by David Burkus
“I realized I didn’t have to meet only the people Warner Bros. happened to be doing business with that day,” Grazer recalled. If he structured his request properly, he figured he could probably get a meeting with pretty much anyone in the business. He could simply call and ask for an appointment, and if he was persistent, he would eventually get one. So he developed a brief pitch and starting dialing. He would call the assistants of powerful executives and give them a well-rehearsed but simple ask: “Hi, my name is Brian Grazer. I work for Warner Bros. Business Affairs. This is not associated with studio business, and I do not want a job, but I would like to meet Mr. So-and-So for five minutes to talk to him.” Then he would finish with a specific reason why he was interested and why meeting with him would be worth the person’s time.6
Just like his speech when delivering legal documents, Grazer’s new pitch worked more often than not. He met producers from other studios, he met directors, and each time he left with useful information and the name of someone else to meet. “Talking to one person in the movie business suggested a half dozen more people I could talk to,” Grazer reflected. “Each success gave me the confidence to try for the next person. It turned out I really could talk to almost anyone in the business.”7
Sometimes it would take repeated efforts to get a meeting, but those meetings were almost always worth it. One such long-awaited conversation was Grazer’s first sit-down with Lew Wasserman. Wasserman was the head of MCA, the studio that would eventually become NBCUniversal. Through MCA, Wasserman had worked with movie stars like Judy Garland, Fred Astaire, and Jimmy Stewart, and he had even worked with Alfred Hitchcock. At the time Grazer was seeking him out, Wasserman was the most powerful man in movies.
Wasserman met Grazer for only ten minutes, but his advice would shape Grazer’s career forever. “Go write something,” Wasserman told him. “You have to bring the idea.”8 As great as his conversations were, and as numerous as his contacts were becoming, Grazer needed to use those contacts to develop ideas . . . otherwise he would never become a producer.
It was this search that ultimately led Grazer to his lifelong business partner, Ron Howard.
Grazer first saw Howard on the lot at Paramount Studios, where he was working as a producer on smaller projects like television movies. Similar to his strategy for reaching out to just about anyone, Grazer called Howard and just said, “I think we have similar goals. Let’s meet and talk about it.”9 A few days later, they met in person and discussed their goals of working on mainstream media, and a partnership was born. They did two movies together at first, and when one of them, Splash, became a hit, they formed their own company, Imagine Entertainment. They have been working together ever since. “My relationship with Ron has been the most important in my life, outside of my family,” Grazer reflected. “He’s my closest work colleague, and my best friend.”10
But his longtime partnership with Howard hasn’t stopped Grazer from continuing to cultivate new connections. He’s still seeking out new people to have what he calls “curiosity conversations.” For more than thirty-five years, he’s made conversations with interesting people a regular part of his routine. “My goal was always at least one every two weeks,” he explained. Just to name a few: He’s met President Bill Clinton and scientist Carl Sagan. He’s met vaccine inventor Jonas Salk and billionaire Carlos Slim. He’s met rapper 50 Cent and oceanographer Jacques Cousteau.11 He even met Princess Diana—and shared a bowl of ice cream with her.12
A good number of Grazer’s meetings have been deliberately outside of the world of movies and television, but many of them have influenced his ideas for new shows and new movies. In addition, he’s pretty much met everyone he needs to know inside the movie business. Knowing them is part of his everyday job as a producer. “Work means meetings with actors, writers, directors, musicians. The phone calls—with agents, producers, studio heads, stars—start well before I reach the office, and often follow me home,” Grazer said. In many ways, that is the job of a producer—to be the hub that connects all of these different groups working on a film or a television show. For that reason, his desire to know as large and diverse a set of people as possible is no doubt one of the major contributors to his success. Grazer is a super-connector. He’s one of a small group of individuals in a social network who amass a collection of contacts that is shockingly larger than average. And for Grazer at least, the size of that collection explains the size of his success.
Above-Average Networks (Far Above Average)
If Brian Grazer’s story seems like an anomaly, it’s because it is . . . at least compared to our mental model of how big someone’s network should be. Most of us assume that other people have networks about the same size as our own—that because we can only keep so many relationships in our head, everyone has around the same number of connections. We may think that a few “lucky ones” have a more powerful collection of contacts, but it’s doubtful (we assume) that they know more people than average. They were just dealt a better hand. When we do think about those super-connectors with abnormally large networks, we reassure ourselves that they must not know everyone as closely as we know the people in our network. And there’s a good scientific argument around that idea.
The origins of that argument belong to Robin Dunbar, an evolutionary psychologist from the University of Oxford. In the early 1990s, Dunbar was studying the social connections among groups of primates—monkeys and apes mostly.13 Through his observations, Dunbar worked up a theory that the size of the groups observed must have been influenced by the size of the animals’ brains. It takes brainpower to interact with other animals, to socialize and bond with them and remember past interactions. So by extension, how many of those interactions a primate can keep straight must correlate to how much brainpower the animal has. That, in turn, must correlate to how large the animal’s brain is—specifically to the animal’s neocortex.
