Super Thinking P6 PDF

goals and skill level. Think about the soccer practice where you can arrange the cones just so. Inside an organization, by contrast, constructing these ideal practice sessions is more challenging because you do not control the environment. For example, you might want to work on your presentation ski...

goals and skill level. Think about the soccer practice where you can arrange the cones just so. Inside an organization, by contrast, constructing these ideal practice sessions is more challenging because you do not control the environment. For example, you might want to work on your presentation skills, but you may have only so many possibilities to do so given your position. Additionally, these real-world situations come with constraints and consequences. However, you can still try your best to nd learning opportunities. An excellent start would be declaring your intention to engage in deliberate practice for a particular skill set and recruiting a mentor who is willing to coach you regularly. Then you and your mentor can look out for situations where you can work on improving your skills without much consequence to the organization. For example, you can take a role where you practice your skills on a project that won’t have a big impact on the company. Of course, you can also nd additional practice opportunities in your time outside the organization. How can the organization help you determine which projects are suitable for practicing your skills, or, as a mentor yourself, how can you help determine the same for your team members? Venture capitalist Keith Rabois developed a related mental model, called the consequence- conviction matrix, to do just that. As he explained in his lecture “How to Operate”: You basically sort your own level of conviction about a decision on a grate, extremely high or extremely low. There are times when you know something is a mistake and there’s times when you wouldn’t really do it that way, but you have no idea whether it’s the right or wrong answer. And then there is a consequence dimension. There are things that if you make the wrong decision are very catastrophic to your company and you  will fail. There are things that are pretty low impact. At the end of the day they aren’t really going to make a big di erence, at least initially. Where there is low consequence and you have very low con dence in your own opinion, you should absolutely delegate. And delegate completely, let people make mistakes and learn. On the other side, obviously where the consequences are dramatic and you have extremely high conviction that you are right, you actually can’t let your junior colleague make a mistake. Conviction-Consequence Matrix High conviction Low conviction High consequence Don’t delegate Delegate sometimes Low consequence Delegate sometimes Delegate completely The consequence-conviction matrix can help you free up your time as a leader, and also categorize situations into learning opportunities for your team members. You can even apply this matrix to family situations. For example, we try to have our kids attempt things that won’t cause much harm if they fail, such as buying something at a store themselves or making their own lunch. Tying it all together, activities in the high-conviction, low-consequence quadrant are perfect to help someone (or yourself) with deliberate practice. These are times when what should be done is known, so that coaching can happen e ectively, and at the same time it won’t be so consequential to the organization if the associated tasks initially fail. These situations are perfect deliberate practice exercises where the particular tasks that are on (or just outside) the edge of someone’s ability are delegated, and then that person is given radically candid feedback about how to improve. That’s a compelling method to help people grow quickly, including in new roles. UNLOCKING POTENTIAL Certain psychological mental models repeatedly arise when you are helping people unlock their potential. First, even if someone agrees to the idea of pursuing deliberate practice and receiving radically candid feedback, the process simply doesn’t work very well if the person doesn’t have the right mindset. Psychologist Carol Dweck developed the xed mindset versus growth mindset model, which explains this wrong-versus-right frame of mind, popularized in her book Mindset: The New Psychology of Success. A xed mindset means you believe that your personal attributes and abilities are xed, with no ability to grow or change. For example, you may believe that you are “just bad at math,” and this lack of ability is “just part of who you are.” Of course, if you believe that your abilities (or lack thereof) are xed, then you are naturally going to resist feedback to improve them. The opposite of the xed mindset is the growth mindset, where you believe that you can grow and change over time. When you have a growth mindset, you are more open to critical feedback, since you believe that you can grow your abilities and recognize that receiving and acting on constructive criticism is a necessary part of the process. You have to be careful about your mindset, especially with things you’re already pretty good at. The reason is that when you’re good at something (e.g., math), being good at that thing can become part of your identity (“I am a person who is good at math”). Yet e ectively growing that skill, such as through deliberate practice, requires consistently getting out of your comfort zone and periodically failing. If you have a xed mindset, this process is perceived as an attack on your identity (“How can I be a person who is good at math and keep failing at these math problems?”). As you start looking at whether people have a xed or growth mindset, you will nd that these concepts can apply selectively to certain characteristics (e.g., public speaking, athletics, etc.), though for certain people a xed or growth mindset can be pervasive across most of their endeavors. What do you personally have a xed or growth mindset about? Originally, Dweck theorized that delivering educational instructions in school might encourage one mindset over another. For example, telling children they are smart encourages a xed mindset because then students may take fewer educational risks in an e ort to protect their “smartness.” On the other hand, praising students for working hard encourages a growth mindset because then they want to put in more e ort, including taking on new challenges. Since Dweck’s original studies in the 1970s, many others have been conducted. A recent meta-analysis in March 2018 in Psychological Science found that these types of “growth-mindset interventions” have a positive e ect, though a modest one. However, there is an opportunity to replace these subtle interventions, like praising hard work instead of intelligence, with a much more direct approach, which is simply talking through this model explicitly with the person being coached. You will reap signi cant bene ts if you can get someone to commit to having a growth mindset for a particular skill. It is similarly important for you to believe in the growth potential of your team members, as your expectations may in uence their performance. The Pygmalion e ect is a model that states that higher expectations lead to increased performance, as people try to meet the expectations set for them. (It’s named after the Greek myth of Pygmalion, a sculptor who crafted his ideal spouse, whom Aphrodite then gave life to as Galatea.) Conversely the golem e ect is the phenomenon where lower expectations lead to lower performance. (That one’s named after a clay creature in Jewish mythology that came to life, grew increasingly corrupt and violent, and eventually had to be destroyed.) Both are types of self-ful lling prophecies. As with xed and growth mindsets, there is an ongoing debate on the strength of these e ects across di erent circumstances. The original studies in classroom settings have also been criticized, but stronger e ects have been shown in other settings, such as organizational leadership. For example, a meta-analysis in the October 2009 issue of Leadership Quarterly found the Pygmalion leadership style to be the most e ective of the methods studied. This meta-analysis of two hundred di erent studies on leadership methods was sponsored by the U.S. Department of Defense and compared Pygmalion leadership interventions with traditional methods (popular ideas from the 1970s and earlier) as well as newer techniques described variously as charismatic, inspirational, transformational, or visionary methods. Setting high expectations came out on top. If you set high expectations for your kids or colleagues, that alone will likely not be enough to propel them to reach their full potential. But setting low expectations or lacking expectations altogether will likely create a signi cant barrier for them and prevent them from reaching their full potential. Again, being explicit can help: if people understand what they are shooting for, they can rise to the occasion. However, setting high expectations for people and repeatedly putting them into challenging situations can be exhausting or unsettling for them. You may have experienced these feelings yourself. E ective leaders need to be sensitive to this reality and put support systems in place to help people overcome the psychological barriers that can arise. There are several psychological models to look out for in these settings. First is impostor syndrome, in which someone is plagued with the feeling that they are an impostor, fearing being exposed as a fraud, even though in reality they are not. Surveys indicate that 70 percent of people become in icted with impostor syndrome at some point in their careers. Have you? Dunning-Kruger E ect When people fall victim to impostor syndrome, they dismiss their successes as luck or deception and focus on their failures or fear of failure. This constant focus on failure can lead to high stress and anxiety, and negative behaviors like overexertion, perfectionism, aggression, or defeatism.  You can take the following steps to help people overcome impostor syndrome: Highlight its prevalence (“Everyone’s felt this way before; I’ve felt this way before”). Explain that small failures are expected when you are operating out of your comfort zone. This explanation can help people recharacterize mistakes as learning opportunities. Connect them with other peers or mentors who have faced impostor syndrome. A second model to consider is the Dunning-Kruger e ect, named after social psychologists David Dunning and Justin Kruger. This model describes the con dence people experience over time as they move from being a novice to being an expert. You usually make a lot of progress when you start out learning something, because there is so much new to learn. For example, you can learn to juggle three tennis balls relatively quickly. This quick progress up the learning curve propels you to have high con dence in your abilities. However, you may trick yourself into thinking that this must be a really easy skill, when in reality you are not yet fully grasping everything you don’t know about the skill and how you could be better. Your con dence plummets and, as you learn more, you start to realize everything you don’t know, and see how much e ort it will take to truly become an expert at the skill. For juggling, trying to juggle more than three balls or switching to di erent objects quickly drives this point home. Then your con dence gradually builds back up as you put in that e ort and gain meaningful experience. As a coach, you should keep in mind the Dunning-Kruger e ect and be aware of where your team members are along the curve. When you are working with people who have less expertise, help them properly recognize their level of abilities so they don’t become overcon dent, but at the same time praise their learning progression so they don’t become discouraged. It’s a balancing act. As they get closer to the middle of the curve, they will need more and more encouragement as their con dence plummets. And don’t forget to also keep the model in mind when you are learning a skill yourself. While the Dunning-Kruger e ect explains what happens psychologically across the whole learning curve, it is often used to refer to just the rst spike, i.e., the phenomenon where low-ability people think they are high- ability, unable to recognize their own skill level (or lack thereof) in a particular area. This is really the opposite of impostor syndrome: instead of thinking they are much worse than they are, they think they are much better than they are.  