9
The Future of Work

What to Expect and How to Make Yourself “Robot-Proof”

Technology has always changed work. As businesses deploy the technologies outlined in this book, workplaces will evolve rapidly. To understand the future of work, it's helpful to review how technology changed work in the past.

The History of Work: From the Physical to the Knowledge Era

At the dawn of the industrial revolution, most people survived by working the land. In 1790, about 90% of the U.S. population worked in agriculture (Source: USDA). Workers were measured by the amount of physical work they could do in a day. What mattered was strength, stamina, and technique.

The industrial revolution moved people out of the fields and into factories and offices. Today, less than 3% of U.S. jobs are in farming (Source: USDA). It's a similar situation in much of Western Europe, Canada, South Korea, Japan, Israel, Argentina, Australia, and other industrialized nations. This happened because we created mechanical muscles: The steam engine, mechanization, electrification, production lines, and computer control boosted production and created enormous wealth. Mechanical muscles commoditized most physical labor and many workers transitioned to knowledge work.

In the knowledge era, workers are valued for their experience, their knowledge, their creativity, their cognitive abilities, and their capacity to tap into their personal network to get things done. Most of today's knowledge jobs were unimaginable in 1790. Millions of people now make a living as web designers, pharmacists, and party planners. The average standard of living has risen dramatically. People live longer, healthier lives. While some people still do jobs that involve physical work—construction workers, truckers, janitors, warehouse workers, manufacturing technicians, baggage handlers, and so on—many of them are assisted by tools and machines.

The Next Era of Work: Augmented Work

Mechanical muscles launched us into the knowledge era. With the advent of artificial intelligence, we are creating mechanical minds. These mechanical minds will fundamentally reshape work.

AI Commoditizes Some Knowledge Work, and More Physical Work, Too

Powerful AIs are already doing some knowledge work. They leverage using diagnostic, decision making, and other capabilities previously only possessed by humans. Autonomous machines fitted with powerful sensors and controlled by AI will commoditize more physical work, work that was impossible to perform using previous generations of automation. Robots that can hang drywall, repair potholes, and sort recycling are all in development.

Automation won't replace all work. Some physical work will remain—jobs that require dexterity, craftsmanship, and physical interaction with humans are all safe for now. And not all knowledge jobs are doomed either. Far from it. Many knowledge jobs will be elevated rather than replaced by automation. Automation will reduce repetitive, dangerous, and boring work and enable workers to focus on meaningful work that is more rewarding. That said, the rise of AI is a major inflection point on the scale of the industrial revolution. For decades, software has eaten jobs. The advent of artificial intelligence accelerates this process and pushes technology into places that seemed like science-fiction fantasy just a decade ago.

Our ability to make and wield tools is one of the skills that differentiates us from other animals. From hammers and fishing rods to combine harvesters and computers, we use tools to get stuff done. These tools are subordinate to us. We are in control. With AI, machines are gaining autonomy and our relationship with technology is changing. Rather than controlling tools to perform a task, we will collaborate with them. Technology will have agency and make some of its own decisions. We will partner with technology to co-create content and we will think of AI and robots more like teammates than tools.

Collaborative technology boosts our existing capabilities. Super sensors and wearable technology will help us to see more, hear more, and feel more. Digital intelligence will augment our own intelligence. Robots will help us to achieve more. The SAM-100 robot, made by Construction Robotics, works in partnership with a bricklayer. Together, they can lay six times as many bricks as the human could alone.

The future of work is a tight collaboration between human intelligence and machine intelligence. Mechanical minds will augment and extend our own capabilities, both cognitive and physical. This doesn't require a physical merging of our bodies. We can augment ourselves without taking such drastic measures. My sense of direction is improved without having to implant a GPS in my brain; I simply need the mapping capabilities of my phone.

Welcome to the augmented era of work … an era when humans work in partnership with cognitive machines to get work done. I've already presented many early examples of augmentation technologies throughout this book. To make my case for this new augmented era of work, let's quickly review four of them.

