As a result of all these factors, many workers in this labor Holocene enjoyed what was known as “a high-wage middle-skilled job,” explained Stefanie Sanford, chief of global policy and advocacy for the College Board.
Well, say goodbye to all that, too.
The high-wage, middle-skilled job has gone the way of Kodak film. In the age of accelerations, there is increasingly no such animal in the zoo anymore. There are still high-wage, high-skilled jobs. And there are still middle-wage, middle-skilled jobs. But there is no longer a high-wage, middle-skilled job.
Average is officially over. When I graduated from college I got to find a job; my girls have to invent theirs. I attended college to learn skills for life, and lifelong learning for me afterward was a hobby. My girls went to college to learn the skills that could garner them their first job, and lifelong learning for them is a necessity for every job thereafter. Today’s American dream is now more of a journey than a fixed destination—and one that increasingly feels like walking up a down escalator. You can do it. We all did it as kids—but you do have to walk faster than the escalator, meaning that you need to work harder, regularly reinvent yourself, obtain at least some form of postsecondary education, make sure that you’re engaged in lifelong learning, and play by the new rules while also reinventing some of them. Then you can be in the middle class.
That’s not a great bumper-sticker slogan, I know. And I say that with no delight—I liked the old world. But we terribly mislead people by saying otherwise. Thriving in today’s workplace is all about what LinkedIn’s cofounder Reid Hoffman calls investing in “the start-up of you.” No politician in America will tell you this, but every boss will: You can’t just show up. You need a plan to succeed.
Like everything else in the age of accelerations, securing and holding a job requires dynamic stability—you need to keep pedaling (or paddling) all the time. Today, argues Zach Sims, the founder of Codecademy, “you have to know more, you have to update what you know more often, and you have to do more creative things with it” than just routine tasks. “That recursive loop really defines work and learning today. And that is why self-motivation is now so much more important”—because so much of the learning will now have to happen long after you have left high school, college, or your parents’ home—not in the discipline of a classroom. “An on-demand world requires on-demand learning for everyone, accessible to anyone around the world, anywhere on your phone or tablet, and this really changes the definition of learning,” added Sims, whose platform provides an easy method to learn how to write computer code. “When I walk into a subway and see someone playing Candy Crush [Saga] on their phone, [I think] there’s a wasted five minutes when they could be bettering themselves.”
For more than a decade after the Internet emerged in the mid-1990s, there was much lamenting about the “digital divide”—New York City had Internet and upstate New York didn’t. America had it and Mexico didn’t. South Africa had it and Niger didn’t. That really mattered because it limited what you could learn, how and where you could do business, and with whom you could collaborate. Within the next decade that digital divide will largely disappear. And when that happens only one divide will matter, says Marina Gorbis, executive director of the Institute for the Future, and that is “the motivational divide.” The future will belong to those who have the self-motivation to take advantage of all the free and cheap tools and flows coming out of the supernova.
During the fifty years after World War II, if the world had a dial on it, that dial was set to the left, and the closer you were to the Soviet Union the more leftward the dial pointed. And what it pointed to was a sign that said “You live in a world of defined benefits: just do your job every day, show up, be average, and here are the benefits you will get.” Since the emergence of the supernova, that dial has whipped sharply to the right, and the sign it points to today says “You live in a world of defined contributions—your wages and benefits will now be more and more directly correlated to your exact contribution, and with big data we will get better and better at measuring just exactly what your contribution is.” It’s a 401(k) world now. To paraphrase an old World War II poster, Uncle Sam wants—to put more on—you.
General Electric’s CEO, Jeff Immelt, put it bluntly in a May 20, 2016, commencement address to graduates at New York University’s Stern School of Business: “Technology has raised the competitive requirements for companies and people.” John Hagel, the management expert, puts it even more bluntly: “There is mounting performance pressure on all of us—as individuals and institutions. All of this connectivity means significantly lower barriers to entry and movement, accelerating change and the increasing occurrence of extreme, disruptive events, all of which put significant pressure on our institutions … On a personal level, the example I use is a billboard that used to be up on a highway here in Silicon Valley which asked a simple question: ‘How does it feel to know that there are at least one million people around the world who can do your job?’ While we might argue whether it is one thousand or one million, it would have been an absurd question to ask twenty or thirty years ago because it didn’t really matter—I’m here and they are somewhere else. Now it is increasingly a central question, and one might add, ‘How does it feel to know there are at least one million robots who can do your job?’ We are all feeling mounting performance pressure at a very personal level.”
The New Social Contract
But can everyone keep up?
This is one of the most important socioeconomic questions of our day—probably the most important. Here is one way to think about it: in every major economic shift, “a new asset class becomes the main basis for productivity growth, wealth creation, and opportunity,” argued Byron Auguste, a former economic adviser to President Obama who cofounded Opportunity@Work, a social venture that aims to enable at least one million more Americans to “work, learn, and earn to their full potential” in the next decade. “In the agrarian economy, that asset was land,” Auguste said. “In the industrial economy it was physical capital. In the services economy it was intangible assets, such as methods, designs, software, and patents.”