A third mental model about psychological barriers was proposed by psychologist Abraham Maslow (of Maslow’s hammer fame) in his 1943 paper “A Theory of Human Motivation,” and is now known as Maslow’s hierarchy of needs. Maslow says that to reach your full potential (a state he calls “self-actualization”), you rst need to satisfy basic psychological and material needs: physical (food, water, etc.), safety (shelter, freedom from fear, etc.), love (relationships, support, etc.), and self-esteem. He represents these categories of needs as a hierarchy, with self-actualization at the top. Maslow’s Hierarchy of Needs Maslow suggests that you can focus on self-actualization (the top layer) only once all of the more basic needs are met (bottom layers). Through the lens of this model, impostor syndrome re ects an unmet need in the esteem part of the hierarchy, since you feel somehow undeserving of success. Thus, it is preventing your growth into ultimate success at the top layer. A couple of other examples: If you’re in the middle of a tumultuous personal relationship (like breaking up), then the middle-layer needs (love/ belonging) may be unmet. Or children who live with food insecurity or in a violent environment may have trouble learning due to their safety-layer needs being unmet. Critics have raised questions about whether Maslow’s hierarchy di ers across cultures or circumstances, or even if there is an actual hierarchy at all. Nevertheless, thinking about this model can help you identify why you or others are not reaching full potential. Finally, let’s suppose you’re coaching someone and together you have been able to work through all their psychological barriers. You are helping them engage in deliberate practice. You are actively providing actionable feedback on a regular basis. When you are helping them analyze past situations to give such feedback, you still need to consider another psychological phenomenon: that sometimes your memories of the past, even the very recent past, can be biased or distorted. We covered some of these biases way back in Chapter 1 with availability bias and the like. One other mental model to consider is hindsight bias, where, after an event occurs, in hindsight, there is a bias to see it as having been predictable even though there was no real objective basis on which it could have been predicted. Monday morning quarterbacking and hindsight is twenty-twenty are formulations of the same concept. Turn on the TV after any major event to see hindsight bias in action. Talking heads will explain why something occurred, and yet, if you had watched coverage before the event, you would not have found many predicting it ahead of time. Think of the 2007/2008 nancial crisis or the U.S. 2016 election cycles. Hindsight bias arises in many other situations: judges weighing evidence in court cases, historians analyzing past events, and physicians assessing earlier clinical decisions. For example, in negligence cases, for guilt to be found, it must be shown that the person who committed the negligent act would have known that their actions would endanger others. When experimental subjects are presented with various negligence scenarios, they typically rate an outcome as more foreseeable the worse the outcome is, even when the negligent act is the same. In other words, the worse the outcome, the worse the hindsight bias. In the context of leadership and learning new roles, hindsight bias can keep you from learning from past events. If you believe an event was predictable when it was not, you may take away that you made the wrong choices leading up to the event, when in reality you may have made the right choice given the information available at the time. For example, if you make an investment in a new technology or even personally in a stock or startup company, and it doesn’t work out, it doesn’t mean it wasn’t a good bet at the time. The odds may have been in your favor, but the luck of the draw simply didn’t go your way. The questions to ask are how accurate your risk assessment was at the time, and whether it could have been any more accurate given the time and resources available. Answering these questions moves you away from black-and-white thinking (the event was totally predictable or not) and into more nuanced thinking (considering how predictable it really was). Counterfactual thinking (see Chapter 6) can reduce hindsight bias because it forces you to consider other ways events could have unfolded. Ask yourself how things would have changed if you had done X, Y, or Z instead. Another related model is survivorship bias (see Chapter 5), which, as applied here, tells you that when looking to see what past failures had in common, you should consider that past successes might have also had these things in common. For instance, when analyzing past investment decisions, you need to look at how your decision-making criteria applied to the winners and the losers as a whole, and not just to one of those subgroups, or else you may take away the wrong message. Another way to counteract hindsight bias is to take notes as events occur in real time. That way you have a more objective record of what happened and are not relying solely on potentially compromised recollections. Of course, literal recordings are the most objective record and are increasing in popularity. Some organizations record some meetings or produce structured notes, journalists record interviews with sources, and police are increasingly using body cams to document encounters. It is important to realize, though, that hindsight bias can a ect you only in instances where the outcome could not be foreseen. Hindsight bias is not a factor when you are reviewing the many instances of predictable errors out there. The key is distinguishing between the two situations. Self-serving bias (see Chapter 1) suggests that you will be more inclined to say that your own or your group’s mistakes could not have been predicted (“Who could have known?”) and you are more likely to apply hindsight bias to be critical of others. The mental models from this section can help correct psychological mischaracterizations (e.g., impostor syndrome), arti cial roadblocks (e.g., xed mindset), and misinformation (e.g., hindsight bias), all in the service of helping people, including yourself, think objectively about current performance and ways to improve. TOGETHER WE THRIVE So far in this chapter, we’ve covered the mental models that help people reach their full potential and thrive as members of 10x teams. There is another set of mental models, however, that can dramatically increase (or decrease) the likelihood of creating these special teams—those related to the makeup of organizational culture. Every group of people has a culture. Often described on an ethnic, national, or regional level, culture as a concept also applies to smaller groups: organizations, immediate family units, extended families, groups of friends, and o ine and online communities built around common interests. Culture describes the common beliefs, behavioral patterns, and social norms of group members. For example, di erent families have di erent norms for resolving disputes: some talk openly about emotions, some hardly ever; some have heated discussions, some much less so. What is the norm in your family? Similarly, two highly functioning organizations can have widely di erent norms and processes for information control (open versus need-to- know), communication delivery (spoken versus written), how new ideas get proposed (ad hoc versus formal), punctuality (always on time versus exible), and many other dimensions. In any group setting, it is important to understand the culture, including whether it is one that prefers high-context or low-context communication. A low-context culture is explicit and direct with information, preferring that you be real and tell it like it is. You need a low amount of context to understand low-context communication, because most everything you need to know is clearly expressed.  High-Context/Low-Context Continuum At the other extreme, in a high-context culture, information is conveyed much more indirectly, less confrontationally. For example, how things are going in a project or role is communicated less explicitly. You need a high amount of additional context to fully understand such high-context communication, appreciating the nuances of nonverbal cues, voice intonation, and adherence (or lack thereof) to usual processes as clues. In other words, what isn’t said is just as important as what is said, if not more so. This high-context/low-context continuum applies to all cultures, from small-group ones all the way up to the cultures of whole countries. As with personality traits, there are many dimensions that sociologists use to describe culture. Some other commonly cited dimensions besides low context versus high context include the following: Tight (many norms and little tolerance for deviation from those  norms) versus loose: In a loose organizational culture, you might see people doing the same thing (like organizing a project) in many di erent ways, whereas tight cultures develop stricter rules and procedures. Hierarchical (lines of power are clear) versus egalitarian (more shared power): You will see more consensus and group decision making in an organization with a more egalitarian culture. Collectivist (group success is more important than individual success) versus individualist: Performance-ranking systems like stacked ranking (where managers are forced to rank their direct reports) occur in individualist organizational cultures. Objective (favoring empirical evidence) versus subjective: Organizational cultures that are more data-driven fall on the objective side of this spectrum. In any case, when you recruit new members to your organization, it may take signi cant time for them to adapt to its culture. For example, someone who is used to extremely low-context environments will expect you to be very direct, whereas someone used to extremely high-context environments may be o ended by your directness, and such low-context communication could potentially hurt their morale. While new hires can grow accustomed to a new culture, they may be resistant initially. So the more up-front you are about the culture of the organization, the better. In fact, being explicit about your cultural norms is one of the most high-leverage activities you can do as an organizational leader (see Chapter 3). It can help prospective team members gure out whether your organization is a good t for them. Strengthening cultural norms also helps existing team members work together more e ciently.  