In This Episode
In our last broadcast of 2019, Dan Ferris quickly recaps what a year it’s been from a 27% gain for the stock market to $12 trillion negative interest-yielding bonds in the world, to the third-ever impeachment of a U.S. President.
Even so – this episode isn’t dedicated to a year-end review of markets, which Dan notes just about everyone is doing now. Instead, it’s about reviewing the very best and most important episodes of Stansberry Investor Hour – including a few that have changed Dan’s life.
In that vein, we’re re-upping the most important interview of the year, in Dan’s estimation, to your inbox – his June interview with success scientist Albert Laszlo Barabasi.
As Dan says on Albert’s book “The Formula: The Universal Laws of Success,” he read a lot of self-improvement books growing up, but “I wish I hadn’t done any of that. I wish I’d read Barabasi’s book instead.”
NOTES & LINKS
- To follow Dan’s most recent work at Extreme Value, click here.
5:34: Dan recalls Aaron Edelheit’s appearance on Stansberry Investor Hour, where he spoke of his book “The Hard Break: The Case for 24/6” which convinced Dan to stop working on Saturdays.
12:33: Another book we discussed this year that Dan recommended listeners read, “Models Behaving Badly” by Emmanuel Derman, teaches the difference between models and full-blown theories.
13:47: The one financial episode Dan does want to point readers back to, published on June 13 this year, centers on the basic mechanics of how to value a business. It’s a window into Dan’s thinking in Extreme Value and explains how he and his team assess value in a bottom-up way, unlike any other service at Stansberry.
19:29: Dan looks back at his favorite episode of the year, on June 28 with Albert Laszlo Barabasi on his book “The Formula: The Universal Laws of Success.” As Dan says, he read a lot of self-improvement books growing up, but “I wish I hadn’t done any of that. I wish I’d read Barabasi’s book instead.”
25:21: Dan replays his favorite interview of 2019, bringing on the recorded interview of Albert-László Barabasi. Albert-László is the author of Bursts: The Hidden Pattern Behind Everything We Do, and Linked: The New Science of Networks. He is the author of Network Science and the co-editor of The Structure and Dynamics of Networks and Network Medicine. His work has led to many breakthroughs, including the discovery of scale-free networks in, which continues to make him one of the most cited scientists today.
29:46: Albert-László explains the field of networking science, developed 20 years ago to make sense of the often-unnoticed networks that govern our lives and interactions. “Even our very biological existence is made possible by the intricate networks within ourselves.”
30:32: Albert-László explains the difference between performance and success in a worldview where communities and individuals are inextricably linked. “Your performance is about you, but your success is about us. From a data perspective, that’s a very important distinction.”
39:14: An exceptional data center allowed Albert-László to track the career of every major artist over the last 40 years – and he found something surprising in this field where performance is statistically unmeasurable. “Give me your favorite artist’s name, and the last five exhibits, I can fast-forward her career.”
55:35: Silicon Valley is no different from other areas of performance – but there’s a reason, Albert-László says, why the biggest-impact founders tend to be so young.
Broadcasting from Baltimore, Maryland, and all around the world, you're listening to the Stansberry Investor Hour. Tune in each Thursday on iTunes for the latest episodes of the Stansberry Investor Hour, sign-up for the free show archive at investorhour.com. Here is your host, Dan Ferris.
Dan Ferris: Hello and welcome to the Stansberry Investor Hour, I'm your host Dan Ferris, I'm also the editor of Extreme Value published by Stansberry Research. Well dear listener of mine, this is it, our last episode of 2019 and what a year it's been. You know the stock market up like 27% or so here, still $12 trillion of negative yielding bonds in the world, U.S. House of Representatives is trying to impeach the President, it would only be the third time in our history. And I mean it's been a little crazy, $120,000 bananas, I mean this guy that we talked about last time, he was saying buy, buy, buy it all, buy it, there's no such thing as risk in stocks, it's getting a little weird. Great year in stocks, a little weird overall. Now it would be sort of typical to do a look back at 2019, right? The typical thing.
I don't want to take the typical look back at the markets, anybody can look back at what happened in various price movements and markets and make up a reason why and tell you what it means. But you know it's all, it's all backward looking, we know where we are, I mean I've certainly talked about it to death. You know and it's a, kind of a typical, mostly useless sort of an exercise although there are a few sort of annual looks back that, that are kind of valuable to me. One is by a guy named Dave Collum who you can find on Twitter at Dave B. Collum, C-o-l-l-u-m, and he publishes a nice look back. So you know go read his look back and just leave it at that. And you know then there will be a zillion more looks forward to 2020 and it's all kind of a useless exercise because what happened – happened, what's going to happen nobody knows. So what should we do instead?
