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“An economy of tech millionaires or billionaires and gig workers, with middle-income jobs undercut by automation, will not be politically sustainable.” “Whatever we mean by the economy growing, by things getting better, the gains will have to be more evenly shared than in the recent past,” she writes. In her new book Cogs and Monsters: What Economics Is, and What It Should Be, Diane Coyle, an economist at Cambridge University, argues that the digital economy requires new ways of thinking about progress. BacklashĪnger over AI’s role in exacerbating inequality could endanger the technology’s future. “I don’t think it’s an accident that we have so much emphasis on automation when the future of technology in this country is in the hands of a few companies like Google, Amazon, Facebook, Microsoft, and so on that have algorithmic automation as their business model,” he says. And, he says, AI researchers have “no compunction working on technologies that automate work at the expense of lots of people losing their jobs.”īut he reserves his strongest ire for Big Tech, citing data indicating that US and Chinese tech giants fund roughly two-thirds of AI work. While human labor is heavily taxed, there is no payroll tax on robots or automation. Government, AI scientists, and Big Tech are all guilty of making decisions that favor excessive automation, says Acemoglu. Acemoglu points to digital technologies that could allow nurses to diagnose illnesses more accurately or help teachers provide more personalized lessons to students. One reason is that companies are often choosing to deploy what he and his collaborator Pascual Restrepo call “so-so technologies,” which replace workers but do little to improve productivity or create new business opportunities.Īt the same time, businesses and researchers are largely ignoring the potential of AI technologies to expand the capabilities of workers while delivering better services. Early in the 20th century and during previous periods, shifts in technology typically produced more good new jobs than they destroyed, but that no longer seems to be the case. And Acemoglu worries that AI-based automation will make matters even worse. That’s mostly before the surge in the use of AI technologies. In fact, he says, 50 to 70% of the growth in US wage inequality between 19 was caused by automation. But it doesn’t have to be that way.ĭaron Acemoglu, an MIT economist, provides compelling evidence for the role automation, robots, and algorithms that replace tasks done by human workers have played in slowing wage growth and worsening inequality in the US. These inventions have generated good tech jobs in a handful of cities, like San Francisco and Seattle, while much of the rest of the population has been left behind. Because of the choices that researchers and businesses have made so far, new digital technologies have created vast wealth for those owning and inventing them, while too often destroying opportunities for those in jobs vulnerable to being replaced. Guiding the trajectory of the technology is critical, however. Recent advances in AI have been impressive, leading to everything from driverless cars to human-like language models.

The emphasis on automation rather than augmentation is, he argues in the essay, the “single biggest explanation” for the rise of billionaires at a time when average real wages for many Americans have fallen.

The excessive focus on human-like AI, he writes, drives down wages for most people “even as it amplifies the market power of a few” who own and control the technologies. But, he says, the obsession with mimicking human intelligence has led to AI and automation that too often simply replace workers, rather than extending human capabilities and allowing people to do new tasks.įor Brynjolfsson, an economist, simple automation, while producing value, can also be a path to greater inequality of income and wealth. The title, of course, is a reference to Alan Turing and his famous 1950 test for whether a machine is intelligent: Can it imitate a person so well that you can’t tell it isn’t one? Ever since then, says Brynjolfsson, many researchers have been chasing this goal. In an essay called “ The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence,” Erik Brynjolfsson, director of the Stanford Digital Economy Lab, writes of the way AI researchers and businesses have focused on building machines to replicate human intelligence.