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The Smart Machine Age:
A New Game Requires New Rules

We can be humble and live a good life with the aid of the machines or we can be arrogant and die.

—Norbert Wiener

Norbert Wiener, an MIT mathematics professor and computer science pioneer, wrote those words in 1948 in a recently discovered unpublished essay for the New York Times. He literally meant them as an apocryphal warning about the dangers to humanity of uncontrolled advances in automation and artificial intelligence. For decades, such dire predictions remained on the fringe of societal concerns and relevant only to science fiction fans. The technologies that were only a gleam in Wiener’s eye, however, have finally come to fruition.

Smart machines are becoming autonomous and able to tackle nonroutine cognitive tasks previously thought the exclusive purview of people. Machines are gaining natural language capabilities, voice and facial recognition, and the ability to draft sports columns and analyze due diligence documents better and faster than many human reporters or lawyers. Thanks to advances in automated perception, sensors, and robotics, machines are now able to handle what had previously prevented them from tackling nonroutine manual jobs as well, such as driving cars, picking out products from warehouse shelves, and sorting mail. High-functioning human-oid robots can now be seen on hospital floors and in hotels, restaurants, museums, and shopping malls. They aren’t just flipping burgers behind the scenes: they’re interacting with patrons and patients—like “Connie,” the robot concierge Hilton began rolling out in 2016 in lobbies across the country in partnership with IBM Watson.

With respect to nonroutine cognitive jobs, using automated tools and algorithms, machines can now handle data analytics, pattern recognition, and deductive reasoning. Machines are becoming better than a roomful of Wharton graduates at devising portfolio investment theory for hedge funds and better than a team of Sloan-Kettering doctors at diagnosing illnesses.¹ With investments from companies like Google, implantable biometric sensors will soon allow us to monitor our own health.² Facial expression analysis software will detect the emotions and engagement of others better than our own minds.³ A group of researchers from MIT and the Masdar Institute, who conducted the first quantitative study of skill content changes in occupations between 2006 and 2014, concluded, “For any given skill one can think of, some computer scientist somewhere may already be trying to develop an algorithm to do it.”⁴

Combining the development of artificial neural codes and networks that model the human brain with access to Big Data, programmers can give machines the ability to process information and learn on a level that rivals and may soon exceed that of the human race.

Machines quite literally are now beating us at our own games. In March 2016 in what many artificial intelligence (AI) experts touted as the match of the century, AlphaGo—a computer program developed by Google’s DeepMind AI company—defeated South Korean Go master Lee Se-dol four matches to one in the ancient Chinese strategy game. Almost twenty years after IBM’s supercomputer DeepBlue bested the chess champion Gary Kasparov, AlphaGo’s victory still surprised many experts who predicted that it would take at least another decade to develop a computer program with the ability to outwit and out-strategize a Go master in arguably the most complicated human board game ever invented. The CEO of DeepMind, Demis Hassabis, said that algorithms used for AlphaGo “one day can be used in all sorts of problems, from health care to science.”⁵

Plenty of today’s technology experts, from Silicon Valley entrepreneurs to current MIT and University of Oxford academics, have sounded alarms about the potentially devastating impacts to our economy and society because of such recent and imminent technology advances.⁶ We repeat Wiener’s warning here, however, not because we believe that the robot apocalypse is around the corner but because we believe that it’s crucial to our relevancy as human workers and the vitality of the organizations for which we work that we pause and acknowledge the drastic changes coming and prepare ourselves to not only survive but to thrive.

We believe that there’s a path to successfully navigating these strange new highly automated waters, but many of us will have to fundamentally change our views of what it means for humans to be “smart” and what it takes for humans to succeed and reach their fullest potential. To do otherwise—to ignore the impact and fail to prepare for what’s to come—would indeed be a foolhardy exercise in human arrogance.

Smart Machines and a New Era

There’s a growing consensus among most computer science experts, economists, and business leaders that smart machines—whether humanoid robots or invisible networked connections—that can learn, think, and perform both manual and cognitive tasks in most cases better than their human counterparts could be the biggest game changer both personally and organizationally since the Industrial Revolution. It’s likely that the business, education, and leadership models created for the Industrial Revolution could become obsolete. Technological and scientific advances in artificial intelligence, the Internet of Things, virtual reality, robotics, nanotechnology, deep learning, mapping the human brain, and biomedical, genetic, and cyborg engineering could fundamentally change how all of us—from laborers to knowledge workers—live and find livelihood.

