{"id":7348,"date":"2026-10-23T06:10:17","date_gmt":"2026-10-23T06:10:17","guid":{"rendered":"https:\/\/www.vaultinsider.top\/?p=7348"},"modified":"2026-10-23T06:10:17","modified_gmt":"2026-10-23T06:10:17","slug":"ai-could-displace-one-in-11-workers-goldman-says-but-the-layoffs-are-waiting-for-a-recession","status":"publish","type":"post","link":"https:\/\/www.vaultinsider.top\/?p=7348","title":{"rendered":"AI could displace one in 11 workers, Goldman says, but the layoffs are waiting for a recession"},"content":{"rendered":"
<\/p>\n
The AI layoff panic that peaked this spring has gone quiet. Goldman Sachs thinks the layoffs are waiting for a recession.<\/p>\n
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\u201cConcerns of imminent AI-related labor displacement have cooled in recent months after hitting a fever pitch this spring,\u201d Goldman economist Joseph Briggs wrote in a note published Thursday. Then he laid out what may be coming. Under the bank\u2019s forecast that AI eventually lifts productivity by 15% in the U.S. and other developed economies, he found that up to 9% of workers could be \u201cdisplaced during the AI transition,\u201d with slower hiring weighing on employment by another 3%. That\u2019s roughly one in 11 workers.<\/p>\n
The bigger finding is about timing. Briggs went back through decades of data on how technology has replaced workers and found that \u201cautomation-related labor disruption is limited when the overall economy is healthy but accelerates during cyclical slowdowns.\u201d<\/p>\n
\u201cAI-related labor disruptions could well amplify the broader business cycle,\u201d he wrote.<\/p>\n
Goldman has now made this argument twice, a year apart, from two different teams. In October 2025, Goldman\u2019s U.S. economists David Mericle and Pierfrancesco Mei called \u201cjobless growth\u201d the likely new normal, Fortune<\/em> reported at the time. \u201cThe modest job growth alongside robust GDP growth seen recently is likely to be normal to some degree in the years ahead,\u201d they wrote. Then came their warning: \u201cHistory also suggests that the full consequences of AI for the labor market might not become apparent until a recession hits.\u201d<\/p>\n
Briggs reached the same conclusion. \u201cThe timing and extent of automation and labor disruption will likely track the overall economic cycle,\u201d he wrote.<\/p>\n
The pattern goes back decades. \u201cHistorically automation-related labor disruption is quite cyclical,\u201d Briggs writes, \u201cwith companies tending to automate work and shed labor costs when the economy starts to slow.\u201d Mericle and Mei cited a leading explanation last year: \u201cCompanies use recessions to restructure and streamline their workforce by laying off workers in less productive areas.\u201d<\/p>\n
The chart that makes the case tracks the share of Americans in routine jobs: clerks, secretaries, factory machine operators, mechanics. That share \u201chas remained stable during expansions but declined sharply during downturns,\u201d Briggs writes. The line drops in steps for the recessions of the early 1980s, 2001, 2008, and 2020, and it doesn\u2019t climb back to where it was. \u201cHistorically, most of the labor headwinds from routine job automation are concentrated in economic recessions,\u201d he wrote.<\/p>\n\n
<\/div>\n<\/figure>\n
Mericle and Mei pointed to the same pattern last year with the \u201cjobless recovery\u201d that followed the dotcom bust. \u201cDuring that recovery, despite a soft labor market, productivity growth remained elevated, and GDP growth rebounded earlier than employment growth,\u201d they wrote.<\/p>\n
Looking industry by industry, Briggs found that \u201clabor market displacement is more severe when the economic backdrop is less favorable.\u201d The hit to unemployment from a technology-driven productivity gain was 0.1 to 0.2 percentage points bigger when the overall jobless rate was rising. Across 25 occupations that past technology upended, employment fell an extra 4% over five years for every point the national unemployment rate climbed.<\/p>\n
\u201cA more concerning takeaway from our analysis is that a cyclical slowdown would likely accelerate AI-related automation and job losses, thereby amplifying the broader business cycle,\u201d he wrote.<\/p>\n
How 6% became 9%<\/strong><\/h2>\n
Briggs has been raising this number for a while. In August 2025, he and Goldman economist Sarah Dong estimated AI could displace 6% to 7% of the U.S. workforce if widely adopted, within a range of 3% to 14%. That used the same 15% productivity assumption. By June, on Goldman\u2019s Exchanges<\/em> podcast, Briggs put the figure at about 9%, or 15 million workers over a decade. He first described them as workers who would be \u201creallocated to new positions.\u201d Thursday\u2019s note calls them displaced. <\/p>\n