1. Cobots are a class of robot specifically designed to work in partnership with humans. Cobots will work alongside humans in retail stores, warehouses, research labs, food service, and many stages of the manufacturing process.
2. Collaborative AI co-creates content in partnership with human workers. This type of AI will swiftly invade many roles, going far beyond its origins in engineering and architecture as generative design. Ten years from now, a collaborative AI might help me to write my next book or put together my presentation materials. Collaborative AI will help programmers to code, teachers to teach, and lawyers to build prosecution and defense strategies.
3. Digital personal assistants will support most workers. A digital intelligence will guide us through our day, help us stay focused on key tasks, and take care of errands.
4. AR workers blend the physical dexterity and intelligence of a human with the digital intelligence and vast online resources of a machine. Augmented reality (AR) will offer step-by-step visual instructions to workers, showing them what to do next. The result is a hybrid of human and machine, intimately combined to create an augmented worker.

Cobots, collaborative AI, digital personal assistants, and AR workers are just four examples of the ways that technology will augment humans in the workplace. We are entering the augmented era of work. And nothing will ever be the same again.

Automation Armageddon: Plan a “Robot-Proof” Career

As we enter the augmented era, two things are clear: Some jobs will be elevated through a deeper partnership with intelligent technology, while other jobs will be replaced entirely, putting millions of people into a state of turmoil.

Which Jobs Are at Risk?

There is much debate on the topic of job automation. Many factors and dynamics will determine how the next phase of automation plays out. Just because a job function can be automated doesn't mean that it will be. To project the impact of automation on employment, we must consider economic factors (sometimes a human will work for less than a robot costs to build and maintain) and technical, political, regulatory, and other practical issues. It's often not the right business decision to replace a human with an AI, even if it's technically possible. For example, nobody wants to be told they have stage-three liver cancer by a robot.

Well-informed and intelligent analysts have researched AI's likely impact on the global workforce. Their conclusions vary wildly but they all agree that a substantial number of jobs are at high risk of automation in the next 10 to 25 years.

A 2013 study by the University of Oxford kicked off much of the interest in this topic. The study, by Carl Benedikt Frey and Michael A. Osborne, concluded that 47% of U.S. jobs are at risk of automation. A 2017 PricewaterhouseCoopers (PwC) study suggests that 38% of U.S. jobs are at risk of automation by 2030. In a 2014 study, Bruegel concluded that 54% of EU jobs—European jobs skew heavily toward the service sector—were at risk of automation. Global studies by the Organisation for Economic Co-operation and Development (OECD) came to a less startling, but still sobering conclusion. In 2016, the OECD concluded that only 9% of global jobs were at risk of automation, though by March 2018 they updated their estimate to 14% of all jobs.

Varied predictions make it difficult to decide quite how concerned we should all be about the impending automation tsunami. Comparisons are difficult because each prediction model uses different assumptions and different input variables and makes predictions for different markets in different time frames. Models vary because they must estimate complicating factors that include the rate of job destruction versus job creation, the likely speed of technology development and adoption, and the amount of short-term labor market contraction caused by the retirement of the baby boomers (i.e., how long will boomers linger in the workforce).

Historically, technology has always created more jobs than it has destroyed. Typically, new jobs require higher levels of skill, create more value, and thus command higher wages. Jobs created by new technology tend to be less physically demanding and provide more job satisfaction. Some analysts argue this process will continue, and that AI will create more high-quality jobs than it will destroy. A 2018 PwC report focused on the United Kingdom predicts that while artificial intelligence will destroy 7 million UK jobs, it will create 7.2 million new jobs in the process. Another PwC report predicts that AI will deliver a $15.7 trillion boost to global GDP by 2030. Almost half of this increase comes from AI enhancements to products that make them more affordable, more personalized, and available in a wider variety. This, predicts PwC, will stoke increased consumer demand and boost the economy.

If we assume that the average of these predictions is accurate, it leads to two major challenges for society. First, newly created jobs typically require higher skills than the jobs they displace. Accessible adult education programs will be vital for society to safely navigate this transition. Social safety nets may be needed to help people who aren't able to make the leap. A 50-year-old truck driver from Omaha, Nebraska, may not be able to learn how to design virtual objects for augmented reality interfaces.

