Why this book
The authors' central historical parallel is deliberate: just as the steam engine and its successors kicked off a first machine age that automated physical labor and reshaped the arc of human development starting around 1765, digital technology, artificial intelligence, and robotics are now doing the equivalent for cognitive labor — tasks like driving, translation, medical diagnosis, and pattern recognition that recently seemed safely human-only. Their argument unfolds in three movements: first, that this shift is powered by three compounding forces — the exponential growth described by Moore's Law, the digitization of nearly everything into a form computers can process, and combinatorial innovation, where new technologies are built by recombining existing ones rather than starting from scratch; second, that the economic result is a tension they name the bounty and the spread — enormous, genuine gains in the volume, variety, and cheapness of goods and services, alongside growing inequality as returns increasingly flow to the highly skilled, to capital owners, and to a small number of superstar performers and firms; and third, a set of policy and personal recommendations — investing in education focused on distinctly human skills, revising tax and immigration policy, and, above all, learning to race with machines as collaborators rather than trying to compete against them head-on.
The book became a New York Times bestseller and one of the most influential general-audience treatments of automation's economic consequences, praised for weaving rigorous economics together with vivid technical examples — DARPA's early, failed self-driving car trials giving way within a decade to unremarkable autonomous vehicles, IBM's Watson demolishing champion Jeopardy players — into an accessible narrative. It's also, a decade on, a useful marker of how fast the underlying trend accelerated: written before the more recent wave of large language models, it nonetheless anticipated the shape of the disruption with real precision, even as some of its concrete technology examples have since been superseded by developments the authors didn't specifically foresee.
Who should read it
Best for professionals, managers, and policymakers who want a rigorous, example-rich framework for thinking about automation's economic effects rather than either breezy techno-optimism or blanket doom. It will frustrate readers hoping for a harder edge on the book's genuinely optimistic tone, which some reviewers have argued underweights risks like social unrest, privacy erosion, and the possibility that this time the new jobs don't fully replace the old ones.
About the author
Erik Brynjolfsson and Andrew McAfee are both economists and researchers affiliated with MIT, where they co-direct research on how digital technology is transforming business and the economy; this book followed their earlier, shorter work Race Against the Machine.