Is the concern about automation costing jobs a recent one only, or are there other examples from the past?

Is the concern about automation costing jobs a recent one only, or are there other examples from the past?

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Jobs always have been replaced. Were people in the past concerned about how technology might deprive them of their jobs?

To make the question less broad: Were people at the time of the industrial revolution in Europe as apprehensive about automation as they are today?

Example from NY times (April 1963):

Fallacies and Facts About Automation; Like 'abolition,' like 'prohibition,' the word is one that sparks great controversy. An expert tries to sort out the truth about it. Automation: Fallacies And Facts

Not new: the simplest previous example is related to the Luddite movement in the early industrial revolution era

As the industrial revolution grew in its influence, the threat to jobs among textile workers evoked a response to the point that some workers rose up and destroyed factory equipment (the advancing technology that was displacing their labor).

Over time, however, the term {Luddite} has come to mean one opposed to industrialisation, automation, computerisation or new technologies in general. The Luddite movement began in Nottingham and culminated in a region-wide rebellion that lasted from 1811 to 1816. Mill owners took to shooting protesters and eventually the movement was suppressed with military force.

An agricultural variant of Luddism, centering on the breaking of threshing machines, occurred during the widespread Swing Riots of 1830 in southern and eastern England.

There is a decent summary of this at, and numerous books have been published about this phenomenon. (When I was taking classes for a master's degree in management thirty years ago, this very issue and its relationship to unions and collective actions by labor were a mini course within the course I was taking on modern labor relations).

It is worth noting that by the late 19th century, as the rise of the labor movement coincided with the spread of the industrial revolution, the ability to form a social/political organization to oppose the problems of those in the laboring classes made possible a different form of reaction to the social problems that technology brought with it: the strike, and a variety of other labor action that takes us somewhat outside of the scope of your question.

The word 'sabotage' comes from the act of throwing a clog in a machine, to make it malfunction/destroy it. So yes, people in the early industrial age were not at all happy being replaced by machines.

Yes, there's a job creation argument for automation and technology

It may not be obvious, but the U.S. presidential election offers critical lessons about how policy and technology leaders should think about the future of artificial intelligence. In fact, just days before Donald Trump was sworn into office, these lessons were a focus of the Davos meeting of the global elite.

Technology executives expressed concern over a growing fear throughout the world that robots destroy jobs and discussed the possibility of a backlash against innovation. It was this same fear of job loss that has contributed to the recent backlash against trade agreements.

After all, proponents of trade agreements won every argument except one: that trade increases employment. That made killing the Trans-Pacific Partnership (TPP) a central message of candidate Trump’s campaign. It was one of the first things President Trump Donald TrumpChinese apps could face subpoenas, bans under Biden executive order: report Kim says North Korea needs to be 'prepared' for 'confrontation' with US Ex-Colorado GOP chair accused of stealing more than 0K from pro-Trump PAC MORE did after taking office.

The lesson is clear. When it comes to artificial intelligence, the industry might win every argument about innovation, progress, and new goods and services, but lose the jobs argument. If that happens, technology companies could face new limits on digital commerce, reduced investments in research and development, burdensome tax treatment, and more.

The opportunity of automation is enormous. Consider that, as autonomous vehicles become the primary means of transportation, accidents will decline by 90 percent, saving lives and billions of dollars. Furthermore, automation will actually return jobs to the United States. One-quarter of the decline in U.S. manufacturing jobs is due to competition from China, driven largely by lower labor costs. But this offshoring is a station on the way to the new globally-competitive automated U.S. factories that are creating good paying jobs for skilled workers.

Of course, computer technology does affect the nature of work. It has eliminated some tasks and lowered demand for some workers. A recent study by McKinsey & Company estimates that almost half of all current tasks are subject to automation, providing fodder for arguments that widespread technological unemployment is near. But the story is more complex. Computers can eliminate all job-required tasks in only 5 percent of occupations, and there will still be plenty of tasks to perform in existing occupations, while many new tasks will be created.

We’ve already seen the way automation creates efficiencies that lower production costs, thereby stimulating demand and creating more jobs. Recent history is filled with examples of lowering operating costs. ATM machines led to increased bank teller employment, and cost savings created by robots have actually increased human employment in warehouses. In the overall economy, automation has led to a greater need for non-routine, high-skill work that pays high wages and for low-skill work that pays lower wages.

While all this may be true, the reality is that the world is focused on bridging income divides and spreading economic opportunity. We have a responsibility to make certain that the bounty of automation can benefit everyone.

An important step is to match computers with human skills. On the computer side, this means creating programs that augment human skills. As described by IBM data scientists, humans and machines will “need to collaborate to produce better results, each bringing their own superior skills to the partnership.”

On the human side, people need to be trained for tasks computers cannot perform. This means prioritizing science, technology, engineering and math (STEM) education. But that’s not the only solution. Our computer-intensive work environment is creating high-paying jobs for those with credentialed skills from quality technical schools or training programs. Reauthorizing the career and technical education program with adequate funding will jump-start the programs that can match human skills with the new workplace, which has many unfilled jobs waiting for skilled workers.

Even with these efforts, some workers will not be able to gain the skills needed to flourish. A late-career truck driver without a college education can’t be expected to become a coder. For many of these workers, a social safety net is essential, and that net can be supported by the wealth that technology generates. Policy and technology leaders must work together on programs that support the collective good.

Ultimately, technology can continue to create more jobs than it displaces, while driving U.S. economic gains. But the only way to achieve the full measure of this opportunity is to ensure that the benefits are clearly realized by those who see technology as more of a foe than a friend.

Mark M. MacCarthy is senior vice president of public policy at the Software & Information Industry Association. He has been a consultant on technology policy issues for the Organization for Economic Cooperation and Development and the Aspen Institute. He is an adjunct professor of communication and technology at Georgetown University, where he teaches courses on artificial intelligence and the future of work.

The views of contributors are their own and are not the views of The Hill.

The destruction of jobs is clear and direct: a firm automates a conveyor belt, supermarket checkout, or delivery system, keeps one-tenth of the workforce as supervisors, and fires the rest. But what happens after that is far less obvious.

The standard economic argument is that workers affected by automation will initially lose their jobs, but the population as a whole will subsequently be compensated. For example, the Nobel laureate economist Christopher Pissarides and Jacques Bughin of the McKinsey Global Institute argue that higher productivity resulting from automation “implies faster economic growth, more consumer spending, increased labor demand, and thus greater job creation.”

But this theory of compensation is far too abstract. For starters, we need to distinguish between “labor-saving” and “labor-augmenting” innovation. Product innovation, such as the introduction of the automobile or mobile phone, is labor-augmenting. By contrast, process innovation, or the introduction of an improved production method, is labor-saving, because it enables firms to produce the same quantity of an existing good or service with fewer workers.

True, new jobs created by product innovation may be offset by a “substitution effect,” as the success of a new product causes the labor employed in producing an old one to become redundant. But the biggest challenge comes from process innovation, because this only ever displaces jobs, and does not create new ones. Where process innovation is dominant, only compensatory mechanisms can help to prevent rising unemployment, or what the British economist David Ricardo called the “redundancy” of the population.

There are several such mechanisms. First, increased profits will lead to further investment in new technology, and hence new products. In addition, competition between firms will lead to a general reduction in prices, increasing demand for products and hence labor. Finally, the reduction in wages caused by initial technological unemployment will increase demand for labor and induce a shift back to more labor-intensive methods of production, soaking up the redundant workers.

Technology Isn't Destroying Jobs, But Is Increasing Inequality

Amid the concern around the automation of jobs, a long-standing truism has perhaps been overlooked. Whilst the likes of the Frey and Osborne paper predicted a pretty widespread demolition of 47% of all jobs, the reality is that those with low-skilled, routine jobs are far more at risk.

The thing is, those with low skills have been on the receiving end of pretty much every shift in the labor market over the past decade. For instance, MIT research found that not only has the recent economic recovery generally passed low-skilled workers by, the same has been true for much of the last 50 years. Since the financial crisis, jobs have returned en masse, with 300,000 or so created in December alone, with income rising at a similar pace. That is not true for low-skilled work however, as incomes for this group have barely moved for 50 years!

