powerful room-cleaning robot was a Roomba, which moved around vacuuming at random and squeaked when it got caught under the couch. Technological prediction is particularly chancy, given that technologies progress by a series of refinements, halted by obstacles and overcome by innovation. Many obstacles and some innovations can be anticipated, but more cannot. In my own work with experimentalists on building quantum computers, I typically find that some of the technological steps I expect to be easy turn out to be impossible, whereas some of the tasks I imagine to be impossible turn out to be easy. You don’t know until you try. In the 1950s, partly inspired by conversations with Wiener, John von Neumann introduced the notion of the “technological singularity.” Technologies tend to improve exponentially, doubling in power or sensitivity over some interval of time. (For example, since 1950, computer technologies have been doubling in power roughly every two years, an observation enshrined as Moore’s Law.) Von Neumann extrapolated from the observed exponential rate of technological improvement to predict that “technological progress will become incomprehensively rapid and complicated,” outstripping human capabilities in the not too distant future. Indeed, if one extrapolates the growth of raw computing power—expressed in terms of bits and bit flips—into the future at its current rate, computers should match human brains sometime in the next two to four decades (depending on how one estimates the information-processing power of human brains). The failure of the initial overly optimistic predictions of AI dampened talk about the technological singularity for a few decades, but since the 2005 publication of Ray Kurzweil’s The Singularity is Near, the idea of technological advance leading to superintelligence is back in force. Some believers, Kurzweil included, regard this singularity as an opportunity: Humans can merge their brains with the superintelligence and thereby