The 5 Universal Laws of Success

We’re told that talent and hard work pays off. But we’ve all seen instances where people who were equally or even less talented and hard working than we are, still got the raise, the buzz, the promotion, or the recognition that we so keenly wanted for ourselves.

It can make a man downright cynical.

My guest today says that instead of getting jaded, you need to understand that hard work and talent, while necessary, aren’t sufficient for success. His name is Albert-László Barabási, and he’s a professor of network science and the author of the book The Formula: The Universal Laws of Success. We begin our conversation discussing how László’s work in network science helped him uncover the hidden connections that lead to success. László then explains the difference between performance and success, and how it’s possible to be a high performer, but not be successful. We then dig into the five universal laws that László and his researchers found cut across the achievement of success in every field, along with practical takeaways you can start implementing in your life to experience more success yourself.

Show Highlights

What is “network science” and how that field of study led to this book
How people generally define success (and how they think it’s attained)
Distinguishing between performance and success
What the real-life Red Baron can teach us about this distinction
The rare phenomenon of posthumous recognition
How important is fame? How does fame interact with the idea of success?
The importance of networks to your success
Why performance is bounded, and success is unbounded
Why success leads to more success (and how to reach success in the first place)
What role does visibility play in success?
How pop culture “Bestselling” lists actually come about
The importance of a good start
Why collaboration isn’t all it’s cracked up to be (and how to get more credit for things)
Why your best work often happens early in your career (and why that doesn’t have to be the case)

Resources/People/Articles Mentioned in Podcast

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Laszlo on Twitter

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Read the Transcript

Brett McKay: Welcome to another edition of The Art of Manliness Podcast. Ever since we were little kiddos, we’ve been told that talent and hard work pays off, but as we’ve gone to adulthood we’ve all seen instances where people who were equally or even less talented than we are, or even less hard-working than we are, still got the raise, the buzz, the promotion, or the recognition that we so keenly wanted for ourselves. Can make a man downright cynical. Well, my guest today says that instead of getting jaded, you need to understand that hard work and talent, while necessary, aren’t sufficient for success.

His name is Albert-Laszlo Barabasi, and he’s a professor of network science and the author of the book The Formula: Universal Laws of Success. We begin our conversation discussing how Laszlo’s work in network science helped him uncover the hidden connections that lead to success. Laszlo then explains the difference between performance and success, and how it’s possible to be a high performer, but not be successful. We then dig into the five universal laws that Laszlo and his researchers have found that cut across achievement of success in every field, along with practical takeaways you can start implementing in your life to experience more success yourself. After the show is over, check out our show notes at aom.is/formula. Laszlo joins me now via Clearcast.io.

Albert Laszlo Barabasi, welcome to the show.

Laszlo Barabasi: It’s a pleasure to be here Brett.

Brett McKay: You just recently published, not too long ago, a book called The Formula: The Universal Laws of Success, the science behind why people succeed or fail. Now the story of how this book came to be is really interesting because for a living what you spent most of your career doing is studying complex networks. In fact, you run the Center for Complex Network Research. For those who aren’t familiar with that, what exactly do you do there?

Laszlo Barabasi: Sure. I’m a network scientist officially, professor of network science. We study all kind of networks. The reason we do so because virtually all our social and biological existence depends on networks. We embedded in the social network and professional network all professional opportunities depend on access to the right network. But even our very biological existence depends on let’s say chemical, biochemical network within ourselves, and genetic network within ourselves. Our consciousness depends really on the wiring of our brain.

We don’t think often too much about it, but really the fact that we are alive and can exist and do what we do is all depends on myriads of networks. Network science aims to study and understand these type of networks. We study at the same time the biological networks, like genetic networks, but also the internet, social networks, and eventually, in the last few years, networks that determine your success.

Brett McKay: How did that happen? How did you go from looking at say a biological network, say in our brain or within our genes to studying how successful people become successful?

Laszlo Barabasi: Sure. There are two ways of thinking. One of them was really by an accident, which is I had a fabulous student, who is now a professor at Kellogg School of Business at Northwestern, but at that time he was just coming off a project about disasters. That is to try to understand how people change their behavior when they experience some kind of disaster in their neighborhood. We use mobile phones to track human behavior and try to understand whether we could detect something odd happening in your neighborhood just the way you behave and use your phone. It was a fabulous project and we wrote a great paper about it, yet journal after journal rejected the paper. One day one of the students who was on the project, came to me and said, “Okay what’s next? We’re done with this kind of. What should be my next project?” I said, “What would you like to do?” He said, “Whatever, but not another disaster.” I kind of said, “Okay, well how about success? How about science of success?” We kind of laughed about it, but then we looked at each other and said, hmm this is not such a bad idea.

Why it wasn’t such bad idea, because we’re network scientists and we have spent quite a bit of time [inaudible] looking at the structure of networks, but we hardly ever ask the question, how you [inaudible] is experience the network that you’re part of, and whether the network will actually help you succeed in certain areas or pull you back.

So kind of that night from this kind of random direction or this discussion, came a new subject of study for us that is still ongoing in my lab. How do we quantify success? What are networks? And how do we really describe performance and success in the language of science?