Well this week I will look back, but I'll look back at all the non-investment topics we looked at this year which we got a lot of great feedback on I have to say, people really enjoyed this stuff because all of that stuff is probably much more timeless. It can help you not only become a better investor but also become a better person, hopefully a happier person. And yes, you know hopefully a wealthier one too, right? We spoke with a lot of guests who dealt with a lot of different sort of general life wisdom topics and how fitting that the first one of 2019 was Shane Parrish from the Farnam Street website who we spoke with during episode 84 published on January 10. Among other titles Shane is the "wisdom seeker of Farnam Street," a website he started anonymously while working for an intelligence agency. We spoke with Shane about a variety of topics, the most important one in my mind being mental models, the mental toolkit we use to understand the world. Shane lists 113 mental models on his website, I'd say that makes him an expert on mental models, OK? And he came out with a book this year called The Great Mental Models: (Volume One) General Thinking Concepts – it's the first volume in a five-volume series that they intend to do at Farnam Street. I have it on my shelf. It's a highly readable resource that deals with nine general thinking sort of mental models. The very first chapter is called "The Map is Not the Territory." That's the first mental model he discusses.
And so "The Map is Not the Territory," it sort of explains itself quite well doesn't it? The model you have in your head is not reality and what perfect, what a perfect way, what a perfect first model to start off with in the book series that he's doing. And I sure wish the folks modeling risk and mortgage securities had taken heed of this back before the financial crisis right? If they had we might have avoided the crisis. And other things too you know like long-term capital management, they were modeling bond markets in a certain way and they didn't behave that way and they blew the heck up. Shane says his work is about learning about the world in a multi-disciplinary way, it's about applied thinking, thinking about thinking is what he told us. I highly recommend his book, his website, his work – it's a great way to learn to think about the world and by all means check out that episode.
The very next episode after that one with Shane Parrish was Episode 85 published on January 18, that's when we interviewed Aaron Edelheit. Aaron is an entrepreneur and an investor who wrote a book called The Hard Break: The Case For The 24/6 Lifestyle which advocates taking one day a week off from week, e-mail, and smartphones to be more productive and healthier, more creative and have a more satisfying, happy life. I read this book and I decided that I would not work on Saturdays anymore. When you work at home like I do you get into this rut where you feel guilty if you're not always working every single day of the week because your work is always just a few steps away and it's kind of beckoning to you all the time. But that's really unhealthy so I decided I'd just take one day every week and Saturday is it and I've stuck to it and it's made me not less productive but more productive. I know that no matter what I'm always going to honor my need for a hard break, for a day of rest once a week so you know I can really push harder on the other days. I also try not to work past 5 p.m. but that's harder, I get up at 5 a.m. in the morning and I try to work no later than 7:00 p.m., so 5:00 p.m., that's you know a 10-hour day, that ought to be enough. If it's not enough maybe I should do something else for a living huh? Aaron talked about his own struggle with being a workaholic which really messed up his health, wound up in the hospital, messed up his relationships, he talked about how stressed-out he was because his fund was down just 5% in 2003, the year the dot-com bust bottomed out. He also talked about how he started buying rental homes after the housing bubble blew up, including one year when he individually bought 2000 homes, I mean that's a lot of homes, that's like what more than five a day or something, it's ridiculous. So yeah, workaholic. And our conversation just led us into all kinds of areas, for example we talked about a phenomenon called the Zeigarnik effect.
Aaron said, "It's from Bluma Zeigarnik, a Soviet psychologist who noticed that waiters could immediately remember unfinished orders but had trouble remembering completed orders in a restaurant, and this has been duplicated a number of times. And if you think about the modern e-mail and the tasks, they're always unfinished tasks, so this part of that whir of technology, of e-mail, of notifications, and there's constantly someone to reach out to – to respond to, something to do, and that's why you're always on the phone and that's why it's so exhausting." On the flipside of that, I have to interject here a similar effect is discussed in a book I recommended called How We Learn by Benedict Carey. And Carey notes that when you're practicing your violin or your piano or studying for your test or doing work or whatever, you should stop when everything is clicking and you're all warmed up, not after you feel like you've done everything you wanted to do.
You cut yourself off to get that Zeigarnik effect of not having finished and it helps you remember, and it keeps it alive in your memory, and much of learning is just remembering. So my point is this Zeigarnik effect Aaron described is a double-edged sword, but I think you can manage it to your advantage. Aaron's got a great story, I strongly encourage you to read all about him in his book, The Hard Break which is one of the few titles I keep right here on my desk, I've got about, I don't know, 800 books in the office here now, I think, and I've got just like maybe 35 or so that I keep on the desk at all time at arm's length, and The Hard Break is one of them.
All right so next we published episode 103 on May 23, it's called "How to Succeed in the Stock Market and in Life." And in this episode, I discussed one of the most important of these mental models like we covered in Shane Parrish's episode and like he covers in his book, and it's called circle of competence. This is what I said, "It's called circle of competence – this idea was created by Warren Buffet as a way to help investors stay out of investments they don't understand. Now imagine a circle with a smaller circle inside of it, the bigger outer circle, that's what you think you know, the inner circle is what you really know, it's a subset of that bigger thing, that's your true circle of competence, what you really know." That's what I said on the episode. So circle of competence is a great way to reflect on what your real strength and skills and talents are and what your limits are, too, right, that's what that circle is, it's a limit around your real talents and strengths. And I've struggled with this my whole life, I used to always want to do more than I could realistically expect to do, and I was always saying, "Yeah, yeah, let's do it, let's do more."
