Following is the full text of network theorist Albert-László Barabási’s talk titled “The Real Relationship Between Your Age & Your Chance of Success” at TED conference. In this talk, he explores the hidden mechanisms that drive success and uncovers an intriguing connection between your age and your chance of making it big.
Albert-László Barabási – TED Talk TRANSCRIPT
Today, actually, is a very special day for me, because it is my birthday. And so, thanks to all of you for joining the party.
But every time you throw a party, there’s someone there to spoil it. Right?
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.
Now, you don’t need to check Wikipedia that I’m beyond 30.
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.
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.