Brett McKay: Okay. So yeah, basically it was like … The question was, tell me why … figure out why our paper got rejected. Why wasn’t it a success? Before we get into what you guys uncover with your research, let’s talk about how people in general think about success. When you ask people, just on the street or maybe a colleague, how do you become a success? What are some of the most common answers or assumptions that we have about that?

Laszlo Barabasi: I’m so glad you asked that, because I was very surprised that when I went around and asked people really that question, I realized that most people are really shy to talk about the measures of success in society considers success, from money, to citations, to visibility. But they talk about their personal successes, like their pride in their children. The satisfaction of achieving something in their life of being where they are and so on.

We were at that time curious about how you quantify success, because anything we do has to be quantified and measurable and so on. For us, it was a big dilemma, how do we describe that? For us actually, we realized that we have to make a very big distinction between performance, which is what you do and how you feel about what you do and so on, and success?

This is interesting that we had to distinguish that because in the language, they’re often used interchangeably, these two terms. There … We do so because we learn early on in school that performance leads to success, as if you’re successful you must have performance, if you have performance, you will be successful. But from a data perspective, we realize that these are very different quantities, because performance is something that you do, how fast you run, what kind of research papers you write, what kind of deals you put together as a business man, what kind of paintings you paint.

Success however, is mostly about, what does the community see from that performance, and whether they acknowledge it or not, and whether they reward you and how they reward you for that. In other words, your performance is about you, but your success is really about us, about the community that acknowledges and rewards you for that performance. Which from a data perspective was very interesting, because probably as we go on we will realize in this discussion that performance often is hard to measure. But success is easily measurable, because it reflects the community’s opinion about you, [inaudible] multiple data points about your success.

Brett McKay: So you can measure success in ways like, okay, number of citations a journal article gets for example, a number of books a book sells, the number of I guess time … I guess nowadays it’s not albums sold, but it’s downloads of a song. Those are metrics of success you can measure.

Laszlo Barabasi: Yes and it’s important to understand that there is not a single measure of success, it’s not just money say, or fame. But depending on what you do, there are different measures of success in the community as you said, for a scientist that’s impacted, is often measures in terms of citations. For a musician, it’s downloads or how many people show up at the concert. For an author, it’s audience, how many people listen to them. For a politician, may actually be fame, because that kind of translates into…

So for each area, one has to find the right performance measure and the right success measure, but one of the things I discuss in the formula is that despite the fact that there are multiple measures of performance and multiple measures of success, fundamentally the laws that describe the relationship with performance and success are rather universal and apply to all different areas.

Brett McKay: Well you give a great example in the book just sort of showing the difference between success and performance, it comes from World War I. Now people are in America, they’ve probably had Rad Baron Pizza. If the Red Baron was this famous ace pilot during World War I, people know about him today, Snoopy made him famous, the Charlie Brown comic. But you also, so there’s a formula with success, because he performed well, but also people knew him, but you also highlight there is also another World War I ace that had pretty much the same performance level as the Red Baron, but no one knows about him.

Laszlo Barabasi: Yes, indeed. That really kind of shows to me how different success can be. So indeed, the Red Baron or [inaudible] what was his name in the First World War, was a very famous ace pilot who has had really every measures of success one can imagine, and he’s very well known to us because he holds officially the record number of planes shot down, I believe in the vicinity of eight years so … Because of that, movies were written about him, books have been written about, documentaries and so on. He was a person who was no shy to hide his success. He’s called Red Baron because at a certain moment he went against of the principle that we have today, navigation to build planes that are invisible but it’s that he painted his own airplane red, so everyone knows that it’s him and it’s coming.

So what is interesting when you look at the data, is that why he was in the German side on the allied side, there was another person, his name is Renee Faulk, who was just as good at fights actually, as himself. Not only that he himself counts that about 120 planes that he shot down, which is much higher than von Richthofen, but about 70 had been confirmed that most likely he has actually shot down more than the Red Baron. But most importantly, he himself has never been shot down and never even been scratched by bullet, while the Red Baron has been shot down three times during his career and third time he even lost his life in the battle.

So yet, all the movies are about the Red Baron and you hardly hear about Renee. That’s really the mystery of the formula. This is one of the reasons I wrote the formula, is for people and myself to understand why is it that with virtually indistinguishable performance, some succeed and some are just plainly forgotten?

Brett McKay: Yeah and as I was reading that chapter, it made me think of there’s some artists, writers in particular, who their performance level was phenomenal at the time they were alive. Made me think of Herman Melville, with Moby Dick, because they’re a masterpiece now. Same with The Great Gatsby by F. Scott Fitzgerald. We consider them masterpieces, great American novels. But it was … That didn’t happen until after they died. They didn’t become a success the way you define it until after they died, even though they masterfully wrote it when they were alive.

