9:59:59.000,9:59:59.000
Today actually is[br]a very special day for me,

9:59:59.000,9:59:59.000
because it is my birthday.

9:59:59.000,9:59:59.000
(Applause)

9:59:59.000,9:59:59.000
And so thanks for all of you[br]for joining the party.

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
But every time you throw a party,[br]there's someone there to spoil it. Right?

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
And I'm a physicist,

9:59:59.000,9:59:59.000
and this time I brought[br]another physicist along to do so.

9:59:59.000,9:59:59.000
His name is Albert Einstein, also Albert,

9:59:59.000,9:59:59.000
and he's the one who said that the person

9:59:59.000,9:59:59.000
who has not made his main contributions[br]to science by the age of 30

9:59:59.000,9:59:59.000
will never do so.

9:59:59.000,9:59:59.000
Now you don't need to check Wikipedia

9:59:59.000,9:59:59.000
that I'm being 30.

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
So effectively what[br]he is telling me, and us,

9:59:59.000,9:59:59.000
is that when it comes to my science,

9:59:59.000,9:59:59.000
I'm a dead wood.

9:59:59.000,9:59:59.000
Well, luckily, I had my share[br]of luck within my career.

9:59:59.000,9:59:59.000
Around age 28, I became[br]very interested in networks,

9:59:59.000,9:59:59.000
and a few years later we managed[br]to kind of publish a few key papers

9:59:59.000,9:59:59.000
that reported the discovery[br]of scale-free networks

9:59:59.000,9:59:59.000
and really gave birth to a new discipline[br]that we call network science today.

9:59:59.000,9:59:59.000
And if you really care about it,[br]you can get a PhD now in network science

9:59:59.000,9:59:59.000
in Budapest, in Boston,

9:59:59.000,9:59:59.000
and you can study it all over the world.

9:59:59.000,9:59:59.000
A few years later,

9:59:59.000,9:59:59.000
when I moved to Harvard[br]first as a sabbatical,

9:59:59.000,9:59:59.000
I became interested[br]in another type of networks,

9:59:59.000,9:59:59.000
and that time the networks[br]within ourselves,

9:59:59.000,9:59:59.000
how the genes and the proteins[br]and the metabolites link to each other,

9:59:59.000,9:59:59.000
and how they connect to disease.

9:59:59.000,9:59:59.000
And that also, that interest led[br]to a major explosion within medicine,

9:59:59.000,9:59:59.000
including the Network Medicine[br]Division at Harvard

9:59:59.000,9:59:59.000
that has more than 300 researchers[br]who are using this perspective

9:59:59.000,9:59:59.000
to treat patients and develop new cures.

9:59:59.000,9:59:59.000
And a few years ago, I thought that I[br]would take this idea of networks

9:59:59.000,9:59:59.000
and expertise we had in networks[br]in a different area,

9:59:59.000,9:59:59.000
that is to understand success.

9:59:59.000,9:59:59.000
And why did we do that?

9:59:59.000,9:59:59.000
Well, we thought that to some degree,

9:59:59.000,9:59:59.000
our success is determined[br]by the networks we are part of,

9:59:59.000,9:59:59.000
that our networks can push us forward,[br]they can pull us back,

9:59:59.000,9:59:59.000
and I was curious if we could use[br]the knowledge in big data and expertise

9:59:59.000,9:59:59.000
that we develop on networks to really[br]quantify how these things happen.

9:59:59.000,9:59:59.000
This is a result from that.

9:59:59.000,9:59:59.000
What you see here is a network[br]of galleries in museums

9:59:59.000,9:59:59.000
that connect to each other,

9:59:59.000,9:59:59.000
and through this map[br]that we mapped out last year,

9:59:59.000,9:59:59.000
we are able to predict very accurately

9:59:59.000,9:59:59.000
the success of an artist

9:59:59.000,9:59:59.000
if you give me the first five exhibits[br]that he or she had in her career.

9:59:59.000,9:59:59.000
Well, as we thought about success,

9:59:59.000,9:59:59.000
we realized that success[br]is not only about networks.

9:59:59.000,9:59:59.000
There are so many[br]other dimensions to that.

9:59:59.000,9:59:59.000
And, you know, one of the things[br]we need for success obviously

9:59:59.000,9:59:59.000
is performance.

9:59:59.000,9:59:59.000
So let's define what's the difference[br]between performance and success.

9:59:59.000,9:59:59.000
Well, performance is what you do,

9:59:59.000,9:59:59.000
how fast you run,[br]what kind of paintings you paint,

9:59:59.000,9:59:59.000
what kind of papers you publish.

