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Albert-László Barabási: The real relationship between your age and your chance of success
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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.
Albert-László Barabási - Network scientist
A pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems. Full bio
A pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems. Full bio
Double-click the English transcript below to play the video.
00:12
Today, actually, is
a very special day for me,
a very special day for me,
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because it is my birthday.
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(Applause)
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And so, thanks to all of you
for joining the party.
for joining the party.
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(Laughter)
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But every time you throw a party,
there's someone there to spoil it. Right?
there's someone there to spoil it. Right?
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(Laughter)
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And I'm a physicist,
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and this time I brought
another physicist along to do so.
another physicist along to do so.
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His name is Albert Einstein --
also Albert -- and he's the one who said
also Albert -- and he's the one who said
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that the person who has not made
his great contributions to science
his great contributions to science
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by the age of 30
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will never do so.
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(Laughter)
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Now, you don't need to check Wikipedia
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that I'm beyond 30.
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(Laughter)
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So, effectively, what
he is telling me, and us,
he is telling me, and us,
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is that when it comes to my science,
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I'm deadwood.
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01:04
Well, luckily, I had my share
of luck within my career.
of luck within my career.
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Around age 28, I became
very interested in networks,
very interested in networks,
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and a few years later, we managed
to publish a few key papers
to publish a few key papers
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that reported the discovery
of scale-free networks
of scale-free networks
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and really gave birth to a new discipline
that we call network science today.
that we call network science today.
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And if you really care about it,
you can get a PhD now in network science
you can get a PhD now in network science
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in Budapest, in Boston,
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and you can study it all over the world.
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A few years later,
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when I moved to Harvard
first as a sabbatical,
first as a sabbatical,
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I became interested
in another type of network:
in another type of network:
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that time, the networks within ourselves,
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how the genes and the proteins
and the metabolites link to each other
and the metabolites link to each other
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and how they connect to disease.
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And that interest led
to a major explosion within medicine,
to a major explosion within medicine,
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including the Network Medicine
Division at Harvard,
Division at Harvard,
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that has more than 300 researchers
who are using this perspective
who are using this perspective
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to treat patients and develop new cures.
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02:09
And a few years ago,
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I thought that I would take
this idea of networks
this idea of networks
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and the expertise we had in networks
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in a different area,
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that is, to understand success.
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And why did we do that?
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Well, we thought that, to some degree,
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our success is determined
by the networks we're part of --
by the networks we're part of --
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that our networks can push us forward,
they can pull us back.
they can pull us back.
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And I was curious if we could use
the knowledge and big data and expertise
the knowledge and big data and expertise
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where we develop the networks
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to really quantify
how these things happen.
how these things happen.
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This is a result from that.
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What you see here is a network
of galleries in museums
of galleries in museums
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that connect to each other.
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And through this map
that we mapped out last year,
that we mapped out last year,
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we are able to predict very accurately
the success of an artist
the success of an artist
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if you give me the first five exhibits
that he or she had in their career.
that he or she had in their career.
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03:01
Well, as we thought about success,
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we realized that success
is not only about networks;
is not only about networks;
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there are so many
other dimensions to that.
other dimensions to that.
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And one of the things
we need for success, obviously,
we need for success, obviously,
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is performance.
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So let's define what's the difference
between performance and success.
between performance and success.
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Well, performance is what you do:
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how fast you run,
what kind of paintings you paint,
what kind of paintings you paint,
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what kind of papers you publish.
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However, in our working definition,
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success is about what the community
notices from what you did,
notices from what you did,
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from your performance:
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How does it acknowledge it,
and how does it reward you for it?
and how does it reward you for it?
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In other terms,
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your performance is about you,
but your success is about all of us.
but your success is about all of us.
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And this was a very
important shift for us,
important shift for us,
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because the moment we defined success
as being a collective measure
as being a collective measure
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that the community provides to us,
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it became measurable,
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because if it's in the community,
there are multiple data points about that.
there are multiple data points about that.
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04:00
So we go to school,
we exercise, we practice,
we exercise, we practice,
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because we believe
that performance leads to success.
that performance leads to success.
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But the way we actually
started to explore,
started to explore,
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we realized that performance and success
are very, very different animals
are very, very different animals
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when it comes to
the mathematics of the problem.
the mathematics of the problem.
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And let me illustrate that.
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So what you see here is
the fastest man on earth, Usain Bolt.
the fastest man on earth, Usain Bolt.
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And of course, he wins most of
the competitions that he enters.
the competitions that he enters.
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And we know he's the fastest on earth
because we have a chronometer
because we have a chronometer
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to measure his speed.
