My paternal grandfather, Preston William Staats Sr., grew up in New Braunfels, Texas, where his father owned the Candy Kitchen, a small store that sold sweets and soft drinks. Recognizing a growth opportunity when he saw one, my great-grandfather, Preston Senior’s father, had secured Coca-Cola bottling rights for the region. Preston Senior worked at the plant as a child and then, when the time was right, headed forty-five miles up the road to Austin to attend the University of Texas. After college he returned to New Braunfels to run the Coke plant. My maternal grandfather, Brooks Woolford, grew up in Houston, where he worked as a credit manager for most of his adult life. My grandfathers lived outside their respective hometowns only while they served in World War II.

Contrast their experience with that of their two grandchildren—my brother, Trent, and me. We grew up in Austin, and we both went to the University of Texas at Austin for our undergraduate degrees (Hook ’em, Horns!). Trent stayed on to get his PhD in engineering and then started a company that enabled real-time monitoring of power transmission lines for electricity traders. After he sold the company, he headed to Harvard Business School to get an MBA. He stayed in Boston for the next ten years, working at startups in biotechnology, biofuels, and chemical waste reclamation.

After I graduated from UT-Austin, I went to work in investment banking at Goldman Sachs, first in New York and then in Houston. I moved to Boston to get my own MBA at Harvard Business School (HBS), and then I worked at Dell Computer in Austin, doing strategic planning, and at a venture capital firm in Tampa that focused primarily on technology and health care services. I headed back to Boston to get a doctorate at HBS and moved to Chapel Hill as a professor at the University of North Carolina’s Kenan-Flagler Business School, where I’ve been ever since, with the exception of a year as a visiting professor at the University of Pennsylvania’s Wharton School in Philadelphia.

The contrast between our grandfathers’ experience and Trent’s and mine is not atypical. Careers today usually involve multiple employers and often multiple industries. Data that tracks individuals over time is sparse, but a Bureau of Labor Statistics report that followed workers aged eighteen to forty-eight over the years 1978 to 2012 found that on average, workers held twelve different jobs.² At the end of that period, only 3.3 percent were holding the same job they’d held from age twenty-five to twenty-nine, and only 5.4 percent were holding the same job they’d held from age thirty to thirty-four. For most people, the only constant is change.

To succeed in this new environment requires continual learning—how to do existing tasks better and how to do entirely new things. If we fail to learn, we risk becoming irrelevant. We end up solving yesterday’s problems too late instead of tackling tomorrow’s problems before someone else does.

But we’re bad at learning. Supremely bad. In fact, we’re our own worst enemies. Instead of doing the things that will help us learn, we often do just the opposite. We are unwilling to take risks that might lead to failure. We obsess about outcomes while neglecting to examine carefully the process through which we achieve them. We rush to answers instead of asking questions. We want to be seen doing something—anything—so we don’t step back to recharge and reflect. We follow the path that others have beaten rather than forge one of our own. We look to fix irrelevant weaknesses instead of playing to our strengths. We focus narrowly rather than draw on broad experience. We treat learning as an individual exercise and neglect the important role played by others.

That’s why I’ve written Never Stop Learning: to help you learn how to learn. It presents a framework for staying relevant in a world of continual change. I will detail the processes you need to follow to become a dynamic learner and explain the behavioral science that shows why we fail to do what we need to do. I’ll also offer practical, proven strategies for overcoming the challenges. You may be your own worst enemy, but you are the one person over whom you have the most control.

The Rise of the Learning Economy

Learning is so vital today that we can think of ourselves as living in a learning economy. We can’t just be knowledge workers; we must also be learning workers. As Microsoft’s CEO, Satya Nadella, has said, “Ultimately, the ‘learn-it-all’ will always do better than the ‘know-it-all.’ ” Four interleaved dynamics—routinization, specialization, globalization, and individual scalability—have led us to a place where our capability to learn and then accomplish our objectives defines whether we can create an individual competitive advantage, and this determines if we can stay relevant, reinvent ourselves, and thrive.

Nonroutine Cognitive Labor

Each innovation requires new skills—a truism that stretches back to the origins of human history but was dramatically highlighted by the Industrial Revolution and over the course of the twentieth century. This is borne out by employment data. For example, in 1910 the US workforce was 32 percent agricultural, down from 65 percent in 1850, and in 2015 it was 2 percent agricultural.³ Manufacturing employment peaked in 1953, at a total of 30 percent of the workforce.⁴ The second half of the twentieth century saw a steady decline: by 2015 manufacturing jobs accounted for less than 10 percent of the total workforce.⁵ The percentages look different in other parts of the world, but the dramatic shifts of workers between industries are similar.

Part of the story is that US agricultural and manufacturing jobs moved to parts of the world where labor costs were lower. In the 1800s the United States offered cheap and willing labor, so manufacturing grew, but from the 1950s on, countries such as Japan, Taiwan, and China offered lower labor costs and captured more of the manufacturing jobs.