Laszlo Barabasi: Mh-hmm. That’s actually an interesting story and I probably didn’t devote enough time in the formula. Is this the idea of posthumous success. That people will recognize what I do when I … how great is what I do after I die. Despite of all the examples that you mention and I could add more. The data is pretty clear about it. It’s not common. It’s extremely rare that someone is recognized after their death. What do I mean by that? When you go back in the encyclopedia and you look at the people whom we admire and remember today, and scientists have done that, there’s a so called [inaudible] who focuses on that, mostly written in psychology. What they realized is that 99% of the people who we consider important for us today from the past, were very, very important to their contemporaries and they have gotten all the recognition that was possible at that moment of their career. So if you think from Michelangelo, to Leonardo, from Beethoven, to Bach and others, they were revered in their times.

And they’re very, very few, less than one percent of the individuals who were recognized after their death. But those one percent present such a powerful storyline for us, that we end up writing most of our books, and most of our movies about them. Therefore, they occupy a bigger space in our brain than those who really did not follow that pattern.

So if I look at the data, my recommendation to you and to your audience, if you want to be successful, don’t count on the next generation to recognize that. Make sure that you follow the patterns that I describe in the formula and get your recognition while you can see it and enjoy it.

Brett McKay: Well so, here’s another question. Perception by the community is one of the necessary factors in order to be a success. That could lead people to the conclusion, well if I just … if I’m just famous, if I’m known by a lot of people, then I’m a success. Is that necessarily the case?

Laszlo Barabasi: Well, there are certain forms where fame is the goal and pretty much the celeb culture is really in that particular category and I know often people ditch the celeb culture, oh all they do is they want to be famous and they don’t do really anything. The truth is that those [inaudible] very, very hard to continue staying in the kind of in the attention of the community, or the world at large. So it’s not so easy to continue doing that.

Perhaps, the reason why we think less of them is because we don’t perceive that they’re doing something good for the society, so it’s the for the sake of becoming famous, that’s what it’s for. In most other areas, people who are famous, from Einstein to let’s say Lady Gaga, they became famous through some activity that they have done, some professional activity that we as a community or as a society, really appreciate. So there is this dichtomy between that, so you can become famous for the sake of famousness or as a result of something good that you’ve done for the society. It’s a value system we obviously appreciate better of those who have just done their job and then recognized them and made them famous.

Brett McKay: Well that leads nicely to the first law of success you lay out. What is the first law in the formula?

Laszlo Barabasi: Sure. The first law really kind of addresses the relationship between performance and success. Performance drives success, but when performance can’t be measured, networks drive success. There’s lots of information acting to that, because on one end it actually acknowledges the fact that in areas where performance is measurable, that it determines success. Unfortunately, there are very few areas where performance is accurately measurable. Sports is one of them and perhaps, investment is another one. What we have shown in my research I discuss in the formula, is the fact that when really you can measure performance, how fast you can run or whether you’re winning or losing your tennis games, that all measurable success quantities are purely derived from that end, they are predictable.

But the problem comes is that most people in this society more can lead in areas where performance is not as easily measurable as it is the case for a runner. Whether you are teaching at the school or university, or whether you’re putting together bills for a business, or whether you have painting, you are in areas where performance is very, very difficult to measure. So then the question is, when performance is not measurable, what determines success and as I discuss in the first law, networks do.

Brett McKay: So and what are the implications of this law? What can people do to I don’t know … guide their life or their career decisions knowing this law?

Laszlo Barabasi: Sure. So perhaps let’s kind of roll out a little bit the network piece. One of the areas where network … where performance is not measurable at all, and as I discuss in the book, one area that is clearly that case is art, because is the microphone in front of me, is a work of art or purely a microphone? Well, in front of me is purely a microphone. If you would see the same microphone on pedestal under a wide box, it would be an artwork. So artists, modern art or contemporary artists, one of those areas, where you cannot just look at the object from … in isolation from the artwork and decide what is it’s worth? It’s worth is determined by who was the artist who put it out there? What did the artist do before where he or she was exhibited before? What happened to him afterwards? What institutions were engaged with that artist?

We have taken this to such an extreme that we mapped out the artwork in the last forty years, every single artists career, and we were able to show that we can map out the invisible network that determines the success in the artwork and that network is extremely predictable, has extreme predictive power. If you give me your favorite artist in the last five exhibits, I can fast forward his or her career 20 years into the future and tell you whether he or she will make it or not. Why is that? Because art is one of those pieces where performance is possible to measure and is owning the network that determines the future success. You have to engage with the network that determine the value in the art. In our case with the art, those are the institutions, galleries, galleries curators.

So coming back to the original question, what does that mean? The first question I would ask, sit down and think yourself, are you in a career path where you have an objectively measurable performance and in that case indeed, the key … the path to success is to improve your performance, run faster, make better deals and so on. If however, you’re in an area where performance is not accurately measurable, or not measurable at all, then beyond a certain point, improving performance does not give you more results. You need to start paying attention to those influence and power networks that determine success.

Brett McKay: So does that mean you have to work on building up your network?

Laszlo Barabasi: Yes, but it’s not as simple as simply mindless networking, and myself as a network scientist . . . that’s not what you should be doing, networking. What you need to do, is to understand what is that network that determines success in your area. In the case of the artwork, it’s not the network between the art, it’s the artists are totally irrelevant, they’re puppets in the show. The network that really matters is the institutions, the curators, all curators as well as the galleries. So just hanging around with lots of artists is not the path to success in the artwork, kind of understanding these forces that determine how artists and artwork [inaudible] within the institutions is the key.