It’s sometimes said, “Culture is what happens when managers aren’t in the room.” It’s what people do when they’re left to their own devices. And that’s exactly why it is so high-leverage to develop and reinforce culture: You can’t look over people’s shoulders all the time. It takes time and energy that is usually best spent elsewhere. If your team makes progress in the way you want only when you’re scrutinizing them, then they won’t get very far in the direction you desire. Additionally, if you don’t shape your organization’s culture, it will shape itself, and may develop in ways you don’t want. Some organizations, such as Uber, were at one time infamous for having a toxic culture. Characteristics of a toxic culture include preoccupation with status, territorialism, aggression, poor communication, fear of speaking up, unethical behavior, harassment, and general unhappiness. Fortunately, there are many straightforward ways to positively shape culture: Establishing a strong vision—“Our north star, our vision for the future, is X” (see Chapter 3). De ning a clear set of values—where your organization sits along the various cultural dimensions, e.g., “Our organization values taking calculated risks, even if they fail.” Reinforcing that vision and those values through frequent communications—including at all-hands meetings and through team-wide broadcasts. Creating processes that align with that vision and those values— such as how you decide on hiring new team members. Leading by example—making sure leaders adhere to the norms  and values you want everyone else to adhere to. Establishing traditions—gatherings that celebrate stated values, such as holiday celebrations, group volunteer events, or recurring award ceremonies. Fostering accountability—e.g., reviewing previous experiences for lessons learned in post-mortems (see Chapter 1) or giving honest feedback on performance reviews. Rewarding people for exhibiting exemplary cultural behaviors— giving them promotions, awards, etc. Taken together, these techniques clearly express cultural norms to everyone in the organization and show that they are taken seriously. They also convey that a person who aligns with the organization’s vision, values, and related cultural norms and processes is more likely to excel within the organization. Winning hearts and minds is a related mental model. It was rst introduced in 1895 in a military context by French general Hubert Lyautey as part of a strategy to counter the Black Flags rebellion along the Indochina–Chinese border. It is a recognition that making direct appeals to people’s hearts and minds through communication can e ectively win them over. In relatively recent history, the U.S. has led hearts-and-minds campaigns that directly explain its perspective to the populations of foreign countries like Vietnam and Iraq. In a business context, the concept has been successful when upstarts like Airbnb have made direct appeals to citizens to contact their representatives and lobby against regulations that would negatively impact consumer (and business) interests. Establishing and communicating a shared vision, values, and cultural norms helps organizations win the hearts and minds of its members, and thus intrinsically motivates them to reach their full potential. Otherwise, motivation tends toward the extrinsic, such as compensation and title. Venture capitalist Fred Wilson uses the idea of loyalists versus mercenaries to explain the way members view an organization. According to a June 23, 2015, blog post, he believes loyalists are devoted to an organization even in the face of adversity. Mercenaries, by contrast, are in it primarily for the money, and are much more likely to leave for greater rewards elsewhere. Wilson explains some factors that draw more loyalists: 1. Leadership. At the end of the day, people are loyal to a leader they believe in.... 2. Mission. People are loyal to a mission. I’ve seen super talented people walk away from compensation packages 2–3x what they currently make because they believe in what they are working on and think it will make a di erence in their lives and the lives of others. 3. Values and Culture.... People want to work in a place that feels right to them. They need to feel comfortable at work. In the way that a welcoming home with comfortable furniture is pleasant to be in, a company with good values and culture is pleasant to work in. 4. Location.... In the Bay Area and NYC, your employees are constantly getting hammered to leave for more cash, more equity, more upside, more responsibility, and eventually it leads to them becoming mercenaries.... If you are building  your company in Ljubljana, Waterloo, Des Moines, Pittsburgh, Detroit, or Indianapolis, you have a way better chance of building a company full of loyalists than if you are building it in the Bay Area or NYC. As Wilson notes, culture is one of the key ways to attract and retain loyalists, which should be the goal if you’re seeking 10x teams for the long term. When working to craft a positive organizational culture, there are a few tactical models to keep in mind as well. First, you can show employees that you value their contributions by understanding that people in di erent positions need di erent kinds of support to make progress on challenging e orts. Consider what startup investor Paul Graham, in a July 2009 blog post, called the manager’s schedule versus maker’s schedule: The manager’s schedule is for bosses. It’s embodied in the traditional appointment book, with each day cut into one-hour intervals. You can block o several hours for a single task if you need to, but by default you change what you’re doing every hour.... But there’s another way of using time that’s common among people who make things, such as programmers and writers. They generally prefer to use time in units of half a day at least. You can’t write or program well in units of an hour. That’s barely enough time to get started. When you’re operating on the maker’s schedule, meetings are a disaster. A single meeting can blow a whole afternoon, by breaking it into two pieces each too small to do anything hard in. Plus you have to remember to go to the meeting. That’s no problem for someone on the manager’s schedule. There’s always something coming on the next hour; the only question is what. But when someone on the maker’s schedule has a meeting, they have to think about it.  When you work with people who could bene t from a maker’s schedule, you must work to create a culture that allows them these long uninterrupted blocks of time. To do so, you need to ensure that people on the manager’s schedule are not consistently interrupting the ow of people on the maker’s schedule. Gabriel’s company (DuckDuckGo) has a policy of no standing meetings on Wednesdays and Thursdays, which facilitates schedules that incorporate deep-work blocks (see Chapter 3). Another approach is to allow people to work in environments more conducive to deep work, such as away from the central o ce, where they will get fewer interruptions from colleagues. Next, you must be wary of culture eroding as your organization grows. Consider Dunbar’s number—150—which is the maximum group size at which a stable, cohesive social group can be maintained. (It’s named after anthropologist Robin Dunbar.) The idea behind Dunbar’s number is that at about 150 group members and below, you can relatively easily know everyone in the group and their roles within it. Above this number, however, you cannot easily remember everyone and what they do. Organizational processes that worked before Dunbar’s number is reached all of a sudden seem to break down when it is exceeded, and new ones need to be constructed for the organization to be similarly e ective again. A group of more than 150 people needs more explicit structures. This concept of the stability of group dynamics being dependent on the size of the group extends to smaller groups as well. Two other well-known breakpoints are when the size of a small organization or team reaches about ten to fteen people and when it further expands to between thirty and fty. When your group consists of only a few people, such as an immediate family unit or a tiny company, everyone can be involved in most major decisions and understand everything relevant to the group. At ten to fteen people, though, this simple system breaks down, and you need some more organizational structure (subgroups, discrete projects, etc.), or chaos ensues. At thirty to fty people, the same thing happens again—you need to create even more structure (teams, formal management, etc.) to avoid another round of disruption. And when you get to 150, Dunbar’s number, you start to need more traditional corporate structure (strict policies and procedures, processes to interact with other departments, etc.). If you are a leader in a growing organization or team, you need to plan for periods of adjustment when you cross these thresholds. You should also be wary of growing your organization too fast. If you have too many new people—who by de nition aren’t ingrained in your culture—come in at once, then that culture you’ve worked so hard to craft can quickly become diluted and much less e ective. Anecdotally, this type of hyper-growth scenario risks signi cant trouble if an organization grows its team more than 50 percent in one year. Another model to keep in mind that can quickly erode culture and morale is the mythical man-month. It comes from computer scientist Fred Brooks, who originally presented it in a book with the same name. Man- month, or person-month, is a unit of measurement for how long projects take (e.g., this project will take ten person-months). Brooks declares that this entire way of measurement is awed, based on a myth that you can simply add more people (person-months) to a project and get it done faster. A silly though memorable example is gestating a baby, which takes about nine months no matter how many people you try to add! The same is true for more mundane projects, especially the later it is in the project life cycle. If you bring someone in to a project late, you need to get them up to speed, and usually this onboarding actually slows down the project timeline. Often it is just faster for the existing people to complete the project. However, you run the risk of burning out the team, especially if you have a strict deadline. But if you extend the deadline and do bring people in, you risk demoralizing the team that way as well, since you had to bring in reinforcements. Better planning can prevent you from ending up in this no- win situation. A nal tactical model to keep in mind when building positive culture is another military one: boots on the ground. It refers to actual troops on the ground in a military con ict, who are wearing boots as part of their uniforms. It is often referenced in the context of making the point that you need boots on the ground to be successful in a military campaign, and that conducting a war just from afar—for example, using only air power—will not achieve the ultimate goals. A military case is also often made that to really win hearts and minds, you need boots on the ground to interact with the population and humanize the outside intervention. That is, you cannot just broadcast and enforce your message from afar. This concept has taken hold within the U.S. through community policing, where police spend time in the community building ties and therefore trust with the population they are policing. The same is true in an organizational setting when you want people to buy in to your organization’s vision and culture. You can’t just de ne the culture from afar and hope that it will take hold. Instead, leaders must lead by example and put their own boots on the ground. This is way more e ective than leaders who are always set apart, seen as sitting in their ivory tower. There are several common phrases that showcase the desired behavior: rolling up your sleeves, getting in the trenches, showing that you are one of us. As a leader, your job of winning hearts and minds and setting your teams up for success is never done. You must continually reinforce vision and values, doing your best to evolve culture in a more optimal direction, setting up the conditions for those around you to grow. If you can do that well, then the culture you create will help set up your organization to be one that supports and cultivates 10x teams. KEY TAKEAWAYS People are not interchangeable. They come from a variety of backgrounds and with a varied set of personalities, strengths, and goals. To be the best manager, you must manage to the person, accounting for each individual’s unique set of characteristics and current challenges. Craft unique roles that amplify each individual’s strengths and motivations. Avoid the Peter principle by promoting people only to roles in which they can succeed. Properly delineate roles and responsibilities using the model of DRI (directly responsible individual). People need coaching to reach their full potential, especially at new roles. Deliberate practice is the most e ective way to help people scale new learning curves. Use the consequence- conviction matrix to look for learning opportunities, and use radical candor within one-on-ones to deliver constructive feedback. When trying new things, watch out for common psychological failure modes like impostor syndrome and the Dunning-Kruger e ect. Actively de ne group culture and consistently engage in winning hearts and minds toward your desired culture and associated vision. If you can set people up for success in the right roles and well- de ned culture, then you can create the environment for 10x teams to emerge.  