Technology that can learn and even program itself will become ubiquitous in homes, factories, and offices and soon displace even the highly educated people who have thought that their professions are immune to the risks of automation, including accountants, business managers, doctors, lawyers, journalists, researchers, architects, higher-education teachers, and consultants. Artificial intelligence—deep learning or machine learning—will be especially transformative in this regard. Speaking at a technology industry conference in May 2016, Jeff Bezos, the founder of Amazon, stated, “It’s probably hard to overstate how big of an impact it’s going to have on society over the next 20 years.”⁷

Andrew Ng, an associate professor of computer science at Stanford University, a chief scientist at Baidu, and chairman and cofounder of Coursera, recently told the Wall Street Journal: “The age of intelligent machines will see huge numbers of individuals unable to work, unable to earn, unable to pay taxes. Those workers will need to be retrained—or risk being left out in the cold. We could face labor displacement of a magnitude we haven’t seen since the 1930s.”⁸

Similarly, Kevin Kelly, co-founder of Wired magazine, says in his new book The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future: “It is hard to imagine anything that would ‘change everything’ as much as cheap, powerful, ubiquitous artificial intelligence.… The advantages gained from cognifying inert things would be hundreds of times more disruptive to our lives than the transformations gained by industrialization.”⁹

In the next two decades, technological advances could displace as many as eighty million US workers, according to the chief economist of the Bank of England,¹⁰ or 47 percent of the US workforce, based on a 2013 study by leading researchers at Oxford University.¹¹ According to a study by McKinsey & Company, by adapting technologies already demonstrated as of 2015, as many as 45 percent of the job tasks US workers are currently paid to do could be automated. Not even the most highly skilled or highly paid are safe. McKinsey also estimated that current technology could be adapted to replace at least 20 percent of a CEO’s work activities.

The result is that no longer will human scale be necessary for value creation in most fields. Without question, technology will transform how most businesses operate and are staffed in terms of both numbers and job requirements and skills. Routine jobs in hierarchical organizations—both those requiring manual and those requiring cognitive skills—will rapidly disappear. Most businesses in the near future will be staffed by some combination of smart robots, smart machines, and humans, and the job and skill requirements for each will be in flux.

In addition, the kind of long-term employment at stable organizations that characterized previous generations will be rare. The percentage of “contingent workers,” including part-time, temporary, and independent contractors, has been on the rise and recently made up a whopping 40 percent of the workforce, according to an April 2015 report of the US Government Accountability Office.¹² Another recent study predicted that by 2020, over half of the country’s workforce will be consultants, freelancers, and independent contractors, cobbling together their own gigs.¹³

Martin Ford, a Silicon Valley entrepreneur and the author of Rise of the Robots: Technology and the Threat of a Jobless Future, recently argued that “emerging industries will rarely, if ever, be highly labor-intensive”; rather, they’ll be more like You-Tube and Instagram, “where we’ve come to expect tiny work forces and huge valuations and revenues.”¹⁴ Similarly, Tony Wagner argues: “While the Intels, IBMs, and Genentechs of the last century employed hundreds of thousands (the majority of whom were low- and middle-skilled workers), the Googles, Facebooks, and Twitters of the 21st century will employ an order of magnitude fewer employees. Almost all of them will be creative problem-solvers.”¹⁵ Howard Gardner made a similar statement: “The future belongs to those organizations, as well as those individuals that have made an active lifelong commitment to learning.”¹⁶

In the age of these smart machines—what we’re calling the Smart Machine Age or SMA—operational excellence may well become almost totally technology-driven, making human innovation the key to value creation. Organizations will need their people to be hyperlearners who can adapt to rapidly changing environments. These needs are unlike what was required in the command-and-control-style organizations of the Industrial Age or more recently with respect to the repetitive and routine nature of knowledge work. Agility, adaptability, and responsiveness also will be required for most, and thus organizational efficiency will be necessary but no longer sufficient. The type of human learning that will be required is continuous and iterative learning, where one’s beliefs are constantly stress-tested against changing phenomena and adapted to better reflect reality. Those human processes are not efficient. In fact, they are hard and emotionally messy.

What’s Left for Humans to Do?

