{"id":2387,"date":"2026-06-27T18:09:32","date_gmt":"2026-06-27T18:09:32","guid":{"rendered":"https:\/\/crosscountrymovingteams.com\/?p=2387"},"modified":"2026-06-27T18:09:32","modified_gmt":"2026-06-27T18:09:32","slug":"its-not-going-away-the-stanford-economist-who-called-the-ai-entry-level-jobs-crisis-early-has-the-receipts","status":"publish","type":"post","link":"https:\/\/crosscountrymovingteams.com\/?p=2387","title":{"rendered":"\u2018It\u2019s not going away\u2019: The Stanford economist who called the AI entry-level jobs crisis early has the receipts"},"content":{"rendered":"
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The findings were stark: a significant relative decline in employment for workers ages 22 to 25 in the most AI-exposed occupations since the widespread adoption of generative AI \u2014 even after controlling for other economic shocks. Critics pushed back immediately. Google economists said it was interest rates, while others blamed tech-sector overhiring, remote work distortions, pandemic noise. Earlier this month, Apollo Global Management\u2019s Torsten Slok continued to argue that entry-level hiring woes are a feature of the low-hire, low-fire job market, asking \u201cwhere is the AI jobs crisis?\u201c<\/p>\n

Read more S&P keeps U.S. sovereign rating at AA+ with stable outlook<\/a><\/p>\n

Not only did Brynjolfsson keep updating the data, but he partnered with ADP Research, the economics arm of the private payroll data provider, which serves roughly one in six American workers. The effect hasn\u2019t faded after a closer look<\/p>\n

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\u201cWhatever it is,\u201d Brynjolfsson told Fortune<\/em>, \u201cit\u2019s not going away.\u201d<\/p>\n

The new numbers come from the Canaries Dashboard, the centerpiece of an expanded partnership between Brynjolfsson\u2019s Stanford Digital Economy Lab and ADP Research, building on that bombshell paper last summer. It draws on information about 4.6 million workers across more than 730 occupations, and the Digital Economy Lab considers this to be its highest-profile dashboard among the several freely accessible, continuously updated AI economic indicators that it maintains to track AI\u2019s effects on the labor market in near-real time.<\/p>\n

For Brynjolfsson, it is a partial answer to every critic who said his original finding was a blip. \u201cWe are flying blind into one of the most consequential periods in world history,\u201d Brynjolfsson said at the platform\u2019s launch. \u201cWe need timely, trusted evidence to understand where AI is creating value and where it is disrupting work.\u201d<\/p>\n

The dashboard processes payroll data covering roughly one in six American workers. What it shows, broken down by age and AI exposure level, is a widening fault line.<\/p>\n

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Across all workers, the numbers remain muted. The most AI-exposed occupations contracted just 0.2% year over year as of April 2026, compared to 0.1% growth for the least-exposed roles. Since ChatGPT\u2019s introduction in late 2022, annual employment growth across AI-exposed occupations has actually increased<\/em> by 1.1%, compared to 2% for the least-exposed. At the headline level, the sky hasn\u2019t fallen.<\/p>\n

Cut the data by career stage, and the story changes.<\/p>\n

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For workers ages 22 to 25, employment in highly AI-exposed occupations is now shrinking at 3.8% per year and the early-career decline sharpened after year one \u2014\u00a02.8% decrease to April 2024, growing to a more than 4% decline per year since. The average decline on a month-to-month basis averages about \u22120.3% but Brynjolfsson notes that trend is noisy, compared to the year-over-year deceleration.<\/p>\n

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The least-exposed jobs in that same age group are growing at 2% annually. Mid-career workers ages 31 to 34 are also contracting, down 1.7% year-over-year. Workers ages 35 to 40, by contrast, are growing at 2%. The technology isn\u2019t eliminating work across the board. It\u2019s eliminating the on-ramp \u2014 and it\u2019s doing so with increasing precision as the data accumulates.<\/p>\n

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The mechanism isn\u2019t mysterious. AI absorbs tasks before it absorbs jobs, and the tasks it reaches first are the ones that don\u2019t require years of experience: retrieving, summarizing, scheduling, formatting, the mechanical assembly of information. These are disproportionately the tasks handed to people at the beginning of their careers. Senior workers have accumulated the hard-to-codify, job-specific skills that still buffer against displacement. Junior workers haven\u2019t yet.<\/p>\n

ADP chief economist Nela Richardson \u2014 Brynjolfsson\u2019s partner on the research \u2014 has argued the distinction between automation and augmentation is the key variable. Occupations where AI augments human work show more enduring employment growth; those where AI automates tasks outright show contraction. Early-career workers, concentrated in the most automatable layer of any occupation, sit squarely in the second category.<\/p>\n

\u201cIn the aggregate, AI\u2019s impact on jobs remains modest,\u201d Richardson stressed in a June 16 blog post on the first batch of dashboard data. But when AI\u2019s impact is measured by career stage, she continued, \u201cdramatic differences emerge.\u201d <\/p>\n

