The real relationship between your age and your chance of success
-
0:00 - 0:03Today, actually, is
a very special day for me, -
0:03 - 0:05because it is my birthday.
-
0:05 - 0:09(Applause)
-
0:09 - 0:12And so, thanks to all of you
for joining the party. -
0:12 - 0:13(Laughter)
-
0:13 - 0:18But every time you throw a party,
there's someone there to spoil it. Right? -
0:18 - 0:19(Laughter)
-
0:19 - 0:21And I'm a physicist,
-
0:21 - 0:25and this time I brought
another physicist along to do so. -
0:25 - 0:29His name is Albert Einstein --
also Albert -- and he's the one who said -
0:29 - 0:34that the person who has not made
his great contributions to science -
0:34 - 0:36by the age of 30
-
0:36 - 0:37will never do so.
-
0:37 - 0:38(Laughter)
-
0:38 - 0:41Now, you don't need to check Wikipedia
-
0:41 - 0:42that I'm beyond 30.
-
0:42 - 0:44(Laughter)
-
0:44 - 0:47So, effectively, what
he is telling me, and us, -
0:47 - 0:50is that when it comes to my science,
-
0:50 - 0:51I'm deadwood.
-
0:52 - 0:58Well, luckily, I had my share
of luck within my career. -
0:58 - 1:02Around age 28, I became
very interested in networks, -
1:02 - 1:06and a few years later, we managed
to publish a few key papers -
1:06 - 1:10that reported the discovery
of scale-free networks -
1:10 - 1:15and really gave birth to a new discipline
that we call network science today. -
1:15 - 1:18And if you really care about it,
you can get a PhD now in network science -
1:19 - 1:21in Budapest, in Boston,
-
1:21 - 1:23and you can study it all over the world.
-
1:23 - 1:25A few years later,
-
1:25 - 1:28when I moved to Harvard
first as a sabbatical, -
1:28 - 1:31I became interested
in another type of network: -
1:31 - 1:34that time, the networks within ourselves,
-
1:35 - 1:38how the genes and the proteins
and the metabolites link to each other -
1:38 - 1:41and how they connect to disease.
-
1:41 - 1:46And that interest led
to a major explosion within medicine, -
1:46 - 1:50including the Network Medicine
Division at Harvard, -
1:50 - 1:53that has more than 300 researchers
who are using this perspective -
1:53 - 1:56to treat patients and develop new cures.
-
1:57 - 1:59And a few years ago,
-
1:59 - 2:02I thought that I would take
this idea of networks -
2:02 - 2:04and the expertise we had in networks
-
2:04 - 2:05in a different area,
-
2:05 - 2:07that is, to understand success.
-
2:08 - 2:09And why did we do that?
-
2:09 - 2:11Well, we thought that, to some degree,
-
2:11 - 2:15our success is determined
by the networks we're part of -- -
2:15 - 2:18that our networks can push us forward,
they can pull us back. -
2:19 - 2:23And I was curious if we could use
the knowledge and big data and expertise -
2:23 - 2:24where we develop the networks
-
2:25 - 2:28to really quantify
how these things happen. -
2:28 - 2:30This is a result from that.
-
2:30 - 2:33What you see here is a network
of galleries in museums -
2:33 - 2:34that connect to each other.
-
2:35 - 2:39And through this map
that we mapped out last year, -
2:39 - 2:44we are able to predict very accurately
the success of an artist -
2:44 - 2:48if you give me the first five exhibits
that he or she had in their career. -
2:49 - 2:52Well, as we thought about success,
-
2:52 - 2:55we realized that success
is not only about networks; -
2:55 - 2:58there are so many
other dimensions to that. -
2:58 - 3:01And one of the things
we need for success, obviously, -
3:01 - 3:03is performance.
-
3:03 - 3:06So let's define what's the difference
between performance and success. -
3:06 - 3:08Well, performance is what you do:
-
3:08 - 3:12how fast you run,
what kind of paintings you paint, -
3:12 - 3:13what kind of papers you publish.