Humans can no longer add value by merely accumulating or analyzing knowledge. The creation of new knowledge is increasing exponentially, and it’s now believed that most knowledge has a less than three-year shelf life. What you think you “know” is so quickly out of date that you must continually update your learning. Moreover, it’ll be impossible for humans to know more facts or concepts than a smart machine or be able to process, remember, recall, pattern match, and synthesize more data faster or more accurately than smart machines such as Google’s AlphaGo and IBM’s Jeopardy!-winning Watson.

Instead, to be marketable and stay relevant in the SMA, humans will need to excel at the kinds of jobs and skills that either complement technology or are those that technology cannot do well—at least not yet. That list includes critical thinking, innovative thinking, creativity, and high emotional engagement with others that fosters relationship building and collaboration. Collectively we refer to these as the SMA Skills. (Note that by creativity we mean to refer to the original expression of ideas and thoughts, including through art and otherwise. By innovation, we mean to refer to the commercialization of new ideas, methods, or things.)

Other jobs that will remain in the near future are those manual jobs requiring customized tasks and physical dexterity, but here we’re focusing on the cognitive skills remaining for the majority of us who consider ourselves knowledge workers. Regardless of job or position, most of us will have to think and behave more like scientists, entrepreneurs, and artists and better engage socially and emotionally with others. The SMA Skills amount to our summary of the conclusions drawn by leading business and education leaders, economists, and researchers at MIT, Oxford, McKinsey & Company, the World Economic Forum, and the National Educational Association, among many other experts on the most important human skills in the twenty-first century.¹⁷

The purpose of this book, however, is not to justify or debate the primacy of the four SMA Skills or to address, for example, when and if computers will ever achieve a human level of creativity. Much has already been written about the need to better incorporate twenty-first-century skills into primary and secondary education and job training programs and to close the skills gap to maintain US competitiveness in the global economy. Our purpose is to focus on how we humans can excel at those skills and thrive in the SMA. Unfortunately, for reasons of both nature and nurture, most of us face challenges in that regard.

Why SMA Skills Are So Hard for Humans

While the SMA Skills are what humans increasingly will need to master to stay relevant, they’re far from easy to execute well. We need to understand that many of today’s business leaders and managers have not been trained to develop or cultivate critical and innovative thinking, creativity, and high emotional engagement with others. They were raised, educated, and trained instead in an era when higher-order thinking and emotional skills were not deemed essential for the majority of workers. Most of today’s adults have had no formal training in how to think, how to listen, how to learn and experiment through inquiry, how to emotionally engage, how to manage emotions, how to collaborate, or how to embrace mistakes as learning opportunities. This is because US society (note that we’re addressing these issues from the perspective of Western and particularly US culture) favors high grades over mastery, aggressiveness and competitiveness, and the avoidance of failure at all costs—all of which hinder thinking, creating, relating, and learning at our best.

Our humanness is a blessing and a curse

We can all probably agree that SMA Skills constitute what humans can do at their best and brightest. When we’re functioning at our highest level, we’re able to think critically and innovatively, be creative, and relate socially and emotionally to and collaborate with others. That’s our human advantage over the “bots” and algorithms. The good news is that recent research in neuroscience and cognitive, social, and educational psychology has begun to show us the environments, mindsets, and behaviors most conducive to enabling this kind of higher-order thinking, relating, and creating. The bad news is that most of us are really bad at creating those environments and embodying those underlying mindsets and behaviors because of both human nature and how we’ve been nurtured, which together generate two big inhibitors to learning and thinking: a preoccupation with protecting our own egos and a fear of failing and looking bad.

Let’s take critical thinking, for instance. The Oxford English Dictionary defines it as “the objective analysis and evaluation of an issue in order to form a judgment.” The key word is objective, and it’s this objectivity that underlies the cognitive psychologist Daniel Willingham’s more elaborate definition of critical thinking: “seeing both sides of an issue, being open to new evidence that disconfirms your ideas, reasoning dispassionately, demanding that claims be backed by evidence, deducing and inferring conclusions from available facts, solving problems, and so forth.”¹⁸

Critical thinking is different from our usual way of thinking precisely because being “objective” is so difficult to do. You may believe that you’re thinking critically much of the time, but chances are you aren’t doing it as well as you think you are, as well as you could, or as well as increasingly you’ll need to. Scientific research has revealed just how hard it can be for humans to think and behave at their best in our modern world because of basic human biology and evolution. Our strong inclination is to be confirmation-biased and emotionally defensive thinkers.

