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AI + ML
Machine-learning models are automating away some entry-level roles
AI-POCALYPSE Researchers with the Stanford Digital Economy Lab say that workers between the ages of 22 and 25 in occupations most exposed to AI, like software developers, have seen a 13 percent relative decline in employment compared to other occupations.
This comes as employment for more experienced workers in the same jobs, and workers of all ages in occupations less exposed to AI, has been stable or growing.
The findings, detailed in a working paper titled, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” echo prior reports about the challenging job market for recent graduates in computer-related fields.
Data released in February 2025, by the Federal Reserve Bank of New York, for example, found that the unemployment rate for recent college graduates was 5.8 percent compared to 4.0 percent for workers on average. And among those who studied computer science, the US unemployment rate was 6.1 percent, spiking to 7.5 percent for computer engineering.
A report from SignaFire in May found that the hiring of new graduates dropped to 50 percent of pre-pandemic levels. The report attributed the shift in part to AI, describing the situation as a hiring reset where firms want more experienced workers and fewer entry-level hires.
This, however, appears to have been the status quo for some time. Last September, UC Berkeley computer science professor James O’Brien published a post to LinkedIn confirming anecdotes reported in a Wall Street Journal report about how tech jobs have dried up – or moved overseas, as some commenters pointed out.
“Previously, a Berkeley CS graduate, even if not a top student, would receive multiple appealing job offers in terms of work type, location, salary, and employer,” he wrote. “However, outstanding students, like those with a 4.0 in-major GPA, are now contacting me worried because they have zero offers. I suspect this trend is irreversible and likely part of the broader trend impacting almost every employment sector.”
The new Stanford Digital Economy Lab paper, by Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen, is based on an analysis of monthly administrative data from US payroll firm ADP between January 2021 and July 2025, representing millions of workers and tens of thousands of firms.
The study focuses on six findings and relies on data from not entirely disinterested AI firms like Anthropic [PDF] and OpenAI to define occupational exposure to AI. Microsoft, another company with a dog in this race, has also published an AI role hit-list. The most exposed occupations are said to include customer service representatives, accountants and auditors, software developers, secretaries, and administrative assistants.
The researchers say that dwindling prospects in AI-exposed jobs is contributing to lackluster overall employment growth for the 22-to-25 age group, even as employment for other workers has grown at a higher rate. The authors also posit that not all uses of AI are accompanied by declining employment. The effects depend on whether the AI usage automates roles away or merely augments and assists with job tasks.
The boffins also note that AI appears to affect employment more than wages at the moment. So the softening job market for recent computer science students isn’t bringing down salaries across the board.
Finally, they say that their findings are not explained away by other factors, such as the offshoring or outsourcing of entry-level computer and support roles.
“We find that our results are not driven solely by computer occupations or by occupations susceptible to remote work and outsourcing,” the authors state.
The authors acknowledge that other researchers have come to different conclusions, such as an April study (A Humlum) of Danish workers that found generative AI had no real impact on jobs or wages and a February study (M Hampole) that found AI had limited impact, with lower demand for some roles balanced by higher productivity in others. But the Stanford authors say those papers are flawed.
“These prior papers use data that lack either sufficient granularity or immediacy to reliably study employment changes by AI exposure and age,” the authors claim. “In contrast, this paper uses large-scale, close to real-time data to take a step towards resolving the ongoing debate on the employment effects of AI on young workers.” ®
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