The second challenge is the speed of the transition. In the industrial revolution, people moved from agriculture to industry over 10 generations, a 200-year period. The coming automation tsunami may require half of the workforce to transition in a single generation. Our current education system, designed 150 years ago to move people from fields to factories, does not scale to cope with this challenge. Government, employers, and the education system will all need to step up. An August 2019 declaration by the Business Roundtable acknowledges as much. The statement on the evolving purpose of business, signed by 181 CEOs of major corporations, specifically refers to the need for companies to support employees “through training and education that help develop new skills for a rapidly changing world.”

AI automates tasks, not jobs. Jobs that consist of a few routine, repetitive tasks are more likely to be automated than jobs that comprise a high variety of nonroutine tasks. Truck drivers perform one main task: they drive. Once the task of driving is automated, the whole job of a truck driver can be automated. Some of the many, varied tasks performed by a marketing manager can be automated, but the majority of tasks cannot. Automation will allow the marketing manager to focus more of their time on tasks where they add unique value. To accurately predict job losses, models must comprehend all the major tasks involved in every job on the planet and whether each task can be automated. A tall order, and another reason that models disagree.

Whether you choose to believe the OECD model, the PwC predictions, or another analyst, the issue is clear. In the next two decades we must remobilize huge chunks of the global workforce. The debate now is only over the scale of the challenge to society: merely massive, or monumental? This challenge should capture everyone's attention. Efforts like the Emma Coalition (emmacoalition.com) seek to raise awareness of the issue, engage policy makers, and prepare the workforce for change.

The Sky Isn't Falling … Yet

The scale of the challenge is uncertain, and we still have some time to plan for the coming transition. But time is short, and this should be an issue we are all talking about. If we get this transition right, we will enter the augmented era in a way that elevates human work, creates a higher standard of living, and makes work more emotionally rewarding. If we get it wrong, the economy could collapse.

People and politicians remain in sweet denial on this topic. Amazingly, 94% of Americans think that it is unlikely they will lose their job to automation (Source: NPR/Marist). In 2017, U.S. Treasury Secretary Steve Mnuchin was asked by Axios whether he was concerned about automation. His disconcerting response was that it's “not even on our radar screen” and that he thought the issue was “50 to 100 years away.” Automation-related job losses are already happening. The National Bureau of Economic Research reported in March 2017 that 670,000 jobs were lost to robots between 1990 and 2007, a time period when robots did not yet have the benefit of modern AI-based intelligence. Research conducted by Daron Acemoglu at MIT and Pascual Restrepo at Boston University shows that every robot installed in a manufacturing environment displaces an average of 5.6 workers and depresses average wages by 0.5%. It's harder to ask for a raise when you're competing for jobs against robots that never get tired, call in sick, or request a paycheck.

Over the next two decades, AI-powered automation will affect a wide variety of jobs. Some roles—including farm laborers, radiologists, underwriters, polishers, telemarketers, cashiers, bookkeepers, tax preparers, butchers, bank tellers, clerks, paralegals, taxi drivers, auditors, customer support specialists, and food preparers—may become fully automated. Roles that involve high levels of creativity, empathy, human interaction, collaboration, adaptation, cultural understanding, systems thinking, complex problem solving, or that involve a high variety of nonroutine tasks will remain safe from automation. These roles will incorporate AI in a way that allows workers to elevate their efforts and focus on tasks only humans can do.

Becoming “Robot-Proof”

The most common question from my keynote audiences is some version of “what can I do to survive the automation tsunami?” People ask for themselves, but more often out of concern for children and grandchildren. The high-level answer is simple. To survive, we must make ourselves robot-proof. We must nurture and improve the skills that machines will never master. We must double down on our own humanity.

AI will continually improve. Robots will become nimbler and more dexterous. Machine vision, speech recognition, and voice platforms will become more capable. Machines will improve their creative capabilities. As this occurs, human workers must shift their focus toward skills that are uniquely human. For some this may feel like a retreat. For others it will feel like liberation. Robots and AI will take on lower-level tasks and free us to focus on more meaningful, impactful, and rewarding work.

