Have no fear as machines rise, just prepare well

Intelligent machines are transforming the way we produce, work, learn, and live throughout the world. Almost every aspect of our economies will be radically altered. Major logistics companies and individual drivers are using new technologies to optimise their route planning. Companies like BMW and Tesla have already released self-driving features in their automobiles, which are produced with the help of sophisticated robots. The Associated Press is using artificial intelligence to help write news stories. 3D printers are being used to produce replacement parts — for both machines and humans. AT&T, in collaboration with Udacity, is offering online “nanodegrees” in data analytics. Drones are delivering health supplies to remote locations in poor countries.

These marvelous new technologies promise higher productivity, greater efficiency, and more safety, flexibility, and convenience. But they are also stoking fears about their effects on jobs, skills, and wages. History and economic theory, however, suggest that anxieties about technological unemployment, a term coined by John Maynard Keynes nearly a century ago, are misplaced.

In the future, as in the past, technological change is likely to fuel productivity gains and income growth, boosting demand for labour. Add to that lower prices and rising quality, and demand for goods and services will also increase. Many of the jobs created cannot even be imagined today, just as few people a century ago could have anticipated that automobiles would give rise to drivethrough restaurants and roadside motels.

A new MGI report finds that under a moderate scenario for the speed and breadth of automation, about 15 per cent of the global workforce, or 400 million workers, could be displaced between now and 2030. A faster pace of automation would trigger greater displacement.

The good news is that as a result of projected increases in demand for goods and services — driven primarily by rising incomes, the growing healthcare needs of aging populations, and investment in infrastructure, energy efficiency, and renewables — new jobs are likely to be created to offset job losses. But the new jobs will differ mightily from the jobs displaced by automation, imposing painful transition costs on workers, businesses, and communities.

Depending on the pace of automation, 75-375 million workers, or 3-14 per cent of the global workforce, will need to change occupational categories by 2030. In the United States and other developed economies where automation is likely to occur more rapidly, 9-32 per cent of the workforce may need to change occupational categories and the skills associated with them.

In these countries, jobs in major occupational categories like production and office support, and jobs requiring a high school education or less, are likely to decline, while jobs in occupational categories like health and care provision, education, construction, and management, and jobs requiring a college or advanced degree, will increase.

So, what can be done to speed and ease the occupational transitions that automation will compel? For starters, fiscal and monetary policies to sustain full-employment levels of aggregate demand are critical. Policies to promote investment in infrastructure, housing, alternative energy, and care for the young and the aging can boost economic competitiveness and inclusive growth, while creating millions of jobs in occupations likely to be augmented, rather than displaced, by automation.

A second response must be a dramatic expansion and redesign of workforce training programmes. Over the past two decades, government outlays for skills training and labour-market adjustment have fallen in most OECD countries. That has been compounded in the US by a sizeable decline in business spending on training as well.

These trends must be reversed. Lifelong learning needs to become a reality. Jobs will change as machines take over some tasks, and human activities will require different skills. MGI’s analysis shows that higher-level cognitive abilities — such as logical reasoning, stronger communication skills, and enhanced social and emotional skills — will become more important, while machines take over routine capabilities common in the workplace today, including in cognitive tasks like data collection and processing.

Governments will need to offer universal and portable social benefits as well as transition support to workers who are forced to change jobs and employers frequently

For mid-career workers with children, mortgages, and other financial responsibilities, training that is measured in weeks and months, not in years, will be necessary, as will financial support to undertake such training. Sending people for two-year degrees at their own expense is not the answer.

Instead, nanodegrees and stackable credentials are likely to gain in importance. Tax and other incentives to encourage more business investment in workforce training, especially by small and medium-size companies, may be necessary. Governments will also need to offer universal and portable social benefits like healthcare, child care, and retirement security, as well as transition support, to workers who are forced to change jobs, occupations, and employers frequently. Like previous technologies, automation today promises major productivity gains, benefiting individuals, communities, and societies. But the path to an automated future could be difficult. It is up to us to make the policy and investment choices that can ease the transition, reduce its costs, and ensure that the income gains are equitably shared. —Project Syndicate Laura Tyson is a professor at the Haas School of Business at the University of

California, Berkeley. Susan Lund is a partner of McKinsey & Company.