As Daniel Kahneman, a psychologist and Nobel Laureate, explains in his treatise Thinking, Fast and Slow, we’ve evolved to have two systems of thinking. System 1 is fast, automatic, and subconscious—we can think of this as our intuition, which is not flying by the seat of our pants necessarily but relying on the internal beliefs, ideas, and perceptions that we consciously or unconsciously form from our experiences. Psychologists refer to this bundle of beliefs, ideas, and perceptions as our “mental models.” They enable us to pattern match and make connections and associations that are quick and often subconscious. System 2 is our slow, deliberate, and effortful process of reasoning—it’s closer to critical-type thinking, but not always quite there, as we’ll explain further.

Our reactivity

System 1 was the first to develop in our evolutionary biology, and you can see why—there’s no need to pause to deliberate about what to do when you hear the telltale signs of a predator approaching. Our minds developed several cognitive biases and heuristics as shortcuts to help us survive. In many cases our cognitive biases are wrong, however, and compromise our thinking and decisions. But when you’re in actual survival mode—Is that woolly mammoth about to charge?—you’re better safe than sorry. Not so in our modern world, where our cognitive biases have us often making faulty judgments based on, for example, stereotypes and groupthink.

As Kahneman explains, our minds are limited by “excessive confidence in what we believe we know, and our apparent inability to acknowledge the full extent of our ignorance.”¹⁹ “We can be blind to the obvious,” he says, and also “blind to our blindness.”²⁰ Also, “our memories are heavily influenced by ease of recall; our emotions (likes and dislikes); and our inherent comfort with coherence that leads to overconfidence.”²¹

The other important point to note when considering these two systems is that even when we set out to be more deliberate and thoughtful in our decision making and use System 2, our thinking is still often “biased, distorted, partial, uninformed, or downright prejudiced,” as Richard Paul and Linda Elder explain on their Critical Thinking Community website. Our thinking even when deliberate is also always influenced by our subconscious perceptions of reality that are colored not only by implicit biases but by our beliefs, assumptions, and experiences about the world that can inhibit us from seeing other sides of an issue or thinking outside the box.

Another problem is that we’re bad at recognizing when our own thinking is faulty because, as Kahneman states, “laziness is built deep into our nature” and “it is much easier, as well as far more enjoyable, to identify and label the mistakes of others than to recognize our own.”²² We tend to brush off those who do recognize our biases and critique our thinking or beliefs, because, as another Nobel Laureate, Herbert Simon, once said, “People who agree with you are apt to seem a little more intelligent than those who don’t.”²³ Thus it’s clear that to effectively think in ways that smart machines can’t think, we need to acknowledge that we need the help of others to open our eyes to disconfirming data and different perspectives, which is why relationship building with other people will be even more important in the SMA.

Our irrationality

Another crucial point to understand about human thinking is that reason cannot be separated from emotional processes, and thus rationality is a myth. Psychologists and neuroscientists have made tremendous discoveries in the last two decades that confirm that cognitive and emotional processes are inextricably intertwined in our minds and that learning, attention, memory, and decision making are profoundly affected by emotion and in fact subsumed within the processes of emotion. This is at odds with the Descartian belief in rationalism so preeminent in our Western learning traditions.²⁴

Ignoring emotions can be as debilitating as allowing excessive emotions to take over. Emotions inform, mediate, and sometimes cloud our cognitive processing, learning, and social interactions. This isn’t a flaw; it’s just a fact. Research has shown that positive emotions and mood are associated with broader attention and more expansive and flexible thinking, while negative emotions such as stress, anger, anxiety, or defensiveness can impede decision making and problem solving.²⁵

The reality of this situation—the two systems of thinking, the subconscious biases, the importance of other people in helping us recognize those biases, and the interconnection of cognition and emotional processes—is why to do our best, most high-level, critical thinking, we need to first acknowledge our limitations and then slow down, be mindful, and learn to manage our thinking processes, our emotions, and our thinking behaviors to understand and account for all the factors affecting our judgment. It also requires that we listen reflectively and with an open mind to the perspectives of others.