“In today’s knowledge-human economy it will be human capital—talent, skills, tacit know-how, empathy, and creativity,” he added. “These are massive, undervalued human assets to unlock”—and our educational institutions and labor markets need to adapt to that.” We need—at all costs—to avoid a growth model based on assets or opportunities that are accessible only to a fortunate few. The massive redistribution of wealth that would be required to support such a society is not politically sustainable.
“We need to focus on a growth model based on investment in human capital,” argued Auguste. “That can produce a more dynamic economy and inclusive society, since talent and human capital are far more equally distributed than opportunity or financial capital.”
So where do we begin? The short answer, says Auguste, is that in the age of accelerations we need to rethink three key social contracts—those between workers and employers, students and educational institutions, and citizens and governments. That is the only way to create an environment in which every person is able to realize their full talent potential and human capital becomes a universal, inalienable asset.
Let’s Hire More Bank Tellers
To understand the necessary components of such a new set of social contracts, we have to start with a clear picture of what is actually happening in the labor market, so we know what exactly we are trying to fix.
Here, I rely on the excellent work of the economist James Bessen, a researcher and lecturer at Boston University School of Law and author of Learning by Doing: The Real Connection Between Innovation, Wages, and Wealth. There are a lot of myths and misunderstandings surrounding this issue.
The central challenge we need to be focusing on, Bessen argues, is the issue of skills—not the issue of jobs per se. There is a huge difference, he insists, between automating tasks and completely automating a job—and dispens
ing with the human beings entirely. To be sure, we have jobs that have completely disappeared because the industry completely disappeared. There is probably no one in America, or anywhere for that matter, who makes their living today producing buggy whips—not since the horse and buggy gave way to the automobile. But it is critical to remember that even 98 percent automation of a job is not the same as 100 percent automation. Why? In the nineteenth century, 98 percent of the labor involved in weaving a yard of cloth got automated. The task went from 100 percent manual labor to 2 percent.
“And what happened?” asked Bessen. “The number of weaver jobs increased.”
Why? “Because when you automate a job that has largely been done manually, you make it hugely more productive.” And when that happens, he explained, “prices go down and demand goes up” for the product. At the beginning of the nineteenth century, many people had one set of clothes—and they were all man-made. And by the end of that century, most people had multiple sets of clothing, drapes on their windows, rugs on their floors, and upholstery on their furniture. That is, as the automation in weaving went up and the price went down, “people found so many more uses for cloth, and so demand exploded enough to actually offset the substitution of more machines for labor,” explained Bessen.
Using government data, Bessen studied the impact of computers, software, and automation on 317 occupations from 1980 through 2013. In a research paper he published on November 13, 2015, he concluded: “Employment grows significantly faster in occupations that use computers more.” He cited the example of cash machines, which began to be deployed in the 1990s in large numbers and now are everywhere. Everyone assumed that they would replace bank tellers. It didn’t happen.
The ATM is sometimes taken as a paradigmatic case of technology substituting for workers; the ATM took over cash handling tasks. Yet the number of fulltime equivalent bank tellers has grown since ATMs were widely deployed during the late 1990s and early 2000s. Indeed, since 2000, the number of fulltime equivalent bank tellers has increased 2.0 percent per annum, substantially faster than the entire labor force. Why didn’t employment fall? Because the ATM allowed banks to operate branch offices at lower cost. This prompted them to open many more branches, offsetting the erstwhile loss in teller jobs. At the same time, teller skills changed. Non-routine marketing and interpersonal skills became more valuable, while routine cash handling became less important. That is, although bank tellers performed relatively fewer routine tasks, their employment increased.
Even though the ATM automated routine cash handling tasks, the technology alone did not determine whether employment of tellers grew or fell; economics mattered. New technology can increase demand for an occupation, offsetting putative job losses. Nor is this example exceptional:
• Barcode scanners reduced cashiers’ checkout times by 18–19 percent, but the number of cashiers has grown since scanners were widely deployed during the 1980s.
• Since the late 1990s, electronic document discovery software for legal proceedings has grown into a billion dollar business doing work done by paralegals, but the number of paralegals has grown robustly.
• E-commerce has also grown rapidly since the late 1990s, now accounting for over 7 percent of retail sales, but the total number of people working in sales occupations has grown since 2000.
Bessen’s point is that technology’s impacts are not uniform: It can reduce demand for certain activities—routine tasks such as answering the phone and taking a message, for instance, have been largely wiped out by voice mail. But technology can also transfer tasks from one occupation to another. “There are still receptionists who answer phones and take messages,” noted Bessen, “but do other things as well. So the number of telephone operators has declined dramatically (317,000 full-time equivalent in 1980 to 57,000 today) while the number of receptionists has grown by more (438,000 to 896,000); receptionists require new and different skills, of course, than switchboard operators.”
At the same time, he pointed out, technology can create demand for totally new jobs—think data science engineers—even as it transforms the skills needed for some very old routine jobs, such as bank tellers and paralegals and store clerks, that would seem to be made obsolete by computers and robots but actually aren’t. And it can vastly increase the skills needed to practice old jobs that have been transformed by technology—graphic designers, for instance. Which is why graphic designers who can use computer-aided design software make a lot more money than a typesetter of old.
