-
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)