Robot-proof skills include complex critical thinking, creative problem solving, social intelligence, and what I'm calling a “twenty-first-century psyche.”

Complex Critical Thinking

Our brains can understand the intricate linkages and interactions that occur between disparate parts of a complex system. We seek out the information needed to understand these relationships and step back to see the bigger picture. We analyze situations and contrast them with other situations, understanding parallels and differences that might exist. We apply judgment and test ideas against preexisting standards and expectations. These standards might be cultural, professional, societal, or other criteria that require complex knowledge and experience—insight machines have no concept of. Humans can weigh evidence and draw conclusions, even in highly complex scenarios. We can generate new ideas and judge these ideas on their merits. Machines cannot.

Creative Problem Solving

To solve complex problems, we use our ability to imagine new potential scenarios and plan ways to build solutions. To prepare children for the postautomation economy, we should fuel their imaginations and curiosity and teach them practical problem solving, for example, in Maker Labs. While machines have gained limited creative capabilities, they are mostly useful for co-creating content in partnership with people. Human creativity is still a powerful asset that makes us robot-proof. Entrepreneurial skills—the ability to see opportunity, create business and marketing plans, raise capital, attract talent, and build value—are unique to humans and will be even more important in the postautomation economy. We should invest in organizations that nurture these skills in children.

Social Intelligence

Understanding people—how to talk to them, motivate them, and work productively alongside them—will be a vital postautomation skill. Leadership skills, defined as the ability to inspire others to follow you, will remain important and highly relevant. Interpersonal communication skills enable us to share complex ideas, influence others, and gather important insights and wisdom. Teaming skills—the ability to work effectively with others—will become even more important as teams become more dynamic, cross-functional, and diverse. Empathy remains one of the main skills that differentiates us from machines. Many robot-proof roles require the ability to understand another person, to put yourself in their shoes, and to show compassion for their situation—for example, nursing, teaching, and counseling. “Culture” captures the wonderful weirdness of why humans behave the way they do. To understand culture is to understand all the rich subtleties of people's origin, background, identity, and outlook. Culture informs how we see the world, how we see ourselves, how we behave, what is important to us, what our unique needs are, and many other things that machines find baffling. Machines can't understand cultural constructs. This difficulty is often used to provide comic relief in science fiction; consider the Star Trek characters Spock and Data.

Twenty-First-Century Psyche

Careers for life are long gone. People entering the workforce today may have two, three, or more careers in their lifetime. They may enter careers that don't exist today and that will become obsolete before they retire. To navigate a world of constant change, people will need to be adaptable, mobile, constantly curious, optimistic, and always learning. They will need to take charge of their destiny in a way that perhaps their parents and grandparents never had to. They will need tenacity and grit. This twenty-first-century psyche will enable people to navigate a vibrant, rapidly shifting labor market, transition into exciting new roles, and participate fully in the postautomation economy.

You might also hear this set of robot-proof skills described as the 4Cs of the twenty-first century: creativity, communication, collaboration, and critical thinking. However you slice it, to thrive in a postautomation economy you should focus on building uniquely human skills.

Complex, Diverse Teams of T-Shaped Individuals

The next set of human challenges are big and complex. They cannot be solved by any one organization or any single approach. These challenges will only be addressed by high-functioning, diverse teams that span many disciplines.

Consider the evolution of the automobile. A car-design team in the 1950s needed mechanical, petrochemical, electrical, and industrial engineers who worked with aerodynamics experts and designers. Cars were made of steel, copper, wood, leather, glass, and rubber. A modern car design team adds new disciplines: electronic engineers, semiconductor design, programmers, experience designers, safety engineers, and so on. Designs incorporate aluminum, metal alloys, carbon fiber, plastics, and electronics. Autonomous vehicles require yet more expertise in AI, machine ethics, simulation, data science, and sensor technology.

