Anxiety about automation is prevalent in this era of rapid technological advances, especially in artificial intelligence (AI), machine learning (ML), and robotics. Accordingly, how human labor competes, or cooperates, with machines in performing a range of tasks (what we term "the race between human labor and machines") has attracted a great deal of attention among the public, policymakers, and researchers.14,15,18 While there have been persistent concerns about new technology and automation replacing human tasks at least since the Industrial Revolution,8 recent technological advances in executing sophisticated and complex tasks--enabled by a combinatorial innovation of new techniques and algorithms, advances in computational power, and exponential increases in data--differentiate the 21st century from previous ones.14 For instance, recent advances in autonomous self-driving cars demonstrate the way a wide range of human tasks that have been considered least susceptible to automation may no longer be safe from automation and computerization. Another case in point is human competition against machines, such as IBM's Watson on the TV game show "Jeopardy!" Both cases imply that some tasks, such as pattern recognition and information processing, are being rapidly computerized. Furthermore, recent studies suggest that robotics also plays a role in automating manual tasks and decreasing employment of low-wage workers.3,22

Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that productivity gains also make society wealthier and thereby contribute to additional labor demand. This analysis enables ethically-minded companies creating or deploying AI systems as well as researchers and policymakers to take into account the effects of their actions on labor markets and inequality, and therefore to steer progress in AI in a direction that advances shared prosperity and an inclusive economic future for all of humanity.

A lot of the conversation about the future of AI and automation focuses on the AGI endgame ("will humans still work when artificial general intelligence can do everything?"). But there are more interesting, tractable, and concrete questions to answer about the effects of "narrow," task-specific AI that looks more or less like what we have today. In the near future, we can expect more advanced robotics, autonomous cars, customer service chatbots, and other applications powered by such narrow AI to take over certain tasks from humans. Should we be optimistic about labor in the next 10-50 years, when parts of industries will be automated by narrow AI? What early signs of those trends should we be concerned about now?

Every day we hear claims that Artificial Intelligence (AI) systems are about to transform the economy, creating mass unemployment and vast monopolies. But what do professional economists think about this? Economists have been studying the relationship between technological change, productivity and employment since the beginning of the discipline with Adam Smith's pin factory. It should therefore not come as a surprise that AI systems able to behave appropriately in a growing number of situations - from driving cars to detecting tumours in medical scans - have caught their attention. In September 2017, a group of distinguished economists gathered in Toronto to set out a research agenda for the Economics of Artificial Intelligence (AI).

ML systems are very strong at learning empirical associations in data but are less effective when the task requires long chains of reasoning or complex planning that rely on common sense or background knowledge unknown to the computer. Ng's "one-second rule" (4) suggests that ML will do well on video games that require quick reaction and provide instantaneous feedback but less well on games where choosing the optimal action depends on remembering previous events distant in time and on unknown background knowledge about the world (e.g., knowing where in the room a newly introduced item is likely to be found) (12). Exceptions to this are games such as Go and chess, because these nonphysical games can be rapidly simulated with perfect accuracy, so that millions of perfectly self-labeled training examples can be automatically collected. However, in most real-world domains, we lack such perfect simulations.

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. The technology-driven world in which we live is a world filled with promise but also challenges. Cars that drive themselves, machines that read X-rays, and algorithms that respond to customer-service inquiries are all manifestations of powerful new forms of automation. Yet even as these technologies increase productivity and improve our lives, their use will substitute for some work activities humans currently perform--a development that has sparked much public concern. 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. The results reveal a rich mosaic of potential shifts in occupations in the years ahead, with important implications for workforce skills and wages. 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.