The same thing applies to thinking innovatively. The research is clear that most innovation occurs when diverse teams work together and use innovation ideation and experimentation processes. Diversity brings different perspectives to the table that make it more likely that someone can more easily see what you can’t see. To be good at doing what smart machines can’t do well, then, requires us to admit that we need to work and collaborate with others and that we need to be the type of person whom others want to work and collaborate with. That means we must be good listeners, trustworthy, and socially sensitive. In the SMA, it doesn’t matter whether you’re a freelancer, entrepreneur, employee, manager, or leader, you’ll need to engage with others in what we call “making meaning together” collaboration, which is very different than normal meeting talk.

Our fight-flee-or-freeze tendencies

Another way in which our evolutionary nature affects our ability to master SMA Skills is that we are also prehistoric when it comes to responding to stress and anxiety in ways that inhibit our ability to learn, create, or innovate for fear of failure. Our minds haven’t caught up to modern life and still respond to any stress as if it threatens our very survival—triggering the older emotional center of our brains (the amygdala) to send out a cascade of hormones and physiological responses that bypass the later-evolved part of the brain where reasoning occurs (the prefrontal cortex) and causing an almost instantaneous fight-flee-or-freeze response. Such a response made sense when saber-toothed tigers were on the loose, but not so much when modern professional demands require that we slow down and think critically and creatively in response to the pressures of the global economy. In today’s world, humans cannot fight, flee, or freeze in response to the necessary risks and failures involved in iterative learning.

We’re not the first ones to evangelize about how learning, skill development, innovation, and creativity come directly from mistakes and failures. Everyone from Thomas Edison and his ten thousand failed inventions before the light bulb to Michael Jordan and his nine thousand missed shots has made this point. The philosopher Daniel Dennett describes the importance of mistakes in Intuition Pumps and Other Tools for Thinking: “Mistakes are not just opportunities for learning; they are, in an important sense, the only opportunity for learning or making something truly new.”²⁶

Having the courage to try, experiment, and learn from the inevitable failures can make sense to most of us logically, but remember we’re only human beings, not smart machines (or Michael Jordan, for that matter), and thus we rarely think and behave logically or in our best interests even when we think we are. Our subconscious emotions and behaviors influence our willingness and ability to fail in the process of creating or innovating. It’s not just the failure itself—most of us don’t even like dealing with the mere uncertainty involved in experimenting. Research has shown that we generally prefer certainty to uncertainty. One study found that we would all rather definitely get an electric shock now than maybe get shocked later, and we show greater nervous-system activation when we’re waiting for an unpredictable shock than an expected one.²⁷ Our fear of uncertainty is increasingly a problem, because in the SMA, the advance of technology is increasing uncertainty as well as the need to adapt and experiment to stay afloat at work and in daily life.

We are inwardly focused

A profound problem for us in executing uniquely human SMA Skills is that we usually perceive and process the outside world in an inwardly focused, self-protective manner. This is a result of both nature and nurture. In general, we’re cognitively blind, confirmation-seeking, and emotionally defensive and reflexive thinkers. We operate more like a defensive closed system than a system open to disconfirming information, differing opinions, or new information that may challenge our stories about who we are and how the world works or to experimenting and opening ourselves up to learning from mistakes and failures.

Staying relevant and optimizing our thinking, listening, relating, and working with others in order to excel at the four SMA Skills will require us to become more of an open system—more open to what’s going on in the world outside our heads and more open to others. Our inward focus will need to change to an outward focus with respect to others because it’ll be very hard for most all of us to excel at the SMA Skills by ourselves. We’ll need the help of others, and that requires that we emotionally relate and connect to them.

Connecting to and relating with other human beings is fundamental to human motivation. That’s not anecdote; science has proved it over and over. This need to belong with and attach to others is something innate across cultures, ethnicities, and gender.²⁸ Many studies have shown that connecting emotionally and building relationships are not just about finding love and friendship and being happy in our personal lives; they’re embedded within our drive to live, learn, and succeed. Research shows that students who emotionally connect with a teacher do better in school; employees who emotionally connect with coworkers are more productive; and emotional connection improves client and customer service. We know this intuitively without the data, yet we don’t seem to understand or acknowledge the fact that our tendencies to be self-obsessed and our individualistic, hypercompetitive culture are often at odds with making these emotional connections and building these meaningful relationships at work.

