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