All areas have their own respective network. Where in the process for example, to start a project to map out the networks and the forces that lead to entrepreneurial success and we already see the multiple networks that are important there, from actually getting access to the resources or the way to kind of getting funded, the people that you bring in your company and so on. So I’m giving this example, end of art example to people understand that really there is no one size fits all and depending on what you do, it may be a completely different network that is responsible.

The first step of the process, understand, map it mentally out and then try to think what do you need to do to position yourself well within that network?

Brett McKay: Well, let’s go to your world. The world of academia. What would be the network that you need to develop to say, get that paper published that didn’t get published?

Laszlo Barabasi: Sure. Actually, academia is somewhere between art and sciences, because performance does matter and why it does matter is that if you and I actually write down the formula for let’s say, predicting the success of tennis players, then the formula can be tested on the data and the community can decide whether your formula or my formula is better. If yours is better, then you will actually carry the success and my formula will be very quickly forgotten. But networks are still important because not everything is worthwhile or it’s possible to study. There are so called disciplines and within disciplines there are kind of breaking areas and a little bit, there is a community division of what are the areas that really we should be focusing on and you could get fabulous results in areas that no one really cares and therefore really, you will not have an impact.

So in science performance and networks together. The networks determine what is worthwhile to explore and then within that area, there’s a clear performance measure, whether your theory, or your formula, or your prediction is better than mine.

Brett McKay: Got you. All right, so to recap the first law. The first law is if an activity can be measured, performance is going to matter, but if it can’t be measured than the network is going to matter more. Did I get that right?

Laszlo Barabasi: Correct.

Brett McKay: Okay. So let’s move on to the second law. What is the second law of the formula?

Laszlo Barabasi: The second law really talks about the fact that performance and success are very, very different animals, mathematically. It’s formulated like that. Performance is bounded, but success is unbounded. But we need to unpack what that means. So think about runners. The runners are determined really … their performance is determined by their speed that we …. and we can measure it. Of course, we know that the fastest man on earth is Usain Bolt. What is interesting about him, when I look at his performance, is that when he wins a race, he doesn’t truly win by outrunning his competition. He runs at most one percent faster than the loser of that particular competition. And particularly when I look at his speed, he’s not running 10 times faster than I do and trust me, I’m not a good runner at all.

So when we measure performance like speed of running, or any other really measurable human performance, what we realize is that there are huge variability between the performance of the individuals, that is the best is not really much, much better than the second best, but only slightly better. This has important consequences, this is what we call that performance is bounded. One of the consequences to this is that no matter how good you are in terms of performance, you will never be much better than your competition, and there will be others who are so closely similar to you in performance that is almost indistinguishable.

Now put to that to the other piece, the fact that in many areas performance is not possible to measure in an objective manner. So now if performance is pondered and you can’t even measure performance in an objective way. It means that no matter what you do, you can count on that there are several people who are indistinguishably good as you are at your job. Now this is not to say that we can not distinguish bad, good singer from bad singer, good businessman from bad businessman, but what is difficult is to do, is to distinguish the good singer from the good singer, the good one from the good one and so on. So performance is bounded and that’s a humbling result because it really tells me that it doesn’t matter what I do, I cannot truly be the absolute best in a measurable way at what I do. I have to co-exist with many others who are comparable to me.

But success is unbounded. So what does this mean? It means that when we look at the success measures. How much money the number one earns versus number two? How many citations the best scientist earns compared to the second one and so on? The differences are not tiny, but it can be orders of magnitude. Indeed, this is kind of a known that the income distribution is not really … and the top people actually are not just earning one percent more than the second one, but often a factor of 10 could be the difference at the end. So that’s really what the second law tells us. Performance is bounded. That is very hard to distinguish those at the top, but success is unbounded. That is the number one are not just slightly better rewarded but often greatly more rewarded than number two.

Brett McKay: This goes to the power laws right, where you’re talking about success is unbounded?

Laszlo Barabasi: Correct. So mathematically every time that we measure performance, it follows bounded, but every time we measure performance whether it’s citations, now downloads of songs, it follows an unbounded distribution. In the economics literature this is often called the Pareto’s law, from the 19th century economist in Italy who realized the so called 80/20 percent rule that 20 percent of the individuals earned 80% of the money and that time in Italy, that is true even today, except it became more extreme, particularly in the U.S. 80% of the money in the U.S. is probably earned by the top two, three percent of the population.

Brett McKay: So I mean, what’s the takeaway from this? This is going to be kind of depressing right, because like well, I’m just as good as that guy who’s getting all the book sales and money. Why am I not getting that? What do I need to do to compete with that guy, or can you even compete with that superstar who’s at the top end of the power law?

Laszlo Barabasi: Yes you can and the key actually, is to really understand that beyond a certain point, the competition is not based on performance because those performance differences are not visible and then you need to pay attention to other effects, namely to the third law.

Brett McKay: Okay so what is the third law?