What's more, when jobs return after a recession or other economic shock, they are nearly always requiring higher skills than before the shock. Far from being a destroyer of jobs therefore, what technology does seem to do is help inequality between those with skills and those without.

Rising inequality

A good example of this comes from a recently published study from the University of California, Los Angeles, which explores how technology (in the broadest sense) has affected wages over the years. The research examined the introduction of something as relatively mundane as broadband into Brazil between 2000 and 2009.

It revealed that the technology coincided with an increase in wages across the labor market, but whereas the average employee saw wages rise by just 2.3%, those in managerial positions saw a 9% rise, and those in the boardroom saw an even more impressive 19% boost to the income. The hypothesis proposed by the researchers is that the new technology allowed the more productive workers to be even more productive, thus widening the income gap between them.

This is common with most new technologies, as it tends to improve the relative position of skilled workers. In other words, technology often does the routine tasks for us, thus allowing highly-skilled people to focus more on non-routine, abstract tasks that really set them apart. The routine tasks are often the bread and butter of the low-skilled worker however, so the new technology harms their prospects.

Unequal skills

This unequal boost to earnings from new technologies is compounded by incredibly unequal digital skills. Back in 2016, the OECD found that over 50% of adults could only just complete the most basic of digital skills. They could write an email, but spreadsheets or word processing were largely beyond them.

What's more, there is little sign that those skills are going to be developed. In 2017 a report from the U.K. government explored both attitudes and access to adult education among those with low skill levels.

The report reveals that adult education in the U.K. is declining, and participation declines more as we age. What’s more, those that do engage in education as adults tend to be wealthier and come from a high existing skill level.

Those with fewer qualifications to begin with would often cite barriers such as a lack of confidence, lack of interest and a sense that they're too old.

So what can be done? A good place to start is in the early educational opportunities that people receive. The report found that the single biggest predictor of later participation in education is earlier participation. In other words, if people enjoyed learning at school and found it interesting and engaging, then they are more likely to carry that on into adult life.

Those from lower socioeconomic groups could also benefit from more support to help them learn effectively. For instance, while people of all sorts encountered barriers to learning, those from higher socioeconomic groups were better able to overcome those barriers, whereas those from lower socioeconomic groups succumbed to them.

The report also commends the route being taken by many MOOC platforms of breaking down courses into more manageable chunks that can allow a more flexible approach to learning that allows the student to overcome time pressures.

They also advocate adopting a unique approach to targeting specific groups. For instance, face-to-face contact is particularly valuable in engaging new learners, especially if it comes from intermediary bodies with strong roots in the local community.

"A longitudinal study of people who had undertaken community learning courses in the U.K. found that many benefits, including improved basic skills and motivation to apply for work, were felt most strongly among learners who lacked qualifications, came from black and minority ethnic backgrounds, and/or lived in the most deprived areas," the report says.

Inequality as a result of technological innovation isn't a forgone conclusion, but it's clear that society as a whole needs to get much better at improving the skills development of all citizens if the dividend is to be spread more widely. Sadly, there is little evidence that governments even understand this dilemma, much less are actively looking to address it.

How Technology Is Destroying Jobs

Given his calm and reasoned academic demeanor, it is easy to miss just how provocative Erik Brynjolfsson’s contention really is. ­Brynjolfsson, a professor at the MIT Sloan School of Management, and his collaborator and coauthor Andrew McAfee have been arguing for the last year and a half that impressive advances in computer technology—from improved industrial robotics to automated translation services—are largely behind the sluggish employment growth of the last 10 to 15 years. Even more ominous for workers, the MIT academics foresee dismal prospects for many types of jobs as these powerful new technologies are increasingly adopted not only in manufacturing, clerical, and retail work but in professions such as law, financial services, education, and medicine.

That robots, automation, and software can replace people might seem obvious to anyone who’s worked in automotive manufacturing or as a travel agent. But Brynjolfsson and McAfee’s claim is more troubling and controversial. They believe that rapid technological change has been destroying jobs faster than it is creating them, contributing to the stagnation of median income and the growth of inequality in the United States. And, they suspect, something similar is happening in other technologically advanced countries.

Perhaps the most damning piece of evidence, according to Brynjolfsson, is a chart that only an economist could love. In economics, productivity—the amount of economic value created for a given unit of input, such as an hour of labor—is a crucial indicator of growth and wealth creation. It is a measure of progress. On the chart Brynjolfsson likes to show, separate lines represent productivity and total employment in the United States. For years after World War II, the two lines closely tracked each other, with increases in jobs corresponding to increases in productivity. The pattern is clear: as businesses generated more value from their workers, the country as a whole became richer, which fueled more economic activity and created even more jobs. Then, beginning in 2000, the lines diverge productivity continues to rise robustly, but employment suddenly wilts. By 2011, a significant gap appears between the two lines, showing economic growth with no parallel increase in job creation. Brynjolfsson and McAfee call it the “great decoupling.” And Brynjolfsson says he is confident that technology is behind both the healthy growth in productivity and the weak growth in jobs.

It’s a startling assertion because it threatens the faith that many economists place in technological progress. Brynjolfsson and McAfee still believe that technology boosts productivity and makes societies wealthier, but they think that it can also have a dark side: technological progress is eliminating the need for many types of jobs and leaving the typical worker worse off than before. ­Brynjolfsson can point to a second chart indicating that median income is failing to rise even as the gross domestic product soars. “It’s the great paradox of our era,” he says. “Productivity is at record levels, innovation has never been faster, and yet at the same time, we have a falling median income and we have fewer jobs. People are falling behind because technology is advancing so fast and our skills and organizations aren’t keeping up.”

Brynjolfsson and McAfee are not Luddites. Indeed, they are sometimes accused of being too optimistic about the extent and speed of recent digital advances. Brynjolfsson says they began writing Race Against the Machine, the 2011 book in which they laid out much of their argument, because they wanted to explain the economic benefits of these new technologies (Brynjolfsson spent much of the 1990s sniffing out evidence that information technology was boosting rates of productivity). But it became clear to them that the same technologies making many jobs safer, easier, and more productive were also reducing the demand for many types of human workers.

Anecdotal evidence that digital technologies threaten jobs is, of course, everywhere. Robots and advanced automation have been common in many types of manufacturing for decades. In the United States and China, the world’s manufacturing powerhouses, fewer people work in manufacturing today than in 1997, thanks at least in part to automation. Modern automotive plants, many of which were transformed by industrial robotics in the 1980s, routinely use machines that autonomously weld and paint body parts—tasks that were once handled by humans. Most recently, industrial robots like Rethink Robotics’ Baxter (see “The Blue-Collar Robot,” May/June 2013), more flexible and far cheaper than their predecessors, have been introduced to perform simple jobs for small manufacturers in a variety of sectors. The website of a Silicon Valley startup called Industrial Perception features a video of the robot it has designed for use in warehouses picking up and throwing boxes like a bored elephant. And such sensations as Google’s driverless car suggest what automation might be able to accomplish someday soon.

A less dramatic change, but one with a potentially far larger impact on employment, is taking place in clerical work and professional services. Technologies like the Web, artificial intelligence, big data, and improved analytics—all made possible by the ever increasing availability of cheap computing power and storage capacity—are automating many routine tasks. Countless traditional white-collar jobs, such as many in the post office and in customer service, have disappeared. W. Brian Arthur, a visiting researcher at the Xerox Palo Alto Research Center’s intelligence systems lab and a former economics professor at Stanford University, calls it the “autonomous economy.” It’s far more subtle than the idea of robots and automation doing human jobs, he says: it involves “digital processes talking to other digital processes and creating new processes,” enabling us to do many things with fewer people and making yet other human jobs obsolete.