And you know it, things pile up and they don't work out the way you want. One example of how I've improved on this is with my, my interest in music. I love to play everything – classical, jazz, rock, blues, showtunes, standards... just everything. But I'm really best at classical guitar – that's what I studied in college. I've been doing it for what 40 years now, over 40 years. And not many people are really very good at it and at this point in my life I'm playing pretty well, and I can play stuff that nobody I know personally these days could ever hope to play. It's been a long time since I hung out with lots of classical guitar players so I'm sure you know I could quickly change things so that I'd no longer be the best classical guitarist I know.
But the point is my current circle of musical acquaintances, you know it's a random enough sample of musical humans and none of them are really good classical players so you know that kind of lets me stand out a little bit. And let's face it, you know, most people who play the guitar are not like a really good classical player so I just feel like it's something I'll have to keep doing. And because of that, I've gotten better at it. So I thought, you know, well right... if this thing is, you know most guitar players don't bother with it, that's pretty cool right? So I focus on that.
And wouldn't you know it? It's helped me with those other styles because I always cheat a little, I go into the next room, pick up my electric guitar every now and then and play some jazz and stuff. So I can still dabble in those other things, primarily because I pay less attention to them... and the most attention to the one most suited to me which also just happens to be the most demanding one technically, allowing me to pick-up techniques and other disciplines much faster. So by giving up those other disciplines as a primary focus, I actually got better at them – a totally unexpected and fortuitous outcome resulting from me trying to figure out what my true circle of competence is and trying to stay within the limits where I'm most effective. I think good mental models are like that. They lead to these non-linear effects which is mostly a fancy way of saying you never know where, how they're going to benefit you, the unintended favorable consequences that practicing a good mental model can provide.
There's also this other book that I discussed this year and recommended you read called Models.Behaving. Badly by Emanuel Derman, D-e-r-m-a-n, a physicist working as a Wall Street quantitative analyst. Among other things the book teaches you: the difference between models and full-blown theories. Derman writes, "Theories offer us the most successful and accurate way to describe the physical world. They are deep and difficult to discover; they require verification but no explanation; they are right when they are right. Models, however, live in the shallows and are easier to find; they require explanation as well as verification. We need both types of understanding." I think that's really cool that we need both types of understanding and so, you need to learn about theories and models, you know, including these mental models. He's talking about like computer models and things, but they have the same – they're the same rough thing as the mental models we're talking about because they just kind of approximate reality and they're easier to find than theories, you get it? That's pretty neat.
So one financial thing that I do want to sort of point you back to is episode 106 published on June 13 where I talked about the basic mechanics of how to evaluate business. I think this was the most concise explanation I've ever given of the basic idea behind the valuation work we do in the Extreme Value newsletter (published by Stansberry, of course) which I write with a guy named Mike Barrett who does some really good work. And that work that we do there, it's unlike anything done by any other publication at Stansberry, we do real deep bottom up valuation work. What most other people do is like relative valuation, they do a kind of pricing where they say, you know, "The P/E of this company is 10... The P/E of all the other companies in this space is 20... This one has some upside." Like that. We don't really do that. We assess bottom-up valuation in a completely different way which I think is really valuable. I think you have to learn to do something like it to be effective in the stock market.
All right, next in episodes 111 and 112, episode 111 – we spoke with poker champ and author Annie Duke who wrote a book called Thinking in Bets. And then in episode 112, we spoke with someone who's actually a friend of hers, Michael Mauboussin, who wrote a few books too, one of which is called The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing. And Mauboussin gave us a little tip on how to figure out if there's skill involved in a particular activity instead of just luck. He said you just ask yourself if it's possible to fail on purpose. With roulette or slot machines it's impossible to fail on purpose, right? It's luck, it's pure luck. There's no skill. You can't fail on purpose. With basketball and tennis and other sports as just you know, one kind of example, it is possible to fail on purpose. It doesn't mean luck isn't involved in those sports. It just means there's plenty of skill involved as well.
And Mauboussin did definitely affirm that with investing it's better to be smart than lucky because over time investing skill will win out even if you're unlucky sometimes, right, that was the good news that we got from him. And Annie Duke dealt with some of those same issues, like when she taught us how the great hand that you might be playing in a game of poker, has no bearing on the quality of the decisions you'll make about it. So the game involves some luck because if you're dealt a fantastic hand, it's better than being dealt a garbage hand for sure. But real success depends on your ability to make decisions and that's a real skill you can learn. If you recall our interview with Annie Duke, she also came from a really competitive family and I'm sure that didn't hurt her either, OK? So there's lots involved here. But that intersection of skill and luck, they both talked about it and it was really, really an interesting, and I think, valuable conversation.
OK in episode 123 published October 10, I recorded the show in Las Vegas during the annual Stansberry Conference. And in that episode I really did like a podcast version of the presentation I had done there which really covers a lot of my overall views on stock and bond markets and has a few funny bits in it that I think you'll enjoy, too. For example I did my impression of the now-former European Central Bank President Mario Draghi and I did my impression of these two German bankers, so if you want a laugh go back and listen to episode 123.
