Artificial Intelligence, Automation and Work Daron Acemoglu , Massachusetts Institute of Technology Pascual Restrepo , Boston University View Abstract

Download Preview (PDF, 339.21 KB) Abstract We summarize a framework for the study of the implications of automation and

AI on the demand for labor, wages, and employment. Our task-based framework

emphasizes the displacement effect that automation creates as machines and

AI replace labor in tasks that it used to perform. This displacement effect

tends to reduce the demand for labor and wages. But it is counteracted by a

productivity effect, resulting from the cost savings generated by

automation, which increase the demand for labor in non-automated tasks. The

productivity effect is complemented by additional capital accumulation and

the deepening of automation (improvements of existing machinery), both of

which further increase the demand for labor. These countervailing effects

are incomplete. Even when they are strong, automation increases output per

worker more than wages and reduce the share of labor in national income. The

more powerful countervailing force against automation is the creation of new

labor-intensive tasks, which reinstates labor in new activities and tends to

increase the labor share to counterbalance the impact of automation. Our

framework also highlights the constraints and imperfections that slow down

the adjustment of the economy and the labor market to automation and weaken

the resulting productivity gains from this transformation: a mismatch

between the skill requirements of new technologies, and the possibility that

automation is being introduced at an excessive rate, possibly at the expense

of other productivity-enhancing technologies.

What Can Machines Learn, and What Does It Mean for the Occupations and Industries? Erik Brynjolfsson , Massachusetts Institute of Technology Tom Mitchell , Carnegie Mellon University Daniel Rock , Massachusetts Institute of Technology View Abstract Abstract The increased availability of high quality data and rapid advances in machine learning (ML) algorithms have the potential to generate significant economic value in the coming decade. Yet recent estimates suggest that median wage stagnation is in part due to increased automation of routine information processing work and the use of robots has been linked to declines wages and employment for factory workers, raising questions about the distributional effects of more widespread use of ML in the economy. We develop a model in which firms adopt ML and compete with heterogeneous inputs. In this model, some production inputs are complementary to ML while others can be substituted. Worker, firms and industries with complementary investments (e.g. relevant skills, large databases) are well-positioned to grow their value with ML, but the digital nature of ML investments makes these industries susceptible to superstar effects and increased concentration. We develop a taxonomy of task suitable for ML and estimate some implications of our model by applying natural language processing techniques to data from a major online job postings site. We use our taxonomy to predict the adoption of ML applications for skills, jobs, firms, industries, and regions. The potential of ML is widespread, though the current the distribution of ML value is very uneven. Once a task is learned once within a firm, it can be rapidly scaled and applied across the company at near zero marginal cost. Employment at the industry level is affected by changes in the production function as well as changes in market power, with higher value accruing individuals and firms that control essential complements for ML use.

Linking Advances in Artificial Intelligence to Skills, Occupations, and Industries Rob Seamans , New York University Edward W. Felten , Princeton University View Abstract

Download Preview (PDF, 549.93 KB) Abstract Prior episodes of automation have led to economic growth and also to many changes in the workplace. In some cases automation has substituted for labor and in other cases automation has complemented labor. We expect that artificial intelligence (AI) will boost economic growth while affecting labor in different ways. The link between AI and labor is complex, however. Our paper provides a method that we believe can help researchers and policy makers to better understand the link between AI and labor. We also demonstrate the method in several applications, including predicting which occupation descriptions will change the most due to advances in AI.