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 fifty-to-one and she wins, then you have achieved a remarkable success. It is the difference between coming up with the next hot idea and being the fifth 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 Buffett puts it this way in Warren Buffet 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) reflect 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 Jeff 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.
Jeff Bezos again, in a 1997 letter to shareholders:
Given a ten percent chance of a 100 times payoff, 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 first-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 financial 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 film 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 efforts 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 fields started out as secrets. You can find 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 influence models from Chapter 7, such as reciprocity.
Mental models themselves are somewhat secret. The central theme of this book is that certain models from different fields can be applied to help you solve problems in other areas. Common knowledge in one field 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 first 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 fiction 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 different fields, 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 find 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 field 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 difficult to get right. A contrarian idea will almost inevitably face a fight 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 sufficiently 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 tabl
et 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 Cliff Stroll entitled “The Internet? Bah!” which basically said that the internet’s potential impact was wildly overstated. Cliff 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 fizzle out before they take off. Additionally, some of the ideas best primed for takeoff 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 “filmless” 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 filmless radiology departments, the other presenter, Sir Peter Mansfield, 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 firm 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 confluence 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 difference 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 offers 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 specifically 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 first to market with the idea, you will still lose out to the competition if your product cannot create the necessary behavioral change. The first person or organization to try to capitalize on a secret can indeed have a first-mover advantage, crafting a competitive advantage derived from being the first to move into a market with a product. However, they can also experience a first-mover disadvantage if they make a lot of mistakes. Fast-followers can copy the first mover, learn from their mistakes, and then quickly surpass them, leaving the first mover ultimately disadvantaged even though they were first.
For a first mover, the difference between success and failure hinges on whether they can also be first to achieve product/market fit. That’s when a product is a such a great fit for its market that customers are actively demanding more. This model was also developed by Andy Rachleff, who explained in “Demystifying Venture Capital Economics, Part 3,” “First to market seldom matters. Rather, first to product/market fit is almost always the long-term winner. . . . Once a company has achieved product/market fit, it is extremely difficult to dislodge it, even with a better or less expensive product.”
A company without product/market fit finds it extremely hard to obtain customers; in contrast, a company with product/market fit finds it relatively easy to obtain customers. This concept can be widened to “fits” in a variety of situations: person/organization fit, member/group fit, culture/strategy fit, message/audience fit, 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 specific 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 different 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 fit, the effect is similar. When this happens, results are not just a little better, they’re dramatically better. Product is flying off the shelves. That’s what you’re looking for with product/market fit or any other fit—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.
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One way to increase your chances of getting to product/market fit 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 find a sustainable business model by applying the scientific 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 find out whether you have something people really want, signaling product/market fit.
Of course, you probably won’t make something people really want on the first 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 refining 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 fit. 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).
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