It is this onslaught of digital processes, says Arthur, that primarily explains how productivity has grown without a significant increase in human labor. And, he says, “digital versions of human intelligence” are increasingly replacing even those jobs once thought to require people. “It will change every profession in ways we have barely seen yet,” he warns.

McAfee, associate director of the MIT Center for Digital Business at the Sloan School of Management, speaks rapidly and with a certain awe as he describes advances such as Google’s driverless car. Still, despite his obvious enthusiasm for the technologies, he doesn’t see the recently vanished jobs coming back. The pressure on employment and the resulting inequality will only get worse, he suggests, as digital technologies—fueled with “enough computing power, data, and geeks”—continue their exponential advances over the next several decades. “I would like to be wrong,” he says, “but when all these science-fiction technologies are deployed, what will we need all the people for?”

New Economy?

But are these new technologies really responsible for a decade of lackluster job growth? Many labor economists say the data are, at best, far from conclusive. Several other plausible explanations, including events related to global trade and the financial crises of the early and late 2000s, could account for the relative slowness of job creation since the turn of the century. “No one really knows,” says Richard Freeman, a labor economist at Harvard University. That’s because it’s very difficult to “extricate” the effects of technology from other macroeconomic effects, he says. But he’s skeptical that technology would change a wide range of business sectors fast enough to explain recent job numbers.

Employment trends have polarized the workforce and hollowed out the middle class.

David Autor, an economist at MIT who has extensively studied the connections between jobs and technology, also doubts that technology could account for such an abrupt change in total employment. “There was a great sag in employment beginning in 2000. Something did change,” he says. “But no one knows the cause.” Moreover, he doubts that productivity has, in fact, risen robustly in the United States in the past decade (economists can disagree about that statistic because there are different ways of measuring and weighing economic inputs and outputs). If he’s right, it raises the possibility that poor job growth could be simply a result of a sluggish economy. The sudden slowdown in job creation “is a big puzzle,” he says, “but there’s not a lot of evidence it’s linked to computers.”

To be sure, Autor says, computer technologies are changing the types of jobs available, and those changes “are not always for the good.” At least since the 1980s, he says, computers have increasingly taken over such tasks as bookkeeping, clerical work, and repetitive production jobs in manufacturing—all of which typically provided middle-class pay. At the same time, higher-paying jobs requiring creativity and problem-solving skills, often aided by computers, have proliferated. So have low-skill jobs: demand has increased for restaurant workers, janitors, home health aides, and others doing service work that is nearly impossible to automate. The result, says Autor, has been a “polarization” of the workforce and a “hollowing out” of the middle class—something that has been happening in numerous industrialized countries for the last several decades. But “that is very different from saying technology is affecting the total number of jobs,” he adds. “Jobs can change a lot without there being huge changes in employment rates.”

What’s more, even if today’s digital technologies are holding down job creation, history suggests that it is most likely a temporary, albeit painful, shock as workers adjust their skills and entrepreneurs create opportunities based on the new technologies, the number of jobs will rebound. That, at least, has always been the pattern. The question, then, is whether today’s computing technologies will be different, creating long-term involuntary unemployment.

At least since the Industrial Revolution began in the 1700s, improvements in technology have changed the nature of work and destroyed some types of jobs in the process. In 1900, 41 percent of Americans worked in agriculture by 2000, it was only 2 percent. Likewise, the proportion of Americans employed in manufacturing has dropped from 30 percent in the post–World War II years to around 10 percent today—partly because of increasing automation, especially during the 1980s.

While such changes can be painful for workers whose skills no longer match the needs of employers, Lawrence Katz, a Harvard economist, says that no historical pattern shows these shifts leading to a net decrease in jobs over an extended period. Katz has done extensive research on how technological advances have affected jobs over the last few centuries—describing, for example, how highly skilled artisans in the mid-19th century were displaced by lower-skilled workers in factories. While it can take decades for workers to acquire the expertise needed for new types of employment, he says, “we never have run out of jobs. There is no long-term trend of eliminating work for people. Over the long term, employment rates are fairly stable. People have always been able to create new jobs. People come up with new things to do.”

Still, Katz doesn’t dismiss the notion that there is something different about today’s digital technologies—something that could affect an even broader range of work. The question, he says, is whether economic history will serve as a useful guide. Will the job disruptions caused by technology be temporary as the workforce adapts, or will we see a science-fiction scenario in which automated processes and robots with superhuman skills take over a broad swath of human tasks? Though Katz expects the historical pattern to hold, it is “genuinely a question,” he says. “If technology disrupts enough, who knows what will happen?”

To get some insight into Katz’s question, it is worth looking at how today’s most advanced technologies are being deployed in industry. Though these technologies have undoubtedly taken over some human jobs, finding evidence of workers being displaced by machines on a large scale is not all that easy. One reason it is difficult to pinpoint the net impact on jobs is that automation is often used to make human workers more efficient, not necessarily to replace them. Rising productivity means businesses can do the same work with fewer employees, but it can also enable the businesses to expand production with their existing workers, and even to enter new markets.

Take the bright-orange Kiva robot, a boon to fledgling e-commerce companies. Created and sold by Kiva Systems, a startup that was founded in 2002 and bought by Amazon for $775 million in 2012, the robots are designed to scurry across large warehouses, fetching racks of ordered goods and delivering the products to humans who package the orders. In Kiva’s large demonstration warehouse and assembly facility at its headquarters outside Boston, fleets of robots move about with seemingly endless energy: some newly assembled machines perform tests to prove they’re ready to be shipped to customers around the world, while others wait to demonstrate to a visitor how they can almost instantly respond to an electronic order and bring the desired product to a worker’s station.

A warehouse equipped with Kiva robots can handle up to four times as many orders as a similar unautomated warehouse, where workers might spend as much as 70 percent of their time walking about to retrieve goods. (Coincidentally or not, Amazon bought Kiva soon after a press report revealed that workers at one of the retailer’s giant warehouses often walked more than 10 miles a day.)

Despite the labor-saving potential of the robots, Mick Mountz, Kiva’s founder and CEO, says he doubts the machines have put many people out of work or will do so in the future. For one thing, he says, most of Kiva’s customers are e-commerce retailers, some of them growing so rapidly they can’t hire people fast enough. By making distribution operations cheaper and more efficient, the robotic technology has helped many of these retailers survive and even expand. Before founding Kiva, Mountz worked at Webvan, an online grocery delivery company that was one of the 1990s dot-com era’s most infamous flameouts. He likes to show the numbers demonstrating that Webvan was doomed from the start a $100 order cost the company $120 to ship. Mountz’s point is clear: something as mundane as the cost of materials handling can consign a new business to an early death. Automation can solve that problem.

Meanwhile, Kiva itself is hiring. Orange balloons—the same color as the robots—hover over multiple cubicles in its sprawling office, signaling that the occupants arrived within the last month. Most of these new employees are software engineers: while the robots are the company’s poster boys, its lesser-known innovations lie in the complex algorithms that guide the robots’ movements and determine where in the warehouse products are stored. These algorithms help make the system adaptable. It can learn, for example, that a certain product is seldom ordered, so it should be stored in a remote area.

Though advances like these suggest how some aspects of work could be subject to automation, they also illustrate that humans still excel at certain tasks—for example, packaging various items together. Many of the traditional problems in robotics—such as how to teach a machine to recognize an object as, say, a chair—remain largely intractable and are especially difficult to solve when the robots are free to move about a relatively unstructured environment like a factory or office.

Techniques using vast amounts of computational power have gone a long way toward helping robots understand their surroundings, but John Leonard, a professor of engineering at MIT and a member of its Computer Science and Artificial Intelligence Laboratory (CSAIL), says many familiar difficulties remain. “Part of me sees accelerating progress the other part of me sees the same old problems,” he says. “I see how hard it is to do anything with robots. The big challenge is uncertainty.” In other words, people are still far better at dealing with changes in their environment and reacting to unexpected events.

For that reason, Leonard says, it is easier to see how robots could work with humans than on their own in many applications. “People and robots working together can happen much more quickly than robots simply replacing humans,” he says. “That’s not going to happen in my lifetime at a massive scale. The semiautonomous taxi will still have a driver.”