This led Dunbar to another conclusion. If the size of the neocortex plays a role in limiting the social circles of primates, then this must extrapolate out to humans. “Since the size of the human neocortex is known, the relationship between group size and neocortex size in primates can be used to predict the cognitive group size for humans,” Dunbar and his colleague Russell Hill wrote.14
Using the known average size of a human neocortex, Dunbar calculated the upper limit of a human’s information-processing capacity and brainpower to socialize in a network at around 150 contacts. This became known in scientific and popular literature as Dunbar’s number. Dunbar then went looking for evidence of this number in human social groups. He studied anthropological field reports from tribal societies and modern ones and (along with Hill) even studied the average numbers of Christmas cards sent by individuals every year. Each time he saw this average of around 150. Elsewhere, 150 was also the typical size of military units in the Roman Empire and of infantry units during World War I.15 There’s evidence that modern businesses and military groups still tend to divide their units up at around 150, though this is an average and the deviation from the mean varies widely.
But there is a problem with Dunbar’s number when it comes to estimating the size of a particular human’s social network—two problems really. The first is that Dunbar’s research mostly focused on the tribes and groups of nonhumans (monkeys and apes) and then extrapolated that data to estimate an average for humans. The bigger problem, however, is that 150 just doesn’t seem to be the right number. Moreover, the real number may shatter our concept of “average.”
In 2010, a trio of researchers led by Tyler McCormick, then a PhD student in statistics at Columbia University, attempted to estimate the average size of an individual’s network using surveys and statistical calculations in lieu of brain size.16 Measuring the size of an individual’s collection of contacts is difficult, for a variety of reasons. Researchers cannot just ask how big someone’s network is. Nor can they just scroll through the address book on each participant’s smartphone. To get a more accurate coun
t, McCormick and his colleagues tested a variety of methods, all of which used specific prompts to trigger known contacts. In this case, they asked 1,370 adults in the United States how many people they knew with certain first names (for example, “How many Michaels do you know? How many Jennifers?”). This technique prompted individuals to remember specific people, and the results could be compared to widely available data on how many people share specific names. (In the United States, the Social Security Administration keeps data on names of newborns by year.) Using a few statistical calculations, the researchers could then arrive at an estimate of network size for each individual.
They found that the average (mean) network size of those surveyed was 611 people. Taken by itself, this number is dramatically larger than Dunbar’s estimate. But another insight hidden in the data is even more dramatic. While the mean network size was 611 contacts, the median was 472 contacts. This difference might not seem like a big deal to you or me, but to a statistician it’s a clear signal. The assumption in Dunbar’s research was that not only was the average network size around 150, but that if drawn on a graph, the results would follow what is known as a normal distribution. In a normal distribution, you tend to have a line climbing from the beginning of the graph, reaching a peak in the middle, and then tapering back down. This is the bell curve, or upside-down U shape, that many of us are familiar with from high school mathematics.
In a normal distribution, you would also see the median network size equal the average (the mean). Real human network sizes actually might be four times larger than Dunbar’s estimate, but if 611 people is the average and the distribution is normal, it should also be the median. This wasn’t the case with the results that McCormick and his colleagues arrived at, and, in fact, those results indicated an entirely different shape to the graph.
To them, network sizes looked more similar to a power law.
A power law is a different kind of distribution. Instead of a bell curve, a power law looks like the steepest hill you have ever seen. It starts high and then drops quickly before almost leveling off near the horizontal axis. (In a pure power law, neither point of the line would touch the axes, but real samples tend to look a little messier.) Indeed, this is what McCormick and his colleagues found. While a lot of people had network sizes around 600 people, a few had dramatically larger networks. Those few people with massive networks skewed the distribution into this power law shape.
To be fair to Dunbar, he did hedge his bets when it came to calculating the number. He actually allowed for a few different numbers, which increased with orders of magnitude as the list of contacts became less intimate. In each case, however, he also assumed that there was an upper limit to how many contacts any one person could have in their network. There may well be an upper limit, but it’s nowhere near the average.
McCormick and his colleagues are not the only people studying the presence of power laws in networks. They are not even the first. Credit for that discovery goes to Albert-László Barabási and Réka Albert. As early as the mid-1990s, Barabási and Albert were studying networks, both person-to-person and technological networks like the world wide web.17 Because they were studying webpages on the Internet in addition to personal networks, they had noticed fairly early on that many websites, nodes in the network, had very large collections of hyperlinks compared to other webpages. As the world wide web evolved, certain places became the preferred starting point for Internet users, and over time these websites were linked to much more frequently than average. In addition, many of these websites happened to be linking to the large collections of other websites. (In the early days of the Internet, websites like Yahoo.com and Excite.com tried to act as a front door to the Internet, linking to a diverse array of other informational websites.) To the researchers, it was fairly easy to see that the pattern didn’t follow an assumed normal distribution—instead, it followed a power law.