-
3:14 - 3:16However, in our working definition,
-
3:16 - 3:21success is about what the community
notices from what you did, -
3:21 - 3:22from your performance:
-
3:22 - 3:26How does it acknowledge it,
and how does it reward you for it? -
3:26 - 3:28In other terms,
-
3:28 - 3:32your performance is about you,
but your success is about all of us. -
3:33 - 3:37And this was a very
important shift for us, -
3:37 - 3:41because the moment we defined success
as being a collective measure -
3:41 - 3:43that the community provides to us,
-
3:43 - 3:44it became measurable,
-
3:44 - 3:49because if it's in the community,
there are multiple data points about that. -
3:49 - 3:54So we go to school,
we exercise, we practice, -
3:54 - 3:57because we believe
that performance leads to success. -
3:58 - 4:00But the way we actually
started to explore, -
4:00 - 4:03we realized that performance and success
are very, very different animals -
4:03 - 4:06when it comes to
the mathematics of the problem. -
4:06 - 4:08And let me illustrate that.
-
4:08 - 4:13So what you see here is
the fastest man on earth, Usain Bolt. -
4:14 - 4:18And of course, he wins most of
the competitions that he enters. -
4:18 - 4:22And we know he's the fastest on earth
because we have a chronometer -
4:22 - 4:23to measure his speed.
-
4:23 - 4:27Well, what is interesting about him
is that when he wins, -
4:27 - 4:32he doesn't do so by really significantly
outrunning his competition. -
4:32 - 4:37He's running at most a percent faster
than the one who loses the race. -
4:38 - 4:41And not only does he run only
one percent faster than the second one, -
4:41 - 4:44but he doesn't run
10 times faster than I do -- -
4:44 - 4:46and I'm not a good runner,
trust me on that. -
4:46 - 4:48(Laughter)
-
4:48 - 4:51And every time we are able
to measure performance, -
4:51 - 4:53we notice something very interesting;
-
4:53 - 4:56that is, performance is bounded.
-
4:56 - 4:59What it means is that there are
no huge variations in human performance. -
5:00 - 5:03It varies only in a narrow range,
-
5:03 - 5:06and we do need the chronometer
to measure the differences. -
5:06 - 5:09This is not to say that we cannot
see the good from the best ones, -
5:09 - 5:12but the best ones
are very hard to distinguish. -
5:12 - 5:15And the problem with that
is that most of us work in areas -
5:15 - 5:19where we do not have a chronometer
to gauge our performance. -
5:19 - 5:21Alright, performance is bounded,
-
5:21 - 5:24there are no huge differences between us
when it comes to our performance. -
5:24 - 5:25How about success?
-
5:26 - 5:29Well, let's switch to
a different topic, like books. -
5:29 - 5:34One measure of success for writers is
how many people read your work. -
5:35 - 5:39And so when my previous book
came out in 2009, -
5:39 - 5:41I was in Europe talking with my editor,
-
5:41 - 5:43and I was interested:
Who is the competition? -
5:44 - 5:47And I had some fabulous ones.
-
5:47 - 5:48That week --
-
5:48 - 5:49(Laughter)
-
5:49 - 5:53Dan Brown came out with "The Lost Symbol,"
-
5:53 - 5:56and "The Last Song" also came out,
-
5:56 - 5:57Nicholas Sparks.
-
5:57 - 6:00And when you just look at the list,
-
6:00 - 6:04you realize, you know, performance-wise,
there's hardly any difference -
6:04 - 6:05between these books or mine.
-
6:05 - 6:07Right?
-
6:07 - 6:11So maybe if Nicholas Sparks's team
works a little harder, -
6:11 - 6:13he could easily be number one,
-
6:13 - 6:16because it's almost by accident
who ended up at the top. -
6:16 - 6:20So I said, let's look at the numbers --
I'm a data person, right? -
6:20 - 6:24So let's see what were
the sales for Nicholas Sparks. -
6:24 - 6:26And it turns out that
that opening weekend, -
6:26 - 6:29Nicholas Sparks sold more than
a hundred thousand copies, -
6:29 - 6:31which is an amazing number.
-
6:31 - 6:34You can actually get to the top
of the "New York Times" best-seller list -
6:34 - 6:36by selling 10,000 copies a week,
-
6:36 - 6:40so he tenfold overcame
what he needed to be number one. -
6:40 - 6:42Yet he wasn't number one.
-
6:42 - 6:43Why?