One of the friendlier, more flexible robots meant to work with humans is Rethink’s Baxter. The creation of Rodney Brooks, the company’s founder, Baxter needs minimal training to perform simple tasks like picking up objects and moving them to a box. It’s meant for use in relatively small manufacturing facilities where conventional industrial robots would cost too much and pose too much danger to workers. The idea, says Brooks, is to have the robots take care of dull, repetitive jobs that no one wants to do.

It’s hard not to instantly like Baxter, in part because it seems so eager to please. The “eyebrows” on its display rise quizzically when it’s puzzled its arms submissively and gently retreat when bumped. Asked about the claim that such advanced industrial robots could eliminate jobs, Brooks answers simply that he doesn’t see it that way. Robots, he says, can be to factory workers as electric drills are to construction workers: “It makes them more productive and efficient, but it doesn’t take jobs.”

The machines created at Kiva and Rethink have been cleverly designed and built to work with people, taking over the tasks that the humans often don’t want to do or aren’t especially good at. They are specifically designed to enhance these workers’ productivity. And it’s hard to see how even these increasingly sophisticated robots will replace humans in most manufacturing and industrial jobs anytime soon. But clerical and some professional jobs could be more vulnerable. That’s because the marriage of artificial intelligence and big data is beginning to give machines a more humanlike ability to reason and to solve many new types of problems.

Even if the economy is only going through a transition, it is an extremely painful one for many.

In the tony northern suburbs of New York City, IBM Research is pushing super-smart computing into the realms of such professions as medicine, finance, and customer service. IBM’s efforts have resulted in Watson, a computer system best known for beating human champions on the game show Jeopardy! in 2011. That version of Watson now sits in a corner of a large data center at the research facility in Yorktown Heights, marked with a glowing plaque commemorating its glory days. Meanwhile, researchers there are already testing new generations of Watson in medicine, where the technology could help physicians diagnose diseases like cancer, evaluate patients, and prescribe treatments.

IBM likes to call it cognitive computing. Essentially, Watson uses artificial-­intelligence techniques, advanced natural-language processing and analytics, and massive amounts of data drawn from sources specific to a given application (in the case of health care, that means medical journals, textbooks, and information collected from the physicians or hospitals using the system). Thanks to these innovative techniques and huge amounts of computing power, it can quickly come up with “advice”—for example, the most recent and relevant information to guide a doctor’s diagnosis and treatment decisions.

Despite the system’s remarkable ability to make sense of all that data, it’s still early days for Dr. Watson. While it has rudimentary abilities to “learn” from specific patterns and evaluate different possibilities, it is far from having the type of judgment and intuition a physician often needs. But IBM has also announced it will begin selling Watson’s services to customer-support call centers, which rarely require human judgment that’s quite so sophisticated. IBM says companies will rent an updated version of Watson for use as a “customer service agent” that responds to questions from consumers it has already signed on several banks. Automation is nothing new in call centers, of course, but Watson’s improved capacity for natural-language processing and its ability to tap into a large amount of data suggest that this system could speak plainly with callers, offering them specific advice on even technical and complex questions. It’s easy to see it replacing many human holdouts in its new field.

Digital Losers

The contention that automation and digital technologies are partly responsible for today’s lack of jobs has obviously touched a raw nerve for many worried about their own employment. But this is only one consequence of what ­Brynjolfsson and McAfee see as a broader trend. The rapid acceleration of technological progress, they say, has greatly widened the gap between economic winners and losers—the income inequalities that many economists have worried about for decades. Digital technologies tend to favor “superstars,” they point out. For example, someone who creates a computer program to automate tax preparation might earn millions or billions of dollars while eliminating the need for countless accountants.

New technologies are “encroaching into human skills in a way that is completely unprecedented,” McAfee says, and many middle-class jobs are right in the bull’s-eye even relatively high-skill work in education, medicine, and law is affected. “The middle seems to be going away,” he adds. “The top and bottom are clearly getting farther apart.” While technology might be only one factor, says McAfee, it has been an “underappreciated” one, and it is likely to become increasingly significant.

Not everyone agrees with Brynjolfsson and McAfee’s conclusions—particularly the contention that the impact of recent technological change could be different from anything seen before. But it’s hard to ignore their warning that technology is widening the income gap between the tech-savvy and everyone else. And even if the economy is only going through a transition similar to those it’s endured before, it is an extremely painful one for many workers, and that will have to be addressed somehow. Harvard’s Katz has shown that the United States prospered in the early 1900s in part because secondary education became accessible to many people at a time when employment in agriculture was drying up. The result, at least through the 1980s, was an increase in educated workers who found jobs in the industrial sectors, boosting incomes and reducing inequality. Katz’s lesson: painful long-term consequences for the labor force do not follow inevitably from technological changes.

Brynjolfsson himself says he’s not ready to conclude that economic progress and employment have diverged for good. “I don’t know whether we can recover, but I hope we can,” he says. But that, he suggests, will depend on recognizing the problem and taking steps such as investing more in the training and education of workers.

“We were lucky and steadily rising productivity raised all boats for much of the 20th century,” he says. “Many people, especially economists, jumped to the conclusion that was just the way the world worked. I used to say that if we took care of productivity, everything else would take care of itself it was the single most important economic statistic. But that’s no longer true.” He adds, “It’s one of the dirty secrets of economics: technology progress does grow the economy and create wealth, but there is no economic law that says everyone will benefit.” In other words, in the race against the machine, some are likely to win while many others lose.

Made in the USA - Episode 2: The Automation Puzzle

There is a fundamental question we need to answer when we talk about automation: To what extent is automation an answer to the skilled workforce shortage, and to what extent is automation vs. Skilled labor the wrong comparison to make in the first place?


Read Next

Increased reliance on automation may explain some of the drop in manufacturing employment discussed in Episode 1 of &ldquoMade in the USA.&rdquo But to what extent is automation an answer to the skilled workforce shortage, and to what extent is automation vs. skilled labor the wrong comparison?

Listen to Episode 2 here, or visit your favorite podcast platform to subscribe to &ldquoMade in the USA.&rdquo

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The following is a complete transcript for Episode 2 of the &ldquoMade in the USA&rdquo podcast. In addition to the hosts, the commentary in this episode is provided by:

  • Daron Acemoglu, Economist and Professor, MIT
  • Robert Atkinson, President, Information Technology & Innovation Foundation
  • James Besson, Professor, Boston University School of Law
  • Mike DiMarino, President, Linda Tool
  • Rob Ireton, Plant Manager, Plethora
  • Scott Smith, Group Leader, Intelligent Machine Tools, Oak Ridge National Laboratory

Brent Donaldson: Welcome to &lsquoMade in the USA, a podcast that dives deep into the past, present and future of American manufacturing. I&rsquom Brent Donaldson.

Pete Zelinski: I&rsquom Pete Zelinski. And in the last episode we talked about a number of factors that contributed to the decline in manufacturing employment that turned into a plummet in U.S. manufacturing employment for more than a decade starting around the year 2000.

When we look for root causes there, increased reliance on automation is one piece of the puzzle. And in this episode, we are going to talk about that: automation. Last episode, there were reasons why we said, &ldquonot so fast,&rdquo in response to those who saw automation as the principal explanation for the U.S. manufacturing job declines just after the year 2000. Still, automation played a role &mdash it would be incorrect to say that advances in automation don&rsquot affect employment levels. But there&rsquos an even more fundamental question that I think we need to answer when we talk about automation, and that question is: to what extent is automation an answer to the skilled workforce shortage, and to what extent is automation vs. Skilled labor the wrong comparison to make in the first place?

Brent Donaldson: Exactly. When a company opens a new manufacturing plant or expands an existing one, we rightly celebrate the jobs it will create. But ultimately, and this sounds awful to even say, but ultimately are jobs the point? When it comes to the health and welfare of our economy and our people, are jobs the ultimate goal?