To Richardson, much of the debate around AI and jobs comes down to \u201cguesswork,\u201d given all of the variables involved and the huge range of uncertainties. Her conclusion, as she recently told Fortune<\/em>, is that the reality is more \u201cnuanced,\u201d with AI disrupting tasks from the bottom up, not jobs from the top down. \u201cIn occupations and career stages where AI amplifies human abilities and potential,\u201d she wrote, \u201cwe see employment growth.\u201d<\/p>\n

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Brynjolfsson has now stress-tested the finding against every major counter-argument. The interest rate hypothesis points the wrong direction \u2014 the most rate-sensitive occupations, like construction, have the lowest AI exposure. He removed the entire tech sector. He isolated remote-work effects. The pattern held every time. \u201cIf you take out the entire tech industry, or take out all tech-related occupations, or you slice it different ways, you still get this effect,\u201d he said.<\/p>\n

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The original paper covered data through August 2025. The new dashboard extends that to April 2026 \u2014 nearly four years of post-ChatGPT labor market data. The effect hasn\u2019t mean-reverted. It\u2019s grown by roughly half a percentage point per month, consistently, month after month.<\/p>\n

Daron Acemoglu, the MIT economist and Nobel laureate, has become the most prominent voice of AI skepticism within the field \u2014 and the two have been publicly sparring for months. Acemoglu\u2019s models produce far lower productivity estimates than Brynjolfsson\u2019s, a gap that frustrates Brynjolfsson even as he maintains deep respect for his former MIT colleague.<\/p>\n

Brynjolfsson said that in fact he had just been \u201cgoing back and forth\u201d with Acemoglu on the morning of our interview. \u201cWe\u2019re trying to find some common ground.\u201d<\/p>\n

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There is some. Both agree that AI should be deployed to complement human workers rather than replace them \u2014 and both have been \u201ctrying to beat that drum,\u201d as Brynjolfsson put it. But on the productivity question, the distance between them remains wide. Acemoglu has argued that if AI is used correctly, it could still deliver significant gains \u2014 a position Brynjolfsson finds somewhat contradictory. \u201cI don\u2019t get how he has such low productivity numbers,\u201d he said. \u201cI tell him that. But time will tell. Pretty soon we\u2019re going to see who\u2019s right.\u201d<\/p>\n

Acemoglu, for his part, hasn\u2019t softened his public skepticism. He recently told Fortune<\/em> that much of the AI productivity discourse is \u201cbrainless\u201d \u2014 speculative to the point of fiction, he clarified, not stupid per se.<\/p>\n

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What\u2019s notable is that the argument is no longer about whether AI is transformative. It\u2019s about the magnitude and the timeline. That, in itself, is a shift. A year ago, Brynjolfsson was still convincing mainstream economists to take the question seriously. Now the debate has moved to his turf, and the data he\u2019s building is designed to settle it.<\/p>\n

Brynjolfsson is careful about scale. He positions himself between Silicon Valley catastrophists and mainstream economists who see AI adding fractions of a percent to productivity. But the middle ground he occupies is still historically large. His comparison for this disruption isn\u2019t the internet. It isn\u2019t even globalization.<\/p>\n

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Alluding to the comparison he made in his 2016 New York Times<\/em> bestseller The Second Machine Age<\/em>, this is like the Industrial Revolution \u2014 the last time humanity built machines that changed work and productivity completely. \u201cThat one automated, augmented our muscles, and now we\u2019re doing it for our minds. How can that not be as big or bigger?\u201d he said. \u201cI think it\u2019s going to be bigger and 10 times faster.\u201d<\/p>\n

But early-career workers in AI-exposed occupations are, right now, bearing a cost that doesn\u2019t yet appear in the headline numbers. The Canaries Dashboard takes its name from that logic: canaries in coal mines didn\u2019t stop the danger. They just told you the clock was running.<\/p>\n

Brynjolfsson shared that he has a friendly wager with Northwestern economist Bob Gordon \u2014 a 10-year bet on longbets.com that productivity will be significantly higher by the end of the decade.<\/p>\n

\u201cI\u2019m already ahead,\u201d he said. \u201cAnd I always figured it was backloaded because of my J-curve theory. So barring war or catastrophe \u2014 the AI part should be positive.\u201d<\/p>\n

Read more The 2 billion-print, $2-pack last hurrah for a World Cup legend: the Panini sticker album\u2019s last ride<\/a><\/p>\n

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Stanford’s Erik Brynjolfsson is partnering with ADP’s Nela Richardson on a live dashboard tracking what they found last summer: AI is eating entry-level work.<\/p>\n","protected":false},"author":1,"featured_media":2386,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-2387","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-jobs"],"yoast_head":"\n\u2018It\u2019s not going away\u2019: The Stanford economist who called the AI entry-level jobs crisis early has the receipts - Cross Country Moving Team<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/crosscountrymovingteams.com\/?p=2387\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u2018It\u2019s not going away\u2019: The Stanford economist who called the AI entry-level jobs crisis early has the receipts - 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