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Well, what is interesting about him
is that when he wins,
is that when he wins,
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he doesn't do so by really significantly
outrunning his competition.
outrunning his competition.
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He's running at most a percent faster
than the one who loses the race.
than the one who loses the race.
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And not only does he run only
one percent faster than the second one,
one percent faster than the second one,
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but he doesn't run
10 times faster than I do --
10 times faster than I do --
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and I'm not a good runner,
trust me on that.
trust me on that.
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(Laughter)
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And every time we are able
to measure performance,
to measure performance,
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we notice something very interesting;
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that is, performance is bounded.
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What it means is that there are
no huge variations in human performance.
no huge variations in human performance.
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It varies only in a narrow range,
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and we do need the chronometer
to measure the differences.
to measure the differences.
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This is not to say that we cannot
see the good from the best ones,
see the good from the best ones,
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but the best ones
are very hard to distinguish.
are very hard to distinguish.
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And the problem with that
is that most of us work in areas
is that most of us work in areas
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where we do not have a chronometer
to gauge our performance.
to gauge our performance.
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Alright, performance is bounded,
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there are no huge differences between us
when it comes to our performance.
when it comes to our performance.
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How about success?
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Well, let's switch to
a different topic, like books.
a different topic, like books.
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One measure of success for writers is
how many people read your work.
how many people read your work.
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And so when my previous book
came out in 2009,
came out in 2009,
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I was in Europe talking with my editor,
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and I was interested:
Who is the competition?
Who is the competition?
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And I had some fabulous ones.
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That week --
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(Laughter)
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Dan Brown came out with "The Lost Symbol,"
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and "The Last Song" also came out,
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Nicholas Sparks.
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And when you just look at the list,
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you realize, you know, performance-wise,
there's hardly any difference
there's hardly any difference
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between these books or mine.
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Right?
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So maybe if Nicholas Sparks's team
works a little harder,
works a little harder,
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he could easily be number one,
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because it's almost by accident
who ended up at the top.
who ended up at the top.
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So I said, let's look at the numbers --
I'm a data person, right?
I'm a data person, right?
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So let's see what were
the sales for Nicholas Sparks.
the sales for Nicholas Sparks.
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And it turns out that
that opening weekend,
that opening weekend,
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Nicholas Sparks sold more than
a hundred thousand copies,
a hundred thousand copies,
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which is an amazing number.
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You can actually get to the top
of the "New York Times" best-seller list
of the "New York Times" best-seller list
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by selling 10,000 copies a week,
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so he tenfold overcame
what he needed to be number one.
what he needed to be number one.
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Yet he wasn't number one.
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Why?
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Because there was Dan Brown,
who sold 1.2 million copies that weekend.
who sold 1.2 million copies that weekend.
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(Laughter)
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And the reason I like this number
is because it shows that, really,
is because it shows that, really,
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when it comes to success, it's unbounded,
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that the best doesn't only get
slightly more than the second best
slightly more than the second best
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but gets orders of magnitude more,
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because success is a collective measure.
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We give it to them, rather than
we earn it through our performance.
we earn it through our performance.
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So one of things we realized is that
performance, what we do, is bounded,
performance, what we do, is bounded,
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but success, which is
collective, is unbounded,
collective, is unbounded,
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which makes you wonder:
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How do you get these
huge differences in success
huge differences in success
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when you have such tiny
differences in performance?
differences in performance?
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And recently, I published a book
that I devoted to that very question.
that I devoted to that very question.
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And they didn't give me enough time
to go over all of that,
to go over all of that,
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so I'm going to go back
to the question of,
to the question of,
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alright, you have success;
when should that appear?
when should that appear?
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So let's go back to the party spoiler
and ask ourselves:
and ask ourselves:
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Why did Einstein make
this ridiculous statement,
this ridiculous statement,
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that only before 30
you could actually be creative?
you could actually be creative?
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Well, because he looked around himself
and he saw all these fabulous physicists
and he saw all these fabulous physicists
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that created quantum mechanics
and modern physics,
and modern physics,
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and they were all in their 20s
and early 30s when they did so.
and early 30s when they did so.
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And it's not only him.
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It's not only observational bias,
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because there's actually
a whole field of genius research
a whole field of genius research
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that has documented the fact that,
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if we look at the people
we admire from the past
we admire from the past
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and then look at what age
they made their biggest contribution,
they made their biggest contribution,
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whether that's music,
whether that's science,
whether that's science,
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whether that's engineering,
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most of them tend to do so
in their 20s, 30s, early 40s at most.
in their 20s, 30s, early 40s at most.
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But there's a problem
with this genius research.
with this genius research.
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Well, first of all, it created
the impression to us
the impression to us
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that creativity equals youth,
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which is painful, right?