9 Flex Your Market Power IN 2016, HATCHIMALS WERE the hottest toy of the Christmas season. They are cute little electronic bird toys that you can take care of, kind of like a modern Furby. Supply was short, and people went to great lengths to get their hands on them. As RetailMeNot reported on December 6, 2016: Last Sunday, Toys “R” Us stocked its shelves with Hatchimals, and shoppers lined up overnight to get their hands on the toys. Toys “R” Us handed out tickets to those in line, and reports revealed that some people turned right around to sell those tickets for over $100 to others in line. Long story short, you should likely expect to have to camp out for these toys, especially if Target decides to implement a ticket system. You don’t want to (literally) be left out in the cold. While Hatchimals retailed for about $60 at the time, they were selling for as much as $1,200 on eBay. As you can see, people will pay very in ated prices when the supply available for a product is low relative to its demand.  With that type of pro t opportunity, enterprising individuals perennially buy Christmas-season toys from retail stores so that they can resell them at higher prices on secondary markets (like ticket scalpers do for desirable concert tickets). When you take advantage of price di erences for the same product in two di erent settings, it’s called arbitrage.  Back in the nineties, when eBay launched, it created numerous arbitrage opportunities by connecting newly minted salespersons to customers anywhere in the world. In college, Lauren found eBay to be a great source for making spare cash by pairing small-town anime lovers, who didn’t have anime shops in their own towns, with products she bought at a local anime shop near MIT. Sometimes she would even nd arbitrage opportunities within eBay itself. She found she could make a pro t by relisting items in a better category or by using better keywords so that more people would nd the listing. For example, she once found a designer wedding gown on sale for fty dollars in the dress-up and costume section but thought she could resell it for hundreds of dollars if she listed it in the pre-owned wedding dress section. She was right—it went for more than two hundred dollars! Price di erences like these tend to not last very long, because others notice and pursue the same discrepancies until they no longer exist. It can certainly be pro table to take advantage of these short-term opportunities, but you need to keep nding new ones to continue turning a pro t. In this chapter we explore the opposite of arbitrage: sustainable competitive advantage. This mental model describes a set of factors that give you an advantage over the competition that you can sustain over the long term. A working ywheel (see Chapter 4) can drive such advantage—think of what Amazon has on its competition with regard to shipping because of its size and investments in warehousing and delivery. The signature of sustainable competitive advantage is what economists call market power, the power to pro tably raise prices in a market. For instance, when Amazon has raised its Prime price, it hasn’t lost many customers. An extreme showing of market power is a monopoly. Monopolies have vast market power because they have little competition. As an example, consider the EpiPen, a medical device needed by people with severe allergies to treat potentially fatal allergic reactions. In 2016, Mylan, the company behind this well-known brand, controlled more than 90 percent of the market for these types of devices. From 2007, when it bought the brand from Merck, through 2016, its market share hadn’t decreased despite having raised the price for the device by more than 500 percent. This price increase is an incredible showing of market power, aided by the recall of a competitor’s device and the U.S. Food and Drug Administration’s rejection of another one during this time frame. If a monopoly raises its prices, you can either pay the higher price or forgo its product, which in many cases (such as for needed life-saving devices) is not an attractive option. The other extreme is perfect competition, markets where many competitors provide the exact same product, perfect substitutes (also known as commodities). A thirty-two-ounce bottle of isopropyl alcohol is thirty-two ounces of isopropyl alcohol no matter whom you buy it from. If a commodity supplier raises prices, you just buy from another supplier at the lower price. Consequently, these commodity providers have no market power. Market power also applies to you personally in the labor market. If you are just starting out in an industry and have only basic, undi erentiated skills, you can be a commodity. That means you have no advantage over other potential employees for the same job. From the employer’s perspective, you are interchangeable with other applicants for the position. In this situation, you have no ability to negotiate compensation and must accept the market rate for your services. However, this situation doesn’t necessarily mean you get paid minimum wage. As with the Hatchimals, market price, in this case compensation, is a function of supply and demand. Plenty of people right out of school can make good money because the demand is high for their services. For example, there are many newly minted nurses each year, but nurses are in high demand right now (at least in the U.S.), so new graduates can nd work at attractive starting salaries. On the other hand, even though the number of new graduates with PhDs in history is relatively small, there are even fewer tenure-track teaching positions available for them, and consequently it is very di cult even to secure one of these jobs. When you are undi erentiated—with no sustainable competitive advantage and therefore no market power—you are completely subject to the supply-and-demand forces in the market, and the price they deliver to you. That speaks to picking an industry in high demand for the long term, such as nursing. It also speaks to the need to di erentiate yourself from your peers by developing a unique set of skills that the market values. Then you have the opportunity to demand higher compensation by demonstrating the distinctive value you bring to your employers or clients. For example, in nursing, such di erentiation could be achieved by a combination of experience and continuing education in a nursing specialty like critical care, anesthesiology, or pain management. Of course, if there is no demand for your special set of skills, then there is no opportunity for you to ex market power. For example, many Olympic athletes need day jobs because there just isn’t enough market demand for their sport. They cannot support themselves on their extraordinary skills alone.  Having market power—individually or organizationally—is an attractive position because you can use your advantages to sustain pro t for a long time. That’s why it is called sustainable competitive advantage. Nothing lasts forever, though. New technologies arise that disrupt the old. Monopolies fall. Patents expire. Regulations evolve. New job skills crop up, supplanting the way things were done before. In Chapter 4, we examined how to watch out for, anticipate, and even engender such change. In this chapter, we examine super models to help you nd and hang on to market power. SECRET SAUCE Major life, career, and organizational choices can be thought of as bets on the future. You can be either right or wrong in those bets. If wrong, you won’t achieve the success you wanted; if right, you will. However, to achieve a really high degree of success, you need something extra: to be contrarian in your bet. In “Demystifying Venture Capital Economics, Part 1,” venture capitalist Andy Rachle summarized this concept, originally formulated by investor Howard Marks, with the consensus-contrarian matrix: The investment business can be explained with a two-by-two matrix. On one dimension you can be either right or wrong. On the other you can be consensus or [contrarian]. Obviously you don’t make money if you’re wrong.... The only way to generate outstanding returns is to be right and [contrarian]. Consensus-Contrarian Matrix Wrong Right Consensus No return Regular-sized returns Contrarian No return Outsized returns Rachle elaborates: Being willing to intelligently take this leap of faith is one of the main di erences between the venture rms who consistently generate high returns—and everyone else. Unfortunately, human nature is not  comfortable taking risk; so, most venture capital rms want high returns without risk, which doesn’t exist. As a result they often sit on the sideline while other people make the big money from things that most people initially think are crazy. The vast majority of my colleagues in the venture capital business thought we were crazy at Benchmark to have backed eBay. “Beanie babies... really? How can that be a business?” Consider the analogy of horse racing. If everyone bets on the same winning horse, no one gets a big payout. If you make the same choice as everyone else, a consensus bet, then there isn’t much ability for you to individually stand out, and so you can at most get a modest success. Venture capitalist Bill Gurley put it this way: “Being ‘right’ doesn’t lead to superior performance if the consensus forecast is also right.” But if you like a horse at fty-to-one and she wins, then you have achieved a remarkable success. It is the di erence between coming up with the next hot idea and being the fth self-serve frozen yogurt franchise in your town. As Charlie Munger said in Poor Charlie’s Almanack, “Mimicking the herd invites regression to the mean” (see Chapter 5). His investing partner Warren Bu ett puts it this way in Warren Bu et Speaks: “Most people get interested in stocks when everyone else is. The time to get interested is when no one else is. You can’t buy what is popular and do well.” In horse betting, the crowdsourced odds (see Chapter 6) re ect how many people agree with your bet. As a result, you get the highest returns when you bet on a horse that hardly anyone else is betting on. However, there is likely a good reason that no one is betting on that horse. As Je Bezos said at Vanity Fair’s New Establishment Summit on October 20, 2016, “You just have to remember that contrarians are usually wrong.” A contrarian bet is therefore most likely to be successful when you know something that almost everyone else doesn’t. In other words, you know that the chance of being right is much greater than the crowd realizes, such as when you know a particular bet has a 10 percent chance of success, but the crowd thinks it’s 1 percent. Je Bezos again, in a 1997 letter to shareholders: Given a ten percent chance of a 100 times payo , you should take that bet every time. But you’re still going to be wrong nine times out of ten. We all know that if you swing for the fences, you’re going to strike out a lot, but  you’re also going to hit some home runs. Knowing something that is important yet mostly unknown or not yet widely believed is what investor Peter Thiel calls a secret. This has the same meaning as its colloquial use, just applied to innovation. As Thiel wrote in his 2014 book, Zero to One: Great companies can be built on open but unsuspected secrets about how the world works. Consider the Silicon