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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.

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So it looks as if
they would not be creative.

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In reality, they stopped trying.

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So when we actually put the data together,
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.

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It's totally random
in the space of the projects.

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It is the productivity that changes.

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Let me illustrate that.

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Here is Frank Wilczek,
who got the Nobel Prize in Physics

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for the very first paper he ever wrote
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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who, at age 70, was forcefully retired
by Yale University.

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They shut his lab down,

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and at that moment, he moved
to Virginia Commonwealth University,

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opened another lab,

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and it is there, at age 72,
that he published a paper

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for which, 15 years later, he got
the Nobel Prize for Chemistry.

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And you think, OK,
well, science is special,

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but what about other areas
where we need to be creative?

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So let me take another
typical example: entrepreneurship.

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Silicon Valley,

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the land of the youth, right?

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And indeed, when you look at it,

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you realize that the biggest awards,
the TechCrunch Awards and other awards,

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are all going to people

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whose average age
is late 20s, very early 30s.

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00:14:24,465 --> 00:14:30,067
You look at who the VCs give the money to,
some of the biggest VC firms --

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all people in their early 30s.

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Which, of course, we know;

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there is this ethos in Silicon Valley
that youth equals success.

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Not when you look at the data,

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because it's not only
about forming a company --

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forming a company is like productivity,
trying, trying, trying --

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when you look at which
of these individuals actually put out

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a successful company, a successful exit.

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And recently, some of our colleagues
looked at exactly that question.

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And it turns out that yes,
those in the 20s and 30s

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put out a huge number of companies,
form lots of companies,

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but most of them go bust.

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And when you look at the successful exits,
what you see in this particular plot,

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the older you are, the more likely that
you will actually hit the stock market

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or the sell the company successfully.

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This is so strong, actually,
that if you are in the 50s,

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you are twice as likely
to actually have a successful exit

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than if you are in your 30s.

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(Applause)

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So in the end, what is it
that we see, actually?

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What we see is that creativity has no age.

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Productivity does, right?

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Which is telling me that
at the end of the day,

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if you keep trying --

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(Laughter)

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you could still succeed
and succeed over and over.

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So my conclusion is very simple:

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I am off the stage, back in my lab.

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00:15:58,162 --> 00:15:59,333
Thank you.

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(Applause)