to automate.
  172. On the other end of the spectrum
    are "wicked learning environments,"
  173. where next steps and goals
    may not be clear.
  174. Rules may change.
  175. You may or may not get feedback
    when you do something.
  176. It may be delayed, it may be inaccurate,
  177. and work next year
    may not look like work last year.
  178. So which one of these sounds like
    the world we're increasingly living in?

  179. In fact, our need to think
    in an adaptable manner
  180. and to keep track of interconnecting parts
  181. has fundamentally changed our perception,
  182. so that when you look at this diagram,
  183. the central circle on the right
    probably looks larger to you
  184. because your brain is drawn to
  185. the relationship
    of the parts in the whole,
  186. whereas someone who hasn't been
    exposed to modern work
  187. with its requirement for adaptable,
    conceptual thought,
  188. will see correctly that
    the central circles are the same size.
  189. So here we are in the wicked work world,

  190. and there, sometimes
    hyperspecialization can backfire badly.
  191. For example, in research
    in a dozen countries
  192. that matched people
    for their parents' years of education,
  193. their test scores,
  194. their own years of education,
  195. the difference was
    some got career-focused education
  196. and some got broader, general education.
  197. The pattern was those who got
    the career-focused education
  198. are more likely to be hired
    right out of training,
  199. more likely to make more money right away,
  200. but so much less adaptable
    in a changing work world
  201. that they spend so much less time
    in the workforce overall
  202. that they win in the short term
    and lose in the long run.
  203. Or consider a famous,
    20-year study of experts

  204. making geopolitical
    and economic predictions.
  205. The worst forecasters
    were the most specialized experts,
  206. those who'd spent their entire careers
    studying one or two problems
  207. and came to see the whole world
    through one lens or mental model.
  208. Some of them actually got worse
  209. as they accumulated
    experience and credentials.
  210. The best forecasters were simply
    bright people with wide-ranging interests.
  211. Now in some domains, like medicine,

  212. increasing specialization has been
    both inevitable and beneficial,
  213. no question about it.
  214. And yet, it's been a double-edged sword.
  215. A few years ago, one of the most popular
    surgeries in the world for knee pain
  216. was tested in a placebo-controlled trial.
  217. Some of the patients got "sham surgery."
  218. That means the surgeons make an incision,
  219. they bang around like
    they're doing something,
  220. then they sew the patient back up.
  221. That performed just as a well.
  222. And yet surgeons who specialize
    in the procedure continue to do it
  223. by the millions.
  224. So if hyperspecialization isn't always
    the trick in a wicked world, what is?

  225. That can be difficult to talk about,
  226. because it doesn't always
    look like this path.
  227. Sometimes it looks like
    meandering or zigzagging
  228. or keeping a broader view.
  229. It can look like getting behind.
  230. But I want to talk about what
    some of those tricks might be.
  231. If we look at research on technological
    innovation, it shows that increasingly,
  232. the most impactful patents
    are not authored by individuals
  233. who drill deeper, deeper, deeper
    into one area of technology
  234. as classified by the US Patent Office,
  235. but rather by teams
    that include individuals
  236. who have worked across a large number
    of different technology classes
  237. and often merge things
    from different domains.
  238. Someone whose work I've admired
    who was sort of on the forefront of this

  239. is a Japanese man named Gunpei Yokoi.
  240. Yokoi didn't score well
    on his electronics exams at school,
  241. so he had to settle for a low-tier job
    as a machine maintenance worker
  242. at a playing card company in Kyoto.
  243. He realized he wasn't equipped
    to work on the cutting edge,
  244. but that there was so much
    information easily available
  245. that maybe he could combine things
    that were already well-known
  246. in ways that specialists
    were too narrow to see.
  247. So he combined some well-known technology
    from the calculator industry
  248. with some well-known technology
    from the credit card industry
  249. and made handheld games.
  250. And they were a hit.
  251. And it turned this playing card company,
  252. which was founded in a wooden
    storefront in the 19th century,
  253. into a toy and game operation.
  254. You may have heard of it;
    it's called Nintendo.
  255. Yokoi's creative philosophy

  256. translated to "lateral thinking
    with withered technology,"
  257. taking well-known technology
    and using it in new ways.
  258. And his magnum opus was this:
  259. the Game Boy.
  260. Technological joke in every way.
  261. And it came out at the same time
    as color competitors from Saga and Atari,
  262. and it blew them away,
  263. because Yokoi knew
    what his customers cared about
  264. wasn't color.
  265. It was durability, portability,
    affordability, battery life,
  266. game selection.
  267. This is mine that I found
    in my parents' basement.
  268. (Laughter)

  269. It's seen better days.

  270. But you can see the red light is on.
  271. I flipped it on and played some Tetris,
  272. which I thought was especially impressive
  273. because the batteries had expired
    in 2007 and 2013.
  274. (Laughter)

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

  276. In a fascinating study of what leads
    some comic book creators
  277. to be more likely to make
    blockbuster comics,
  278. a pair of researchers found
  279. that it was neither the number of years
    of experience in the field
  280. nor the resources of the publisher
  281. nor the number of previous comics made.
  282. It was the number of different genres
    that a creator had worked across.
  283. And interestingly,
  284. a broad individual
    could not be entirely replaced
  285. by a team of specialists.
  286. We probably don't make as many
    of those people as we could
  287. because early on,
    they just look like they're behind
  288. and we don't tend to incentivize anything
    that doesn't look like a head start
  289. or specialization.
  290. In fact, I think in the well-meaning
    drive for a head start,
  291. we often even counterproductively
    short-circuit even the way
  292. we learn new material,
  293. at a fundamental level.
  294. In a study last year,
    seventh-grade math classrooms in the US

  295. were randomly assigned
    to different types of learning.
  296. Some got what's called "blocked practice."
  297. That's like, you get problem type A,
  298. AAAAA, BBBBB, and so on.
  299. Progress is fast,
  300. kids are happy,
  301. everything's great.
  302. Other classrooms got assigned
    to what's called "interleaved practice."
  303. That's like if you took all the problem
    types and threw them in a hat
  304. and drew them out at random.
  305. Progress is slower,
    kids are more frustrated.
  306. But instead of learning
    how to execute procedures,
  307. they're learning how to match
    a strategy to a type of problem.
  308. And when the test comes around,
  309. the interleaved group blew
    the block practice group away.
  310. It wasn't even close.
  311. Now, I found a lot of this research
    deeply counterintuitive,

  312. the idea that a head start,
  313. whether in picking a career
    or a course of study
  314. or just in learning new material,
  315. can sometimes undermine
    long-term development.
  316. And naturally, I think there are
    as many ways to succeed
  317. as there are people.
  318. But I think we tend only to incentivize
    and encourage the Tiger path,
  319. when increasingly, in a wicked world,
  320. we need people who travel
    the Roger path as well.
  321. Or as the eminent physicist
    and mathematician
  322. and wonderful writer,
    Freeman Dyson, put it --
  323. and Dyson passed away yesterday,
  324. so I hope I'm doing
    his words honor here --
  325. as he said: for a healthy ecosystem,
    we need both birds and frogs.
  326. Frogs are down in the mud,
  327. seeing all the granular details.
  328. The birds are soaring up above
    not seeing those details
  329. but integrating
    the knowledge of the frogs.
  330. And we need both.
  331. The problem, Dyson said,
  332. is that we're telling everyone
    to become frogs.
  333. And I think,
  334. in a wicked world,
  335. that's increasingly shortsighted.
  336. Thank you very much.

  337. (Applause)