Introduction: The Local Maximum Trap: A Universal Challenge

John's earliest memory is of playing with his father's stethoscope. He's always dreamed of following in his dad's footsteps and becoming a surgeon. Most of his college classes have been pre-med, but, by the end of sophomore year, he's felt a pull toward biomedical engineering. His academic advisor has been encouraging him to specialize (his father has, too), and it's time to declare a major. John's not positive which career path will lead to the best outcome. His dad's surgical residency has led to a very nice life for the family, but he wonders if he can make a greater impact on people's lives from the engineering side. Part of the dilemma is that he's still learning and gathering information. He's not sure he has enough to go on to decide, but the registrar needs an answer.

Lucy has been playing the dating game for 15 years, and she's eager to get married and start a family. She's dated over 80 guys; three of which were serious. She wonders, is she being too picky, or was one of those three the "one"? Should she stay in the dating pool and keep looking for her dream partner, or should she let go of some of the "must haves" in lieu of what "will do"?

Fred is a top-level marketing executive with a high salary and lifestyle expenses to match. From the outside, his life seems enviable. He's been on a steady career trajectory for 15 years, gathering prestige and properties along the way. But when he thinks about how he really wants to spend his time, marketing is not it. The problem is his salary is so high, he'll have to take a severe loss to go in a different direction. He's trapped in the proverbial golden handcuffs.

John, Lucy, and Fred are each stuck in a Local Maximum. In nearly every field of human endeavor and facet of decision-making, in which we aim to go as high as possible, a Local Maximum is a point from which we can only go down. Crucially, we may not be at the highest point. There are higher peaks around us, but we find ourselves trapped on our own peak within our own Local Maximum, with significant costs and implications, but without the necessary tools, or even the language, to describe them.

Though humans may lack the nomenclature to articulate their predicaments (beyond the word, "stuck"), there are several cutting-edge industries in which the challenge of Local Maximum is well-known. And because of the enormous costs it can incur, it has been given careful attention. These include fusing alloys, telecom routing, weather forecasting, mobile advertising, oil and gas mining, molecular modeling, aerodynamics, cryptography, and many more. They are encountered by the brightest coders and engineers at tech giants like Google and Amazon.1 Professionals within these arenas readily acknowledge that getting stuck with suboptimal solutions is one of the biggest issues they face. Critically, the challenge of a Local Maximum is not exclusive to the tech industry, to computer scientists, or to programmers. The challenge exists in ways both large and small, personally and professionally, for everyone, as evidenced by John, Lucy, and Fred.

In my own life, the concept of Local Maximum has crept in through the back door and become a key factor in determining an initiative's success or failure. As an intelligence officer in national security, I have been trained to think in new and innovative ways to reverse engineer my regiment's way out of both common and uncommon military challenges: how to analyze the field, how to define its parameters, and how to tackle complex problems effectively and efficiently under duress. In my career first at a hedge fund and then as co-founder and managing partner of Hetz Ventures, an international investment fund focused on early stage Israeli start-ups led by hi-tech entrepreneurs, and as a board member of many of those companies, I have the opportunity to help founders identify and avoid Local Maximums every day. The CEOs of these companies are dealing with hundreds of oversight-related items, very few of which have to do with actual innovation, strategy, or implementation (or, not nearly as much as they would like). They are focused on the nuts and bolts of daily operations, so when we sit down to talk, they're craving a high-level perspective as to whether their efforts, or the organization's efforts, are driving them to the optimal outcome. They want to know: Are we climbing toward the highest point? In these two very different environments, my military colleagues and the start-up entrepreneurs are amongst the most talented, driven, educated, and thoughtful people in their fields. But it turns out, running as fast as you can toward the highest point is not always the best strategy.

How to Navigate This Book

For decades, since the advent of computer programming, talented programmers have used their best efforts to teach computers human logic. A classic example is - if A is bigger than B, and B is bigger than C, then A must be bigger than C. In recent years, though, computers have begun to develop a logic of their own. This book turns the tables and asks what we humans can now learn from how computers make decisions. Specifically, it focuses on the challenge of avoiding the Local Maximum trap. Now that the brightest minds of tech giants such as Amazon and Google have devoted years to addressing this challenge and saving billions of dollars and millions of work-hours, this book explores what computers can teach us about avoiding the Local Maximum trap in our own lives.

Rest assured this book does NOT require a background in computing, engineering, or math. In fact, as you will see, the major obstacles we face emerge from human psychology far more than from algorithms. At the end of each chapter, I have included a short section titled "A Little Byte of Data Science," which you should feel fine to skip. Although they do not require knowledge of, or interest in, technology, they demonstrate how the ideas from each chapter are used in the tech world.

In Chapter 1, we explore the concept of Local Maximum through the metaphor of a combat paratrooper in training. He is dropped into the middle of the desert and given the task of climbing the tallest mountain. How does he decide which mountain to climb, and how does he know it is the tallest? Through his experience searching for a solution to a seemingly simple problem, we will see that Local Maximum is a challenge with far-reaching implications and applications.

In Chapter 2, we focus on understanding why Local Maximums can be so attractive and the dangers of the most common methods used by marketers, strategic planners, and others to assess potential courses of action. We will learn the extraordinary and simple power of tweaking our thinking and metrics by moving from A/B testing to A/B/X testing.

In Chapter 3, we ask how we can get off a Local Maximum when we find ourselves stranded on one. We will analyze "death valleys" - the seemingly insurmountable canyons between where we are and where we need to be. And we will encounter practical examples from fields as diverse as Military Intelligence to career choices of Ethiopians living in Israel to international Judo competitions.

In Chapter 4, we turn inward and understand how certain personal or organizational characteristics make us more susceptible to falling foul of a Local Maximum. We will consider how we can develop practices, such as balancing agility and muscle, to achieve our current targets while reducing the potential pain of hitting a Local Maximum.

In Chapter 5, we address the psychological dimension and contrast the Local Maximum mindset with a Global Maximum way of thinking. With lessons from the Israeli Air Force and the future of Healthcare 3.0, we'll see that often our greatest mental strengths can also be our most forceful opponent.

In Chapter 6, we discover that time can both constrain and expand our realm of possible outcomes. We learn to view time as a variable rather than a fixed factor, and - like YouTube, drone units, and early-stage start-ups - utilize this knowledge to our advantage.

In Chapter 7, we dig down into our core values and consider how the degree to which we see ourselves, as individuals or as part of a collective, impacts our ability to overcome obstacles and reach higher ground. Charles Darwin, Robin Dunbar, and the Joker from Batman may believe our DNA limits our potential, but ants may suggest otherwise.

In Chapter 8, we consider not only the height of the mountain we are aiming to climb, but its unique shape. We will learn to recognize the topography of different types of dangerous mountains. With examples from hedge fund managers and public company CEOs, greatest of all-time (GOAT) athletes and major start-up busts, we will meet four mountain shapes to be wary of.

In Chapter 9, we pause to consider whether there may be situations in which a Local Maximum is not a bug, but actually something to aim for. Wildfires, viruses, and US sports leagues all offer unique insights to utilizing the tools from earlier chapters to our advantage.

Finally, in Chapter 10, we put the lessons we've learned from artificial intelligence to the test by examining five fundamental global challenges. We examine whether the tools we have acquired can provide a fresh look at education, globalization, governance, healthcare, and technological...