And then in episode 130 published on November 27, I talked about Henry Singleton, an iconic investor and businessman, and we talked about share repurchases which has become a very important topic today. And you can go back and read the transcript, listen to the episode, but the main point was that Singleton pioneered share repurchases and later in his career noticed everyone was doing them. So he said at the time that must mean there's something wrong with it, and he was right, nobody does share repurchases as well as he did it with his sense of discipline. He issues stock, which is really like selling it when his company shares were super-expensive, and he used the shares to acquire cash flow-positive businesses. Then when the bear market came, he used those ample cashflows to repurchase 90% of his company's stock which made the shares soar into the stratosphere and created a huge multi-bag of return. And just overall though, it's one of the most brilliant careers in all of finance and it's something you ought to know about... that intersection of, like, the businessman and the investor. And there's a lot of other kind of life lessons in it for you and a pretty good account of Singleton's career is in a book called The Outsiders by William Thorndike. And that book has great chapters on seven other brilliant investor CEOs besides Singleton –including people like Tom Murphy, Katharine Graham, Warren Buffett, and John Malone – and others you might not have heard of, but also deserve your attention, like Bill Stiritz of Ralston Purina and Dick Smith of General Cinema. And like I said, it's a book about investing but it's a book about business thinking, too, so it's pretty cool.
OK and one more really important thing now, looking back over the year my favorite interview of the year was definitely, definitely episode 108 published June 27, the guest was Albert-László Barabási. And he wrote that book called The Formula: The Universal Laws of Success. And as I said during that interview, I read a lot of self-help books when I was young just trying to figure things out like everybody else, but I wish I'd not done any of that, I wish I'd had Barabási's book, The Formula, instead, because his work has a solid scientific underpinning rooted in math and science of – the math and science of networks which itself is rooted in something called graph theory that, you know, I don't know anything about. And we had a wonderful conversation which I think is really super-valuable to you and so, you know, we want to replay this interview for you, I hope you enjoy it as much as, as I enjoyed doing it. We got a lot of great feedback, so I expect if you didn't get a chance to hear it the first time you'll want to hear it now. And if you did hear it the first time listen to it again man, it's that valuable. So without further ado, let's replay our wonderful interview with Albert-László Barabási right now.
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Dan Ferris: Today's guest is Albert-László Barabási, he's the Robert Gray Dodge Professor of Network Science and a distinguished university professor at Northeastern University where he directs the Center for Complex Network Research and holds appointments in the Department of Medicine at Harvard Medical School and the Central European University in Budapest.
A native of Transylvania, Romania, he received his Master's in Theoretical Physics at the, forgive me for the pronunciation, Eötvös University in Budapest, Hungary, and Ph.D. at Boston University. Barabási is the author of Bursts: The Hidden Pattern Behind Everything We Do, Linked: The New Science of Networks. He is also the author of Network Science and the co-editor of The Structure and Dynamics of Networks and Network Medicine. The titles have been translated into more than 15 different languages. His work has led to many breakthroughs, including the discovery of scale-free networks, which continues to make him one of the most cited scientists today. László, welcome to the program. Thank you for being here.
Albert-László Barabasi: It's a pleasure to be with you. Thanks for inviting me.
Dan Ferris: So László, the first thing we need to do is: you're not our usual type of guest. We usually have people who manage money for a living. So let's just talk about what you do as a network scientist. My wife said, "Networks? People networks or computer networks?" And it's all networks right?
Albert-László Barabasi: All of the above, so network science is a discipline that was born about 20 years ago that tries to develop a language to think about all kind of networks that permeate our life. And, you know, this just kind of grew out from the realization that just about everything we do... we do through networks. We communicate through networks – our friendships and professional links can be really perceived as a network and have to be perceived as a network. Our business ties are there. But even our very biological existence is made possible by the intricate metabolic and genetic networks within our cells. And about 20 years ago a movement has started in science to quantify these networks and hence today I'm a network scientist who studies all of these networks and many more.
Dan Ferris: OK now I want to talk mostly about your book, The Formula, which I could not put down. I think it's an excellent book. And the first question I have for you is: you said a couple of times in here how – the book is about the laws of success. It's called The Formula: The Universal Laws of Success. Are we really talking about like the law of gravity-type laws? I mean, that's what you say in the book is – it's really that kind of a law?
Albert-László Barabasi: I tend to think it is. These are patterns that we extracted by studying the career of millions of individuals. And if you are in the right conditions of your career we think you cannot avoid that. Hence, they really elevate at the level of laws. Now, not all laws always apply to us you know, as I'm walking around talking with you – you know, Bernoulli's law doesn't apply to me. But if I start to fly, which is a gas law, then suddenly the only reason the airplane can fly with me is because of Bernoulli's law applied to us. So fundamentally, when these laws apply to you depends on the circumstances you find yourself in. But these are very generic patterns that apply to all individuals at that stage of their career.
Dan Ferris: Is it accurate to say that you basically study anything that behaves like a network, and the phenomenon of success among human beings is a very scientifically hard version of that? Success is a science, a real science?
Albert-László Barabasi: Well, we certainly would like to think so. And the reason why I got into this is that much of the studies that we've been doing over 20 years focused on how the network looks like, how it evolves in time, and so on. And about 10 years ago we started to think of a complimentary problem. OK, I'm a node in the network – how does this node affect my success? Does it help me? Does it pull me back? And whether my position in the network will really determine my long-term success.
So we went after this question. And then we realized that if we want to answer it we need to step back and address much deeper questions. Like, what is success? What is performance? How do they relate to that?