9:59:59.000,9:59:59.000
However, in our working definition,

9:59:59.000,9:59:59.000
success is about what does the community[br]notices from what you did,

9:59:59.000,9:59:59.000
from your performance,

9:59:59.000,9:59:59.000
how does it acknowledge it,[br]and how does it reward you for it?

9:59:59.000,9:59:59.000
In other terms,

9:59:59.000,9:59:59.000
your performance is about you,[br]but your success is about all of us.

9:59:59.000,9:59:59.000
And this was a very[br]important shift for us,

9:59:59.000,9:59:59.000
because in the moment we defined success[br]as being a collective measure

9:59:59.000,9:59:59.000
that the community provides to us,

9:59:59.000,9:59:59.000
it became measurable,

9:59:59.000,9:59:59.000
because if it's in the community,[br]there are multiple data points about that.

9:59:59.000,9:59:59.000
So now we believe,[br]and we go to school, we exercise,

9:59:59.000,9:59:59.000
we practice because we believe[br]that performance leads to success.

9:59:59.000,9:59:59.000
But the way we actually[br]started to explore,

9:59:59.000,9:59:59.000
we realized that performance and success[br]are very, very different animals

9:59:59.000,9:59:59.000
when it comes to[br]the mathematics of the problem.

9:59:59.000,9:59:59.000
And let me illustrate that.

9:59:59.000,9:59:59.000
So what you see here is[br]the fastest man on Earth, Usain Bolt.

9:59:59.000,9:59:59.000
And of course, he wins most of[br]the competitions that he enters.

9:59:59.000,9:59:59.000
And we know he's the fastest on Earth[br]because we have a chronometer

9:59:59.000,9:59:59.000
to measure his speed.

9:59:59.000,9:59:59.000
Well, what is interesting about him[br]is that when he wins,

9:59:59.000,9:59:59.000
he doesn't do so by really significantly[br]outrunning his competition.

9:59:59.000,9:59:59.000
He's running at most a percent faster[br]than the one who loses the race.

9:59:59.000,9:59:59.000
And not only does he run only[br]one percent faster than the second one,

9:59:59.000,9:59:59.000
but he doesn't run[br]10 times faster than I do,

9:59:59.000,9:59:59.000
and I'm not a good runner,[br]trust me on that.

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
And every time we are able[br]to measure performance,

9:59:59.000,9:59:59.000
we notice something very interesting.

9:59:59.000,9:59:59.000
That is, performance is bounded.

9:59:59.000,9:59:59.000
What it means is that there are[br]no huge variations in human performance.

9:59:59.000,9:59:59.000
It varies only in a narrow range,

9:59:59.000,9:59:59.000
and we do need the chronometer[br]to measure the differences.

9:59:59.000,9:59:59.000
This is not to say that we cannot[br]see the good from the best ones,

9:59:59.000,9:59:59.000
but the best ones[br]are very hard to distinguish,

9:59:59.000,9:59:59.000
and the problem with that[br]is that most of us work in areas

9:59:59.000,9:59:59.000
where we do not have a chronometer[br]to gauge our performance.

9:59:59.000,9:59:59.000
Alright, performance is bounded,

9:59:59.000,9:59:59.000
there are no huge differences between us[br]when it comes to our performance.

9:59:59.000,9:59:59.000
How about success?

9:59:59.000,9:59:59.000
Well, let's switch to[br]a different topic like books, right?

9:59:59.000,9:59:59.000
One measure of success for writers[br]is how many people read your work?

9:59:59.000,9:59:59.000
And so when my previous book[br]came out in 2009,

9:59:59.000,9:59:59.000
I was in Europe talking with my editor,[br]and I was interested,

9:59:59.000,9:59:59.000
who is the competition?

9:59:59.000,9:59:59.000
And I had some fabulous one.

9:59:59.000,9:59:59.000
That week (Laughter)

9:59:59.000,9:59:59.000
Dan Brown came out with "The Lost Symbol"

9:59:59.000,9:59:59.000
and "The Last Song" also came out,

9:59:59.000,9:59:59.000
Nicholas Sparks.

9:59:59.000,9:59:59.000
And when you just look at the list,

9:59:59.000,9:59:59.000
you realize, you know, performance-wise

9:59:59.000,9:59:59.000
there is hardly any difference[br]between these books or mine.

9:59:59.000,9:59:59.000
Right?

9:59:59.000,9:59:59.000
So maybe if Nicholas Sparks' team[br]works a little harder,

9:59:59.000,9:59:59.000
he could easily be number one,

9:59:59.000,9:59:59.000
because it's almost by accident[br]who ended up at the top.