-
6:43 - 6:47Because there was Dan Brown,
who sold 1.2 million copies that weekend. -
6:47 - 6:49(Laughter)
-
6:49 - 6:53And the reason I like this number
is because it shows that, really, -
6:53 - 6:57when it comes to success, it's unbounded,
-
6:57 - 7:03that the best doesn't only get
slightly more than the second best -
7:03 - 7:06but gets orders of magnitude more,
-
7:06 - 7:08because success is a collective measure.
-
7:08 - 7:13We give it to them, rather than
we earn it through our performance. -
7:13 - 7:18So one of things we realized is that
performance, what we do, is bounded, -
7:18 - 7:21but success, which is
collective, is unbounded, -
7:21 - 7:22which makes you wonder:
-
7:22 - 7:25How do you get these
huge differences in success -
7:25 - 7:28when you have such tiny
differences in performance? -
7:29 - 7:32And recently, I published a book
that I devoted to that very question. -
7:32 - 7:35And they didn't give me enough time
to go over all of that, -
7:35 - 7:37so I'm going to go back
to the question of, -
7:37 - 7:40alright, you have success;
when should that appear? -
7:40 - 7:44So let's go back to the party spoiler
and ask ourselves: -
7:45 - 7:49Why did Einstein make
this ridiculous statement, -
7:49 - 7:52that only before 30
you could actually be creative? -
7:52 - 7:56Well, because he looked around himself
and he saw all these fabulous physicists -
7:56 - 7:59that created quantum mechanics
and modern physics, -
7:59 - 8:03and they were all in their 20s
and early 30s when they did so. -
8:04 - 8:05And it's not only him.
-
8:05 - 8:07It's not only observational bias,
-
8:07 - 8:11because there's actually
a whole field of genius research -
8:11 - 8:13that has documented the fact that,
-
8:13 - 8:16if we look at the people
we admire from the past -
8:16 - 8:19and then look at what age
they made their biggest contribution, -
8:19 - 8:22whether that's music,
whether that's science, -
8:22 - 8:23whether that's engineering,
-
8:23 - 8:29most of them tend to do so
in their 20s, 30s, early 40s at most. -
8:30 - 8:33But there's a problem
with this genius research. -
8:33 - 8:36Well, first of all, it created
the impression to us -
8:37 - 8:40that creativity equals youth,
-
8:40 - 8:42which is painful, right?
-
8:42 - 8:44(Laughter)
-
8:44 - 8:48And it also has an observational bias,
-
8:48 - 8:53because it only looks at geniuses
and doesn't look at ordinary scientists -
8:53 - 8:55and doesn't look at all of us and ask,
-
8:55 - 8:58is it really true that creativity
vanishes as we age? -
8:58 - 9:00So that's exactly what we tried to do,
-
9:00 - 9:04and this is important for that
to actually have references. -
9:04 - 9:07So let's look at an ordinary
scientist like myself, -
9:07 - 9:08and let's look at my career.
-
9:08 - 9:12So what you see here is all the papers
that I've published -
9:12 - 9:17from my very first paper, in 1989;
I was still in Romania when I did so, -
9:17 - 9:18till kind of this year.
-
9:19 - 9:21And vertically, you see
the impact of the paper, -
9:21 - 9:23that is, how many citations,
-
9:23 - 9:27how many other papers
have been written that cited that work. -
9:27 - 9:29And when you look at that,
-
9:29 - 9:32you see that my career
has roughly three different stages. -
9:32 - 9:34I had the first 10 years
where I had to work a lot -
9:34 - 9:35and I don't achieve much.
-
9:35 - 9:37No one seems to care
about what I do, right? -
9:37 - 9:39There's hardly any impact.
-
9:39 - 9:41(Laughter)
-
9:41 - 9:43That time, I was doing material science,
-
9:44 - 9:47and then I kind of discovered
for myself networks -
9:47 - 9:49and then started publishing in networks.
-
9:49 - 9:52And that led from one high-impact
paper to the other one. -
9:52 - 9:55And it really felt good.
That was that stage of my career. -
9:55 - 9:57(Laughter)
-
9:57 - 10:00So the question is,
what happens right now? -
10:01 - 10:04And we don't know, because there
hasn't been enough time passed yet -
10:04 - 10:07to actually figure out how much impact
those papers will get; -
10:07 - 10:08it takes time to acquire.