Dan Ferris: Yes, so let's talk about the difference between performance and success. You gave great examples of performance with tennis players and golf, you know, who'd you talk about? Roger Federer and Tiger Woods. They're examples of great performance, right?
Albert-László Barabasi: So what is interesting is that as we grow up one of the things we learn in school and on the running field and everywhere else we go is that you need to have performance to have success. And in a way that performance really drives success. So the question is: What's the difference between them? Because in our vocabulary, we often use it interchangeably, thanks to the strong belief of the relationship between them. And from a data perspective since I'm a network and data scientist, we have to be able to distinguish them. So our approach was relatively simple and straightforward. Performance is what you do, how fast you run, what kind of deals you put together, how do you invest your money, what kind of paintings you paint.
However, success is: What does the community notice from that performance? Whether it acknowledges and whether it rewards you for that. In other terms your performance is about you, but your success is about us. We as a community reward you with success for your performance. And from a data perspective, that's a very important distinction because as we'll see during the discussion as I discuss in The Formula, in many areas performance is very difficult to measure at the individual level. But success, because it's a collected quantity, it's easily measurable because there are multiple data points around you that pertain to your success. So measuring success and characterizing success becomes a big data problem.
Dan Ferris: So let's talk about a concrete example. Let's talk about the difference between, you know, Tiger Woods and Roger Federer versus the artists you mention in your book, Diaz and Basquiat. What's the difference?
Albert-László Barabasi: Sports is a very special area and kind of unique in terms of human performance because in sports we have very accurate measure of performance. If you are a runner I can, I have a chronometer and I can measure your speed and your speed uniquely determines how well you are regarded as a runner. And indeed we actually ended up analyzing the tennis players like Roger Federer and all men and women tennis players performance, how well they do on the field, as well as their success in terms of how many people actually follow them, how many people visit their Wikipedia page, how many people search for them on Google and so on.
And what we found is that in those cases, performance uniquely determines success, that we were able to actually build a formula that if I plugged in where did Federer play last week against whom and whether he lost or won, that formula tells me uniquely how many people went and visited his Wikipedia page. So starting from a performance measure we were able to very accurately predict a success measure which is curiosity about a particular player.
But as I said, this is unique to sports because in sports we have chronometers. We have accurate measures of performance. Most of us, however, live and work in areas where performance is much harder to gauge. And this is not to say that you can distinguish the good from bad, but there are actually many individuals with kind of comparable performance.
Now coming back to your question, you know, sports is one area where performance can be very accurately measured. The other extreme where performance is inherently impossible to measure is arts. Is this microphone on the table in front of me, is this an artwork or it's purely a microphone? Well right now in front of me it's a microphone. If you see it on the pedestal in MoMA under a glass box, it's an artwork. Would you be able to look at the object itself and make a distinguish whether it's an art or an ordinary object? In contemporary art that's virtually impossible. So therefore you cannot use performance measures to really see how well art does. It all depends on the context and then you have to start developing other rules and tools and laws to describe how success emerges when performance is not measurable.
Dan Ferris: And I was fascinated by the difference between the two artists you talked about, Jean-Michel Basquiat and his partner – what was the name – Al Diaz, and their work. Some of their works were virtually indistinguishable, yet we don't – nobody knows who Diaz is but lots of people know who Basquiat is, and his work sells for millions of dollars. What was the difference between the two of them?
Albert-László Barabasi: Absolutely, I'm glad you kind pointed that out. Indeed for me, the Al Diaz and Basquiat example is a perfect example for what I would call the first law: When performance can't be measured networks drive success. And indeed we're dealing with two artists who started their career together in the 1970s in New York. Not only they started their careers together but they really worked together under one single name, the name SAMO, and they did graffiti art. And they did so for about a year and a half when they broke apart, and within three years their trajectory completed diverged. Al Diaz is still alive today, working in the New York art scene, and if you haven't heard about him there's a reason. However Basquiat, a few years, later paints a painting that became the most expensive painting sold by an American artist a few years ago.
And so what's the difference? Well, fundamentally, networks. When you look at the career of the two individuals, all the other approach art from a performance perspective to say you know it's about making the right artwork at the right place. However, Basquiat immediately recognized that he has to invest himself into the community of artists in New York. And he befriended, within a few months' interval, some of the biggest names of the art world like Andy Warhol and actually moved in and lived in Gagosian's apartment who later on became, of course today, the biggest art dealer out there.
And so Basquiat has mastered the network that really mattered at that moment in the arts, you know, galleries and other artists. Al Diaz, however, continued to approach art from a performance perspective as trying to produce more and more works and of course the difference is huge. And what we did is that of course the book doesn't only rely on anecdotes. I have had the possibility to get access, thanks to Magnus Resch, a gallerist in New York and art historian in New York, to an exceptional data set that allowed us to track the career of every single artist in the last 40 years, both in America and abroad. Half a million artists were in the data base and we could fully reconstruct their career.
And what we found there: That in case of artists where performance is unmeasurable, there's a hidden network of institutions that could be mapped out with this data, and that network uniquely determined their success. And this was not only kind of a finding that that network determines their success but we were able to turn that one into a predictive tool such that if you give me your favorite artist's name and the last five exhibits that he or she had, I can fast forward her career and tell you what level of institutions would actually be really exhibiting in 10 or 20 years from now.
