AI News, Robots won't kill the workforce. They'll save the global economy.

Robots won't kill the workforce. They'll save the global economy.

The United Nations forecasts that the global population will rise from 7.3 billion to nearly 10 billion by 2050, a big number that often prompts warnings about overpopulation.

Still others inspire a chorus of neo-Luddites, who fear that the “rise of the robots” is rapidly making human workers obsolete, a threat all the more alarming if the human population is exploding.

At the same time, owing to rapid advances in health care and medicine, people are living longer , and most of the coming global population increase will be among the retirement crowd.

One simple way to estimate how fast an economy can grow is by adding working-age population growth and productivity growth: If the number of workers and output per worker are both increasing by 1 percent a year, then economic output should rise by roughly 2 percent.

In the United States, productivity growth has fallen by almost half from its postwar average, but growth in the labor force has slid even faster, dropping by two-thirds to an average pace of 0.5 percent, according to calculations performed for my book.

Studies by Evercore ISI, a research firm, show that the elderly share of the population is rising more than twice as fast as it did in the United States and more than four times faster than in France at similar stages of development.

If older generations created tools for use by humans, such as sewing machines, the new forms of automation are imbued with artificial intelligence, capable of “machine learning” and of rapidly replacing humans in a broad swath of jobs, from manufacturing to services — even jobs that involve writing about robots.

If automation was displacing human workers as fast as implied in recent books like Martin Ford’s “The Rise of the Robots,” then we should be seeing a negative impact on jobs already.

In the Group of Seven, the world’s top industrial countries, unemployment has fallen faster than expected in the face of weak economic growth, and faster than in any comparable period since at least the 1970s.

In the postwar era, countries like China escaped poverty by moving a rising young population off the farm and into more productive jobs in factories.

Starting in the 1980s, led by Singapore, nations from Chile to Australia have offered baby bonuses for women to have more children, but many have found that these bonuses are ineffective in the face of stronger cultural forces, including the desire of many women to pursue a career before having children.

Others have tried with some success to boost the workforce directly by raising the retirement age, offering women incentives to join or return to the labor force after having kids, and opening doors to immigrant workers.

The simple math, however, shows that particularly in rapidly aging, conservative societies such as Japan and Germany, none of these groups has the potential to make up for coming declines in the working-age population.

The Countries Most (and Least) Likely to be Affected by Automation

As we’vedescribed previously, our focus is on individual work activities, which we believe to be a more useful way to examine automation potential than looking at entire jobs, since most occupations consist of a number of activities with differing potential to be automated.

Japanese manufacturing has a slightly larger concentration of work hours in production jobs (54% of hours versus the U.S.’s 50%) and office and administrative support jobs (16% versus 9%).

By comparison, the United States has a higher proportion of work hours in management, architecture, and engineering jobs, which have a lower automation potential since they require application of specific expertise such as high-value engineering, which computers and robots currently are not able to do.

On a global level, four economies — China, India, Japan, and the United States — dominate the total, accounting for just over half of the wages and almost two-thirds the number of employees associated with activities that are technically automatable by adapting demonstrated technologies.

Some hardware solutions require significant capital expenditures and could be adopted faster in advanced economies than in emerging ones with lower wage levels, where it will be harder to make a business case for adoption because of low wages.

The pace of adoption will also depend on the benefits that countries expectautomation tobring for things other than labor substitution, such as the potential to enhance productivity, raise throughput, and improve accuracy and regulatory and social acceptance.

Considering the labor substitution effect alone, we calculate that, by 2065, theproductivity growth that automation could add tothe largest economies in the world (G19 plus Nigeria) is the equivalent of an additional 1.1 billion to 2.2 billion full-time workers.

They will need to find ways to embrace the opportunity for their economies to benefit from the productivity growth potential that automation offers, putting in place policies to encourage investment and market incentives to encourage innovation.

Why automation could be a threat to India's growth

Ravi is one of thousands of Indian IT workers who will lose their jobs this year, caught between a slump in India’s previously booming IT industry and new technology threatening to replace human workers.