-
10:08 - 10:10Well, when you look at the data,
-
10:10 - 10:13it seems to be that Einstein,
the genius research, is right, -
10:13 - 10:14and I'm at that stage of my career.
-
10:14 - 10:17(Laughter)
-
10:17 - 10:23So we said, OK, let's figure out
how does this really happen, -
10:23 - 10:25first in science.
-
10:25 - 10:28And in order not to have
the selection bias, -
10:28 - 10:30to look only at geniuses,
-
10:30 - 10:33we ended up reconstructing the career
of every single scientist -
10:33 - 10:36from 1900 till today
-
10:36 - 10:40and finding for all scientists
what was their personal best, -
10:40 - 10:42whether they got the Nobel Prize
or they never did, -
10:42 - 10:46or no one knows what they did,
even their personal best. -
10:46 - 10:48And that's what you see in this slide.
-
10:48 - 10:49Each line is a career,
-
10:49 - 10:52and when you have a light blue dot
on the top of that career, -
10:52 - 10:54it says that was their personal best.
-
10:54 - 10:56And the question is,
-
10:56 - 10:59when did they actually make
their biggest discovery? -
10:59 - 11:00To quantify that,
-
11:00 - 11:04we look at what's the probability
that you make your biggest discovery, -
11:04 - 11:06let's say, one, two, three
or 10 years into your career? -
11:07 - 11:08We're not looking at real age.
-
11:08 - 11:10We're looking at
what we call "academic age." -
11:10 - 11:13Your academic age starts
when you publish your first papers. -
11:13 - 11:15I know some of you are still babies.
-
11:15 - 11:17(Laughter)
-
11:17 - 11:19So let's look at the probability
-
11:19 - 11:21that you publish
your highest-impact paper. -
11:21 - 11:25And what you see is, indeed,
the genius research is right. -
11:25 - 11:28Most scientists tend to publish
their highest-impact paper -
11:28 - 11:31in the first 10, 15 years in their career,
-
11:31 - 11:34and it tanks after that.
-
11:34 - 11:39It tanks so fast that I'm about --
I'm exactly 30 years into my career, -
11:39 - 11:42and the chance that I will publish a paper
that would have a higher impact -
11:42 - 11:44than anything that I did before
-
11:44 - 11:46is less than one percent.
-
11:46 - 11:49I am in that stage of my career,
according to this data. -
11:50 - 11:51But there's a problem with that.
-
11:52 - 11:55We're not doing controls properly.
-
11:55 - 11:57So the control would be,
-
11:57 - 12:01what would a scientist look like
who makes random contribution to science? -
12:01 - 12:04Or what is the productivity
of the scientist? -
12:04 - 12:06When do they write papers?
-
12:06 - 12:09So we measured the productivity,
-
12:09 - 12:11and amazingly, the productivity,
-
12:11 - 12:15your likelihood of writing a paper
in year one, 10 or 20 in your career, -
12:15 - 12:19is indistinguishable from the likelihood
of having the impact -
12:19 - 12:20in that part of your career.
-
12:21 - 12:23And to make a long story short,
-
12:23 - 12:27after lots of statistical tests,
there's only one explanation for that, -
12:27 - 12:30that really, the way we scientists work
-
12:30 - 12:34is that every single paper we write,
every project we do, -
12:34 - 12:38has exactly the same chance
of being our personal best. -
12:38 - 12:43That is, discovery is like
a lottery ticket. -
12:43 - 12:45And the more lottery tickets we buy,
-
12:45 - 12:47the higher our chances.
-
12:47 - 12:48And it happens to be so
-
12:48 - 12:51that most scientists buy
most of their lottery tickets -
12:51 - 12:54in the first 10, 15 years of their career,
-
12:54 - 12:57and after that,
their productivity decreases. -
12:57 - 12:59They're not buying
any more lottery tickets. -
13:00 - 13:03So it looks as if
they would not be creative. -
13:03 - 13:05In reality, they stopped trying.
-
13:06 - 13:09So when we actually put the data together,
the conclusion is very simple: -
13:09 - 13:12success can come at any time.
-
13:12 - 13:16It could be your very first
or very last paper of your career. -
13:16 - 13:20It's totally random
in the space of the projects. -
13:20 - 13:22It is the productivity that changes.
