Too often, public debate rewards dramatic predictions. Think tank gurus and activists win viral attention, warning that a new technology will destroy society, then sell their preferred policy as the only way to avert disaster. The more counterintuitive possibility, that innovation might strengthen workers and expand opportunity, rarely breaks through.
This distortion was on full display when the godfather of modern AI became so certain the machines had won that he urged us to stop training an entire specialty of physicians. In 2016, Geoffrey Hinton delivered the verdict as if it were settled science. “People should stop training radiologists now,” he said. Within five years, he predicted, computers would read medical scans better than any doctor alive. The humans were finished.
A decade later, that verdict has aged poorly. Radiologists are not obsolete. They are among the most sought-after doctors in America, with some compensation packages reaching $571,000. The Mayo Clinic’s radiology staff has grown by more than half in a decade. The American College of Radiology projects the specialty will expand by 26% over the next three decades. Far from a glut of idle image-readers, the country faces its largest radiologist shortage on record, with scans at some centers backlogged for months.
This is worth remembering as a fresh wave of predictions rolls in, each warning that artificial intelligence is about to hollow out the American workforce. The radiologists are not the exception to that story. They are the rule the AI pessimists keep missing.
Consider the news from the past week. The Washington Post reported how recent graduates blaming AI for a brutal entry-level market have the story backward. The real problem, recruiters and economists told the paper, is that the United States is barreling toward what may become the largest labor shortage in its history: shortfalls of nurses, physicians, engineers, pharmacists, construction workers, and airplane mechanics numbering in the tens and even hundreds of thousands. These are jobs AI cannot do, and there are not enough people to fill them.
Then there is the latest data. A new study from Ramp’s Economics Lab, linking corporate AI spending to workforce records across more than 21,000 companies, found that firms investing heavily in AI grew their headcount by 10.2% in the two years after adopting AI. Entry-level hiring at those firms rose even faster, by 12%. The companies betting biggest on automation were not shedding workers but adding employees to their teams.
None of this fits the script we’ve been sold. For a decade, a confident class of experts, “futurists,” and activists insisted emerging innovation would produce mass unemployment, and that the only humane response was to prepare for a world with less work: universal basic income, shorter work weeks, managed decline. This worldview treats the economy as a fixed pie, in which every task a machine performs is a job permanently subtracted from humanity.
Yet the economy is not a fixed pie, and it never has been. When a technology makes workers more productive, it makes their labor more valuable, not less, lowering costs, expanding what a firm can attempt, and creating demand for work nobody had imagined.
The radiologist with an AI assistant is proof. She reads more scans, catches more disease, and grows more essential as imaging reaches problems it once could not touch. The tool supercharges the human rather than erasing her.
This is the oldest lesson in economics, and the one Vitamin D-deficient elites in their ivory towers are most eager to forget. From the mechanical loom to the spreadsheet, every wave of automation summoned prophets who promised the end of work. Every time, the labor market grew, wages increased over the long run, and vocations transformed. The spreadsheet did not eliminate accountants. It multiplied them.
The real risk facing America is not too little work. There are too few workers prepared to do it. The shortage is in skills and training. It is beyond time to revamp the pipelines that turn the next generation into welders, nurse practitioners, and technicians. That is a solvable problem, but only if we stop indulging the fantasy that the robots are coming for everything and start building the human capital a more productive economy demands.
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Hinton, to his credit, has admitted he was wrong about the radiologists. He got the direction of the technology right and its consequence for workers exactly backward. The lesson is not that experts are always wrong. It is that confident predictions about the death of human work have a perfect record of failure.
The machines are getting smarter, and in doing so they are enabling new levels of productivity. That has been very good news for the people who work alongside them.
Nathan Leamer is the Executive Director of Build American AI.
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[ H/T Washington Examiner ]