Market volatility and rising protectionism in countries like the USA, where much of India’s IT outsourcing work comes from, saw Cognizant’s revenue grow at its slowest pace in two decades last year, and its peers in the Indian IT industry are in the same boat.

the very tasks global companies originally outsourced to India But at the same time, rapidly improving automation technology is allowing software to carry out routine IT support work and repetitive back office tasks previously performed by humans –

Meanwhile, India’s third-largest IT firm, Infosys, said automation allowed it to shift 9,000 workers from low-skill jobs to more advanced projects, like machine learning and artificial intelligence, last year.

“New machines and technologies are about helping cut costs, improve efficiencies, and increase sophistication in building and delivering services.

He’s now job hunting, but says opportunities for the kind of work he was doing before are limited and he will probably have to adapt: take a course on automated software testing and then try to secure a position.

Since the 1990s Indian firms have carried out back office tasks, and IT services like data entry, running call centres and testing software for foreign companies at cut-price rates by throwing cheap labour at them.

stable job at one of the big IT companies is a major aspiration for many Indians, which probably explains why fears of technological unemployment have featured so prominently in newspapers here in recent months.

But despite contributing 9.3% of the country’s GDP, according to Nasscom, the IT industry only employs 3.7 million of the nation’s roughly half a billion working adults.

Its working-age population increased by 300 million between 1991 and 2013, according to UN figures, but the number of people employed only rose by 140 million.

His facility isn’t in danger of automation, but he knows union leaders at Hyundai’s plant where the entire body shop and most of the paint shop was automated.

Mohandas Pai, former CFO of Infosys, says it is unlikely to impact high-skill jobs like architects or high-quality coders, or even lower-end jobs in the service industry like restaurant staff and hairdressers.

As technology streamlines routine tasks, middle-skill jobs like clerical workers and machine operators decline while both high-skill and low-skill ones increase.

But pulling away “the ladder to the middle class”, as the report puts it, could be particularly damaging in a developing country like India.

Rural Indians come looking for better earnings, but onerous targets, low wages and urban living costs mean they rarely last long.

Pai says India has the luxury of time compared to developed countries, as labour will remain cheaper than automation for a decade, and huge unmet demand for infrastructure and services can produce lots of jobs.

Future-proofing jobs from robots With automation taking on the routine tasks at the heart of today’s workplace, the jobs of the future will focus on skills like critical thinking, collaboration and creativity.

The jobs of the future will focus on skills like critical thinking, collaboration and creativity India’s education system has a reputation for learning by rote and Indian Institute of Technology Madras engineering professor Ashok Jhunjhunwala agrees most institutions aren’t adequately preparing young people.

He leads a government-sponsored pilot where professors from leading colleges use virtual labs to teach students at struggling engineering colleges.

IBM’s Raghavan says smart machines that automatically analyse students' performance and preferences can help guide them through this fast-moving terrain by combining data on their skills with job opportunities and available courses.

a programme providing LinkedIn training, with progress automatically added to profiles so companies can shortlist candidates, as well as personalised job recommendations.

How Technology Is Destroying Jobs

­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.

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 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).

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.

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.

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.

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.

“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).

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.

“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;

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.

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?

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.

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.

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.

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.

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.

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.

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.

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.

Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages

In an era marked by rapid advances in automation and artificial intelligence, new research assesses the jobs lost and jobs gained under different scenarios through 2030.

Building on our January 2017 report on automation, McKinsey Global Institute’s latest report, Jobs lost, jobs gained: Workforce transitions in a time of automation (PDF–5MB), assesses the number and types of jobs that might be created under different scenarios through 2030 and compares that to the jobs that could be lost to automation.

Our key finding is that while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging—matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.

Other factors include the cost of developing and deploying automation solutions for specific uses in the workplace, the labor-market dynamics (including quality and quantity of labor and associated wages), the benefits of automation beyond labor substitution, and regulatory and social acceptance.

Taking these factors into account, our new research estimates that between almost zero and 30 percent of the hours worked globally could be automated by 2030, depending on the speed of adoption.