To collaborate effectively, people from different disciplines and backgrounds must understand something about each other's expertise. Importantly, they must have respect for other disciplines. All too often, liberal arts students are dismissive of engineering, and engineers pour equal scorn on the liberal arts. Our education system must create well-rounded specialists with a working understanding of many other disciplines and a curiosity to learn beyond their chosen field. These “T-shaped” or “π-shaped” individuals will combine deep knowledge and problem-solving capabilities in at least one discipline with a working understanding of many other disciplines. These people also have skills that allow them to cross boundaries: strong communication skills, interpersonal networking skills, and teaming skills.

To produce world-changing results, companies must build diverse teams of T-shaped (or π- or -shaped people!) people. For those teams to flourish, leaders must foster a culture of constant curiosity and mutual respect.

New Technology Creates New Types of Work

As new technology solves new problems and creates new capabilities, new types of work are generated. These jobs are rarely predicted at the time. When James Watt's steam engine burst onto the scene in the late eighteenth century, few would have predicted it would lead to a wide range of train-related jobs. Aside from all the people needed to build trains, tracks, and stations, many people were employed to manage station operations, run the signals, maintain the tracks, create timetables, check tickets, load luggage, and serve food in the dining car. When the first computers were built, nobody imagined the roles of web designer, game physics programmer, PowerPoint presentation coach, computer-aided designer, smartphone screen repair technician, or cybersecurity tsar.

Full deployment of the six technologies outlined in this book has only just begun. Some new roles have already emerged. Most exist beyond our current imagination. We will certainly need more data scientists, robotics engineers, and machine learning experts. We will need people who can apply these disciplines to solve problems. People with domain expertise that spans multiple disciplines—an engineer with expertise in both AI and medicine—will be in high demand. High-impact projects will require cross-disciplinary collaboration by teams of people from a wide variety of expertise. New roles will be created at the intersection of previously separate disciplines—machine ethics will combine knowledge of artificial intelligence with the philosophy of morality and ethics.

New roles will emerge as technology is used to create new value in new ways. When augmented reality matures, “virtual object designer” may become a bona fide job. A vibrant market may emerge for a wide range of interactive virtual objects: branded sports team avatars, virtual intelligent mirrors, and interactive digital sculptures for the coffee table. Object designers might combine expertise in visual design, experience design, and programming. Advances in biotech may create organism designers. As people live longer, longevity coaches may help us imagine and plan how to live out our golden (and platinum?) years. New roles will be associated with replanning our cities for autonomous vehicles. Autonomous platforms will create new roles—mobile hairdresser? The energy industry will create new roles associated with smart grids and the transition to a carbon-free society. Many jobs will be created that can't be imagined today. Just as they always have.

Keeping the World Spinning in the Postautomation Economy

Automation may displace hundreds of millions of people from their current careers in just the next two decades. Some will need support as they transition to new roles.

Identity, Purpose, and Life 2.0

Many people feel lost when they are asked to imagine what “Life 2.0” looks like for them. Think about your own career. If somebody snapped their fingers and your job, and all jobs like it, disappeared forever in an instant, what would you do instead? What if all your experience and skills training was suddenly irrelevant? How would you choose a new career for the next phase of your life? Often our jobs become a facet of our identities. They help to a form our sense of self. They are one way that we create meaning in our lives and feel a sense of purpose. A major disruption in the workplace can leave people feeling lost, abandoned, worthless, and hopeless. Career-counseling services help people to find a new sense of identity as they plan their path to a new career. Society should invest in these services and scale them to cope with inevitable future demand.

Upskilling and Reskilling Millions

We will need to prepare millions of people for the coming transition. This burden will fall on government, business, and the education system. Businesses must play a key role. They won't be able to rely solely on others to provide the educated talent they need for future jobs. It may be more cost-effective to retrain existing employees than to lay them off and fight in the open market to recruit scarce talent. In July 2019, Amazon announced a plan to spend $700 million to retrain about a third of its workforce by 2025. Workers will gain new skills that either allow them to take on new challenges at the company, or that prepare them for a new role outside Amazon.

The World Economic Forum predicts that by 2022 “no less than 54% of all employees will require significant re- and upskilling. Of these, about 35% are expected to require additional training of up to six months, 9% will require reskilling lasting six to 12 months, while 10% will require additional skills training of more than a year.”