On one hand, the answer is yes. It is perfectly valid to say that the priority in manufacturing or any other industry is to protect jobs. And if you believe that, then you might also believe that automation, and let&rsquos stick with manufacturing here, so&hellip machine tools that run unsupervised, software automation, robots that automate certain tasks &mdash if you are a person who believes that at the end of the day our industrial sectors have a sacred obligation to provide and protect jobs, then you will look at lights-out machining as technology and think maybe that&rsquos cool tech but it&rsquos also scary, right? There used to be a person who ran that machine tool overnight, and now that person is no longer needed.

Pete Zelinski: But the other side of that example is that, because the machine shop automated and innovated a way to produce more parts with less people, that shop gets to keep producing. Its customer isn&rsquot looking for a cheaper solution because the shop is using technology to produce more. And now that shop keeps the contract, pays its employees and maybe even grow its business. Lights-out machining keeps the lights on.

So we are going to talk about automation &mdash its meaning for manufacturing, its meaning for our workforce, and we want to warn you now that automation is a complicated topic with parallel truths that exist at once. But let&rsquos start with a simple one, a simple truth: people have been automating for so long that we rarely think about the automation we use in our daily lives, like washing machines, cars, microwaves, automatic bill paying from your checking account, or even a hand cart&hellip

Scott Smith, Oak Ridge National Laboratory: A lot of people think the robots are coming to take their jobs, right.

Pete Zelinski: This is Scott Smith, who you might remember from the first episode. He&rsquos the group leader for machining and machine tool research at Oak Ridge National Naboratory.

Scott Smith: And there's a lot of articles that are written with that sort of a theme in it, we're afraid of robots, we're going to lose our jobs. But we're not afraid of hand trucks for example. Right? One person with a hand truck can carry a lot more boxes than one person without a hand truck. So, think of all the jobs that were lost because we bought those hand trucks. But nobody suggests that we go back and just have people carry boxes. We're more productive with the hand trucks. So the argument that I'm making is about increasing the production We have to get more productive. That's the way that I see it. So, how do we do that? What's the what's the strategy in a high-wage country? Okay, so we have to innovate.

Pete Zelinski: Scott goes from hand carts to machine tools, and we need a definition here. As we described in the last episode, machine tools are the machines that make machines. They are a technology Brent and I know well &mdash we write about them for Modern Machine Shop magazine. At the same time, talking about manufacturing means talking about machine tools. They are arguably at the foundation of the manufacturing economy, and they are some of the most important representatives of manufacturing automation.

Scott Smith: So if I think about machine tools, when I'm putting machine tools in a high wage country, well, they have to be highly automated, right? And in a low wage country, you have a you have an operator at every machine and the machine doesn't have very much in the way of automation because you can't afford that. But you've got lots of people there and you're not paying them very much. So if you need more things, you buy more machines, and you have more people standing next to those machines. And you just copy that over and over and over. In high wage countries, you can't do that. In high wage country, you have to figure out a way for one person to produce more things. So in high wage country, you have palletized systems. You have robots who are loading parts on the pallets or people who are loading parts on the pallets.

Brent Donaldson: Economist Robert Atkinson gets a bit more into the weeds on this point.

Robert Atkinson, Information Technology and Innovation Foundation:What you see is manufacturing, jobs have grown somewhat since early 2012, 2013. But you have to understand the reason manufacturing jobs are growing &mdash it's just very troubling and hardly anybody's talking about this. For the first time, again, probably in American history, U.S. manufacturing productivity, measured as output for our work, is actually growing more slowly than services. And that's one of the reasons why we're seeing manufacturing job growth &mdash because you're not automating. Manufacturers aren't automating as much as they should be. And that to me is a big serious long-term problem, because while in the short run, yeah, sure, we can create some jobs there. As the overall economy expands, companies get more work, more machine tool work, more machining, so they have to add a couple more workers. That's fine in the short run. But in the medium term, if you can't raise your productivity, you lose price competitiveness in global markets. And so I really worry that &mdashand I&rsquom not saying it's going to happen &mdash but I worry that unless we turn that around, the next big challenge will be how do we be more competitive with places like Mexico, or places like Germany, or places like China or India? You know, we, U.S. manufacturers, we can't compete on low costs. We want to compete good wages, good living standards. But the only way to do that is you have to raise productivity and you have to keep innovating.

Pete Zelinski: So the argument here is how increased productivity is the only way we can compete with low-wage countries. What&rsquos productivity? The amount of output per person, using technology to let each person drive more output. But the topic is about more than that. Sometimes it means doing more with the same amount of space. This is especially true in places where space is at a premium, like New York City.

Mike DiMarino, president, Linda Tool: So I have some automation here. And I was looking at very sophisticated automation until (the pandemic) unfolded here because I needed it to produce. So in New York, for me, people say me all you need to expand, you&rsquore so jammed up in here, why don&rsquot you buy another building? I say, listen, with the way New York is, if I buy another building, I'm not going to put machines in it, I'm gonna put a condominium or a co-op on it, and sell it and sit on the beach and collect money every week, you know? So in order for me to expand here, I've over the past 10 or 12 years, I&rsquove gotten rid of older technology machines, CNC machines, and brought in machines where one machine can take the place of three. So I brought in mill-turns, I brought in a gantry motor, I brought in five-axis. So that allowed me to expand, but not on a real estate footprint.

Brent Donaldson: That&rsquos Mike DiMarino, who you might remember from the first episode. Incidentally, Pete, you and I have been in Mike&rsquos shop in Brooklyn and he&rsquos not kidding when he says he takes advantage of every square foot of that space. He&rsquos even got a rooftop garden where he grows herbs and flowers and several unusual varieties of tomatoes.

At any rate, his arguments are right. He can&rsquot expand his footprint, and he&rsquos limited to how many employees he can fit in his current space. So, growing the business means increasing productivity with the employees that he has. But obviously, he still needs people. I think we need to be careful not to confuse automation with automatic. People need to program those CNC machines to design the parts, to run measurements and do QC. So at some point you have to ask: even if he had room to grow, would he hire more people?

Scott Smith: So if you look in a highly automated system, there's a lot more machines than there are shop floor workers. One person on floor runs a lot of different machines. But all together, there's a lot more people than there are machines because it created all kinds of new jobs to do that. So you have the NC programmers, the process planners and all that kind of stuff. The workers, you know, they're supervisors rather than operators. A lot of people when they talk about the loss of the jobs, they have in their mind the people who put the workpiece in the machine and push the button. But I think you've got to look at all the jobs, right?

Pete Zelinski: So there it is. You have to look at all of the jobs. And economists have done that. Or tried to. And let&rsquos accept that as a nation, we&rsquore more competitive in manufacturing if we embrace automation. But there are costs, and we have to be real about that, too, and those costs are not distributed evenly throughout the country. At the regional level, in communities that benefit from manufacturing plants, it is at best a mixed blessing &ndash and maybe a net loss &ndash if those plants advance into greater automation.

Daron Acemoglu, economist and professor, MIT: You know, what automation does is that it substitutes capital for labor.

Pete Zelinski: This is Daron Acemoglu, professor of economics at MIT.

Daron Acemoglu: And that's why (automation) is such a powerful trend, because if you can efficiently use it, capital would be cheaper. And that's why employers find it profitable to use robots, and you see that some of the sectors that have switched to robots, like car manufacturers have increased their productivity substantially. But that means that, you know, capital owners gain to some degree and also this enables, to some extent, cheaper car being manufactured. And what we do is that we use data and estimates from a variety of sources to compute what the impact of this would be on prices and gains for other parts of the economy. And it turns out, you know, those gains are important, but they're not enough to make up for the employment losses that have happened in the areas where manufacturing used to concentrate.

Brent Donaldson: So yes, clearly there are costs associated with automation. He&rsquos not making a value judgement there. We&rsquore better off to some extent having cheaper cars. But there is a cost, and the cost is often localized. The result of that is, ironically, that at the local level some people might hope their manufacturing plants don&rsquot become too technologically advanced.

