The Stanford Digital Economy Lab, led by economist Erik Brynjolfsson, published a JOLTS-based analysis on 10 April 2026 concluding that AI is preventing roughly 950,000 to 1 million American hires per year against the 2023 pace. The Job Openings and Labor Turnover Survey hiring rate fell to 3.1% in February 2026, the lowest reading since April 2020. Applied to the 158.6 million nonfarm workforce, the 0.6 percentage-point gap against the 2023 baseline produces an annualised shortfall of roughly one million hires. Against Challenger, Gray & Christmas's cumulative AI-attributed layoff tally of 27,645 through March, the ratio is approximately 34 to 1.
JOLTS is a Bureau of Labor Statistics monthly survey of job openings, hires and separations; its hiring rate measures how many workers were hired in a month as a share of total employment. Stanford's reading is that the rate has collapsed not because firms are cutting declared roles but because they are quietly choosing not to replace departing workers and not to open new entry-level requisitions. That is the channel through which most AI displacement actually runs, and it is the channel to which Washington's primary labour instrument, unemployment-insurance claims, is deliberately blind.
Workers aged 22 to 25 in AI-exposed occupations have seen 16% employment decline since late 2022, while colleagues over 30 in the same occupations are up between 6% and 12%. That age profile is the strongest evidence that AI is the mechanism rather than interest rates or cyclical slack. Young software developers sit 20% below their 2022 peak. That age asymmetry matches the SSRN large-scale resume study showing entry-level postings at AI-adopting firms fell sharply, and the Fortune/Columbia finding that most unemployed Americans never file for benefits. Goldman Sachs's own 25,000-per-month substitution model priced the unannounced displacement at roughly three times the Challenger count; Stanford moves that multiple to 34.
Announced layoffs drive the headline count that regulators and Congress respond to; Brynjolfsson's analysis argues the response has been calibrated to a number that captures one thirty-fourth of the real impact. Hires-not-made do not trigger WARN Act filings, do not register as unemployment claims, and do not appear in any official federal AI workforce dataset. They surface years later as cohort scarring, when the young workers who never entered the pipeline emerge as the mid-career shortage the 1980s manufacturing automation literature documented.
The Stanford figure is an analytical derivation rather than a direct measurement, and is sensitive to the 2023 baseline assumption; Brynjolfsson's causal inference rests on the occupation-by-age asymmetry that is hard to explain through general macro channels. Even if the true multiple turns out to be half or double Brynjolfsson's figure, the order-of-magnitude point is that the declared number on which policy has relied since 2023 is not close to describing the labour-market impact of AI deployment.