And the reason we can do so, so accurately is because performance is not measurable. All the predictive power is in the network, where you are in the network, whom are you connected to, what is the precise level of those institutions. So art is a perfect example of the first law of the formula saying when performance is not measurable, it's networks that determine success.
Dan Ferris: Now I'd like to skip over, if you don't mind, the second law of success in your book which is: performance is bounded but success is unbounded. And I want to talk about the third law, which says that previous success times fitness equal future success. Can you tell, what's fitness? How do we define fitness?
Albert-László Barabasi: Sure, and before we go to fitness, let's first talk about the previous success determines success. And this is a discovery I made almost 20 years ago while we were studying the worldwide web. And trying to understand why do we have certain web pages like Google and Yahoo, back at that time, who had such an exceptional number of holdings from other websites. And why is my website hardly acquiring any links from outside individuals? And what we realized there is that the only way to explain mathematically what's happening and what drives the emergence of these hubs on the web is that, is that success drives success. That is, the more links a certain website has, the easier it is to find it and the more likely that it will actually acquire further links from other websites. And then we soon realized that this is not only unique to the worldwide web, but in all areas where you have some measure of success. It's true. It's certainly true for money. People kind of end up acquiring money proportionate to how many they already had – if nothing else, that's what compounding interest is. But it seems to be true also for scientists, of how they acquire impact – that the number of citations my papers gets is proportionate to how many they got in the past.
Now the problem with this rule is that it just simply says if you have a lot you will get more. Well how do you get a lot to begin with? And that's where fitness comes along because we realize that there are differences between website. Some with websites like Google are acquiring links much, much faster than mine. Not only because they have more links but because they offer services that are more desirable to the public at large than, say, what my website offers. And so we named these differences fitness. Fitness is the link, the node's ability to acquire more links, the person's ability to acquire more money, to earn more money, your ability to acquire more friends.
And the way this works is that you know how popularity determines how easy you are to find but once I found you it's your fitness that determines whether I'm going to link to you, whether I want to give money to you, whether I want to become friends with you, where I want to write a paper with you as a scientist, and so on. So really – and fitness is unique to each individual. And it's not something that you as an individual determine, but it's something that the community assigns to you based on what you have to offer to the community. And this fitness is actually measurable in many contexts.
Like in the case of the worldwide web, what's the fitness of the website? And therefore we arrive to this third law saying it's previous success times your fitness that determines your future success. That if you have low success to begin with but high fitness, you will acquire links faster and could be very, very successful – that you have a high level of success. But for whatever reason your fitness drops, then you will actually stop growing and you will stop actually acquiring further links or further money and so on. And their growth will slow down.
Dan Ferris: So László, fitness to me sounds like it's another way in which you're saying this is the network's assessment of your performance or your fitness. They sound very – it sounds to me like it's the network saying whether or not you're good enough... whether or not your performance is good enough. Is that accurate or not?
Albert-László Barabasi: Absolutely, absolutely. So it's a measure that the community assigns to you and it's really a competitive measure, how much more attractive you are than the person next to you. And of course this doesn't happen by simply comparing _____ by _____, but simply if you have a large community who's choosing between different individuals, between different services, before different songs. They, through their joint action, they slowly start assigning a fitness to each node. And that fitness – which it's not written on any of that, but it's kind of measurable – eventually determines their growth rate.
Dan Ferris: Right, and it depends on what the network is looking for. For example, you know if people like a good female singer to also be very pretty, a better female singer who isn't as pretty might not be deemed as fit.
Albert-László Barabasi: Certainly, but it also depends a little bit of what community are looking at it, right? So I'm sure that my parents' generation would assign a completely different fitness measure to a Britney Spears song than my generation or my children's generation, right? So, so the fitness is kind of incorporating the subjective judgments about whether I like it or not. But because so many people are voting on the object or on you, at the end there's a unique number that allows me to say how fast you would be growing in that particular environment.
Dan Ferris: One of the things that gets me about the emphasis on previous success: When I got to that point in the book, I was kind of depressed because I thought, "Well you know, I've had some success but maybe, maybe not so much," let's say. But you give me hope with your fifth law and I'm skipping the fourth one for now, we can get back to it. You give me hope with the fifth law because the fifth law is: with persistence success can some at any time... even late in life, no?
Albert-László Barabasi: Oh I'm so glad that you jumped there because that's really my favorite law. And let me give you some background. I just passed 50 and based on all the data out there, my chance of creating an innovation – writing a research paper that would overcome my earlier work statistically – is close to zero. What do I mean by that? It means that when you look at the career of high-performing individuals, there is overwhelming data to indicate that many people make their most important discoveries, contributions to society, to art, relatively early in their career – in their 20s and 30s. And this is so severe and so persistent that Einstein once claimed that the scientist who doesn't make his big discovery by the age of 30 will never do so.
So the question we ask – well that's definitely very bad news for me because it suggests that really, I should stop doing what I'm doing – which is running a lab and trying to make new and new discoveries and publish them. And maybe I should just write books. You sound like – it sounds like you like my book. Maybe that is my next career because the chances are of making a discovery that will be bigger than I did before is close to zero.