That\u2019s a long way from where Goldman\u2019s U.S. team stood last fall. Mericle and Mei said then that they were \u201cskeptical of the boldest claims that rapid technological progress could lead to very high unemployment,\u201d citing Goldman\u2019s global economics team, which Briggs co-leads. Briggs can square his estimate with that: He says the displacement is temporary. On the podcast he said that \u201ceven under our forecast for 9% of workers being displaced, we\u2019d still expect that the unemployment rate increase in any given year would be less than one percentage point.\u201d<\/p>\n
Part of the answer is in what Briggs counts. Thursday\u2019s note measures gross job losses rather than unemployment, because the jobless rate hides people. \u201cSome workers that are displaced may find jobs relatively quickly,\u201d he writes, \u201cwhile others may decide to exit the labor force.\u201d <\/p>\n
Historically, each 1% gain in technology-driven productivity pushed the unemployment rate up 0.3 to 0.4 points at its peak, fading to zero after two years. Over the same stretch, it raised the cumulative gross job-loss rate by 0.5 to 0.6 points. Multiply the top of that range by 15 and you get 9.<\/p>\n
The occupation data shows how bad it can get in one line of work. Briggs looked at 25 jobs hit hard by past technology, including photo processors, movie projectionists, proofreaders, file clerks, and bank tellers. On average, employment in those jobs fell 25% in the five years after it started to decline, \u201csuggesting that very large job losses are possible when technology makes labor redundant.\u201d He calls those numbers \u201clikely upper bounds\u201d for AI, since he picked the cases where technology hit hardest.<\/p>\n
Most of the shrinkage came from people losing or leaving jobs, more than from employers hiring fewer people. Separation rates rose in 19 of the 25 occupations, by 3.8 points on average. Hiring rates fell in 13, by 0.7 points on average. That\u2019s why Briggs says AI-related layoffs \u201cshould provide a good real-time guide\u201d to disruption.<\/p>\n
He also lists reasons the 9% could be low. Displacement ran higher during the late-1990s\/early-2000s productivity boom, \u201cthe period most widely viewed as comparable to today.\u201d It also ran higher after unusually large productivity jumps, and Goldman expects AI\u2019s boost to beat most past ones. \u201cThis pattern suggests more scope for displacement during the AI transition,\u201d he wrote.<\/p>\n
Why it\u2019s quiet now<\/strong><\/h2>\n
\u201cGiven that our estimates point to a significant amount of technology-related job losses to come, why has the impact been limited so far?\u201d Briggs asks.<\/p>\n
For now, the layoff gauge reads low. AI-attributed layoffs in Challenger\u2019s tracking have pulled back sharply from their spring peak. News coverage of AI-related job losses has fallen back to levels from the start of 2026. Job growth in the most AI-exposed industries has run about 10,000 below its long-run trend over the past three months, roughly offset by hiring for AI-related construction. \u201cWhile these patterns suggest that a modest labor market headwind is emerging, the near-term headwinds remain smaller than feared earlier this year,\u201d Briggs wrote.<\/p>\n\n
<\/div>\n<\/figure>\n
Some of the most prominent voices warning about AI job losses have softened their tone. Anthropic CEO Dario Amodei spent much of 2025 warning that AI could eliminate half of entry-level white-collar jobs. Onstage with JPMorgan Chase CEO Jamie Dimon at an Anthropic event in New York in May, he reached for the Jevons paradox instead, as Fortune<\/em> reported at the time. \u201cIf you automate 90% of the job, then everyone does the 10% of the job,\u201d Amodei said. \u201cAnd the 10% kind of expands to be 100% of what people do and kind of 10xs their productivity.\u201d He kept one caveat, and it\u2019s the one Briggs\u2019s note is about: \u201cAI is moving faster than all these previous technologies,\u201d he said. \u201cIt\u2019s possible you get these weird behaviors and this big disruption.\u201d<\/p>\n
Briggs\u2019s first answer is that it\u2019s still early: Only 23% of U.S. establishments have built AI into regular workflows, and most AI-enabled productivity gains remain in the future. His second is the economy. \u201cGiven that the overall economy and labor market have remained resilient in 2026,\u201d he wrote, \u201ccompanies may have had less incentive to focus on aggressively cutting labor costs.\u201d<\/p>\n
Generative AI was used for research assistance and\/or transcription for this article. The reporter independently reported and verified the factual claims in this article, and a human editor reviewed it before publication.<\/em><\/p>\n<\/div>\n
The AI layoff panic that peake… <\/p>\n","protected":false},"author":1,"featured_media":7349,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[1353,5841,4311,537,619,10427,703,530],"class_list":["post-7348","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-finance-news","tag-displace","tag-goldman","tag-goldman-sachs-group","tag-jobs","tag-layoffs","tag-recession","tag-waiting","tag-workers"],"yoast_head":"\n
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