That’s a real problem in the SMA because higher-level thinking requires us to connect with other people who can help us get past our biases. It’s also crucial to engaging in the kind of teamwork and collaboration that leads to creativity and innovation. Most important, as of yet, smart machines, robots, and AI cannot fully replace the kind of empathetic emotional and social connections that humans have with other humans. Geoff Colvin, the author of Humans Are Underrated,²⁹ has gone so far as to suggest that soon jobs requiring deep human interaction may be the only ones left for the masses. In any case, being able to hone our emotional and social skills remains one of our few advantages. The bottom line is that in the SMA very few of us will succeed on our own. We’ll need the help of others, which means we’ll need to be the kind of people whom others will want to help. That requires much more than being “nice”: it means being a trustworthy helper in return.

Our idea of “smart” no longer works

Another problem for us in developing SMA Skills is that today the dominant definition of “smart” is still quantity based. Today, we think, I’m smarter than you if I know more than you, and the way to determine that is by seeing who makes the fewest mistakes on “tests” of our knowledge and experience. That definition is a legacy of the Industrial Revolution’s need for the mass education of workers who could do routine and repetitive manual and cognitive tasks error-free. It’s also the consequence of a knowledge-based meritocratic economy, which rewards those who “know” more and “tell” more than those who listen and inquire.

Many of us who are college graduates or knowledge workers have probably defined ourselves in large part by being smarter in this way than others. We succeeded because we knew more, and we measured being smart by the grades and extrinsic rewards we received. Higher grades resulted from accuracy and efficiency—knowing facts fast and making few mistakes or at least knowing facts faster and making fewer mistakes than others. Most of our teachers, coaches, and parents instilled that mindset in us, and, later, managers and employers reinforced it. From our childhoods on we learned the importance of knowing more and making fewer mistakes, and we were led to believe that “smarter” people would get good jobs and succeed.

Another problem with the belief in a quantity-based definition of smart is that it encourages a constant need to prove ourselves by “looking” smart. That in turn motivates people to avoid experimenting and risking mistakes, which inhibits learning, improvement, discovery, innovation, and creativity. That’s a huge roadblock because innovation, creativity, and entrepreneurship usually result from iterative learning, when things do not turn out as expected, that is, from surprises or failures.

A quantity-based definition of smart also incites ego protection and reinforces an individualistic culture in which our ultimate goal, even if subconscious, is to view every interaction as a way to compare ourselves or compete with others—a way to prove our intelligence or “win” the conversation or transaction. That kind of self-focus leads to ego defensiveness and fear that inhibits learning and impedes critical thinking, creativity, innovation, and emotional engagement with others. In sum, in the SMA our old quantity-based notion of smart, what we call Old Smart, is the new “stupid.” Knowledge workers, you’ve been warned.

We Need New Mindsets and New Behaviors

Cultivating SMA Skills in today’s and future workforces goes far beyond institutional training or challenges—it goes to the very heart of our human nature, our social and organizational cultures, and our daily behaviors. We believe that to truly excel at the higher-level thinking and emotional engagement underlying the SMA Skills requires us to engage in four key behaviors: Quieting Ego; Managing Self (one’s thinking and emotions); Reflective Listening; and Otherness (emotionally connecting and relating to others).

As we explain in more detail in Part 2, we determined these to be the most fundamental common behaviors underlying SMA Skills, based on researching hundreds of academic articles and over forty-five leading books about those four SMA Skills. Unfortunately, most of us don’t regularly engage in those behaviors. In many ways they’re in fact counterintuitive to us. To thrive and lead others in the SMA, then, requires many of us to work hard at behavioral improvements, and that’s much easier if the new behaviors fit well with our mental models.

Mental models guide our thoughts and actions and predispose us to behave in certain ways. They can help us simplify the world and operate efficiently, but they can also be limiting and destructive when they’re like concrete bunkers, blinding or repelling us from ideas, facts, or perspectives that challenge our views of the world. Many of our mental models are stuck in ideas and perceptions originating in the Industrial Revolution. The SMA is a new reality requiring new ideas and rules.

For most of us, our mental model is dominated by a quantitative definition of smart and an obsessively self-absorbed and individualistic, winner-take-all approach to life and livelihood that inhibits the more outwardly focused behaviors necessary to excel at SMA Skills. Developing the behaviors and ultimately the skills that will give us a chance for human excellence in the SMA, then, requires that we first change our mental model of what it means to be smart and what it takes to succeed.