9:59:59.000,9:59:59.000
So I said, let's look at the numbers.[br]I am a data person, right?

9:59:59.000,9:59:59.000
So let's see what were[br]the sales for Nicholas Sparks.

9:59:59.000,9:59:59.000
And it turns out that[br]that opening weekend,

9:59:59.000,9:59:59.000
Nicholas Sparks sold more than[br]a hundred thousand copies,

9:59:59.000,9:59:59.000
which is an amazing number.

9:59:59.000,9:59:59.000
You can actually get to the top[br]of the New York Times Bestseller List

9:59:59.000,9:59:59.000
by selling 10,000 copies a week,

9:59:59.000,9:59:59.000
so he tenfold overcame[br]what he needed to be number one.

9:59:59.000,9:59:59.000
Yet he wasn't number one. Why?

9:59:59.000,9:59:59.000
Because there was Dan Brown,[br]who sold 1.2 million copies that week.

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
And the reason I like this number[br]is because it shows that really,

9:59:59.000,9:59:59.000
when it comes to success, it's unbounded,

9:59:59.000,9:59:59.000
that the best doesn't only get[br]slightly more than the second best,

9:59:59.000,9:59:59.000
but gets orders of magnitude more,

9:59:59.000,9:59:59.000
because success is a collective measure.

9:59:59.000,9:59:59.000
We give it to them, rather than[br]we earn it through our performance.

9:59:59.000,9:59:59.000
So one of things we realized[br]is that performance,

9:59:59.000,9:59:59.000
what we do is bounded but success,[br]which is collective, is unbounded,

9:59:59.000,9:59:59.000
which makes you wonder,

9:59:59.000,9:59:59.000
how do you get these[br]huge differences in success

9:59:59.000,9:59:59.000
when you have so tiny[br]differences in performance?

9:59:59.000,9:59:59.000
And recently I published a book[br]that I devoted to that very question,

9:59:59.000,9:59:59.000
and they didn't give enough time[br]to go over all of that,

9:59:59.000,9:59:59.000
so I'm going to go back[br]to the question of alright,

9:59:59.000,9:59:59.000
you have success, when should that appear?

9:59:59.000,9:59:59.000
So let's go back to the party spoiler,

9:59:59.000,9:59:59.000
and ask ourselves,

9:59:59.000,9:59:59.000
why did Einstein make[br]this ridiculous statement

9:59:59.000,9:59:59.000
that only before 30[br]you could actually be creative?

9:59:59.000,9:59:59.000
Well, because he looked around himself[br]and he saw all these fabulous physicists

9:59:59.000,9:59:59.000
that created quantum mechanics[br]and modern physics,

9:59:59.000,9:59:59.000
and they were all in their 20s[br]and early 30s when they did so.

9:59:59.000,9:59:59.000
And it's not only him.

9:59:59.000,9:59:59.000
It's not only observational bias,

9:59:59.000,9:59:59.000
because there's actually[br]a whole field of genius research

9:59:59.000,9:59:59.000
that has documented the fact[br]that if we look at the people

9:59:59.000,9:59:59.000
we admire from the past

9:59:59.000,9:59:59.000
and then we look at what age[br]did they make their biggest contribution,

9:59:59.000,9:59:59.000
whether that's music,[br]whether that's science,

9:59:59.000,9:59:59.000
whether that's engineering,

9:59:59.000,9:59:59.000
most of them tend to do so[br]in their 20s, 30s, early 40s at most.

9:59:59.000,9:59:59.000
But there's a problem[br]with this genius research.

9:59:59.000,9:59:59.000
Well, first of all, it created[br]the impression to us

9:59:59.000,9:59:59.000
that creativity equals youth,

9:59:59.000,9:59:59.000
which is painful, right?

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
And it also has an observational bias,

9:59:59.000,9:59:59.000
because it only looks at geniuses

9:59:59.000,9:59:59.000
and doesn't look at ordinary scientists,

9:59:59.000,9:59:59.000
and doesn't look at all of us and asking,

9:59:59.000,9:59:59.000
is it really true that creativity[br]vanishes as we age?

9:59:59.000,9:59:59.000
So that's exactly what we tried to do,[br]and this is important for that

9:59:59.000,9:59:59.000
to actually have references.

9:59:59.000,9:59:59.000
So let's look at an ordinary[br]scientist like myself,

9:59:59.000,9:59:59.000
and let's look at my career.