Laszlo Barabasi: Of course, so the third law is formulated like that. Preview success times fitness = future success. So let’s again and take it apart and what it means. Preview … the law starts with simply saying success derives success. That is the more you have, the more you will get proportionate to what you already have. I have discovered or encountered that is the first time about 20 years ago, when we were studying the world wide web and we tried to understand, why is it the certain webpages have millions of things, like the vast majority of the webpages have a few dozen at most. So what’s the mechanism by which a certain webpage like Google or Yahoo running away with such an exceptional [inaudible] number of links. We realize that mathematical to describe that, you have to assume that success leads to success. That is the more links you have on the worldwide webpage, the more you will get tomorrow. The friends you have, the more friends you will make tomorrow and so on.

This is a very powerful law. It’s called in the scientific literature as preferential attachment, saying that effectively if you have more, you are preferentially chosen . . . if only rich gets richer, which is what preferential attachment says, the question is, how do you become rich, to get richer. So what’s the mechanism by which coming from behind you could actually become the top or that very rich individual? That’s where the fitness comes in. The fitness is really telling us that to have different abilities, or individuals have different abilities to compete for success.

Once again, we discovered first in the case of the worldwide web, trying to understand how can a latecomer webpage turn into most connected page. Facebook was a relatively latecomer on the worldwide web, yet within a few years after it’s appearance it became, the single biggest hub of the worldwide web overcoming even Google.

We realize that there is another concept which is the fitness and once again, fitness is a collective measure that the community of science, the particular note or individual and effectively it tells you how much … how interesting you are for us. Fitness as an individual tells you, if I meet you, do I want to keep your phone number. The webpage tells me, if I go to your webpage, do I want to save that link to go back again and in all areas, there is a measure of fitness and effectively describes the community’s perception of how useful that individual, that product, that webpage is.

The reason why fitness is important is because the rich gets richer phenomena, is really filtered to the fitness. That is visibility means that I can easily find you. Well once I find you, I make a decision if I want to know you and connect to you, and that’s determined on your fitness. Hence, the law of fitness could actually lose, or throw slower, and lose it, but if a high fitness comes into the worldwide web. It very fastly can acquire new links and can overcome the earlier webpages, like Facebook has overcome Google.

So at the end, what we learn is that really your success is determined by your previous success which is your visibility, how easy it is for me to find you, times your fitness, is telling me once I found you, what is the likelihood that I will actually connect to you or work with you.

Brett McKay: Because that’s interesting. I want to … I like that distinction between fitness and visibility, because that visibility factor can be manipulated in unethical ways where you get lots of visibility really fast. So there’s examples of authors who will buy their books in bulk, so they can get on the New York Times list, but what you’re saying, okay that might give you visibility in the short term, but once people start reading it, they actually find out, well it’s not a very good book, you’re not going to be as successful.

Laszlo Barabasi: Absolutely, and I’m glad you raised that example because we in my lab actually analyzed the book’s success, we have purchased sales data from book scan and we looked at kind of [inaudible] like what makes a book successful and what doesn’t, and we actually do see books that are pushed up on the New York Times Bestseller List. That is when they appear, they appear on the bestseller list and generally they sell no more copies in the coming weeks whatsoever. This is a traditional case of the situation that you described. Very strong marketing and often purchase massive purchases to kind of create the numbers to make it the bestseller list, but then when people actually get that book, they realize, no I don’t really want to read that and they will not recommend it to anyone else.

Which actually raises an interesting question in terms of success, does the New York Times Bestseller list or appearing there will help you sell books and actually, that’s a very good question I discussed at the end of the formula that the answer is no. Appearing with the New York Times Bestseller list will not actually boost your sales, for a vast majority of the books at least it will not, the only time it will do so, if you are a new time author and you’ve never been in a bestseller list and for the first time appear there. In that case it acts kind of like a marketing tool that people will find about you … your book about and then therefore, they may by chance by it. Most cases the New York Times Bestseller List is not selling books, it’s reflecting the community’s interest in your book.

Brett McKay: This chapter also you talk about this I’ve always found fascinating is the study, the research on the wisdom of crowds. We have this idea because the internet, well the crowd, if you get them together are going to come up with the best. But there’s a study you talk about in the book where I think it was at Stanford where they looked at music downloads. In one case, people … they couldn’t see what other people were downloading and in that case, people typically rated the songs the same in quality, but then once people could see what other people were downloading, what ended up being the most popular downloaded song changed based on the particular group that they were in. So there’s a social network effect going on there.

Laszlo Barabasi: Absolutely, and this is very interesting because markets can be very volatile … like books, and music, and so on, and we often attribute to the variable quality. But what the study has shown by that time they were working at Yahoo, is that really the volatility is often not in the quality of the song, but it is rather in the crowd effects.

Effectively, when people were not shown the ranking of the song and they were asked to rank songs just simply pure based on the performance, they would come up with a relatively stable ranking that deflects the community’s perception which is a good song, but indeed, in the moment they were actually shown of how many other people have liked that song, then the outcome became totally unpredictable. So they have … they had eight different parallel experiments that different groups of people ended up in the different rooms and the outcome of the eight experiments was drastically different and there was no agreement between the eight groups of which one is the best song.

