Ubiquitous computing, of course, is not the first technology to have effects on jobs. From steam engines to robotic welders and ATMs, technology has long displaced humans—often creating new and higher-skill jobs in its wake. The invention of the automobile threw blacksmiths out of work, but created far more jobs building and selling cars. Over the past 30 years the digital revolution has displaced many of the middle-skill jobs that underpinned twentieth-century middle- class life. The number of typists, travel agencies, bank tellers, and many production-line jobs has fallen dramatically, but there are ever more computer programmers and web designers. Displaced workers with obsolete skills are always hurt, but the total number of jobs has never declined over time (Aeppel 2015).

Paradoxically, although productivity—a crucial indicator of growth and wealth creation—is at record levels, and innovation has never been greater, over the past several decades, median wages have not risen (Galston 2014). This pattern is inconsistent with economic theory, which holds that when productivity increases, any automation that economizes on the use of labor will increase incomes. That will generate demand for new products and services, which, in turn, will create new jobs for displaced workers (The Economist 2014). One explanation for this inconsistency is that advances in information and communications technology are destroying more jobs in developed economies than the advances are creating. In short, technological progress is eliminating the need for many types of jobs, and leaving the typical worker worse off than before (Brynjolfsson & McAfee 2014, Rotman 2013).

Not everyone concurs with this conclusion, however (e.g., Jacoby 2015). Although labor economists generally agree that the digital revolution is creating a great divide between a skilled and wealthy few and the rest of society, hollowing out the middle class (Autor & Dorn 2013), it is not clear whether this can be attributed entirely to the effects of technology, and the data are, at best, far from conclusive. One reflection of this change is the simultaneous increase in both job openings and unemployment relative to the early 2000s (Elsby et al. 2010). This suggests that the types of skills now demanded by employers do not match those of the existing labor force (Katz 2010). 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. The problem is that it is difficult to separate the effects of technology from other macroeconomic effects (Rotman 2013).

The advent of machine learning, in which computers teach themselves tasks and rules by an- alyzing large sets of data (The Economist 2015a) will surely lead to large-scale worker dislocation as areas such as speech recognition, pattern recognition, and image classification eliminate wide swaths of white-collar workers (The Economist 2015b). We agree that many jobs currently per- formed by humans will be substantially taken over by robots or digital agents by 2025. Other jobs will disappear as a result of structural changes in the economy, such as the long-term drop in the demand for coal, as cleaner sources of energy become more popular.

Even if today’s information and communication technologies are holding down employment, however, history suggests it is a temporary, although painful, shock. As workers adjust their skills and entrepreneurs create opportunities based on the new technologies, the number of jobs will rebound. At the same time, we believe that human ingenuity will create new jobs, industries, and ways to make a living, just as it has been doing since the Industrial Revolution (Mabry & Sharplin 1986, Smith & Anderson 2014; see also Bessen 2015 and Stiglitz & Greenwald 2014).

What about the demand for managers and executives? Unlike effective managers, machines have not yet learned to tolerate high levels of ambiguity or to inspire people at every level in organizations. Consider ambiguity. The bigger and broader the question to be addressed, the

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