9:59:59.000,9:59:59.000
So what you see here is all the papers[br]that I published from my first paper,

9:59:59.000,9:59:59.000
it's in 1989 that I was still[br]in Romania when I did so,

9:59:59.000,9:59:59.000
til kind of this year.

9:59:59.000,9:59:59.000
And vertically you see[br]the impact of the paper,

9:59:59.000,9:59:59.000
that is, how many citations,

9:59:59.000,9:59:59.000
how many other papers[br]have been written that cited that work?

9:59:59.000,9:59:59.000
And when you look at that,[br]you see that my career

9:59:59.000,9:59:59.000
has three roughly different stages.

9:59:59.000,9:59:59.000
I had the first 10 years[br]where I work a lot

9:59:59.000,9:59:59.000
and I don't achieve much.

9:59:59.000,9:59:59.000
No one seems to care[br]about what I do, right?

9:59:59.000,9:59:59.000
There's hardly any impact.

9:59:59.000,9:59:59.000
That time, I was doing material science,

9:59:59.000,9:59:59.000
and then I kind of discovered[br]for myself networks,

9:59:59.000,9:59:59.000
and then started publishing in network,

9:59:59.000,9:59:59.000
and that led one high-impact paper[br]to the other one.

9:59:59.000,9:59:59.000
And it really felt good.[br]That was that stage of my career.

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
So the question is,[br]what happens right now?

9:59:59.000,9:59:59.000
And we don't know, because there[br]hasn't been enough time passed yet

9:59:59.000,9:59:59.000
to actually figure out how much impact[br]those papers will get.

9:59:59.000,9:59:59.000
It takes time to acquire.

9:59:59.000,9:59:59.000
Well, when you look at the data,

9:59:59.000,9:59:59.000
it seems to be that Einstein,[br]the genius research, is right,

9:59:59.000,9:59:59.000
and I'm at that stage of my career.

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
So we said, OK, let's figure out[br]how does this really happen

9:59:59.000,9:59:59.000
first in science, right?

9:59:59.000,9:59:59.000
And in order not to have[br]the selection bias,

9:59:59.000,9:59:59.000
to look only at geniuses,

9:59:59.000,9:59:59.000
we ended up reconstructing the career[br]of every single scientist

9:59:59.000,9:59:59.000
from 1900 til today

9:59:59.000,9:59:59.000
and finding for all scientists[br]what was their personal best,

9:59:59.000,9:59:59.000
whether they got the Nobel Prize[br]or they never did,

9:59:59.000,9:59:59.000
or no one knows what they did,[br]even their personal best.

9:59:59.000,9:59:59.000
And that's what you see[br]actually in this slide.

9:59:59.000,9:59:59.000
Each line is a career,

9:59:59.000,9:59:59.000
and when you have a light blue dot[br]on the top of that career,

9:59:59.000,9:59:59.000
it says that was their personal best.

9:59:59.000,9:59:59.000
And the question is,

9:59:59.000,9:59:59.000
when did they actually make[br]their biggest discovery?

9:59:59.000,9:59:59.000
And to quantify that, we look at[br]what's the probability

9:59:59.000,9:59:59.000
that you make your biggest discovery[br]let's say, one, two, three,

9:59:59.000,9:59:59.000
or 10 years into your career?

9:59:59.000,9:59:59.000
We're not looking at real age.

9:59:59.000,9:59:59.000
We're looking at[br]what we call academic age.

9:59:59.000,9:59:59.000
Your academic age starts[br]when you publish your first papers.

9:59:59.000,9:59:59.000
I know some of you are still babies.

9:59:59.000,9:59:59.000
(Laughter)

9:59:59.000,9:59:59.000
So let's look at the probability[br]that you publish your highest impact paper

9:59:59.000,9:59:59.000
and what you see is indeed[br]the genius research is right.

9:59:59.000,9:59:59.000
Most scientists tend to publish[br]their highest impact paper

9:59:59.000,9:59:59.000
in the first 10, 15 years in their career,

9:59:59.000,9:59:59.000
and it tanks after that.

9:59:59.000,9:59:59.000
It tanks so fast that I'm about,[br]I'm exactly 30 years into my career,

9:59:59.000,9:59:59.000
and the chance that I will publish a paper[br]that would have higher impact

9:59:59.000,9:59:59.000
than anything that I did before

9:59:59.000,9:59:59.000
is less than one percent.

9:59:59.000,9:59:59.000
I am in that stage of my career,[br]according to this data.

9:59:59.000,9:59:59.000
But there's a problem with that.