What is interesting about that is that we live in a society where we rely on other people’s opinion in many of our decisions. We go at it and see how many people like a particular product, how many comments it has, and whether we try to go to a hotel, we actually look at how many people like that hotel and what kind of comments they gave and therefore, we’re relying on the crowd to shortcut our decision process. Well what this study shows is that the crowd decision is really not selecting quality, because there’s a huge degree of volatility in the randomness of what the crowd sees first and how they pick it up.

So, but what is also interesting about it is that, if you are actually following the crowds election process, my colleagues were able to come up with formulas that can infer the true value of each of those objects, whether it’s songs, or services, or books. So and tell us which one is truly the best and why is that important is because you can rank things when you have a store for example, based on the popularity, or you can rank things based on the true inner value of that, that you infer from these formulas, and what the data shows is that your consumers are much more likely to make a purchase if the ranking is based on the quality, than it is based on popularity, because partly they look at it, at the ranking and they don’t like what they see at the front, they walk away from it. If you rank on the true quality that you can infer from this data, they’re very likely going to like what they see at the top and they will make a purchase.

Brett McKay: Okay. So the third law is success is visibility times fitness, so fitness is performance basically, whether you’re good, you can replicate it. So what’s the takeaway from that loss. So you said earlier, so how do you get … in order to be successful you go to be successful. Let’s say there’s a young entrepreneur or a young writer who’s just starting out, what can they do understanding that law?

Laszlo Barabasi: Well I mean first of all, number one is understanding that that mechanism takes place and then if you’re completely novice, then the big question is how you get started. I discuss in the books there were studies that show how important is that initial acknowledgement of what you do. My favorite one actually is the kick starter study, where a colleague, a sociologist from Holland has gone and randomly picked 200 kick starter projects that no one has yet supported because they were brand new and group them into two groups, friend only. For one of the group, he actually gave them a little money, proportionate to how much they were asking for and he simply ignored the other group. Then he asked what’s … how did the two groups do? How did the projects in the two groups have done? And what he found a month later is that the groups that he actually gave that initial tiny investment in, have had a much, much higher chance of actually succeeding and collecting the money that they had compared to the random one. Which was really odd because these were … the groups were randomly decided, so it’s not that the group that he chose was any better than the group they didn’t choose.

This experiment and many other experiments he has shown … he has done have shown how important is that initial endorsement? If you get a prize, you are much more likely to get further prize, if you get the support from someone, you’re much more likely to get further support. In particular, people who are in the investment business, let’s say startups, they know that variable. Once a big name company or even not big name company comes and tries to invest into your company, lots of other investors will come by and say I also want to do that. The hardest is to get that very first investment, that very first endorsement.

Why is that first endorsement important, the first prize? Is because we decision makers are very, very risk adverse. So if I have a choice between one that has been already endorsed and supported by people whom I trust and one that has never been, I’m much more likely going to choose the one that has been endorsed through others because I feel like I’m reducing my chance of failure by doing so. So the challenge for a young entrepreneur and for a young person in anything, whether it’s science or business or art, is that how you get that initial endorsement, how you get that initial attention, how you get that first award that will make you award-able in the future.

Brett McKay: And I imagine it goes back to the first rule, building up your network in a smart way can help increase that chances that someone gives you that first endorsement.

Laszlo Barabasi: Absolutely, and you asked earlier if performance is bounded and what is it that I can do and if fitness … if success drives success? The answer is, to pay attention to these random things like this initial endorsement. We have a tendency to say, well, this endorsement will come naturally if I do good work. What the research is showing is that, that’s not necessarily so and how they distinguish the success from failure.

Brett McKay: Okay so let’s move onto the fourth law, because people hear okay, that first endorsement helps, so one thing that a young academic or young writer will think, well if I just collaborate with someone who’s bigger than me, that can help me, but the fourth law says, maybe not.

Laszlo Barabasi: Well I mean, often collaboration and teamwork is not a choice. We live in a society that most big tasks cannot be done alone. You have to work with others to do so and when you work with others, there are two questions that come up. What’s the right team to lead to success? The second one is who gets the credit for the team’s work? In the formula one chapter discussing really the research that came out in the last few years to understand what makes the good team. But personally, I think the most important part of this law is not as much how you make a good team. Well once the team is there and they have achieved the work, and let’s say that they successfully did so, who will walk away with the credit? That is, will the person who came up with the idea? Will it the one who did most of the work? Or perhaps the person who really came up with the eureka moment and sold the problem or maybe the promotion will go to the individual who made sure that the coffee is warm on the table at all times.

The reason why this an important question is, because that we within the team, we understand who did what, but the community outside of the team, the one who rewards you for the teams performance often and typically has no clue about what was the role of each of the player and success is based on the perception, not on performance, so the success eventually and the reward will be determined what the community perceives about who was responsible for the success of the team.

This is not just kind of a theoretical discussion. We have actually written an algorithm that looks at any research paper published in scientific literature and decides how much credit each of the author gets independent on the order of the authors, or how many authors are in the paper. Of course, we have that formula to tell you how much credit you get for the team’s work. Well how do we know that we are right?