-
13:22 - 13:23Let me illustrate that.
-
13:23 - 13:26Here is Frank Wilczek,
who got the Nobel Prize in Physics -
13:26 - 13:30for the very first paper he ever wrote
in his career as a graduate student. -
13:31 - 13:32(Laughter)
-
13:32 - 13:35More interesting is John Fenn,
-
13:35 - 13:39who, at age 70, was forcefully retired
by Yale University. -
13:39 - 13:41They shut his lab down,
-
13:41 - 13:45and at that moment, he moved
to Virginia Commonwealth University, -
13:45 - 13:47opened another lab,
-
13:47 - 13:50and it is there, at age 72,
that he published a paper -
13:50 - 13:54for which, 15 years later, he got
the Nobel Prize for Chemistry. -
13:55 - 13:58And you think, OK,
well, science is special, -
13:58 - 14:01but what about other areas
where we need to be creative? -
14:01 - 14:06So let me take another
typical example: entrepreneurship. -
14:07 - 14:08Silicon Valley,
-
14:08 - 14:11the land of the youth, right?
-
14:11 - 14:12And indeed, when you look at it,
-
14:12 - 14:17you realize that the biggest awards,
the TechCrunch Awards and other awards, -
14:17 - 14:19are all going to people
-
14:19 - 14:24whose average age
is late 20s, very early 30s. -
14:24 - 14:30You look at who the VCs give the money to,
some of the biggest VC firms -- -
14:30 - 14:32all people in their early 30s.
-
14:33 - 14:34Which, of course, we know;
-
14:34 - 14:39there is this ethos in Silicon Valley
that youth equals success. -
14:40 - 14:42Not when you look at the data,
-
14:42 - 14:44because it's not only
about forming a company -- -
14:44 - 14:47forming a company is like productivity,
trying, trying, trying -- -
14:47 - 14:51when you look at which
of these individuals actually put out -
14:51 - 14:54a successful company, a successful exit.
-
14:54 - 14:57And recently, some of our colleagues
looked at exactly that question. -
14:57 - 15:01And it turns out that yes,
those in the 20s and 30s -
15:01 - 15:04put out a huge number of companies,
form lots of companies, -
15:04 - 15:05but most of them go bust.
-
15:06 - 15:10And when you look at the successful exits,
what you see in this particular plot, -
15:10 - 15:14the older you are, the more likely that
you will actually hit the stock market -
15:14 - 15:16or the sell the company successfully.
-
15:17 - 15:20This is so strong, actually,
that if you are in the 50s, -
15:20 - 15:24you are twice as likely
to actually have a successful exit -
15:24 - 15:25than if you are in your 30s.
-
15:27 - 15:31(Applause)
-
15:32 - 15:35So in the end, what is it
that we see, actually? -
15:35 - 15:39What we see is that creativity has no age.
-
15:39 - 15:41Productivity does, right?
-
15:41 - 15:46Which is telling me that
at the end of the day, -
15:46 - 15:48if you keep trying --
-
15:48 - 15:50(Laughter)
-
15:50 - 15:54you could still succeed
and succeed over and over. -
15:54 - 15:56So my conclusion is very simple:
-
15:56 - 15:58I am off the stage, back in my lab.
-
15:58 - 15:59Thank you.
-
15:59 - 16:03(Applause)
- Title:
- The real relationship between your age and your chance of success
- Speaker:
- Albert-László Barabási
- Description:
-
Backed by mathematical analysis, network theorist Albert-László Barabási explores the hidden mechanisms that drive success -- no matter your field -- and uncovers an intriguing connection between your age and your chance of making it big.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 16:16
Brian Greene edited English subtitles for The real relationship between your age and your chance of success | ||
Brian Greene edited English subtitles for The real relationship between your age and your chance of success | ||
Oliver Friedman edited English subtitles for The real relationship between your age and your chance of success | ||
Oliver Friedman edited English subtitles for The real relationship between your age and your chance of success | ||
Brian Greene edited English subtitles for The real relationship between your age and your chance of success | ||
Oliver Friedman approved English subtitles for The real relationship between your age and your chance of success | ||
Oliver Friedman edited English subtitles for The real relationship between your age and your chance of success | ||
Camille Martínez accepted English subtitles for The real relationship between your age and your chance of success |