Valley startups that have harnessed the spare capacity that is all around us but often ignored. Before Airbnb, travelers had little choice but to pay high prices for a hotel room, and property owners couldn’t easily and reliably rent out their unoccupied space. Airbnb saw untapped supply and unaddressed demand where others saw nothing at all. The same is true of private car services Lyft and Uber. Few people imagined that it was possible to build a billion-dollar business by simply connecting people who want to go places with people willing to drive them there. We already had state-licensed taxicabs and private limousines; only by believing in and looking for secrets could you see beyond the convention to an opportunity hidden in plain sight. A secret can be an idea that no one else has thought of, but it can also be an idea about how to achieve something that everyone else currently thinks is too risky. It is possible that an idea is not as risky as it seems, and by taking a rst-principles approach you can come to a more correct risk assessment (see Chapter 1). Many investors actually passed on Airbnb because both sides of an Airbnb transaction seemed so risky that they thought there wouldn’t be a market for it. After all, an Airbnb transaction on one side calls for letting a stranger sleep in your home, and on the other side involves sleeping in a stranger’s home. Of course, the investors who passed were wrong; plenty of people were happy to bear those risks once Airbnb set up a marketplace to do so. The opposite can be true as well, in that people can substantially underestimate risks, such as in the 2007/2008 U.S. housing crisis, which led to a global nancial crisis. The few people who correctly assessed this risk and bet on their secret knowledge made a lot of money, as depicted in the 2015 lm The Big Short, based on a 2010 book of the same name by author Michael Lewis. A secret can also be how to turn someone else’s good idea into a great idea. Thomas Edison didn’t invent the lightbulb, but his concerted e orts made it long-lasting and commercially viable. You could similarly have a chance for great success if you see a viable path for an idea that everyone else is missing. Most currently central ideas in academic elds started out as secrets. You can nd examples throughout this book, from the paradigm shifts of continental drift and germ theory in Chapter 1, to the statistics in Chapter 5 that we now take for granted, to all the in uence models from Chapter 7, such as reciprocity. Mental models themselves are somewhat secret. The central theme of this book is that certain models from di erent elds can be applied to help you solve problems in other areas. Common knowledge in one eld can be a secret in another. In another book by Michael Lewis, Moneyball, he explains how the Oakland Athletics baseball team was one of the rst to use statistics to identify undervalued players by focusing on previously underappreciated statistics like on-base and slugging percentage. As a result, they assembled a world-class team with much less money than their competition. Now most professional sports teams employ a squad of statisticians to look for such anomalies. As Thiel says, many secrets are similarly hidden in plain sight. You just need to know where to look. Science ction writer William Gibson put it like this: “The future is already here—it’s just not very evenly distributed.” By studying future-facing pockets of people and knowledge across di erent elds, you can get closer to secrets. Technologies that people use every day started their growth among small groups of innovators many years before they became commonplace. For example, long before computers were everywhere, enthusiasts gathered into groups such as the Homebrew Computer Club in Silicon Valley, which included Steve Wozniak (cofounder of Apple) and Jerry Lawson (inventor of the video game cartridge) among its members. Academic advances and groundbreaking ideas in every area follow a similar pattern, starting with innovators and early adopters before moving into the mainstream (see the technology adoption life cycle in Chapter 4). Find the Homebrew Computer Club equivalent for whatever area you’re interested in and you’ll nd active discussions of secrets. Seeking out groups like these puts you in the know. You are then in the position to jump on an innovation bandwagon early and be among the groundbreakers in a new eld or industry. However, if you aren’t set on changing the world, secrets can also be used on a smaller scale. Knowing about new technologies can help you improve your day-to-day life, through such current innovations as virtual assistants, new delivery services, or telemedicine. For instance, knowing about new medical advances can help you make better medical decisions, and knowing about the latest car technologies can help you make a safer vehicle choice. Just discovering a secret is not enough; your timing must also be right. Pushing on an idea too soon can result in a lot of wasted time and money, possibly leading you to miss out on the opportunity altogether. Unfortunately, new ideas and ways of doing things can face a lot of challenges that make this timing di cult to get right. A contrarian idea will almost inevitably face a ght against the inertia from the consensus idea (see Chapter 4). This inertia can be a barrier against both the spreading of the idea and the ability to raise capital to fund it. New ideas also often face technological barriers to mass adoption. Uber’s widespread adoption was possible only once everyone had a smartphone. YouTube became a mainstream possibility only once broadband access was prevalent. In both cases there were earlier attempts to accomplish similar things that failed because the timing wasn’t right. The rest of the world wasn’t yet su ciently equipped with the necessary technology. Apple famously introduced the Apple Newton tablet device in 1993 and discontinued it in 1998 after lackluster sales. More than a decade later, Apple introduced a new tablet device—the iPad—which had the fastest initial adoption rate of any mainstream electronic device up to that point, even ahead of the iPhone and the DVD player. What changed? For one thing, the internet: you could do so much more with the iPad relative to the Newton, given the previous twenty years of internet advances. Similarly, in 1995, Newsweek published a now-infamous opinion piece by Cli Stroll entitled “The Internet? Bah!” which basically said that the internet’s potential impact was wildly overstated. Cli Stroll was neither a Luddite nor a noob in the tech world. As stated in his piece, he was an early adopter, having been on the internet already for two decades, even famously catching a hacker. He just couldn’t see that 1995 was the right time for mainstream internet adoption. While it wasn’t yet the right time for a mainstream tablet like the Newton, enough people were coming online to enable sites like Amazon (founded in 1994) and eBay (founded in 1995) to become viable. It is certainly fair and reasonable to question hype, especially because so many overhyped ideas zzle out before they take o. Additionally, some of the ideas best primed for takeo aren’t hyped much at all. Psychologist Robert Sternberg explained to Psychology Today: “Creative ideas usually get a weak reception, at least initially... but contrarians give their lives meaning by attempting to change the way things are to the way they think they should be.” William Brody, former president of Johns Hopkins University, told a story in a 2004 faculty newsletter about giving a presentation about digital radiography as a young faculty member in the late 1970s to a standing- room-only crowd at an international meeting. The promise of this new technology was the totally “ lmless” radiology department, and he had some interesting results to share. Next door, a new imaging technology was presented to only a handful of people, most of whom were collaborators or family of the presenter. While decades later the medical community was still waiting for lmless radiology departments, the other presenter, Sir Peter Mans eld, went on to win a Nobel Prize in 2003 for his contributions to the invention of magnetic resonance imaging (MRI) technology. To address this timing question more systematically, ask yourself why now? This simple yet powerful mental model comes from venture capital rm Sequoia Capital, early investors in Apple, Oracle, PayPal, YouTube, Instagram, Yahoo!, WhatsApp, and many more business ideas that went on to become household names. For every rocket-ship startup, there is a good answer to this question underpinning it, usually based on some rapidly unfolding secret due to a con uence of recent advances and adoption of underlying technology. The same concept applies for almost any change you want to make, from trying out a new organizational process to pursuing a new career. Why now? Would it make a di erence if you waited longer? What would you be waiting for in particular? Given the array of things you can work on, is there another change you should be making right now? You can also consider this question using inverse thinking (see Chapter 1). Instead of asking why now?, ask now what? When you see something change in the world around you, ask yourself what new opportunities might open up as a result. From the political sphere to the personal and organizational, many sweeping changes happen in the wake of a real or impending crisis. Politician Rahm Emanuel o ers this perspective: “You never want to let a serious crisis go to waste. And what I mean by that [is] it’s an opportunity to do things you think you could not do before.” The why now model also explains why there are often concurrent academic discoveries across the world and similar startups independently emerging simultaneously. Wikipedia has a huge list of instances like these, and there is a name for the concept: simultaneous invention, or multiple discovery. Modern calculus was independently formulated around the same time in the seventeenth century by Isaac Newton and Gottfried Leibniz. And as we mentioned in Chapter 4, Charles Darwin and Alfred Wallace jointly published the theory of natural selection after independent discovery. The underlying conditions were ripe for these ideas, and often more than one person will act on the same secret once they have determined the time is right to pursue the opportunity. VISION WITHOUT EXECUTION IS JUST HALLUCINATION Unfortunately, even knowing a secret at the right time still isn’t enough to guarantee success. People with great, timely insights often fail to achieve great returns due to poor execution. In this section we will explore mental models that can improve your chances of successful execution. The title of this section is a modern take on an old Japanese proverb, “Vision without action is a daydream. Action without vision is a nightmare.” Successful, world-changing ideas almost always involve changing the behavior of a large group of people: how they live, work, entertain themselves, or even how they think. For example, as noted earlier, Airbnb has changed the way many people travel. Whether your idea is business- focused or not, you can think of the people whose behavior it seeks to change as your “customers.” In this context, your secret is the insight you have on how the behavior of your customers should be changed, e.g., people should be able to rent out rooms directly from one another. Your “product” is therefore how you speci cally are using your secret to cause a behavioral change in your customers, e.g., creating a marketplace of rentable rooms over the internet. Even if you are the rst to market with the idea, you will still lose out to the competition if your product cannot create the necessary behavioral change. The rst person or organization to try to capitalize on a secret can indeed have a rst-mover advantage, crafting a competitive advantage derived from being the rst to move into a market with a product. However, they can also experience a rst-mover disadvantage if they