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(Laughter)
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And it also has an observational bias,
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because it only looks at geniuses
and doesn't look at ordinary scientists
and doesn't look at ordinary scientists
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and doesn't look at all of us and ask,
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is it really true that creativity
vanishes as we age?
vanishes as we age?
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So that's exactly what we tried to do,
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and this is important for that
to actually have references.
to actually have references.
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So let's look at an ordinary
scientist like myself,
scientist like myself,
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and let's look at my career.
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So what you see here is all the papers
that I've published
that I've published
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from my very first paper, in 1989;
I was still in Romania when I did so,
I was still in Romania when I did so,
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till kind of this year.
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And vertically, you see
the impact of the paper,
the impact of the paper,
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that is, how many citations,
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how many other papers
have been written that cited that work.
have been written that cited that work.
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And when you look at that,
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you see that my career
has roughly three different stages.
has roughly three different stages.
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I had the first 10 years
where I had to work a lot
where I had to work a lot
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and I don't achieve much.
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No one seems to care
about what I do, right?
about what I do, right?
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There's hardly any impact.
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(Laughter)
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That time, I was doing material science,
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and then I kind of discovered
for myself networks
for myself networks
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and then started publishing in networks.
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And that led from one high-impact
paper to the other one.
paper to the other one.
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And it really felt good.
That was that stage of my career.
That was that stage of my career.
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(Laughter)
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So the question is,
what happens right now?
what happens right now?
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And we don't know, because there
hasn't been enough time passed yet
hasn't been enough time passed yet
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to actually figure out how much impact
those papers will get;
those papers will get;
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it takes time to acquire.
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Well, when you look at the data,
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it seems to be that Einstein,
the genius research, is right,
the genius research, is right,
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and I'm at that stage of my career.
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(Laughter)
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So we said, OK, let's figure out
how does this really happen,
how does this really happen,
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first in science.
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And in order not to have
the selection bias,
the selection bias,
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to look only at geniuses,
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we ended up reconstructing the career
of every single scientist
of every single scientist
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from 1900 till today
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and finding for all scientists
what was their personal best,
what was their personal best,
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whether they got the Nobel Prize
or they never did,
or they never did,
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or no one knows what they did,
even their personal best.
even their personal best.
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And that's what you see in this slide.
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Each line is a career,
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and when you have a light blue dot
on the top of that career,
on the top of that career,
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it says that was their personal best.
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And the question is,
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when did they actually make
their biggest discovery?
their biggest discovery?
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To quantify that,
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we look at what's the probability
that you make your biggest discovery,
that you make your biggest discovery,
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let's say, one, two, three
or 10 years into your career?
or 10 years into your career?
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We're not looking at real age.
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We're looking at
what we call "academic age."
what we call "academic age."
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Your academic age starts
when you publish your first papers.
when you publish your first papers.
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I know some of you are still babies.
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(Laughter)
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So let's look at the probability
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that you publish
your highest-impact paper.
your highest-impact paper.
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And what you see is, indeed,
the genius research is right.
the genius research is right.
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Most scientists tend to publish
their highest-impact paper
their highest-impact paper
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in the first 10, 15 years in their career,
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and it tanks after that.
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It tanks so fast that I'm about --
I'm exactly 30 years into my career,
I'm exactly 30 years into my career,
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and the chance that I will publish a paper
that would have a higher impact
that would have a higher impact
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than anything that I did before
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is less than one percent.
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I am in that stage of my career,
according to this data.
according to this data.
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But there's a problem with that.
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We're not doing controls properly.
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So the control would be,
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what would a scientist look like
who makes random contribution to science?
who makes random contribution to science?
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Or what is the productivity
of the scientist?
of the scientist?
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When do they write papers?
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12:18
So we measured the productivity,
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and amazingly, the productivity,
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your likelihood of writing a paper
in year one, 10 or 20 in your career,
in year one, 10 or 20 in your career,
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is indistinguishable from the likelihood
of having the impact
of having the impact
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in that part of your career.
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And to make a long story short,
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after lots of statistical tests,
there's only one explanation for that,
there's only one explanation for that,
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that really, the way we scientists work
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is that every single paper we write,
every project we do,
every project we do,
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has exactly the same chance
of being our personal best.
of being our personal best.
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That is, discovery is like
a lottery ticket.
a lottery ticket.
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And the more lottery tickets we buy,
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the higher our chances.
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And it happens to be so
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that most scientists buy
most of their lottery tickets
most of their lottery tickets
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in the first 10, 15 years of their career,
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and after that,
their productivity decreases.
their productivity decreases.
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They're not buying
any more lottery tickets.
any more lottery tickets.
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So it looks as if
they would not be creative.
they would not be creative.