Pete Zelinski: To be fair, I think plenty of people would have a problem with this line of argument. It seems to suggest we should go slow on automating or not automate at all. It also seems to contradict the world we see. We have jobs in this society, this technologically advanced society. Even during a time when unemployment is high, most people who want a job can have a job, even in a society with lots of automation. We mentioned washing machines as an example of automation. If people don&rsquot have to do their wash by hand, doesn&rsquot this set them free to do more productive work? Doesn&rsquot automation in the end ultimately lead to more jobs? Or better jobs? It seems to. How does this happen?

This gets back to the question we asked earlier: to what extent is automation an answer to the skilled workforce shortage, and to what extent is automation vs. Skilled labor the wrong comparison to make in the first place?

James Besson, Boston University School of Law: Yeah, I think it's the wrong comparison.

Pete Zelinski: This is James Besson, executive director of the technology & policy research initiative at the Boston University School of Law, and someone who writes frequently about automation.

James Besson: Automation can have the effect of increasing the demand for skilled labor I think historically that's often been the case. If you look at something like the textile industry, you know, we think about today the textile industry automation is something that destroys jobs, and employment in textiles has been going down since the late 1940s. In the U.S., part of that is because of global trade, but only the most recent years is that true. But for over 100 years before that automation was accompanied by growing employment in the textile industry, and many other industries as well. You know, it may seem to many people counterintuitive or not even nonsensical, that automation could create more jobs. And in doing so, it often was creating jobs have a higher level of skill or improving the value of skills. So Why was this? How did this happen?

You know, think about textiles. At the time they began automating textiles in the early 19th century. Cloth was extremely expensive, the typical person only had one set of clothing. When automation came along, it reduced the amount of labor needed to produce a yard of cloth. And, but in a competitive market, that meant that the price went down. Well, the price went down, and now all of a sudden, people could afford more cloth. And they bought a whole lot more cloth. In fact, so much more cloth that the number of textile workers increased, even though the labor per yard went down. That that was true for like I said, 100 years, over 100 years. You get to the mid 19, mid 20th century though and all of a sudden people have lots of cloth, you know, they've got full closets. They've got cloth furniture, upholstery, they've got draperies, you know, all sorts of uses of cloth. Automation is still chugging along it's still reducing the relative price of cloth. But demand just doesn't go up that much like it did in the beginning. Demand is become satiated and so, at that point, the labor reducing side of automation takes hold. There seems to be some studies suggesting automation is very closely related to upskilling. You know that they may reduce certain kinds of labor, but there at the same time, they're increasing demand for more skilled workers.

Brent Donaldson: So automation led to an increase in jobs, but only because an expensive product became very cheap and totally changed the market. That&rsquos not happening anymore &ndash textile demand has been stable and employment is no longer growing. So when you talk about automation increasing low-skill jobs, you need a case like that: an expensive product becoming really cheap. Could we get to really cheap cars, really cheap cell phones? The trend lines don&rsquot seem to be moving in that direction.

Meanwhile, it&rsquos not just manual-labor workers like in a textile mill whose work can be displaced by automation. We mentioned software is automation as well. Not just manual work, but also mental work can be automated. Rob Ireton is a plant manager at plethora, another company that does CNC machining. In addition to using automation on the plant floor, this company uses software automation to get the job ready for machining, and that is just as important.

Rob Ireton, Plethora: Most of our automation right now is focused on the software, which as I mentioned earlier, it's the user interface, it is the interaction with the customer on the front end. And then in the background, our proprietary software is doing the analysis of the parts. And then it is moving that information into our programmers, which, we currently have a bank of traditional programmers that take that data, and if it's good to go, they release it to the machine.

We had a study done a couple months ago, and within the $20 billion machining industry, the quick turn R&D focus is $7 to $9 billion of that. And that's where we live. And I think that's one of the reasons why we are expanding is because we have a philosophy of delivering high quality parts in the shortest amount of time possible.

Pete Zelinski: This is weird to say out loud but it&rsquos an important point: thinking takes time. But by automating thinking, this company can run smaller orders faster, deliver them faster, and take on a lot more of this small-order work.

And in this case we are not just talking about button-pushers or low-skill manual laborers. Automation replaces knowledge work as well.

Brent Donaldson: CNC machine tools are a kind of automation. And those machine tools replaced machines that were not automated, at least at some point in the past. But new machine tools are computer controlled &mdash they have to be programmed. Therefore, many machine shops today employ CNC programmers, and that is a type of job that didn&rsquot exist before machining became highly automated. And this goes back to what Scott Smith was saying: the final tally has to include all of the jobs.

Pete Zelinski: I think we&rsquove arrived at the point where this topic become really complicated. Automation creates new categories of employment. Automation creates jobs. But certain communities lose out from automation. The boom in textiles jobs from automation isn&rsquot likely to happen again. And not only that: we now understand how we can automate certain quote-unquote, thinking jobs.

So which is it? Does automation kill jobs or create them?

And the answer is&hellip yes. Both.

Daron Acemoglu: You know, once you continue the right line of reasoning that I was talking about earlier, you know, automation, robotics would be one part of it. But it's true for, you know, computerized numerical control, numerical control and other types of automation. Also, they substitute capital for workers, but much of that takes place at the lower end of the skill distribution. You're not replacing engineers you're not even replacing technicians. In fact, many of these automation technologies need more input from technicians, design workers, engineers. So it's really workers, middle-skilled workers and lower-skilled workers, that are being replaced. And that puts pressure on the wages of these workers and tends to contribute to inequality.

Brent Donaldson: If you remember earlier in this episode, Acemoglu said that with automation, capital replaces labor. Or at least, some labor. It also demands other types of labor. And as anyone in manufacturing can attest, there is not enough of supply of this new type of labor. That is a problem. We are building more advanced pieces of automation faster than we can adapt people&rsquos skills to run that automation. Our wages reflect this. People who can program and run this kind of automation are in demand. Thankfully &mdash and this is the point we&rsquoll finish on &mdash there are examples of how we can meet that demand.

Daron Acemoglu: Well, I think it's complicated with skilled workers. We have some other work which shows that for other countries, and this will happen for the U.S. later on also, that are in the midst of rapid demographic change. Automation is a very powerful tool. So if you look at Germany, South Korea, Japan, three of the most rapidly ageing economies, they are facing acute shortages of middle-age workers, and those are the workers that typically focus on blue collar occupations in manufacturing. Without any response from technology, manufacturing in these economies would have been heavily damaged. But Japan, South Korea and Germany have increased their share of international trade in manufacturing. They have continued to make inroads in manufacturing growth. How did they do that? They did that by responding massively to their demographic trends and investing in automation and, in fact, becoming leaders in the technologies of automation in the world.

Now, that's less easy with skills. Because so far, the automation technologies that we have, don't perform the tasks that skilled workers used to perform. So when U.S. workers, we saw US employers complain about lack of skills. They're really asking for workers with high technical capacity, the ability to flexibly switch from one task to the other, some knowledge of engineering. And all of these are tasks that at the moment, machines cannot perform. You know, some people think AI just around the corner is going to start doing what robotics did to middle-skilled blue-collar occupations in manufacturing. That AI is going to do this for higher skilled workers in, you know, finance and accounting and medicine and so on. We'll see about that. I think it's optimistic to think that's just around the corner. But, manufacturing automation is not really is not really going to be able to replace the very trained highly skilled engineering design and technical workers.

Brent Donaldson: So let&rsquos wrap this up. There are high-skill and low-skill employees in manufacturing. Or maybe a better way to say it is high-expertise versus repetitive, even though some of that repetitive work is pretty high-knowledge. But because of this difference, automation actually needs more employees at the higher end.

And Pete, you and I see all the time when we write about machining: The machine shop owner who says he would buy another CNC machine tool if only he could find another skilled machinist or programmer to run it. This is the &ldquoskills gap&rdquo we talk about all the time. On the one hand, automation displaces and replaces jobs. On the other hand, it needs more jobs. It can&rsquot perform if the right people with the right expertise aren&rsquot there.

