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McKinsey says 11 million US workers may need new careers by 2035 due to AI

McKinsey & Co. estimates that 11 million US workers—about 6.5% of the current labor force—could need entirely new occupations by 2035 because of AI-driven automation. The McKinsey Global Institute report predicts AI will create more jobs than it eliminates over the next nine years, but the shift may require the largest workforce transformation in US history, per the authors. While 36 million…

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Key points

  • McKinsey estimates **11 million US workers** may need new careers by **2035** due to AI-driven automation
  • AI could eliminate **36 million jobs** but create **40 million new ones**, per McKinsey’s base scenario
  • Labor market remains sluggish: job openings at **five-month low**, hiring growth below historical averages

Labor market conditions remain weak, with job openings at a five-month low in August, voluntary quits near a six-year low, and hiring growth still below historical averages. Glassdoor’s Employee Confidence Index hit a record low in September, reflecting rising anxiety over job security and economic uncertainty. Meanwhile, consumer sentiment also declined sharply, with the Conference Board’s index dropping 6.7 points to its lowest level in 12 years, influenced by higher gas prices and geopolitical tensions. Economists expect September’s jobs report to show slower employment growth, with a consensus forecast of 95,000 new jobs (down from 162,000 in August).

Full story from cnn.com · by Alicia Wallace · via Reddit AI communitiesOpen source ↗

AI could force 11 million US workers into new careers by 2035

cnn.com · 29 September 2026

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This text was published by cnn.com and written by Alicia Wallace. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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McKinsey & CoMcKinsey Global InstituteGlassdoorConference BoardBureau of Labor StatisticsFactSetDaniel ZhaoGrace Zwemmer

The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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