A Nobel economist figured out 60 years ago that people learn best on the job. The Atlanta Fed says AI is making that almost impossible
A recent analysis by the Federal Reserve Bank of Atlanta highlights the negative impact of AI on entry-level job opportunities for young graduates. The study references Kenneth Arrow's theory that hands-on experience is crucial for productivity and career growth. As automation increases, the pipeline for developing skilled workers may be jeopardized, leading to long-term consequences for both firms and the economy.
- ▪Kenneth Arrow's theory emphasizes that workers learn best through hands-on experience in their jobs.
- ▪The unemployment rate for young degree-holders is now higher than the overall unemployment rate, partly due to AI replacing entry-level roles.
- ▪The Fed researchers warn that automating entry-level jobs risks creating a shortage of skilled senior workers in the future.
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Sixty years ago, an economist named Kenneth Arrow sat down and worked out something that seemed almost too obvious to say: workers get better at their jobs by doing them. The insight was simple, but Arrow, who would go on to win the Nobel Prize, formalized it into a theory with sweeping implications. Learning, he wrote, “can only take place through the attempt to solve a problem and therefore only takes place during activity.” Experience wasn’t just good for workers, he argued—it was the engine of productivity growth for firms and, ultimately, the entire economy.Recommended Video Now, as artificial intelligence chips away at the entry-level jobs that once served as the on-ramp to white-collar careers, researchers at the Federal Reserve Bank of Atlanta are dusting off Arrow’s 1962 paper…
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