To test that we are right, we went to areas where the community has already decided who gets the reward for that work and namely prizes, in a particular Nobel Prizes. So we’ve taken all the Nobel Prize winning papers, some of them had as many as 175 authors and we use our algorithm to decide who should get the Nobel Prize. In about 95% of the cases, we got it exactly right, that the authors [inaudible] who actually Nobel Prize community awarded the prize for. In a few cases we were wrong. Every time we were wrong, there was a juicy story behind that helping understand really the of allocation of credit when it comes to a team’s work.

Just to give an idea of how it works, you are hosting this broadcast, I am simply the guest. If this podcast will be the most successful podcast that you’ve ever done and I’m sure it will be, then it’s your credit, not mine because you put together the podcast, you chose me as a person to interview, you are asking the question and guiding this conversation, and rightly so, it will be your success if this particular show is well regarded by the community. However, if you and I write a paper about network science and let’s assume that you come up with the idea, and let’s assume that you decide to spend the next year in my lab and work out that idea because you are so passionate about it. The truth is that when the paper is published it’s going to my paper and it’s going to be my paper not because I did anything in that, but because you and I, you have no track record in network science and everybody who will read the paper will say, oh Laszlo has some other paper and here is Brett who is actually helping to make that a reality.

So that credit for work doesn’t really depend on who did what, it depends on who is the person who’s previews and subsegment work most likely aligns with the team’s success. That is that if you are a scientist and if you publish many, many papers in network science like I did and I will continue to do so, you and I publishing a paper means that the credit mostly goes to me because the community sees it as part of my intellectual journey. So which means that really one can actually look at a potential collaboration and decide even before the work has started, whether if that work is successful, will I get credit for it or not.

This is very interesting, because we’re not doing work only to get credit for it. I engage in lots of team activities where I’m not there for credit, I’m there just to make sure that, that project because I deeply care about it. But if the project that you’re working on, you do so because you would like to get credit and acknowledgements for your work in the project, then you need to choose carefully making sure that indeed the project’s outcome lines up with your intellectual journey.

Brett McKay: So what does this mean for a young academic, who teams up with you? I want to be a network scientist, they’re going to work on a paper with you, but you’re going to get all the credit, how can they still benefit from that collaboration with and get that visible success?

Laszlo Barabasi: I am so glad that you asked the question, Brett, because I tell every student of mine when we publish a great or not so great paper, it doesn’t matter together, I taught them congratulations, now your first or second or third publication now, but you need to understand that this is not your paper, but my paper. How will you change that? Well, two things you need to do.

First go out and speak at every possible venue, conferences, workshops about this work, so people will get to know you and they slowly associate the results with you and not with me whom they already know. Second, and that even more important. Go ahead and publish a series of papers on the same topic without me and with that the credit will slowly shift to you. I had for example my student, actually Gonzalez, who worked for several years in my lab and together we started working for the first time on human mobility, using cell phone data to understand how people move around and what are the fundamental laws of human mobility?

What Martha has done after leaving my lab is that she moved to become a faculty at MIT and she ended up writing quite a number of fabulous papers on the same topic, and I personally stopped working on that. So now, when the community would like an expert in human mobility, no one thinks of me, everyone thinks of Martha. She rightly so gets much of the credit for the joint work.

Brett McKay: Okay, that’s useful information. So that can apply to you even if you’re an academic or a business person. At some point you have to differentiate yourself and go off on your own.

Laszlo Barabasi: Absolutely, and it’s … really the key is not necessarily is to say, well you and I made a great business, and I’m now going to become a great singer and I will get the credit for the joint business, no. You have to continue working in the same area so that you can strengthen the credit for the work that you have done alone.

Brett McKay: All right, so let’s move to the fifth law because this came top of mind to me this week because in the Atlantic, we’ll talk about what it is but in the Atlantic, there was an article talking about Your Professional Decline is Coming Much Sooner Than You Think by Arthur C. Brooks. He was saying that yeah, you’re going to have a lot of success early on in your career and then you reach a point where you’re not going to have much success. The fifth law says ah, maybe not so fast. You can still have success even later on in your career.

Laszlo Barabasi: Well I’m glad we get to talk about the fifth law because this is really the favorite part of the book for me, particularly so because I just passed 50 and based on all the previous research on the topic of creativity, the conclusion is clear, I have really very limited chance of overcoming my earlier work. What do I mean by that? There is a [inaudible] research in the genius literature that looks at what age people that we admire today have done their best work, whether in 20s, 30s, or 40s and 50s. The conclusion is pretty clear, notable individuals tend to do the most important, a career defining work relatively early in their career. This is so much so that Einstein once claimed that if a person has not made his or her major discovery by the age of 30, he will never do so.

So a few years ago we were curious, is this only true for geniuses or it’s true for average individuals, average scientists as well? So we ended up reconstructing the career of all scientists from 1900 until today, finding out when they did their best work, whether there was a Nobel Prize winning discovery or something that no one remembers, but it was the best of their own career. What we were surprised to find that it turns out that people do their best … personal best work relatively early in their career. But one … in line with the genius discovery. But when we look more carefully at the data, we realize that not only they do actually most of their rediscovery early in their career, but they do most of the published work relatively early in their career. That is productivity changes during the career of a individual, very high early on, young people try a lot, publish a lot, paint lots of paintings, write lots of music and as they age, they do less and less of that.