Pete Zelinski: Right. At the beginning of this episode, we asked to what extent are jobs sort of &ldquothe point&rdquo of manufacturing. On one hand the point of manufacturing is to make the stuff that humans want and require.

So&hellip if jobs are not the point of manufacturing, why do we worry so much about how many people are employed in manufacturing? Why does the graph that looks like a literal cliff where jobs plummeted from in the early 2000s concern us?

Clearly, jobs are not beside the point. The issue is more complex than that.

Manufacturing is necessary work, work that to some extent defines the health of a nation, manufacturing is valuable work, including valuable work for people to do, because it is work that directly involves value creation by making objects people want or need. Nd manufacturing is work that, even when it is automated, needs people. We are doing this podcast because manufacturing is worth understanding&hellip because it is worth encouraging. And Americans want our manufacturing to be &ldquoMade in the USA&rdquo because it&rsquos work we ought to want our country and people in our communities to be doing.

But the commitment to manufacturing means a commitment to preparing people for manufacturing even as we advance manufacturing so every person in it can do more and more. Companies need to be involved in this. The public sector and the school system need to be involved in this. The question that we, as a country, can answer is: will automation harm communities by costing jobs, even though it struggles to deliver its full promise, or will automation thrive because we have the people who are ready to adapt to the new roles able to oversee this automation?

Brent Donaldson: Made in the USA is a production of Modern Machine Shop and published by Gardner Business Media. The series is written and produced by me and by Peter Zelinski. I edit the show.

This podcast was recorded at the historic Herzog Studio, home of the non-profit Cincinnati USA Music Heritage Foundation. Our outro theme song is by The Hiders.

If you enjoyed this episode, please leave us a nice review. If you have comments or questions, email us at [email protected] dot com. Or check us out at

For our next episode we wanted to really beat ourselves up and focus on a topic that is easily as complex as automation. So, we&rsquore going sort out the benefits and challenges of keeping production in the United States. A look into our manufacturing supply chains, next time on Made in the USA.


Automated From The Start

Turning automation helps this shop produce parts more efficiently.

Is Robotic Automation the Key to Drawing (and Keeping) New Employees?

Robotic automation is transforming a job that was perhaps a machine operator’s least-favorite work assignment into one that is not a heavy lift.  

Choosing The Right Bar Feeder

Take a look at some of the options, and find out how some shops make their decisions.

5. Optimizing Performance

Every company would like to have their enterprise perform like a thoroughbred. In reality, it is more likely to be overburdened with work. Even though advancements in computers make them faster and less expensive every year, the demands on them always catch up and eventually exceed the level of capability that a company’s computer infrastructure possesses. That leaves a lot of companies wanting to improve their system performance.

Two options to improve performance are to upgrade hardware or purchase a newer system—both expensive choices. It’s also possible to tune a system for better performance, but this takes a highly skilled person who is not normally available 24 hours a day. And, once a system is tuned for a specific workload, if the workload changes, the settings are no longer optimum.

The Case for Expanding Trade Adjustment Assistance

Our Most Generous Program for Dislocated Workers

Since the 1960s, Congress liberalized trade with the goal of boosting the economy, knowing that certain groups of workers would be negatively impacted. TAA was meant to cushion these negative impacts, helping redeploy human capital to a changing economy all while bolstering public support for trade. 66 Created in 1962, Trade Adjustment provides federal support both for tuition for retraining, extended income support so workers can provide for themselves and their families while they retrain, and an increasing array of reemployment options. Significant expansions to the program were made in the Trade Act of 2002 and the American Recovery Reinvestment Act in 2009, but then narrowed in 2011. 67 The program was most recently reauthorized in 2015 through 2021. The current benefits provided by TAA include:

  • Trade readjustment (TRA) benefits: TRA benefits provided extended income support beyond what is provided by unemployment insurance. TAA qualifies workers for 104 additional weeks of payments (at the same level) beyond what UI provides. Workers can only receive the full TRA allotment if they are in a retraining program, but can get a waiver of training in limited circumstances for the first twenty-six weeks of assistance. 68
  • Retraining: TAA pays for a wide variety of training programs, including post-secondary education, classroom training, apprenticeship, and customized training, as well as remedial education like language classes for workers for whom English is not their first language. The average per-participant spending on training in TAA is $11,000. That’s far greater than the average short-term training provided by the WIOA dislocated worker services, which is just $2,861 per participant. 69
  • Continued health care benefits: TAA recipients can maintain their employer-based health insurance through the health care tax credit (HCTC), which covers 72.5 percent of a family’s premiums. Like the credits provided by Affordable Care Act, the HCTC is paid each month directly to insurance companies. 70
  • Wage insurance: TAA provides wage insurance, known as Reemployment Trade Adjustment Assistance (RTAA). RTAA recipients receive up to $10,000 over a two-year period. RTAA payments are equal to half of the difference between a TAA recipient’s pre-layoff salary and their new job. Only workers earning $50,000 or less in their new jobs are eligible for RTAA. 71
  • Relocation and job search allowances: TAA recipients can receive up to 90 percent of the expenses of relocating outside of their community in order to secure a good-paying job, up to a maximum of $1,250.
  • Case management and reemployment services: All TAA recipients are eligible for job counseling and case management, including assessments, development of an individualized employment plan, career counseling, and referrals to supportive services like child care.

Eligibility for TAA benefits is limited to workers employed at a firm that is trade-impacted. Each group of workers must petition for eligibility: petitions can be filed by the company, a union, or any group of three workers on behalf of a firm or subdivision of that firm. To prove that trade is a primary cause of their job loss, they must demonstrate one of the following:

  • An increase in competitive imports and a decrease in sales of the petitioning company in a narrowly defined similar good or service
  • A shift in production to a foreign country, including moving of production overseas
  • The U.S. International Trade Commission has found that the firm was a victim of unfair trade or
  • That they have been laid off from a firm that supplies a TAA-certified firm.

The Department of Labor investigates petitions and makes determinations on them. A typical petition is reviewed and decided within fifty days of receipt. 72 Workers have twenty-six weeks after the petition is certified or after the date of the “adverse impact” (layoff or plant closing) to begin services.

TAA has evolved since its initial passage in 1962 to include a comprehensive set of services recommended by experts and based on international experiences. A lack of income support is one of the main reasons unemployed workers cannot complete training. 73 The basic twenty-six weeks of unemployment benefits are not enough time for most workers to find, enroll, and complete a meaningful training course. TAA allows for a wide variety of training options, spanning classroom training to apprenticeship—and it is one of the only retraining programs that would provide long enough retraining for a dislocated worker to claim a post-secondary credential. Unemployment rates remain far lower for workers with college degrees than for those without. 74

Starting with 2002 reform legislation, TAA has been expanded to include services beyond retraining. Most dislocated workers take a pay cut when they are re-employed, and wage insurance compensates them for part of that earnings loss. While wage insurance is not a silver bullet for the major challenges of long-term unemployment, this option is particularly relevant for certain workers who may be less motivated to pursue extended retraining programs. Older workers are one such population. RTAA has slowly increased as part of the TAA program, with 12 percent of participants receiving benefits. In addition, many commentators have noted that globalization has increased geographic inequality, and that workers should have the option of relocating to a new community that has greater employment options TAA now offers such help. The combination of services that TAA provides more than earn its reputation as a Cadillac program. 75

Doomsday Or Development?

Past innovations automated routine, menial work. Displaced workers could (and typically did) simply educate and re-skill themselves back into employability. In the past, they had the time to do it.

In contrast, AI’s looming threat is to replace high-level, judgment-based skill-sets, such as complex analysis, discretionary decision-making and even creative ideation. Robotics will replace many of the services and manufacturing positions. What kind of jobs will be left for people to do?

This is a pressing concern without clear answers, but doomsdayers neglect a crucial fact: Investments cannot capitalize on AI’s gains in the absence of human consumption. I believe AI will only be meaningful if humans are able to capture the benefits of AI technology.

