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Today, actually, is
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.

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(Laughter)

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But every time you throw a party,
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.

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His name is Albert Einstein --
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

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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,

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is that when it comes to my science,

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I'm deadwood.

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Well, luckily, I had my share
of luck within my career.

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Around age 28, I became
very interested in networks,

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and a few years later, we managed
to publish a few key papers

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that reported the discovery
of scale-free networks

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and really gave birth to a new discipline
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

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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,

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I became interested
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

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and how they connect to disease.

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And that interest led
to a major explosion within medicine,

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including the Network Medicine
Division at Harvard,

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that has more than 300 researchers
who are using this perspective

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to treat patients and develop new cures.

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And a few years ago,

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I thought that I would take
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 --

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that our networks can push us forward,
they can pull us back.

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And I was curious if we could use
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.

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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

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that connect to each other.

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And through this map
that we mapped out last year,

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we are able to predict very accurately
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.

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Well, as we thought about success,

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we realized that success
is not only about networks;

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there are so many
other dimensions to that.

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And one of the things
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.

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Well, performance is what you do:

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how fast you run,
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,

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from your performance:

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How does it acknowledge 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.

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And this was a very
important shift for us,

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because the moment we defined success
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.

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So we go to school,
we exercise, we practice,

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because we believe
that performance leads to success.

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But the way we actually
started to explore,

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we realized that performance and success
are very, very different animals

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when it comes to
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.

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And of course, he wins most of
the competitions that he enters.

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And we know he's the fastest on earth
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,

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he doesn't do so by really significantly
outrunning his competition.

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He's running at most a percent faster
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,

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but he doesn't run
10 times faster than I do --

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and I'm not a good runner,
trust me on that.

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(Laughter)

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And every time we are able
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.

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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.

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This is not to say that we cannot
see the good from the best ones,

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but the best ones
are very hard to distinguish.

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And the problem with that
is that most of us work in areas

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where we do not have a chronometer
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.

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How about success?

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Well, let's switch to
a different topic, like books.

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One measure of success for writers is
how many people read your work.

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And so when my previous book
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?

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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

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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,

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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.

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So I said, let's look at the numbers --
I'm a data person, right?

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So let's see what were
the sales for Nicholas Sparks.

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And it turns out that
that opening weekend,

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Nicholas Sparks sold more than
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

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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.

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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.

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(Laughter)

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And the reason I like this number
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

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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.

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So one of things we realized is that
performance, what we do, is bounded,

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but success, which is
collective, is unbounded,

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which makes you wonder:

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How do you get these
huge differences in success

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when you have such tiny
differences in performance?

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And recently, I published a book
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,

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so I'm going to go back
to the question of,

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alright, you have success;
when should that appear?

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So let's go back to the party spoiler
and ask ourselves:

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Why did Einstein make
this ridiculous statement,

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that only before 30
you could actually be creative?

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Well, because he looked around himself
and he saw all these fabulous physicists

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that created quantum mechanics
and modern physics,

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and they were all in their 20s
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

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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

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and then look at what age
they made their biggest contribution,

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whether that's music,
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.

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But there's a problem
with this genius research.

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Well, first of all, it created
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

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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?

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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.

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So let's look at an ordinary
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

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from my very first paper, in 1989;
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,

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that is, how many citations,

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how many other papers
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.

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I had the first 10 years
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?

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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

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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.

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And it really felt good.
That was that stage of my career.

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(Laughter)

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So the question is,
what happens right now?

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And we don't know, because there
hasn't been enough time passed yet

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to actually figure out how much impact
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,

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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,

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first in science.

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And in order not to have
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

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from 1900 till today

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and finding for all scientists
what was their personal best,

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whether they got the Nobel Prize
or they never did,

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or no one knows what they did,
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,

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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?

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To quantify that,

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we look at what's the probability
that you make your biggest discovery,

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let's say, one, two, three
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."

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Your academic age starts
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.

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And what you see is, indeed,
the genius research is right.

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Most scientists tend to publish
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,

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and the chance that I will publish a paper
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.

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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?

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Or what is the productivity
of the scientist?

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When do they write papers?

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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,

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is indistinguishable from the likelihood
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,

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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,

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has exactly the same chance
of being our personal best.

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That is, discovery is like
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

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in the first 10, 15 years of their career,

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and after that,
their productivity decreases.

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They're not buying
any more lottery tickets.

00:12:59.519 --> 00:13:02.963
So it looks as if
they would not be creative.

00:13:02.987 --> 00:13:04.986
In reality, they stopped trying.

00:13:05.509 --> 00:13:09.424
So when we actually put the data together,
the conclusion is very simple:

00:13:09.448 --> 00:13:11.779
success can come at any time.

00:13:11.803 --> 00:13:15.538
It could be your very first
or very last paper of your career.