The dominant driver of the change in employment was actually productivity improvement resulting from routinization. For example, US agricultural production more than doubled from 1948 to 2011, even as the amount of land used and the manual labor required plummeted.⁶ Improvements in seeds, fertilizer, farming techniques, and technology drove the change. Farmers developed better techniques and gave more attention to managing the farming process than to simply following the same approaches to tending fields.

The same is true of manufacturing. Many decry the flight of jobs from the United States in the twenty-first century, as companies have gone looking for cheaper labor. But according to one estimate, from 2000 to 2010, only 13 percent of manufacturing job losses were due to foreign trade (i.e., jobs moving to another country), whereas 87 percent were due to productivity increases (i.e., less need for labor).⁷ Ongoing improvements in technological investment and new labor and management practices can dramatically increase productivity, but they also often reduce the amount of labor required and change what workers must do.

This has important implications for the skills we will need moving forward. The value in repetitive manual labor continues to decline. Such jobs can often be automated, shifting the workforce from human to silicon, or they can be shipped to where costs are lower. Value is created when we can customize, adapt, and innovate—all of which require learning.

A look at employment data from 1983 through 2013 tells this story.⁸ In the figure below, jobs are divided into categories according to how routine or cognitive they are. The number of routine jobs—such as manufacturing worker (manual) or sales professional (cognitive)—has stayed flat over time, despite the growing number of people looking for work. In contrast, nonroutine jobs are growing. This divergence occurs because learning and productivity enhancements eliminate jobs in the middle but leave lower-paying jobs (such as personal caregiving for elders) and higher-paying jobs (managers and scientists).

I have a good friend who spent her early career in charge of check processing at a bank. Her job was to supervise a workforce that opened envelopes, removed the checks, and manually entered the information. These were good jobs at the time for data entry workers, because they required computer skills that not everyone had. But employment fell as the data entry process became more streamlined: checks were scanned rather than entered manually, and eventually the entire process became electronic, often with little human intervention. The workers who remained were highly trained, because they had to know how information technology and electronic processing worked and how to deal thoughtfully with exceptions. But the routine work disappeared.

Specialization

The second driver of the learning economy has been at the core of human progress for millennia: specialization. In 1776 Adam Smith began Book I of The Wealth of Nations by writing, “The greatest improvements in the productive powers of labour, and the greater part of the skill, dexterity, and judgment, with which it is anywhere directed, or applied, seem to have been the effects of the division of labour.”⁹ When work was divided so that individuals specialized in given tasks, those individuals could learn and improve dramatically. Societies prior to Smith’s had gleaned this insight, of course, but the past three centuries have seen it applied ever more intensely.

As we gain deeper familiarity with an area, opportunities to learn increase. The more we learn, the more we realize what we don’t know, so we invest in more learning. The cycle can go on and on.

Perhaps no field exemplifies this better than medicine. In early civilizations, it was simplistic. Absent any true understanding of human anatomy and disease mechanisms, medicine required a careful study of cause and effect to learn what remedies might lead to better outcomes.

To rectify this lack of knowledge, doctors started focusing on anatomy in the sixteenth century. In 1546 Girolamo Fracastoro posited that microorganisms such as bacteria and viruses were the actual cause of disease, and Marcus von Plenciz expanded on that theory some two hundred years later. Louis Pasteur, among others, provided enough support that the theory was widely adopted. Each advance meant that the knowledge required to be a doctor increased. As a result, it became necessary to specialize in certain bodily systems or types of care.

In 2017 the American Board of Medical Specialties recognized 37 specialties and 132 subspecialties.¹⁰ Each requires years of training to qualify. Even for specialists it is usually impossible to know everything in a domain. According to one estimate, a physician would need to read twenty-nine hours a day to stay current with the literature.¹¹ Specialization requires investment, but it helps to determine where to allocate one’s scarce attention.

Globalization

The third driver of the learning economy is globalization, which brings increased competition in the labor market. In the latter part of the twentieth century, numerous economies opened up their labor markets so that their workers and companies could compete globally. Brazil, Russia, India, and China led the pack, but others joined in. The software services industry in India provides a useful example.

In 1979, when IBM left India because of rules that would have required it to sell an equity stake in its operations there, few software engineers were left working in the country. Indian laws and regulations made it difficult to acquire necessary equipment and export services. But some companies carried on, either sending workers to customer locations abroad or doing work in India and then flying to Singapore or other connected hubs to upload reels of tape.

In the 1990s the Indian government recognized that the country was graduating hundreds of thousands of well-trained engineers who weren’t helping its economy as much as they could; they often left the country to find work or stayed in India and took roles that failed to utilize their advanced skills. Meanwhile, technology was changing to enable remote work, and global customers were looking for solutions to their software problems. The government changed the rules so that companies could bring in the necessary technological equipment and export services with little or no tax.