The McKinsey Global Institute recently observed that the only way to realize the productivity dividends of AI will be to have people in place to capture them. Managing this transition will be a competitive imperative.

In other words, all jobs will not disappear — they will undergo a significant metamorphosis.

Preparing for the Future of AI

Helpful or Homicidal: The Fantastical Possibilities of Artificial General Intelligence

Speaking at London’s Westminster Abbey in late November of 2018, internationally renowned AI expert Stuart Russell joked (or not) about his “formal agreement with journalists that I won’t talk to them unless they agree not to put a Terminator robot in the article.” His quip revealed an obvious contempt for Hollywood representations of far-future AI, which tend toward the overwrought and apocalyptic. What Russell referred to as “human-level AI,” also known as artificial general intelligence, has long been fodder for fantasy. But the chances of its being realized anytime soon, or at all, are pretty slim. The machines almost certainly won’t rise (sorry, Dr. Russell) during the lifetime of anyone reading this story.

Hollywood representations of far-future AI tend toward the overwrought and apocalyptic, which many experts disdain as fantasy. Human-level AI will require major breakthroughs and when/if that happens the positive and negative ramifications will be far more complex than what is presented in science fiction movies. | Photo Credit: Shutterstock

“There are still major breakthroughs that have to happen before we reach anything that resembles human-level AI,” Russell explained. “One example is the ability to really understand the content of language so we can translate between languages using machines… When humans do machine translation, they understand the content and then express it. And right now machines are not very good at understanding the content of language. If that goal is reached, we would have systems that could then read and understand everything the human race has ever written, and this is something that a human being can't do. Once we have that capability, you could then query all of human knowledge and it would be able to synthesize and integrate and answer questions that no human being has ever been able to answer because they haven't read and been able to put together and join the dots between things that have remained separate throughout history.”

That’s a mouthful. And a mind full. On the subject of which, emulating the human brain is exceedingly difficult and yet another reason for AGI’s still-hypothetical future. Longtime University of Michigan engineering and computer science professor John Laird has conducted research in the field for several decades.

“The goal has always been to try to build what we call the cognitive architecture, what we think is innate to an intelligence system,” he says of work that’s largely inspired by human psychology. “One of the things we know, for example, is the human brain is not really just a homogenous set of neurons. There’s a real structure in terms of different components, some of which are associated with knowledge about how to do things in the world.”

Aaron Mininger, a graduate student in the University of Michigan Computer SCience and Engineering Department, teaches a robot a task using natural language to convey instructions. | Photo Credit: University of Michigan/John Laird

That’s called procedural memory. Then there’s knowledge based on general facts, a.k.a. semantic memory, as well as knowledge about previous experiences (or personal facts) that’s called episodic memory. One of the projects at Laird’s lab involves using natural language instructions to teach a robot simple games like Tic-Tac-Toe and puzzles. Those instructions typically involve a description of the goal, a rundown of legal moves and failure situations. The robot internalizes those directives and uses them to plan its actions. As ever, though, breakthroughs are slow to come — slower, anyway, than Laird and his fellow researchers would like.

“Every time we make progress,” he says, “we also get a new appreciation for how hard it is.”

Is AGI Really an Existential Threat to Humanity?

More than a few leading AI figures subscribe (some more hyperbolically than others) to a nightmare scenario that involves what’s known as “singularity,” whereby superintelligent machines take over and permanently alter human existence through enslavement or eradication.

The late theoretical physicist Stephen Hawking famously postulated that if AI itself begins designing better AI than human programmers, the result could be “machines whose intelligence exceeds ours by more than ours exceeds that of snails.” Elon Musk believes and has for years warned that AGI is humanity’s biggest existential threat. Efforts to bring it about, he has said, are like “summoning the demon.” He has even expressed concern that his pal, Google co-founder and Alphabet CEO Larry Page, could accidentally shepherd something “evil” into existence despite his best intentions. Say, for example, “a fleet of artificial intelligence-enhanced robots capable of destroying mankind.” (Musk, you might know, has a flair for the dramatic.) Even IFM’s Gyongyosi, no alarmist when it comes to AI predictions, rules nothing out. At some point, he says, humans will no longer need to train systems they’ll learn and evolve on their own.

“I don’t think the methods we use currently in these areas will lead to machines that decide to kill us,” he says. “I think that maybe five or ten years from now, I’ll have to reevaluate that statement because we’ll have different methods available and different ways to go about these things.”

While murderous machines may well remain fodder for fiction, many believe they’ll supplant humans in various ways.

Last spring, Oxford University’s Future of Humanity Institute published the results of an AI survey. Titled When Will AI Exceed Human Performance? Evidence from AI Experts, it contains estimates from 352 machine learning researchers about AI’s evolution in years to come. There were lots of optimists in this group. By 2026, a median number of respondents said, machines will be capable of writing school essays by 2027 self-driving trucks will render drivers unnecessary by 2031 AI will outperform humans in the retail sector by 2049 AI could be the next Stephen King and by 2053 the next Charlie Teo. The slightly jarring capper: by 2137, all human jobs will be automated. But what of humans themselves? Sipping umbrella drinks served by droids, no doubt.

Diego Klabjan, a professor at Northwestern University and founding director of the school’s Master of Science in Analytics program, counts himself an AGI skeptic.

“Currently, computers can handle a little more than 10,000 words,” he explains. “So, a few million neurons. But human brains have billions of neurons that are connected in a very intriguing and complex way, and the current state-of-the-art [technology] is just straightforward connections following very easy patterns. So going from a few million neurons to billions of neurons with current hardware and software technologies — I don't see that happening.”

Some experts believe the real threat from AI isn't malice, but machines being fed faulty incentives by nefarious humans. In other words, if the robots invade, it will likely be because someone directed them to, not because they decided it was a good idea. | Photo Credit: Shutterstock

War Robots & Nefarious Motives: How Humans Might Use AGI Is the Real Threat

Klabjan also puts little stock in extreme scenarios — the type involving, say, murderous cyborgs that turn the earth into a smoldering hellscape. He’s much more concerned with machines — war robots, for instance — being fed faulty “incentives” by nefarious humans. As MIT physics professors and leading AI researcher Max Tegmark put it in a 2018 TED Talk, “The real threat from AI isn’t malice, like in silly Hollywood movies, but competence — AI accomplishing goals that just aren’t aligned with ours.” That’s Laird’s take, too.

“I definitely don’t see the scenario where something wakes up and decides it wants to take over the world,” he says. “I think that’s science fiction and not the way it’s going to play out.”

What Laird worries most about isn’t evil AI, per se, but “evil humans using AI as a sort of false force multiplier” for things like bank robbery and credit card fraud, among many other crimes. And so, while he’s often frustrated with the pace of progress, AI’s slow burn may actually be a blessing.

“Time to understand what we’re creating and how we’re going to incorporate it into society,” Laird says, “might be exactly what we need.”

But no one knows for sure.

“There are several major breakthroughs that have to occur, and those could come very quickly,” Russell said during his Westminster talk. Referencing the rapid transformational effect of nuclear fission (atom splitting) by British physicist Ernest Rutherford in 1917, he added, “It’s very, very hard to predict when these conceptual breakthroughs are going to happen.”

But whenever they do, if they do, he emphasized the importance of preparation. That means starting or continuing discussions about the ethical use of A.G.I. and whether it should be regulated. That means working to eliminate data bias, which has a corrupting effect on algorithms and is currently a fat fly in the AI ointment. That means working to invent and augment security measures capable of keeping the technology in check. And it means having the humility to realize that just because we can doesn’t mean we should.

“Our situation with technology is complicated, but the big picture is rather simple,” Tegmark said during his TED Talk. “Most AGI researchers expect AGI within decades, and if we just bumble into this unprepared, it will probably be the biggest mistake in human history. It could enable brutal global dictatorship with unprecedented inequality, surveillance, suffering and maybe even human extinction. But if we steer carefully, we could end up in a fantastic future where everybody’s better off—the poor are richer, the rich are richer, everybody’s healthy and free to live out their dreams.”

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