A New York Times technology columnist’s timely, counterintuitive, and highly practical guide to success in the age of A.I. and automation.
The machines are here. After decades of sci-fi doomsaying and marketing hype, advanced A.I. and automation technologies have leapt out of research labs and Silicon Valley engineering departments and into the center of our lives. Robots once primarily threatened blue-collar manufacturing jobs, but today’s machines are being trained to do the work of lawyers, doctors, investment bankers, and other white-collar jobs previously considered safe from automation''s reach. The world’s biggest corporations are racing to automate jobs, and some experts predict that A.I could put millions of people out of work. Meanwhile, runaway algorithms have already changed the news we see, the politicians we elect, and the ways we interact with each other.
But all is not lost. With a little effort, we can become futureproof.
In Futureproof: 9 Rules for Machine-Age Humans, New York Times technology columnist Kevin Roose lays out an optimistic vision of how people can thrive in the machine age by rethinking their relationship with technology, and making themselves irreplaceably human.
In nine pragmatic, accessible lessons, Roose draws on interviews with leading technologists, trips to the A.I. frontier, and centuries’ worth of history to prepare readers to live, work, and thrive in the coming age of intelligent machines. He shares the secrets of people and organizations that have successfully survived technological change, including a nineteenth-century rope-maker and a Japanese auto worker, and explains how people, organizations, and communities can apply their lessons to safeguard their own futures.
The lessons include:
• Do work that is surprising, social, and scarce (the types of work machines can’t do)
• Break your phone addiction with the help of a rubber band
• Work in an office
• Treat A.I. like the office gorilla
• Resist “hustle porn” and efficiency culture and do less, slower
Roose’s examination of the future rejects the conventional wisdom that in order to compete with machines, we have to become more like them—hyper-efficient, data-driven, code-writing workhorses. Instead, he says, we should let machines be machines, and focus on doing the kinds of creative, inspiring, and meaningful work only humans can do.