Jobs in unpredictable environments—occupations such as gardeners, plumbers, or providers of child- and eldercare—will also generally see less automation by 2030, because they are technically difficult to automate and often command relatively lower wages, which makes automation a less attractive business proposition.

Globally, we estimate that 250 million to 280 million new jobs could be created from the impact of rising incomes on consumer goods alone, with up to an additional 50 million to 85 million jobs generated from higher health and education spending.

This will create significant new demand for a range of occupations, including doctors, nurses, and health technicians but also home-health aides, personal-care aides, and nursing assistants in many countries.

For the next three trends, we model both a trendline scenario and a step-up scenario that assumes additional investments in some areas, based on explicit choices by governments, business leaders, and individuals to create additional jobs.

Infrastructure and buildings are two areas of historic underspending that may create significant additional labor demand if action is taken to bridge infrastructure gaps and overcome housing shortages.

This so-called marketization of previously unpaid work is already prevalent in advanced economies, and rising female workforce participation worldwide could accelerate the trend.

We estimate that this could create 50 million to 90 million jobs globally, mainly in occupations such as childcare, early-childhood education, cleaning, cooking, and gardening.

When we look at the net changes in job growth across all countries, the categories with the highest percentage job growth net of automation include the following: The changes in net occupational growth or decline imply that a very large number of people may need to shift occupational categories and learn new skills in the years ahead.

We estimate that between 400 million and 800 million individuals could be displaced by automation and need to find new jobs by 2030 around the world, based on our midpoint and earliest (that is, the most rapid) automation adoption scenarios.

Of the total displaced, 75 million to 375 million may need to switch occupational categories and learn new skills, under our midpoint and earliest automation adoption scenarios;

In absolute terms, China faces the largest number of workers needing to switch occupations—up to 100 million if automation is adopted rapidly, or 12 percent of the 2030 workforce.

For advanced economies, the share of the workforce that may need to learn new skills and find work in new occupations is much higher: up to one-third of the 2030 workforce in the United States and Germany, and nearly half in Japan.

History would suggest that such fears may be unfounded: over time, labor markets adjust to changes in demand for workers from technological disruptions, although at times with depressed real wages (Exhibit 2).

We address this question about the future of work through two different sets of analyses: one based on modeling of a limited number of catalysts of new labor demand and automation described earlier, and one using a macroeconomic model of the economy that incorporates the dynamic interactions among variables.

Both analyses lead us to conclude that, with sufficient economic growth, innovation, and investment, there can be enough new job creation to offset the impact of automation, although in some advanced economies additional investments will be needed as per our step-up scenario to reduce the risk of job shortages.

However, low-wage countries may be affected as well, if companies adopt automation to boost quality, achieve tighter production control, move production closer to end consumers in high-wage countries, or other benefits beyond reducing labor costs.

It faces the combination of slower job creation coming from economic expansion and a large share of work that can be automated as a result of high wages and the structure of its economy.

Mexico’s projected rate of future economic expansion is more modest, and it could benefit from the job creation in the step-up scenario plus innovation in new occupations and activities to make full use of its workforce.

But automation also may raise labor productivity: firms adopt automation only when doing so enables them to produce more or higher-quality output with the same or fewer inputs (including material, energy, and labor inputs).

If displaced workers are able to be reemployed within one year, our model shows automation lifting the overall economy: full employment is maintained in both the short and long term, wages grow faster than in the baseline model, and productivity is higher.

In advanced economies, occupations that currently require only a secondary education or less see a net decline from automation, while those occupations requiring college degrees and higher grow.

In India and other emerging economies, we find higher labor demand for all education levels, with the largest number of new jobs in occupations requiring a secondary education, but the fastest rate of job growth will be for occupations currently requiring a college or advanced degree.

Although we do not model shifts in relative wages across occupations, the basic economics of labor supply and demand suggests that this should be the case for occupations in which labor demand declines.

Policy choices such as increasing investments in infrastructure, buildings, and energy transitions could help create additional demand for middle-wage jobs such as construction workers in advanced economies.

In many countries, this may require an initiative on the scale of the Marshall Plan, involving sustained investment, new training models, programs to ease worker transitions, income support, and collaboration between the public and private sectors.

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