9:59:59.000,9:59:59.000
We're not doing controls properly.

9:59:59.000,9:59:59.000
So the control would be,

9:59:59.000,9:59:59.000
how would a scientist look like[br]who makes random contribution to science?

9:59:59.000,9:59:59.000
Or what is the productivity[br]of the scientist?

9:59:59.000,9:59:59.000
When do they write papers?

9:59:59.000,9:59:59.000
So we measured the productivity,

9:59:59.000,9:59:59.000
and amazingly, the productivity,

9:59:59.000,9:59:59.000
your likelihood of writing a paper[br]in year one, 10, or 20 in your career

9:59:59.000,9:59:59.000
is indistinguishable from the likelihood[br]of having the impact

9:59:59.000,9:59:59.000
in that part of your career.

9:59:59.000,9:59:59.000
And to make a long story short,

9:59:59.000,9:59:59.000
after lots of statistical tests,[br]there's only one explanation for that,

9:59:59.000,9:59:59.000
that really the way we scientists work

9:59:59.000,9:59:59.000
is that every single paper we write,[br]every project we do,

9:59:59.000,9:59:59.000
has exactly the same chance[br]of being our personal best.

9:59:59.000,9:59:59.000
That is, discovery is like a lottery,

9:59:59.000,9:59:59.000
and like a lottery ticket,

9:59:59.000,9:59:59.000
and then the more lottery tickets we buy,[br]the higher is the chance,

9:59:59.000,9:59:59.000
and it happens to be so[br]that most scientists

9:59:59.000,9:59:59.000
buy most of their lottery tickets[br]in the first 10, 15 years of their career,

9:59:59.000,9:59:59.000
and after that,[br]their productivity decreases.

9:59:59.000,9:59:59.000
They're not buying[br]any more lottery tickets.

9:59:59.000,9:59:59.000
So it looks as if[br]they would not be creative.

9:59:59.000,9:59:59.000
In reality, they stopped trying.

9:59:59.000,9:59:59.000
So when we actually put the data together,[br]the conclusion is very simple:

9:59:59.000,9:59:59.000
success can come at any time.

9:59:59.000,9:59:59.000
It could be your very first[br]or very last paper of your career.

9:59:59.000,9:59:59.000
It's totally random[br]in the space of the projects.

9:59:59.000,9:59:59.000
It is the productivity that changes.

9:59:59.000,9:59:59.000
And let me illustrate that.

9:59:59.000,9:59:59.000
Here is Frank Wilczek,

9:59:59.000,9:59:59.000
who get the Nobel Prize in Physics[br]for the very first paper

9:59:59.000,9:59:59.000
he ever wrote in his career[br]as a graduate student.

9:59:59.000,9:59:59.000
More interesting is John Fenn,

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who at age 70 was forcefully retired[br]by Yale university.

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They shut his lab down,

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and at that moment, he moved[br]to Virginia Commonwealth University,

9:59:59.000,9:59:59.000
opened another lab,

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and it is there at age 72[br]that he published a paper

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for which 15 years later he got[br]the Nobel Prize for Chemistry.

9:59:59.000,9:59:59.000
And you think, OK,[br]well science is special, right?

9:59:59.000,9:59:59.000
But what about other areas[br]where we need to be creative?

9:59:59.000,9:59:59.000
So let me take another[br]typical example: entrepreneurship.

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Silicon Valley,

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the land of the youth, right?

9:59:59.000,9:59:59.000
And indeed, when you look at it,[br]you realize that the biggest awards,

9:59:59.000,9:59:59.000
the TechCrunch Awards and other awards,

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are all going to people,

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an average age for them[br]is late 20s, very early 30s.

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You look at who the VCs give the money to,[br]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[br]that youth equals success.

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Not when you look at the data,

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because it's not only[br]about forming a company.

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Forming a company is like productivity:[br]trying, trying, trying.

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When you look at which[br]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[br]looked at exactly that question,

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and it turns out that yes,[br]those in the 20s and 30s

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put out a huge number of companies,[br]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,[br]what you see in this particular plot,

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the older you are, the more likely that[br]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,[br]that if you are in the 50s,

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you are twice as likely[br]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 at the end, what it is[br]that see, actually?

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What we see is that creativity has no age.

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Productivity does, right?

9:59:59.000,9:59:59.000
Which is telling me that[br]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[br]and succeed over and over,

9:59:59.000,9:59:59.000
so my conclusion is very simple.

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I am of this age, back in my lab.

9:59:59.000,9:59:59.000
Thank you.

9:59:59.000,9:59:59.000
(Applause)