make a lot of mistakes. Fast-followers can copy the rst mover, learn from their mistakes, and then quickly surpass them, leaving the rst mover ultimately disadvantaged even though they were rst. For a rst mover, the di erence between success and failure hinges on whether they can also be rst to achieve product/market t. That’s when a product is a such a great t for its market that customers are actively demanding more. This model was also developed by Andy Rachle , who explained in “Demystifying Venture Capital Economics, Part 3,” “First to market seldom matters. Rather, rst to product/market t is almost always the long-term winner.... Once a company has achieved product/market t, it is extremely di cult to dislodge it, even with a better or less expensive product.” A company without product/market t nds it extremely hard to obtain customers; in contrast, a company with product/market t nds it relatively easy to obtain customers. This concept can be widened to “ ts” in a variety of situations: person/organization t, member/group t, culture/strategy t, message/audience t, etc. As we explored in Chapter 8, a person in just the right role can produce amazing results, and an organizational strategy attuned perfectly to its culture can be a quick and resounding success. Similarly, a message can strike just the right tone for a speci c audience such that it will deeply resonate. You see this phenomenon repeatedly in politics when certain candidates hit a nerve with a segment of the population, as Bernie Sanders and Donald Trump did in the U.S. 2016 presidential election cycle. A model that captures these phenomena is resonant frequency. This model comes from physics and explains why glass can break if you play just the right note: Each object has a di erent frequency at which it naturally oscillates. When you play that frequency, such as the right tone for a wineglass, the energy of the wave causes the glass to vibrate more and more until it breaks. When you achieve product/market t, the e ect is similar. When this happens, results are not just a little better, they’re dramatically better. Product is ying o the shelves. That’s what you’re looking for with product/market t or any other t—signs of real resonance. In Chapter 8, we also discussed 10x teams. True resonance is like that: not one or two times better, but many, many times better. Resonance One way to increase your chances of getting to product/market t is through customer development, a product development model established by entrepreneur Steve Blank that focuses you on taking a customer-centric view. Customer development’s goal is to help you nd a sustainable business model by applying the scienti c method (see Chapter 4) through rapid experimentation with your customers. You set up a quick feedback loop with them to learn as much as you can about their needs, resulting in a repeatable process to acquire and retain them. Way back in Chapter 1 we explained how you want to de-risk an idea by testing your assumptions as cheaply as possible. Customer development is one way to do that, by talking directly to customers or potential customers. As Blank says, “There are no facts inside the building so get the hell outside!” If you can ask the right questions, you can nd out whether you have something people really want, signaling product/market t. Of course, you probably won’t make something people really want on the rst shot. That’s why you build an MVP (again, see Chapter 1) and run experiments with customers to see how it is actually used (if at all), continually re ning your product as you incorporate real-world feedback via this rapid experimentation process. Customer development works in a wide variety of situations: Talk to residents before you move somewhere. Interview current employees before you take a job. Poll a community before enacting a new policy. For any idea you have, think about who the “customer” is and then go talk to them directly about your “product.” Think focus groups, surveys, interviews, etc. When you are trying to act on a secret by delivering a product or service, you are in a race against your competition for product/market t. To give yourself the best chance of winning this race, you must engage in customer development the fastest. A model from the military can help: the OODA loop, which is a decision loop of four steps—observe, orient, decide, act (OODA). U.S. Air Force colonel John Boyd developed the OODA loop to assist ghter pilots in dog ghts, where there isn’t time for analysis between actions. Each pilot is trying to quickly outmaneuver the other, reacting to the other’s moves and surrounding circumstances. Boyd showed repeatedly that the pilot who can adjust more quickly—who moves faster through the OODA loop—will usually win. They take an observing glance at the changing conditions, immediately re-orient their assessment of the situation, decide the next best course of action, act on it without hesitation, and then repeat this loop. OODA Loop  The faster you can make your OODA loop, the faster you can incorporate external information, and the faster you’ll reach your destination, be that product/market t or something else. The OODA loop applies best in situations where rapid learning will give you an advantage; not every situation is so uncertain and ever changing, though many are. One area that is always changing and evolving is technology, making it a great example of an area where OODA loops are particularly e ective. Increasingly, all major businesses, not just traditional “tech” companies, are trying to utilize technology to gain an edge on their competition. As a result, creating fast OODA loops is becoming more important over time. The organization with the fastest OODA loop learns faster than its competitors, consistently makes better decisions, and adapts faster to the unfolding technology landscape. OODA loops may call natural selection to mind (see Chapter 4). Species that have faster life cycles evolve faster, so you might say they have a faster OODA loop. For example, some bacteria can create a new generation in fteen minutes. This is a primary reason why it doesn’t take long for bacteria to become resistant to the drugs designed to ght them. Similarly, having a faster OODA loop helps you adapt faster to changing circumstances, including reaching product/market t before your competitors. If, after extensive customer development, you still cannot nd this promised land of product/market t, then you must pivot to something di erent. A pivot is a change in course of strategic direction, and there are many famous examples. You may be surprised to know that Twitter started as a podcasting network or that Nintendo actually dates back to 1889, when it was founded as a playing-card manufacturer. Over the course of Nintendo’s history, it tried its hand at a variety of businesses with limited success (taxi service, motel chain, TV network, instant rice sales). After its stock price bottomed out in 1964 as playing-card sales dropped, it was saved by a pivot to the toy industry after a maintenance engineer, Gunpei Yokoi, invented the Ultra Hand toy. Yokoi thereafter took a leading role in transitioning Nintendo into a video game powerhouse. Some pivots are less extreme. For example, PayPal started out as a way to beam payments physically between handheld devices before nding product/market t with online payments. Similarly, Starbucks started out selling roasted whole co ee beans and equipment. In fact, Starbucks didn’t sell its rst latte until thirteen years after the company was founded. After a trip to Italy, employee and future CEO Howard Schultz persuaded the founders to test the co eehouse concept after seeing the popularity of espresso bars in Milan. Schultz eventually bought the company from them and expanded Starbucks to what it is today. Pivoting is usually di cult because it cuts against organizational inertia, involves openly admitting failure, and requires nding a better direction, all at the same time. But it can also be necessary. Pivoting is appropriate when your current strategy is not going to bring you the results you are seeking. Consulting advisers, who can more easily see your situation objectively, can help you determine whether a pivot is a good move. More broadly, pivoting can apply across all areas of life: your career path, a di cult relationship, how you’re approaching meeting your child’s educational needs, and so forth. When considering a pivot, you can use a few mental models to help you decide what to do. Harvard Business School professor Clayton Christensen named and championed the model of jobs to be done, which asks you to gure out the real job that your product does, which can be di erent than what you might initially think. An oft-cited example by Christensen is a power drill: “Customers want to ‘hire’ a product to do a job, or, as legendary Harvard Business School marketing professor Theodore Levitt put it, ‘People don’t want to buy a quarter-inch drill. They want a quarter-inch hole!’” Knowing the real job your product does helps you align both product development and marketing around that job. Apple does this exceptionally well. For instance, it introduced the iPod in 2001 amid a slew of MP3- player competitors but chose not to copy any of their marketing lingo, which was focused on technical jargon like gigabytes and codecs. Instead, Steve Jobs famously framed the iPod as “1,000 songs in your pocket,” recognizing that the real job the product was solving was letting you carry your music collection with you. In a December 8, 2016, podcast with Harvard Business Review, Christensen describes another illustrative example, this one about milkshakes served at a particular fast-food restaurant. You might assume that a milkshake’s job is to be a special treat to cap o a meal. While it is true that many parents order shakes as an after-dinner family treat, this restaurant learned that almost half of their shake customers were using them for a di erent job—to make their long morning commutes more interesting. People felt their trips were more enjoyable as they sipped milkshakes while moving through tra c. Doing two jobs at once sounds great, but that usually means at least one job isn’t being done particularly well. In this case, parents didn’t like how long it took their kids to drink the shakes. Yet that was one of the key features for the commuters. The restaurant chain realized they needed two di erent products to do the two di erent jobs well. They decided to further improve the shake for the commuters by making it even thicker, adding more chunks, and moving the shake machine to the front of the stores for the fast on-the-go service that commuters wanted. They then needed to market a wholly di erent dessert product to kids and their parents. When you truly understand what job people are really trying to get done by using your product, then you can focus your e orts on meeting that need. Asking customers what job they really want done can tell you the root of their problem and eliminate faulty assumptions on either side, ultimately resulting in a solution with a higher chance of success. In your analysis, you want to gure out what job your product is really currently doing and where it might be miscast, as in the milkshake example (see 5 Whys in Chapter 1 for a tactical technique). When you talk to customers, beware of their focus on a speci c solution instead of on the problem they’re trying to solve. For example, as a statistician, Lauren has often been asked to perform statistical analyses using a speci c computational technique. However, the particular techniques suggested are often the wrong ones. That’s because non- statisticians are usually unable to determine the best statistical plan on their own, and so instead they suggest a technique they know, regardless of whether it is appropriate (see Maslow’s hammer in Chapter 6). Telling a client or colleague that they are proposing to solve a problem in completely the wrong way can at times be di cult. Lauren recognizes, though, that using a particular technique is not really the job the client wants done. The client really wants a correct analysis, and will gladly accept that analysis from Lauren regardless of how she gets there. To approach these situations, Lauren tries to get the client to take a step back and de ne their ultimate objective, using layman’s terms. Outside of business, you can ask yourself the same questions in the context of any personal connection (“What does this person really want out of this relationship?”) or anywhere you are contributing (“What did they really hire me to do?”). From this perspective, you can determine whether you are really getting the job done with your current strategy. Is there a di erent approach that might do the job better? Understanding the answer to these questions will help you determine whether you need to make a pivot. Another clarifying model is what type of customer are you hunting? This model was created by venture capitalist Christoph Janz, in a November 4, 2016, post on his Angel VC blog, to illustrate that you can build large businesses by hunting di erent size customers, from the really small ( ies) to the really big (elephants). What Type of Customer Are You Hunting?  