00:13:15.562 --> 00:13:19.850
It's totally random
in the space of the projects.

00:13:19.874 --> 00:13:21.805
It is the productivity that changes.

NOTE Paragraph

00:13:21.829 --> 00:13:23.081
Let me illustrate that.

00:13:23.105 --> 00:13:26.374
Here is Frank Wilczek,
who got the Nobel Prize in Physics

00:13:26.398 --> 00:13:30.499
for the very first paper he ever wrote
in his career as a graduate student.

NOTE Paragraph

00:13:30.523 --> 00:13:31.530
(Laughter)

NOTE Paragraph

00:13:31.554 --> 00:13:34.772
More interesting is John Fenn,

00:13:34.796 --> 00:13:39.394
who, at age 70, was forcefully retired
by Yale University.

00:13:39.418 --> 00:13:41.474
They shut his lab down,

00:13:41.498 --> 00:13:45.164
and at that moment, he moved
to Virginia Commonwealth University,

00:13:45.188 --> 00:13:46.974
opened another lab,

00:13:46.998 --> 00:13:50.031
and it is there, at age 72,
that he published a paper

00:13:50.055 --> 00:13:53.900
for which, 15 years later, he got
the Nobel Prize for Chemistry.

NOTE Paragraph

00:13:54.940 --> 00:13:57.982
And you think, OK,
well, science is special,

00:13:58.006 --> 00:14:01.469
but what about other areas
where we need to be creative?

00:14:01.493 --> 00:14:06.429
So let me take another
typical example: entrepreneurship.

00:14:06.834 --> 00:14:08.413
Silicon Valley,

00:14:08.437 --> 00:14:10.503
the land of the youth, right?

00:14:10.527 --> 00:14:12.122
And indeed, when you look at it,

00:14:12.146 --> 00:14:16.788
you realize that the biggest awards,
the TechCrunch Awards and other awards,

00:14:16.812 --> 00:14:18.985
are all going to people

00:14:19.009 --> 00:14:24.024
whose average age
is late 20s, very early 30s.

00:14:24.465 --> 00:14:30.067
You look at who the VCs give the money to,
some of the biggest VC firms --

00:14:30.091 --> 00:14:32.332
all people in their early 30s.

00:14:32.951 --> 00:14:34.216
Which, of course, we know;

00:14:34.240 --> 00:14:38.693
there is this ethos in Silicon Valley
that youth equals success.

00:14:39.653 --> 00:14:41.836
Not when you look at the data,

00:14:41.860 --> 00:14:44.164
because it's not only
about forming a company --

00:14:44.188 --> 00:14:47.328
forming a company is like productivity,
trying, trying, trying --

00:14:47.352 --> 00:14:50.836
when you look at which
of these individuals actually put out

00:14:50.860 --> 00:14:53.642
a successful company, a successful exit.

00:14:53.666 --> 00:14:57.386
And recently, some of our colleagues
looked at exactly that question.

00:14:57.410 --> 00:15:00.566
And it turns out that yes,
those in the 20s and 30s

00:15:00.590 --> 00:15:03.938
put out a huge number of companies,
form lots of companies,

00:15:03.962 --> 00:15:05.493
but most of them go bust.

00:15:06.089 --> 00:15:10.284
And when you look at the successful exits,
what you see in this particular plot,

00:15:10.308 --> 00:15:14.003
the older you are, the more likely that
you will actually hit the stock market

00:15:14.027 --> 00:15:16.339
or the sell the company successfully.

00:15:16.847 --> 00:15:19.960
This is so strong, actually,
that if you are in the 50s,

00:15:19.984 --> 00:15:23.572
you are twice as likely
to actually have a successful exit

00:15:23.596 --> 00:15:25.486
than if you are in your 30s.

NOTE Paragraph

00:15:26.613 --> 00:15:30.938
(Applause)

NOTE Paragraph

00:15:31.645 --> 00:15:34.654
So in the end, what is it
that we see, actually?

00:15:34.678 --> 00:15:38.761
What we see is that creativity has no age.

00:15:38.785 --> 00:15:40.987
Productivity does, right?

00:15:41.424 --> 00:15:45.559
Which is telling me that
at the end of the day,

00:15:45.583 --> 00:15:47.583
if you keep trying --

NOTE Paragraph

00:15:47.607 --> 00:15:50.010
(Laughter)

NOTE Paragraph

00:15:50.034 --> 00:15:53.606
you could still succeed
and succeed over and over.

00:15:53.630 --> 00:15:56.021
So my conclusion is very simple:

00:15:56.045 --> 00:15:58.138
I am off the stage, back in my lab.

NOTE Paragraph

00:15:58.162 --> 00:15:59.333
Thank you.

NOTE Paragraph

00:15:59.357 --> 00:16:02.666
(Applause)