The Federal Reserve Bank of Atlanta and the National Bureau of Economic Research published a survey of 750 US chief financial officers on 25 March, finding that projected AI-attributed job cuts for 2026 are nine times higher than 2025 levels.1 The raw number is roughly 502,000 roles. As a share of the total US workforce, that is 0.4%.
The figure is the first hard employer-side estimate of AI displacement at scale. It arrives in a quarter where Q1 tech layoffs had already reached 59,000, up from 45,363 at the last count, with one in five cuts explicitly citing AI. The ninefold increase sounds severe. The denominator tells a quieter story: 0.4% falls within normal labour market churn, where monthly separations run at roughly 3.5%.
Only 44% of surveyed CFOs plan AI-related layoffs at all.2 The majority intend no cuts. The survey also documents a productivity paradox: executives perceive AI gains that do not yet appear in revenue. Companies are cutting based on expected capability, not demonstrated return on investment. That pattern produced the 55% regret rate Orgvue found earlier this quarter, when more than half of leaders who cut staff for AI admitted they were wrong.
The Atlanta Fed data does not contradict Harvard Business Review's finding that only 2% of layoffs followed actual AI deployment. It refines it. Firms intend to cut. They have not done so at scale. When they do, the numbers will be smaller than the headlines suggest.