Janz notes that to get to $100 million in revenue, a business would need 10 million “ ies” paying $10 per year, or 1,000 “elephants” paying $100,000 per year. Believe it or not, there are successful $100 million revenue businesses across the entire spectrum, from those seeking “amoebas” (at $1 per year) to those seeking “whales” (at $10 million per year). More commonly, with any project, and certainly with a business, you want to de ne what success looks like and whether it is achievable under reasonable assumptions and time frames. Janz’s framing steers you toward a particular quantitative evaluation: How many “customers” will it take to achieve success? And what exactly do you need them to “pay” (or do)? Once you answer these questions, you can then ask whether there are enough of these types of customers out there. If not, you might consider pivoting toward bigger or smaller types of customers. A key reason why this model matters is because how you interact with your customers depends on the type of customer you are hunting. If you need to reach ten million people, you can’t do that by talking individually to each one. Additionally, it is challenging to get ten million people to pay a high “price” for a product. In contrast, you can deal individually with one thousand customers and get each of them to “pay” you more. In the business context, hunting di erent customers means deciding to put out a free or minimally priced service to millions of people (like Spotify or Snap) versus selling a high-priced product to large enterprises (like Oracle or Salesforce). Or, within an industry, it’s deciding between customer segments that choose to pay much di erent amounts, such as Rolls-Royce and Lamborghini versus Kia and Hyundai in the automobile sector. In a political context, a candidate in a local election can try to meet all their constituents, but this type of outreach becomes impossible in larger elections. And in those larger elections, fundraising becomes increasingly important because of the need to turn to TV and the internet to reach everyone, which is relatively expensive. This political reality has the consequence that statewide and national politicians need to focus considerable attention on courting (hunting) deep-pocketed individuals (whales/dinosaurs/elephants) to foot the bill for this advertising. A quantitative evaluation like this one is an example of a back-of-the- envelope calculation, a quick numerical assessment that you can calculate literally on the back of an envelope. A simple spreadsheet is the modern-day equivalent. This type of exercise forces you to quantify your assumptions and can quickly result in clarifying insights. With jobs to be done, you are asking what stakeholders are “hiring” you for. With what type of customer are you hunting? you are asking how many “customers” you need to be hired by and what you want them to give you in exchange for your doing the job. Like customer development, both of these models ask you to think from the customer perspective. Thinking this way can also help you develop personas, ctional characters that personify your ideal customers, which will help you better reason through a realistic assessment of your idea. What kind of people are your customers exactly—what are their demographics, likes versus dislikes, and hobbies? If you did customer development right, your personas should be modeled on characteristics of real people you’ve already met. Once constructed (say Bob and Sally are your personas), you can ask yourself: Would Bob and/or Sally do X? Thinking in terms of actual people, ctional or otherwise, can really ground you in the customer perspective and help you apply these assessment models more e ectively. However, be careful not to allow availability bias (see Chapter 1) to limit the factors you consider for creating these personas. The most easily collected or available data might not lead to the most useful personas. Looking at the totality of these models, you should now know what success looks like (how many customers you need and what you need them to do) and whether you see a realistic path toward that goal. So, should you pivot? If the answer is still unclear, one litmus test is this: Do you have any bright spots, positive signs in a sea of negative ones? In a business context, this would be a small subset of customers who really like what you’re doing and are highly engaged with your product. Outside of business, you might look to the bright spots in your current job when considering the prospect of pivoting your career: What are the things you really like about your job? Are there enough of them to make you stay? What aspects would you like to retain if you do choose to pivot? If you have no bright spots after some time, it is likely you do need to pivot. It’s like the old phrase “You don’t have to go home, but you can’t stay here.” If you do have some bright spots, you can try to gure out why things are working there and focus on growing out from that base. This is actually a useful strategy for advancing any idea, struggling or otherwise, drawing on the military concept of the beachhead. That’s where a military o ense takes and defends a beach so that more of their force can move through the beachhead onto the greater landmass. In other words, a beachhead is getting a foothold and using it as a launching point. Amazon’s beachhead was books. Tesla’s was its Roadster. These were positions they could stake out in the market, and then use to expand into adjacent markets. For your career, your beachhead might be your current skills and position, which you could use to launch toward a better position or more ful lling career. A beachhead strategy is only one way to navigate the process of taking your secret and turning it into a product that achieves product/market t. More broadly, this process can be compared to navigating a maze, what investor Balaji Srinivasan calls the idea maze. Imagine a physical maze, as in a corn maze at a fall festival or a hedge maze in a formal garden. The entrance is you starting out on your idea, and the exit is your idea’s ultimate success. Within the maze are lots of dead ends, and it is your job to navigate the maze and successfully get to the other side. As he said in a lecture:  A good founder is capable of anticipating which turns lead to treasure and which lead to certain death. A bad founder is just running to the entrance of (say) the “movies/music/ lesharing/P2P” maze or the “photosharing” maze without any sense for the history of the industry, the players in the maze, the casualties of the past, and the technologies that are likely to move walls and change assumptions. Josh Kopelman, another investor, equates founders who can successfully navigate the perils of nding product/market t with heat-seeking missiles. As he wrote on his Redeye VC blog on August 2, 2010: It doesn’t matter where the missile is aimed pre-launch. Successful entrepreneurs are constantly collecting data—and constantly looking for bigger and better targets, adjusting course if necessary. And when they nd their target, they’re able to lock onto it—regardless of how crowded the space becomes. These metaphors can apply to navigating any path in life. Successfully navigating the idea maze means understanding how best to interact with the people in your life through understanding what you want and need from them and what they want and need from you. It’s recognizing when you are on the wrong path in the maze, deciding when and how to pivot, and having the resilience to nd a way to navigate obstacles put in your path. ACTIVATE YOUR FORCE FIELD Once you achieve product/market t or whatever type of t you are trying to achieve, it is time to protect your position. Warren Bu ett popularized the term moat, making an analogy to the deep ditch of water surrounding a castle to describe how to shield yourself from the competition, thereby creating a sustainable competitive advantage. Moats are situationally dependent. The following are some cases in which they are used (not mutually exclusive): Protected intellectual property (copyright, patents, trade secrets, etc.) Specialized skills or business processes that take a long time to develop (for example, Apple’s vertically integrated products and supply chain, which meld design, hardware, and software) Exclusive access to relationships, data, or cheap materials A strong, trusted brand built over many years, which customers turn to re exively Substantial control of a distribution channel A team of people uniquely quali ed to solve a particular problem Network e ects or other types of ywheels (as described in Chapter 4) A higher pace of innovation (e.g., a faster OODA loop) Elon Musk notably sparred with Warren Bu ett on the concept of moats. In Musk’s words from a May 2, 2018, Tesla earnings call: “Moats are lame,” and “If your only defense against invading armies is a moat, you will not last long.” He was pointing out that, in his opinion, the most important sustainable competitive advantage is creating a culture that supports a higher pace of innovation, because that higher pace of innovation can overcome traditional moats. In our opinion, though, a higher pace of innovation is really just another type of moat, and the metaphor of the moat shouldn’t be taken too literally. Instead of a static moat, consider the sci- equivalent of a force eld or a de ector shield, which allows you to move at warp speeds while still o ering protection. You can both continue innovating (at warp speed) and also employ other types of moats (for increased defenses). The Eastman Kodak Company is a great case study on how to build a moat. Founded in 1888, Kodak dominated the camera market for a hundred years. It arguably had signi cant moat protection in all the categories mentioned above, successfully fending o competitors and reaping outsized pro ts for a century: Protected intellectual property: It held many photography patents and trade secrets. Specialized skills or business processes that take a long time to develop: They had a vertically integrated supply chain serving all sides of the market, from cameras to lm to printing. Exclusive access to relationships, data, or cheap materials: It had many exclusive business deals, and being the biggest in the industry, it could negotiate to secure supplies more cheaply than competitors. A strong, trusted brand built over many years: Everyone knew the name of Kodak and what it specialized in. Substantial control of a distribution channel: It had the prime shelf space at retail locked in. A team of people uniquely quali ed to solve a particular problem: In  Kodak Research Laboratories, it had the widest expertise in its technology areas and developed many advances in the eld. Network e ects or other types of ywheels / A higher pace of innovation: While Kodak had no real network e ects, it had a major ywheel going with its research and development department. Since it made outsized pro ts, it could invest more than anyone else in research and development, which kept its outsized pro ts going via faster innovation. When assessing your possible sustainable competitive advantages, be explicit. A list like the one above can be a big help. What are you doing that competitors can’t copy? What will keep the competition at bay and allow you to exercise your market power for the long term? Any single advantage could serve as the basis for your moat, but, as you can see from the Kodak example, several advantages can also work together, amplify one another, and produce an even bigger moat (force eld). As we will see in a bit, though, even the biggest moats don’t last forever. These same moat types can apply to your personal place in an organization or eld as well. For example: You can have the biggest personal network (exclusive access to relationships). You can build a personal following (strong, trusted brand). You can become the expert in an in-demand area (unique quali cations). You can create a popular blog (substantial control of a distribution channel). Each of these and more can create a moat that protects your place in a competitive landscape.  