Well we were curious. Let's look at the data. And let's not only look at geniuses. Let's look at everyone and ask ourselves, "Is it really true that only young people tend to be innovative?
So we analyzed, for example, all the careers of all the scientists from 1900 to today, in all disciplines, and we confirmed that seems to be the case. That is, most scientists write their most important paper – whether that's a Nobel Prize-winning discovery or something that no one remembers 10 years later. All right, they do so relatively in their, kind of like, you know, 10 – roughly 10 years from the beginning of the career which is all we call academic age. So we confirmed what the belief that was out there for a long time.
But then we are data scientists so we started looking a little bit more carefully at the data and look for the placebo effect. What would happen if I make my discoveries completely random? That is, what happens if any of the papers that I ever write could be the most important paper of my career? What happens if any of the business deals you put together could be the most important business deal for your career? What if it's completely random in the space of the deals of the papers you write, and so on?
And so we said, "Let's see what happens in the case of random careers," and to our surprise, we realized they were indistinguishable from the real ones. And once – to make this long story short – what we realized is that really what changes during the career is not the creativity, but the productivity. That is, young people tend to put lots of projects out and as they age the number of projects decreases, the people are not trying any longer. However, what we showed is that each project the scientist puts out has exactly the same probability to be the highest success project in his or her life. So a little bit, you know, discovery and innovation is like buying lottery tickets, you know. Every lottery ticket has roughly the same chance of winning but if you buy most of your lottery tickets when you are young, it appears that you have to be young when you win the lottery. And it appears that old people cannot win the lottery.
So at the end, what the fifth law does is that it summarizes, really, this key discovery that we made a few years ago in my lab that, really, innovation is not about age. Innovation is about persistence, and those individuals who keep trying can be truly innovative relatively late in their life. And my favorite idol in that space and example is what I discuss in The Forumla, John Fenn. John Fenn is a scientist who was a chemist and he worked at Yale University. And at age 65 or 70, actually, he was forcefully retired by Yale University. This was kind of in the '80s – '70s and '80s, when there was still a retirement age.
And he was still full of energy. So instead of actually retiring and leaving his lab because they closed his lab down, he moved onto another university, Virginia Commonwealth University, started a new research lab and it was there where he published his greatest discovery of his life about electrospray ionization that 15 years later, at age 85, landed him the Nobel Prize in chemistry. And I don't think that John Fenn is special, I think John Fenn is just an example of an individual who simply believes that creativity doesn't wane with age and he just kept trying and trying and trying and he just actually passed away a few years ago at the age 90-something and you know three days before he passing away he was still in the lab working on the next research paper.
Dan Ferris: So that, I take that's a message of great hope for me because I'm – I'll be 60 in three years and I really have no interest in slowing down at this point. And you're giving me a lot of hope that my efforts will not be in vain because success could come at any time. But this topic is really good for investors isn't it? Because, you know, we buy a portfolio of stocks not knowing which one will be the best performer. If we knew that, we'd only buy that one. So we buy a portfolio because we – and we persistently, you know, change the portfolio over time, never knowing which investment will become our greatest investment. But the point is to stay in the game, to survive and stay in the game. It's a wonderful message.
Albert-László Barabasi: Absolutely, and if I may add something to that, before you think that this is only something applies to scientists, think twice. Because after the formula was published a colleague of mine – two colleagues of mine from Northwestern University and MIT have looked at the age effect for founders of the startup companies. And if you want to think of one area where, really, youth really dominates, that's Silicon Valley, right? And the belief that you have to be in your 20s to start the next Facebook or Google. And you know, that's certainly to some degree true and held very closely by the community.
Because if you look at the biggest Silicon Valley awards, they all go to people in their late 20s or early 30s. And when you look at the major firms, the major five firms and the age distribution of the funders of the company they support for the first time, it's late 20s, early 30s. So there's a deeply held belief that if you want to make it in Silicon Valley, you need to start early and you need to be successful early. Yet what my colleagues have done is, they look at not only what age do they actually get these kids their investment, but they also asked, "Would they succeed?" "Will the company be sold as an IPO or be sold or get an IPO?"
So they measured success in terms of "is there a successful exit?" And what they found is that, yes, a huge number of investments go to young people but the success is not staying with them. And actually a 50-year-old funder has about three times higher chance of having a successful exit than a 30-year-old funder. So yes, the belief is very strong, but it doesn't mean that it actually leads to success. And you know Silicon Valley is no different from the other areas of performance. You have to keep trying and often, the biggest successes really come relatively age-related time.
So yes, the Google funders were young, but they needed an Eric Schmidt to turn into Google what it is today.
Dan Ferris: Wow, that's great. I had never heard that before. László, we're actually towards the end of our time here, so I usually like to ask, you know, if you could just leave our listeners with one thought about your work with success and networks. I realize it's asking a lot because you've done a lot of work and made a lot of discoveries, but if I ask you to leave them with one thought, what would it be?
Albert-László Barabasi: The most important thing I learned from The Formula is really that your success is not about you but about us. That is, that if you want to be successful, it's not enough to have performance. You really need to see how the performance is recognized and seen in the community around us. And to me, that's a very humbling experience because it really helped me understand that every decision I go for, going forward, if I care about success in that particular domain – and you don't always have to care. But if you do care about success, you need to go outside of yourself and look at your community because they are the key to your success.
