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10 resources
Labor Matters analysis by Mels de Zeeuw and Gad Levanon of remote-work patterns and how they differ across worker groups. Published September 30, 2026; the article is publicly readable.
Jobs for the Future's second-year study finding that 6 in 10 American workers still lack quality jobs, with lower job quality reported among Gen Z workers and people with records.
Anthropic's October 18 announcement proposes committing 5% of shares, subject to stockholder approval, to a wholly owned Public Benefit Fund LLC. An additional 2% may be added at the board's discretion for each 50% valuation increase above a baseline. Existing founder and employee pledges bring the stated total to roughly 20%. Fund equity will not be used for lobbying or political giving; governance details are still to come.
Gartner analysts Brook Selassie and Helen Poitevin argue that current workforce data supports an uneven transition rather than imminent mass unemployment. They discuss adoption constraints, early-career risks, role redesign and reskilling, while acknowledging uncertainty about future employment.
CIO Dive article reporting on Dice survey findings that show growing unease among technology professionals about hiring conditions and job security.
TheGrio reports on Black unemployment rising sharply to 7 percent, with economists pointing to federal policy changes. Described from the headline — the page would not let our reader in.
Labor economist Gad Levanon argues on his Labor Matters newsletter (Oct 1, 2026) that most of the public debate underestimates how much AI has already changed the US economy and labor market. He estimates underlying GDP growth at about 3 percent, driven largely by AI-related business investment, and says his reading of the evidence is not definitive proof.
Profile page for Daron Acemoglu, Institute Professor at MIT, covering his research in macroeconomics, political economy, labor economics and development economics, with links to his publications and papers. Acemoglu has written extensively on what AI means for jobs and wages.
The non-profit, non-partisan research organisation behind most influential US economics working papers, including much of the research on AI and jobs.
Why I recommend it: NBER working papers are free to download, but most are not yet peer reviewed. Treat headline findings as early evidence.
US Census Bureau working paper (CES 26-56, September 2026) by Cody Orr, Lee C. Tucker and Lawrence Warren, using administrative records covering about 29% of US bachelor's degrees conferred 2016-2024. Graduates in the most AI-exposed tenth of majors saw their chance of being employed in the quarter after graduation fall five percentage points, and first full-quarter earnings fall thirteen percent, starting immediately after ChatGPT's release in late 2022. The earnings hit is comparable to graduating into a large recession. About half came from lower pay inside the same industries, the rest from graduates shifting into lower-paying sectors such as restaurants and retail. The effect shrinks to about five percent two years out but does not disappear for the most exposed majors.
Why I recommend it: This is the strongest evidence yet that AI has already moved entry-level pay, because it uses actual wage records rather than employer statements or surveys. Two things to hold onto: the paper says plainly it has not been through Census Bureau review and is not an official position, and exposure is measured by what a major typically leads to, not by whether any particular employer used AI. So it tells you which fields got harder to enter — not that a machine took a named job.