Organizations and individuals that control working moats create lock-in when customers are locked in to their services because perceived switching costs are so high. There are many ways to create switching costs, such as cancellation fees, trusted relationships, new equipment costs, learning curves, network e ects (see Chapter 4), brand a nity, etc. Many people feel locked into Facebook because this is how many of their friends and family choose to share photos and updates of what is going on in their lives. Employers can feel locked into certain key employees, which gives these employees leverage to ask for raises or other bene ts. Some employees are so critical to a business’s operations that there is a whole class of insurance products called key person insurance, which pays out if these key people become incapacitated. These concepts also apply well beyond business situations. Many people feel locked into personal relationships, since the perceived costs (including emotional and psychological costs) of these changes are so high. Or you may feel locked into your housing situation, given the costs of physically moving and the opportunity cost (see Chapter 3) of spending your time on picking a new place, packing your things, making new friends, etc. Even countries get locked into diplomatic arrangements with high switching costs, as in the case of Brexit. A related pair of concepts resulting from moats are barriers to entry and barriers to exit, which prevent people or companies from either entering or exiting a situation or market. A new mobile operating system wanting to compete with Apple’s iOS or Google’s Android would need to re- create an app store populated with thousands of useful apps, a large barrier to entry. Some careers have high barriers to entry, such as expensive years of schooling required. Similarly, some personal contracts, such as noncompetes, partnership agreements, or even marriage, create signi cant barriers to exit. As with switching costs, barriers to entry and exit can come in many forms, such as trade secrets, like the Coca-Cola formula; high capital investment, like the cost of a huge factory; and government regulations that protect incumbents. A speci c model centered on barriers to entry due to regulation is called regulatory capture, in which regulatory agencies or lawmakers get captured by the special interest groups they are supposed to be regulating, ultimately protecting these entities from competition. In 2012, Je Donn reported on a year-long Associated Press investigation of the U.S. Nuclear Regulatory Commission [NRC], resulting in a lengthy four-part series that noted: Federal regulators have been working closely with the nuclear power industry to keep the nation’s aging reactors operating within safety standards by repeatedly weakening those standards, or simply failing to enforce them.... Examples abound. When valves leaked, more leakage was allowed— up to 20 times the original limit. When rampant cracking caused radioactive leaks from steam generator tubing, an easier test of the tubes was devised, so plants could meet standards. Failed cables. Busted seals. Broken nozzles, clogged screens, cracked concrete, dented containers, corroded metals and rusty underground pipes —all of these and thousands of other problems linked to aging were uncovered in the AP’s yearlong investigation. And all of them could escalate dangers in the event of an accident. Yet despite the many problems linked to aging, not a single o cial body in government or industry has studied the overall frequency and potential impact on safety of such breakdowns in recent years, even as the NRC has extended the licenses of dozens of reactors. Industry and government o cials defend their actions, and insist that no chances are being taken. But the AP investigation found that with billions of dollars and 19 percent of America’s electricity supply at stake, a cozy relationship prevails between the industry and its regulator, the NRC. The disheartening part about this example is that nuclear power done right can be a safe and essentially unlimited source of low-carbon energy. Not regulating it e ectively foments fears of nuclear energy and sets back the entire industry. Nobel Prize–winning economist Joseph Stiglitz pioneered the model of regulatory capture. One reason for its common occurrence is that special interest groups often collectively lobby regulators via lobbyists, whereas the individuals a ected do not tend to put together strong lobbying e orts due to their lack of organization. Another reason is that the regulators themselves often operate in a revolving-door pattern in which, after their time as regulators, they take highly compensated jobs in the industry they were just regulating. Regulatory capture can happen outside the government as well, such as in occupational licensing, where certain occupations restrict the supply of professionals through control of their licensing boards and processes. For example, according to a Brookings report, in the U.S. about one-quarter of current jobs require a license, up from just 5 percent in the 1950s. These licenses cover occupations that you might think they should, such as medicine, and also those you might not think of, such as cosmetology. Critics contend that while some licensing can make sense, the trend is to require too much money and time to acquire these licenses, which protects people who already have them at the expense of competition. For example, they found the “number of days to obtain a cosmetology license varies from 232 in New York to 490 in Iowa.”  Another common example is nonpro t or community boards that get overrun by the personal interests and motivations of friends or family. In its worst form, regulatory capture is just plain corruption, though it often also occurs naturally with good-faith intentions through regulators not seeking broad enough input from their constituents or not conducting comprehensive impact assessments (see availability bias and con rmation bias in Chapter 1). There are ways to diminish regulatory capture. As U.S. Supreme Court justice Louis Brandeis famously wrote in Other People’s Money, “Sunlight is said to be the best of disinfectants,” meaning that allowing people to see and understand regulation and its e ects—increasing transparency—can lead to less regulatory capture by special interests. When people are held accountable and made to explain their actions, change can more easily occur. Strong moats, including those built upon regulatory capture and especially those built on network e ects, can also lead to winner-take- most markets. This is where one company, once it reaches critical mass (see Chapter 4) through its network or dominant position based on another sustainable competitive advantage, e ectively wins the market by taking most of the customers within it. For example, with more than two billion people on Facebook, a competitor won’t nd it easy to re-create that network and compete with Facebook’s core o erings. Just because you won the market, however, doesn’t mean you will win in perpetuity. Andy Grove, former CEO of Intel, famously wrote in his 1999 book of the same name, “Only the paranoid survive.” Intel’s early dominance was in memory chips; however, by the mid-1980s, Japanese manufacturers had e ectively erased much of its competitive advantages in this market. But at the height of its dominance, Intel foresaw this existential competitive threat. As a result, it pivoted the focus of the company into microprocessors and reestablished a long-lasting moat (“Intel Inside”). Grove’s words serve as a reminder that even if you establish a working moat, you must be constantly evaluating the strength of it, and even when you have a strong product/ market t, your moat may give way and you may need to eventually pivot. And Intel’s new moat did in fact eventually give out with the rise of the chips that power the smartphone and other smaller devices. Remember Kodak? Its moat was also disrupted, though it didn’t pivot in time as Intel did from memory chips. In the 1990s, Kodak was rapidly disrupted by digital photography, and it ultimately declared bankruptcy in 2012 after a century of market dominance. You might think that it got caught o guard, but that isn’t true, just as it usually isn’t true in similar cases. As mentioned, Kodak’s investment in research and development was part of its moat. Kodak actually developed the very rst digital camera, way back in 1975! But the timing wasn’t right for digital photography to prosper then, due to lack of a supporting ecosystem—graphics cards were not fully developed, physical hard drive size was too big, etc. Meanwhile, Kodak had been making most of its money from selling lm. Digital photography of course has no lm, and once it prospered, it fundamentally disrupted Kodak’s pro table analog model. When disruptive technologies like this rst emerge, they are usually inferior to the current technologies in the ways that most buyers care about. For decades, digital photography was comparatively expensive and produced lower-quality photographs than lm; however, its convenience (in not having to develop pictures) appealed to some buyers and allowed the market to progress. Slowly but surely, the price and performance gap between digital and lm closed. Once it crossed the tipping point (see Chapter 4) of being attractive to most consumers, the digital camera market exploded. Consumer Camera Sales Analog vs. Digital, 1995-2012  Kodak wasn’t blind to these developments either. Initially it was even the market leader in digital cameras too, with a 27 percent share in 1999. However, it didn’t invest heavily enough in the technology relative to its competitors, the way Intel did when pivoting to microprocessors. Kodak simply wasn’t paranoid enough. The overall market for photography quickly and fundamentally shifted from the high-margin lm business to a highly commoditized digital camera business, and Kodak simply wasn’t fast enough to adapt. It didn’t use its ywheel from the lm business to fuel its path to domination in digital, and its share of the exploding digital photography market fell as a result. In 2007 Kodak was number four in digital photography and by 2010 they fell to seventh, at 7 percent market share, behind Canon, Sony, Nikon, and others. Just as this happened, these same digital camera manufacturers were similarly disrupted by Apple, Samsung, and others producing smartphone cameras. Same story: First, these new “cameras” were relatively expensive and produced lower-quality photographs, but they were more convenient. Over time, however, the quality kept increasing and there were more and more reasons to have a smartphone, leaving not enough reasons to have a separate digital camera. It is an interesting question in counterfactual thinking