The economy is being transformed by digital technologies, especially in artificial intelligence, that are rapidly changing how we live and work. But this transformation poses a troubling puzzle: these technologies haven’t done much to grow the economy, even as income inequality worsens. Productivity growth, which economists consider essential to improving living standards, has largely been sluggish since at least the mid-2000s in many countries.
Why are these technologies failing to produce more economic growth? Why aren’t they fueling more widespread prosperity? To get at an answer, some leading economists and policy experts are looking more closely at how we invent and deploy AI and automation—and identifying ways we can make better choices.
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. 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. 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.
For Brynjolfsson, an economist, simple automation, while producing value, can also be a path to greater inequality of income and wealth. 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. 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.
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