Lifelong learning is the new normal. On-the-job training has been part of most career progressions, but automation will accelerate the need for constant learning and personal development. The era of a single education for life is over.

Businesses will compete to maintain a well-educated, adaptable workforce that can perform in a rapidly evolving work environment with skills that complement the latest capabilities of technology. To avoid high turnover and high talent acquisition costs, companies will need to build robust training departments or make strong alliances with third party training providers.

A New Social Contract

While the long-term prospect for new job creation might be strong, the rapid speed of automation and the urgent need for reskilling may create a short- to medium-term (think next 10–15 years) challenge that some conclude will create a class of permanently unemployed and underemployed people. The single-generation transition is unprecedented and may place an enormous strain on society.

To keep the global economy going, we may need to consider changes to the social contract. The three-day week, often discussed in the 1970s, may need to become a reality, at least for some. We may need to find a way to provide people with a decent income in return for just 25 hours of work each week. Some advocate for a partial decoupling of work and income, for example, with schemes like Universal Basic Income that provide a fixed income to all citizens, regardless of their employment status.

Sir Richard Branson, Elon Musk, Mark Zuckerberg, Stephen Hawking, and Ray Kurzweil all seem to have concluded that some kind of Universal Basic Income (UBI) will be required in the future. U.S. presidential candidate Andrew Yang is passionate about the need for what he called “a freedom dividend” to navigate the coming waves of automation. Elon Musk, who endorsed Yang, said, “There is a pretty good chance we end up with a universal basic income or something like that, due to automation.” Sir Richard Branson said of UBI, “The hope is that policies like these can help people struggling just to survive, and allow them to get on their feet, be entrepreneurial and be more creative.” In a 2018 interview with Reddit, the late Stephen Hawking remarked, “If machines produce everything we need, the outcome will depend on how things are distributed. Everyone can enjoy a life of luxurious leisure if the machine-produced wealth is shared, or most people can end up miserably poor if the machine-owners successfully lobby against wealth redistribution. So far, the trend seems to be toward the second option, with technology driving ever-increasing inequality.”

Hawking's insight indicates that automation-related job losses will quickly become a major political issue. Rapid automation will be disruptive to society and an absence of government intervention may lead to extreme inequality, increased poverty, and more homelessness.

As businesses automate, they trade out human labor for capital equipment in the form of robots. Microsoft co-founder Bill Gates has suggested that corporate taxes be updated to reflect this reality. “Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed. … If a robot comes in to do the same thing, you'd think that we'd tax the robot at a similar level.”

Others advocate for a negative income tax that provides supplemental pay from the government for people earning below a certain threshold. The advantage of a negative income tax system is that it provides incentive for people to work, even in low wage jobs. This has been a major criticism of UBI, though early trials of UBI seem to indicate that only a very small percentage of people take advantage of the system and shirk productive work. Most people, it seems, work for many reasons that go beyond generating income—they work for the challenge, social interaction, self-worth, status, to stay occupied, and to contribute to an effort they feel is worthwhile.

There are early signs that populations may seek government intervention to slow the pace of automation: 58% of U.S. adults say that limits should be placed on how many jobs can be replaced with robots and computers, even if that technology makes a business more productive (Source: Pew Research Center). Widespread automation and unemployment may lead to labor strikes and social unrest. The Luddites destroyed the mechanized looms that took their textile-weaving jobs. If we are to avoid a “Luddite 2.0” moment, society will need to address the rising inequality that automation may bring. Societies in Europe and Asia, where people are more receptive to the need for social programs, may be in a better position than the United States to embrace this approach.

All Hands on Deck

When it comes to the topic of automation and job losses, there are more open questions than answers. The scale of the challenge for the 2030s will become clearer in the first half of the 2020s. Corporations, the education sector, and all of our institutions will have to step up with an appropriate and rapid response. We can all play an important role, too. Help others to understand the challenge ahead and encourage meaningful and nonpartisan conversations about potential solutions. We will neither unionize nor deregulate our way out of this one. By virtue of having read this book, please consider yourself deputized as a futurist.

Spread the word. Start the conversation. Today.