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13:14
In reality, they stopped trying.
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13:17
So when we actually put the data together,
the conclusion is very simple:
the conclusion is very simple:
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success can come at any time.
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It could be your very first
or very last paper of your career.
or very last paper of your career.
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It's totally random
in the space of the projects.
in the space of the projects.
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It is the productivity that changes.
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13:33
Let me illustrate that.
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Here is Frank Wilczek,
who got the Nobel Prize in Physics
who got the Nobel Prize in Physics
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13:38
for the very first paper he ever wrote
in his career as a graduate student.
in his career as a graduate student.
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(Laughter)
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More interesting is John Fenn,
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13:46
who, at age 70, was forcefully retired
by Yale University.
by Yale University.
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13:51
They shut his lab down,
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2056
13:53
and at that moment, he moved
to Virginia Commonwealth University,
to Virginia Commonwealth University,
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13:57
opened another lab,
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13:58
and it is there, at age 72,
that he published a paper
that he published a paper
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14:02
for which, 15 years later, he got
the Nobel Prize for Chemistry.
the Nobel Prize for Chemistry.
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14:06
And you think, OK,
well, science is special,
well, science is special,
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but what about other areas
where we need to be creative?
where we need to be creative?
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14:13
So let me take another
typical example: entrepreneurship.
typical example: entrepreneurship.
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14:18
Silicon Valley,
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14:20
the land of the youth, right?
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14:22
And indeed, when you look at it,
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14:24
you realize that the biggest awards,
the TechCrunch Awards and other awards,
the TechCrunch Awards and other awards,
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14:28
are all going to people
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14:31
whose average age
is late 20s, very early 30s.
is late 20s, very early 30s.
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14:36
You look at who the VCs give the money to,
some of the biggest VC firms --
some of the biggest VC firms --
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14:42
all people in their early 30s.
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14:44
Which, of course, we know;
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there is this ethos in Silicon Valley
that youth equals success.
that youth equals success.
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14:51
Not when you look at the data,
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2183
14:53
because it's not only
about forming a company --
about forming a company --
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14:56
forming a company is like productivity,
trying, trying, trying --
trying, trying, trying --
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14:59
when you look at which
of these individuals actually put out
of these individuals actually put out
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15:02
a successful company, a successful exit.
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2782
15:05
And recently, some of our colleagues
looked at exactly that question.
looked at exactly that question.
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15:09
And it turns out that yes,
those in the 20s and 30s
those in the 20s and 30s
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put out a huge number of companies,
form lots of companies,
form lots of companies,
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15:15
but most of them go bust.
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1531
15:18
And when you look at the successful exits,
what you see in this particular plot,
what you see in this particular plot,
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15:22
the older you are, the more likely that
you will actually hit the stock market
you will actually hit the stock market
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15:26
or the sell the company successfully.
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2312
15:28
This is so strong, actually,
that if you are in the 50s,
that if you are in the 50s,
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3113
15:31
you are twice as likely
to actually have a successful exit
to actually have a successful exit
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3588
15:35
than if you are in your 30s.
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923596
1890
15:38
(Applause)
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15:43
So in the end, what is it
that we see, actually?
that we see, actually?
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15:46
What we see is that creativity has no age.
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15:50
Productivity does, right?
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15:53
Which is telling me that
at the end of the day,
at the end of the day,
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if you keep trying --
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15:59
(Laughter)
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you could still succeed
and succeed over and over.
and succeed over and over.
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16:05
So my conclusion is very simple:
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I am off the stage, back in my lab.
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Thank you.
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(Applause)
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ABOUT THE SPEAKER
Albert-László Barabási - Network scientistA pioneer in network science, Albert-László Barabási uncovers the hidden order behind complex systems.
Why you should listen
Albert-László Barabási is fascinated by a wide range of topics, from the structure of the brain and treating diseases with network medicine to the emergence of success in art and how science really works. His work uses the quantitative tools of network science, a research field that he pioneered, and led to the discovery of scale-free networks, helping explain the emergence of many natural, technological and social networks.
Barabási is the Robert Gray Dodge Professor of Network Science at Northeastern University and holds an appointment in the Department of Medicine at Harvard Medical School. He splits his time with Budapest, where he runs a European Research Council project at Central European University. A Hungarian born native of Transylvania, Romania, he received his masters in theoretical physics at the Eötvös University in Budapest, Hungary and his PhD three years later at Boston University.
Barabási’s latest book is The Formula: The Universal Laws of Success. He is also the author of Network Science, Linked and Bursts. He co-edited Network Medicine and The Structure and Dynamics of Networks. His books have been translated into over twenty languages.
Albert-László Barabási | Speaker | TED.com