Looking at the future, we humans typically overestimate the pace of change in the short run and underestimate it in the long run. So what do these disparate data points have in common?

  • In the U.S. law school enrollments are shrinking, as tuition and entry level wages fall.
  • Last year the Associated Press assigned all the writing of its standard corporate earnings announcements to computers.
  • GE now has over 1100 engineers to Silicon Valley doing predictive data analytics and gets 2/3 of the value of its $250 billion order backlog from its intellectual property.
  • Since 2011, each year the American Air Force has been training more “unmanned system operators” (drones) than fighter and bomber pilots combined.
  • Starting around 2000, the gap between labor’s share of US GDP and the share dedicated to capital has soared to a record high.

They suggest the long run is here.

Driven by the steady annual doubling of computer processing power, advances in natural language and perception technology, the rise of mobile always-on interconnectivity, and, most important, an accelerating shift from machines that must be programmed to machines that crunch data and “learn,” every day in every way we are rapidly moving into a world where we will be surrounded by technology that is better than us at an ever wider range of ever more sophisticated tasks. In such a world, what is the role for managers/leaders and what is the role for machines? How will that shift the balance in what we do at work? How will it change our organizations, and re-shape policy making and urban planning?

There are far more questions than answers. But this is the time to begin the discussion – because it’s happening, whether we like it or not!