These factors combined to create an explosion in the industry. Tata Consultancy Services, Infosys, Wipro Technologies, and other Indian companies grew, but then multinationals recognized the opportunity and began scaling up their own operations. For example, in 2005 IBM held its annual investors’ meeting in India and announced plans to invest $6 billion over the ensuing three years in its India operations, which would result in the employment of some estimated 150,000 Indians. Figure 1-2 shows the remarkable growth of the industry over time.

Similar growth stories can be told about other countries and industries.

The implication is clear: as individuals consider their career paths, they must recognize that staying relevant means outlearning not only those immediately around them but also people around the globe. It is easier than ever for companies to contract with employees from anywhere in the world.

This lesson becomes even more important in view of the general shift in employment patterns. My grandfathers’ lifetime employment was not atypical for their generation, but it’s almost unheard of today. The shift is made even more dramatic by individual workers’ increasing independence from companies. Whether they are drivers for Uber and Lyft or knowledge workers contracting directly or through platforms such as Upwork, Guru, and 99designs, people are competing globally more and more frequently. If you want to stay internationally competitive, you must become a dynamic learner.

Scalability

The final important driver of the learning economy is the ability to scale one’s learning. Many years ago, even an excellent surgeon who stayed on the cutting edge of performance faced a limit on the demand for services. People didn’t travel easily, so the expert could serve the local population but rarely beyond. As travel became easier over time, demand (and the ability to raise prices for expertise) increased. Specialization, too, was more common, because a higher income made the investment in specialized training worthwhile.

Information and communication technologies have further broken these bonds. Now one can use the internet not only to market one’s services but to reach a still larger audience. The rise of information technology means that knowledge can be stored—for example, by writing software to capture supply chain management decisions or tax regulations—and then distributed. Even better, because the marginal cost of information products is close to zero (it costs little to sell a second copy once the investment has been made to produce the first one), it is economically attractive to sell one’s work much more broadly than was ever possible before.

Becoming a Dynamic Learner

Staying relevant in the learning economy requires dynamic learning. Dynamic learners both share knowledge broadly and use network effects (the value of services increases as more people use them: think Facebook). Failing to learn and adapt means being left behind. This creates meaningful risk for our organizations, ourselves, and our children. It’s not just knowledge that’s necessary—it’s using that knowledge to build more knowledge. In other words, to learn.

This is the underlying challenge that drew me to academia. As an engineer, an investment banker, and a venture capitalist, I watched intelligent, motivated people struggle to meet their objectives. Although many of them failed, I also encountered those who consistently performed at a higher level. I could not immediately see the differences—in general, these people had similar education, training, and resources. Upon reflection, I realized that some people were learning continually, and others were not.

I had identified the question—What principles lead to dynamic learning?—but I didn’t know the answer. Over the past fifteen years, my research has sought that answer.

To investigate the topic of learning more deeply, I decided to situate myself between two academic fields: operations and behavioral science. I chose the former because at its core, learning is not a theoretical exercise; it’s a practical one. Operations is concerned with improving outcomes. To do that means looking at operational processes—how inputs are converted into outputs—and then making them better. I use the operations toolkit to deconstruct the process of learning—understanding not only the constituent parts, but how they fit together into an overall system.

I chose behavioral science because my experience has taught me that fundamental properties of human nature affect individuals’ ability to learn. Moreover, even as a recovering engineer, I recognized that a process focus divorced from an examination of the people who take part in the process would be incomplete. The epigraph that opens this chapter is correct: what goes on inside our brains can alter outer reality. In the same way, what we fail to achieve internally will alter the opportunities we have going forward.

By marrying these two approaches, I’ve gained a unique perspective on the topic of learning that involves three steps: First, figure out what you need to do to be a dynamic learner. Second, identify why you don’t do those things. Third, understand the steps to take to overcome the challenge.

This process governs the organization of each chapter in this book. Throughout, I incorporate anecdotes for illustration and research for proper grounding in order to explain what we need to do to learn, why we don’t do it, and how to overcome the challenge. In the following eight chapters I will address these key elements, which I see as necessary to becoming a dynamic learner:

• Valuing failure—Dynamic learners are willing to fail in order to learn.

• Process rather than outcome—Dynamic learners recognize that focusing on the outcome is misguided, because we don’t know how we got there, whereas a process focus frees us to learn.

• Asking questions rather than rushing to answers—Dynamic learners recognize that “I don’t know” is a fair place to start—as long as we quickly follow with a question.

• Reflection and relaxation—Dynamic learners fight the urge to act for the sake of acting and recognize that when the going gets tough, the tough are rested, take time to recharge, and stop to think.

• Being yourself—Dynamic learners don’t try to conform; they’re willing to stand out.

• Playing to strengths—Dynamic learners don’t try to fix irrelevant weaknesses; they play to their strengths.

• Specialization and variety—Dynamic learners build a T-shaped portfolio of experiences—deep in one area (or more) and broad in others.

• Learning from others—Dynamic learners recognize that learning is not a solo exercise.

In the ensuing chapters I’ll unpack each of these ideas to illustrate how to become a dynamic learner who not only stays relevant in our changing world but excels in and shapes it.