Then we put productivity and success together, we realize that really there is no age dependence of creativity, rather truly, what we learned is that everything the project and person’s career, has exactly the same probability of becoming his or her most important work. That is success or successful projects are like lottery tickets. That each of them has the same probability of winning, but what it turns out is that most people write, buy their lottery tickets or do most of the projects relatively early in their career and as time goes on they try less and less. So therefore, it appears as if people … only young people can win the lottery or young people can be actually successful. This is … the data indicates that this is not the case at all, there is age dependence, which is fabulous news for me obviously, because it means that if I continue doing research, I could still come up and discover that would overshadow everything that I did until now and there are beautiful examples for that.

I discuss in the book the example of a chemist at Yale University, who was forcefully retired at 70 at the end of his career, but he was not ready to give up. So when they close his lab down at Yale, he moved to a smaller University and it is there in the new research lab where he made the discovery for which 15 years later at age 85, he received the chemistry Nobel Prize. So at the end what we learned from this research is that no … creativity has no age, productivity does, people do tend to slow down, mainly because aging partly because of family responsibilities, often because other opportunities open up, they become research administrators, they start running companies, that will take away from their initial expertise and area of interest, but creativity doesn’t mean . . . Those who actually continuing doing it, they could come up with their break to discovery at any age of their career. John was 93 when he passed away and a few days before his death, he was still in the lab working on the next paper.

Brett McKay: All right, that gives me hope. I like that. So let’s do a quick summary of this.

Laszlo Barabasi: Sure. First law, is really about performance drives success, but when performance can’t be measured, networks drive success. That is really that you have to pay attention to your network if your performance is not so distinguishable from the other ones.

The second law says performance is bounded, but success is unbounded, which really says, performance at the top is very hard to distinguish, but success is easily distinguishable.

The third law helps us understand which of the high performance individuals will succeed, telling us that preview success times fitness is the one that leads to future success and all measures of success from citations, to money, to visibility follow that.

The fourth law talks about teamwork, saying that why team success requires diversity and balance, a single individual will receive credit for the group’s achievement. That is, credit really is a sign based on perception and not on what you did on the team, and you need to manage that performance if you would like, or you need to manage that perception if you would like to get credit for the work you have done in the team setting.

Finally, comes the fifth law, [inaudible] favorite at this age of my career telling us with persistence, success can come at any time.

Brett McKay: I love this and what I love about this as you said throughout this conversation, is they’re sometimes not specific prescriptions you can give on how to apply these things but as you said, just understanding how this works can start getting you to think about how can I use this or apply this in my specific career or setting that I’m in.

Laszlo Barabasi: Oh absolutely, I mean even for me, writing this book and organizing these laws that were scattered in the literature into multiple papers and multiple texts was really a game changer and not as much about my own success, but I’m an educator so these days much of the advice I give is not to myself, but to my students, and other young individuals and I always invoke these laws, it gave me the backbone on which I can really give pertinent advice to each of my trainees how to succeed, what to do next in their career.

Brett McKay: Well Laszlo, this has been a great conversation. Is there some place people can go to learn about the book and your work?

Laszlo Barabasi: Absolutely, so I mean I would obviously start with the formula but there’s also a website, Barabasi.com, which is my personal website or my labs website is connected to that and we also have formulabook.com that has lots of content related to the book. Thanks for having me on the show.

Brett McKay: Laszlo, thanks for us coming on. It’s been a pleasure.

Laszlo Barabasi: My pleasure.

Brett McKay: My guest today was Albert Laszlo Barabasi, he’s the author of the book, The Formula: The Universal Laws of Success. It’s available on amazon.com in bookstores everywhere. You can find more information about his work at his website. It’s spelled B – A – R – A – B – A – S – I .com. Learn more about his work. Also check out our show notes at [inaudible 00:57:15].is/formula, where you find links to resources, where we delve deeper into this topic.

Well that wraps up another addition to the [inaudible] podcast. Check out our website at artofmanliness.com where you can find our podcast archives. There’s over 500 episodes there, as well as thousands of articles written over the years on success, personal development, personal finances, physical fitness. You name it, we’ve got it and if you’d like to enjoy add free additions of The Art of Manliness Podcast, you can do so only on stitcher premium. Sign up at stitcherpremium.com and use code manliness, to get a free month of Stitcher Premium. Once you sign up, you can download the Stitcher IOS or android app and start enjoying The Art of Manliness Podcast ad free. If you haven’t done so already, I’d appreciate it if you’d take one minute to give us a review on iTunes or Stitcher. It helps out a lot and if you’d done that already, thank you. Please consider sharing the show with a friend or family member, who you would think would get something out of it. As always, thank you for the continued support, until next time, this is Brett McKay, reminding you not only to listen to the [inaudible] Podcast, but put what you’ve heard into action.