Fed Study Finds U.S. Programming Job Growth Dropped 50% After ChatGPT Launch

Gate News message, April 27 — A Federal Reserve study by economists Leland D. Crane and Paul E. Soto found that employment growth among U.S. programmers dropped roughly 50% after ChatGPT launched in November 2022. Before the AI model’s release, programming-intensive jobs were growing at approximately 5% annually, well above the overall labor market. Since then, growth in sectors like IT services and software development has essentially flatlined.

The researchers estimate that roughly 500,000 programmer jobs that would have otherwise existed were never filled over the three-year period. The employment gap did not appear until mid-2024, about 18 months after ChatGPT’s launch, suggesting companies needed time to assess AI capabilities before adjusting headcount. The study controlled for other 2022 headwinds—interest rate hikes, the end of the pandemic digital boom, and the crypto crash—and found programmer employment still falling by approximately 3% per year even after removing those effects.

A separate Harvard study of 62 million payroll workers found that junior developer employment drops roughly 9-10% within six quarters when companies adopt generative AI, while senior employment remains largely unchanged. Anthropic CEO Dario Amodei has warned that up to 50% of entry-level roles could disappear within five years. “If A.I. disproportionately affects junior positions, it could have lasting consequences for the college wage premium, upward mobility and income disparities,” Harvard researchers noted.

Programmer wages have not declined; the effect has shown up in headcount rather than pay. Job postings stabilized in 2024 and have ticked slightly upward since. The Fed researchers note that cheaper AI-assisted programming could open new markets and grow total demand for developer labor over the long run. The study was published as a preliminary designation and represents the first Federal Reserve-level analysis directly linking AI adoption to a measurable, occupation-specific decline in hiring.

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