Some economists keep telling us that there is no skills gap—because if there were, median wages would go up in those professions when the supply of skilled labor does not meet demand. They need to look under the hood more, argues Bessen.
“The wages of the median worker tell us only that the skills of the median worker aren’t in short supply,” said Bessen. At the same time, some workers in a given field could still have skills that are in much higher demand and the workforce could have a gap in those able to meet it. “Technology doesn’t make all workers’ skills more valuable; some skills become valuable, but others go obsolete,” explains Bessen. When you look inside many professions what you discover is soaring demand and very high pay for those best able to leverage technology—and the opposite for those least able. That is where the real “skills gaps” show up in many occupations. Try hiring a quality data scientist in Silicon Valley today who can leverage the supernova to find needles in haystacks. Get in line!
For all of these reasons, Bessen concludes: “Jobs are not going away, but the needed skills for good jobs are going up.” And with this new technology platform we’re now on, it’s all happening faster. For instance, new software—such as AngularJS and Node.js, both Java-based programming languages to build Web-based mobile apps—can come out of nowhere and become the industry standard overnight, far faster than any university can adjust its curriculum. When that happens, demand and pay for people with those skills soar.
So now we’re defining the problem a little more accurately—what’s over is not jobs. What’s over is the Holocene era for jobs. Every middle-class job is now being pulled in four directions at once—and if we are going to train our citizens to thrive in such a world, we have to think afresh about each direction and what new skills or attitudes go with it in order to find a job, hold a job, and advance in a job.
For starters, middle-class jobs are being pulled up faster—they require more knowledge and education to perform successfully. To compete for such jobs you need more of the three Rs—reading, writing, and arithmetic—and more of the four Cs—creativity, collaboration, communication, and coding.
Consider a New York Times story from April 22, 2014, that reported:
Something strange is happening at farms in upstate New York. The cows are milking themselves.
Desperate for reliable labor and buoyed by soaring prices, dairy operations across the state are charging into a brave new world of udder care: robotic milkers …
Robots allow the cows to set their own hours, lining up for automated milking five or six times a day—turning the predawn and late-afternoon sessions around which dairy farmers long built their lives into a thing of the past.
With transponders around their necks, the cows get individualized service. Lasers scan and map their underbellies, and a computer charts each animal’s “milking speed,” a critical factor in a 24-hour-a-day operation.
The robots also monitor the amount and quality of milk produced, the frequency of visits to the machine, how much each cow has eaten, and even the number of steps each cow has taken per day, which can indicate when she is in heat.
In the future, a successful cow milker may need to be an astute data reader and analyst.
Every job is also being pulled apart faster. For instance, being a cow milker may become disaggregated. The high-skilled part of that job may move up—now you either have to learn computing or become a veterinarian who understands the anatomy of cows or be a big data scientist
who can analyze a cow’s behavior. At the same time the less skilled part of that job—herding cows into and out of the milking barn and cleaning up their manure—may get pulled down so that it can be done by anyone for a minimum wage (and probably soon by a robot). This is a broad trend in the workplace, as Bessen noted: the skilled part of each job requires more skill and rewards more skill, and the routine, repetitive part, which can much more easily be automated, will pay minimum wages or just be given over to a bot.
At the same time, every job is also being pulled out faster—more machines, robots, and workers in India and China can now compete for all of it or a bigger part of it. That demands more self-motivation, persistence, and grit to learn new technical or social-emotional skills to keep one step ahead of the robots, Indians, Chinese, and other skilled foreigners through lifelong learning.
And, finally, every job is being pulled down faster—it’s being outsourced to history in its present form and made obsolete faster than ever. And that demands more entrepreneurial thinking at every level: a constant searching for new niches, new opportunities to start something from which to profit and create employment.
So, at a minimum, our educational systems must be retooled to maximize these needed skills and attributes: strong fundamentals in writing, reading, coding, and math; creativity, critical thinking, communication, and collaboration; grit, self-motivation, and lifelong learning habits; and entrepreneurship and improvisation—at every level.
The Compounding Solution
Fortunately, new technology tools will aid this endeavor. The new social contracts we need between government, business, the social sector, and workers will be far more feasible if we find creative ways—to borrow a phrase from Nest Labs’ founder, Tony Fadell—to turn “AI into IA.” In my rendering, that would be to turn artificial intelligence into intelligent assistance, intelligent assistants, and intelligent algorithms.
Intelligent assistance involves leveraging artificial intelligence to enable the government, individual companies, and the nonprofit social sector to develop more sophisticated online and mobile platforms that can empower every worker to engage in lifelong learning on their own time, and to have their learning recognized and rewarded with advancement. Intelligent assistants arise when we use artificial intelligence to improve the interfaces between humans and their tools with software, so humans can not only learn faster but also act faster and act smarter. Lastly, we need to deploy AI to create more intelligent algorithms, or what Reid Hoffman calls “human networks”—so that we can much more efficiently connect people to all the job opportunities that exist, all the skills needed for each job, and all the educational opportunities to acquire those skills cheaply and easily.
Thank You for Being Late Page 24