This led them to wonder if the same phenomenon held true for human networks. They chose the Hollywood data set from the now-infamous “six degrees of Kevin Bacon” studies. When they graphed the level of connectivity in the actor network of Hollywood, a power law again emerged: there was certainly an average number of connections, but a small subset of actors were dramatically more connected than the norm. In fact, these extremely well-connected actors were what allowed the small-world effect to happen. Not only were they well connected, but they kept everyone else much more closely connected than they would otherwise have been.
Over time these key individuals (or nodes when speaking of a nonhuman network) became known as super-connectors—not only do they possess super-collections of contacts, but they keep everyone in the network super-connected. Consider Brian Grazer as a super-connector; the importance of his vast collection of individuals to his work is readily apparent. Now he regularly interacts with a diverse set of individuals outside of Hollywood, but because of his early work in getting to know almost everyone in the movie business, he is a super-connector for the industry. Since much of the work of a producer is keeping various parties on a project connected and working together, being a super-connector has made him a super producer. His collection of contacts and conversation is rivaled only by his collection of Oscar and Emmy nominations.
But as we’ll see, being super-connected isn’t just a way to extract value from your network. Creating new and valuable connections inside your existing network is a useful way to become a super-connector.
From Shy to Super-Connected
Like Brian Grazer’s network, Jordan Harbinger’s network rests on the upper limits of the power law. Like Grazer, Harbinger is a super-connector. Unlike Grazer, however, Harbinger didn’t develop his network from a natural curiosity and desire to seek out interesting conversations. Rather, he built his abnormally large network out of necessity. Growing up, Jordan was a shy kid. He skipped school a lot because he experienced social anxiety whenever he was there. He wasn’t a bad student—quite the opposite in fact. He was smart enough and hardworking enough to graduate from the University of Michigan and eventually go on to law school there as well.18
In law school, Harbinger realized just how important it was to build his network. It was during his internship that he learned that “what you know” wasn’t as good a path to success as “who you know.” And he learned that he ought to know a lot of people. During a summer internship, Harbinger was working at a Wall Street law firm, and by luck of the draw, he was paired with a “mentor” whom he could never seem to find. Apparently, his mentor was always out of the office (more on exactly why a little bit later). Harbinger looked around at the firm and saw a company built seemingly on hard work alone. “These guys, they bill in six-minute increments,” Harbinger explained of the lawyers at the firm. “That’d be my job too. You want to bill as many hours as possible. Then a senior partner will decide what a reasonable amount of time is for you to do a specific task.”19
From his perspective, it was all about putting in the work. “You bill 2,000-plus hours a year. You get a bonus. Life is grand,” he said. “It’s a lot of work, though.”
That was when Harbinger finally met his assigned mentor, Dave. “He was never in the office,” Harbinger recalled. “This was a guy from Brooklyn who had a tan . . . so obviously he knew something that nobody else knew.”20 Harbinger only got one meeting with Dave, and the only reason that meeting happened was that human resources, realizing that Dave hadn’t met his mentee for that summer yet, made it happen.
“We were at Starbucks, and he’s standing there and goes ‘Well, ask me whatever you want,’” Harbinger recalled. Dave barely looked up from his BlackBerry as Harbinger started with the most obvious question: “How come you’re never in the office?”21 Harbinger had watched as other lawyers in the firm worked all hours of the day and even into the weekend. Yet Dave was never around and looked like he was earning the same money as a partner.
Dave decided to return the candor. “I bring in the deals,” he said. “I bring in clien
ts. I bring in customers. I’ve got the book of business.”22 Dave explained that he didn’t bill for nearly as many hours as the other lawyers, but he made up for it in compensation with his referral bonuses. Dave saw his job less as working long hours in the office and more as working his contacts to generate new clients for the firm. So while others were working away to grow their tally of billable hours, Dave was working as a “rainmaker” to grow his tally of contacts and connections that might someday help the firm.
When they got back to the office, Harbinger started doing the math. His mentor wasn’t putting in enough billable hours to make his bonus, but he was getting a percentage of the total legal bill for any clients he brought into the firm, which meant he was making more than most of the other partners. For Harbinger, a lightbulb went off; indeed, it was a turning point in his career strategy. As an undergraduate, he could see that intelligence brought a person pretty far. In law school, it seemed like intelligence and hard work were the right combination. But what he now saw was that in the outside world, even intelligence and hard work alone weren’t enough. He would need to start building his network.
Harbinger returned for his final year at law school and immediately started putting what he had learned over the summer into practice. He wasn’t just studying networking. He studied nonverbal communication, body language, vocal tonality, conversational dynamics, and a host of other topics that would help him grow his connections. He borrowed lessons from dating and relationships and took what worked into building professional relationships. He built a small-scale study group that led to huge demand among students to get to know Jordan because they realized it could help their academic performance.