Dan Ferris: Don't hide your light under a bushel.
Albert-László Barabasi: Absolutely. Thanks for having me.
Dan Ferris: OK László, thank you so much and I hope we can have you on again someday. Are you working on another book?
Albert-László Barabasi: No, after every book I like to have a four- or five-year break. So yes, I am working on a book but that's a much more technical book, not general audience. We're writing with my former student, Dashun Wang, a beautiful book called Science of Science that is really taking these ideas into the scientific place and rethinking how science works.
Dan Ferris: OK, well I guess we'll have to wait a few years for another book like The Formula then, all right. But it'll be worth it, I know. Thank you so much for being here László.
Albert-László Barabasi: It's my pleasure. Thanks for picking The Formula to feature in your broadcast.
Dan Ferris: Hey guys, real quick, I just want to tell you something, as host of the Stansberry Investor Hour podcast I also enjoy listening to other podcasts. It helps me figure out ways to make the Stansberry Investor Hour a better experience for you. One podcast I really like is called We Study Billionaires hosted by Preston Pysh and Stig Brodersen of the investorspodcast.com. It's the biggest investor podcast on the planet enjoyed by thousands of listeners every week. Preston and Stig interview legendary billionaires like Jack Dorsey, founder of CEO of Twitter and payments company Square, and billionaire investor Howard Marks whose book, The Most Important Thing, I've recommended dozens of times.
Sometimes Preston and Stig spend a whole episode reviewing lessons learned from billionaires they've studied like Dell Computer founder Michael Dell, tech industry maverick Peter Thiel, and macro trader Stanley Druckenmiller. Before starting the We Study Billionaires podcast, Preston went to West Point and Johns Hopkins, founded an investment company, and his finance videos have been viewed by millions. Stig went to Harvard and worked for a leading European energy-trading firm. They're smart, experienced investors who know the wealth-building secrets of billionaires better than anyone and their listeners love it. And I'm one of those listeners. Head over to the investorspodcast.com and check out We Study Billionaires with Preston Pysh and Stig Brodersen. The investorspodcast.com. Check it out.
In our next episode which airs in January, I will you know again try to avoid doing what everyone else does by making predictions about the coming year. You know, it's kind of silly to make predictions. What I'll do instead, it'll sound like predictions. But what I'm really going to do is I'm going to start out by figuring out what expectations appear to be baked into the financial market right at that moment. And then we'll try to figure out based on that, you know, what would the opposite thing look like, what would surprise the market in 2020? And yes, I got that idea from a guy from Blackstone named Byron Wien who – every year for, like, I don't know, 30 years or something – he's put out his "Ten Surprises for the Coming Year" and – but his surprises are really like predictions and I'm not going to do that. I'm going to say this is an alternative scenario that is not baked into securities' prices right now.
So, like, I'm not going to predict, you know, who's going to be the next president or things like that like he does. I'm just going to do something a little different and I think it'll be a lot of fun. And it'll fulfill this part of our mission that I think has developed into a really good conversation with us about things that go against what you might believe, right? You're looking for alternative ideas. Maybe you're bearish like me... you've got to look for things that would result in the market going up, right? And you know that goes for any of you that you might have. Some people like to say that you should have a strong viewpoint – loosely held. I definitely have a strong viewpoint but it's kind of loosely held because I'm ready to be wrong about it. So that's what we'll do. We'll look ahead and we'll say, "You know, how could the market be wrong right now in 2020 as things play out over the coming year?"
I hope that sounds like fun to you, I'm really looking forward to it and it's been a really great year for me here thanks to you. Without you this podcast is not worth doing, I'm really grateful and you know as tired it's become to say this, I'm humbled by the many listeners who download and listen every week, thank you very much for helping to give me a purpose in life. It is really, truly a privilege to come to you this week and every week, I know your time is precious. I'm thrilled that you've chosen to spend these weeks here with us, you know, for an hour every week. So on behalf of podcast producer Justin Mattas and engineer Greg Bafitis, I thank you for listening. We have some really cool interviews lined up for the coming year, including like some honest-to-goodness TV stars who probably know more about finance than a lot of people in the finance world and we'll have that and a lot more in 2020. I hope you had a great 2019 and happy holidays. I will talk to you next year. Remember go to www.investorhour.com and sign up, put your e-mail in there and you'll get all the updates. And send us lots of feedback, questions, comments, politely worded criticisms. And go to Apple iTunes, download Stansberry Investor Hour, give us a like – that'll push us up in the rankings and invite more people like you into the conversation which is always a good thing. So I will talk to you next year. Bye-bye for now.
Thank you for listening to the Stansberry Investor Hour. To access today's notes and receive notice of upcoming episodes go to InvestorHour.com and enter your e-mail. Have a question for Dan? Send him an e-mail at [email protected] This broadcast is provided for entertainment purposes only and should not be considered personalized investment advice. Trading stocks and all other financial instruments involves risk, you should not make any investment decision based solely on what you hear. Stansberry Investor Hour is produced by Stansberry Research and is copyrighted by the Stansberry Radio Network.
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