Goldman Sachs Says AI Could Displace One in 11 Workers — When Recession Hits

Goldman Sachs now estimates that up to 9% of workers in the U.S. and other developed economies could be displaced during the AI transition — roughly one in 11 workers — and it argues the layoffs are likely waiting for a recession, not around the corner.

"Concerns of imminent AI-related labor displacement have cooled in recent months after hitting a fever pitch this spring," Goldman economist Joseph Briggs wrote in a note published Thursday. Then he laid out the math: under the bank's forecast that AI eventually lifts productivity by 15%, up to 9% of workers could be "displaced during the AI transition," with slower hiring weighing on employment by another 3%.

The bigger finding is about timing, not totals. Briggs went back through decades of data on how technology replaced workers and found that "automation-related labor disruption is limited when the overall economy is healthy but accelerates during cyclical slowdowns." His warning is direct: "AI-related labor disruptions could well amplify the broader business cycle."

What does the history show?

Goldman has now made this argument twice, a year apart, from two different teams. In October 2025, Goldman's U.S. economists David Mericle and Pierfrancesco Mei called "jobless growth" the likely new normal. "History also suggests that the full consequences of AI for the labor market might not become apparent until a recession hits," they wrote.

Briggs reached the same conclusion. "Historically automation-related labor disruption is quite cyclical," he writes, "with companies tending to automate work and shed labor costs when the economy starts to slow." Mericle and Mei cited a leading explanation last year: "companies use recessions to restructure and streamline their workforce by laying off workers in less productive areas."

The chart that makes the case tracks the share of Americans in routine jobs — clerks, secretaries, factory machine operators, mechanics. That share "has remained stable during expansions but declined sharply during downturns," Briggs writes. The line drops in steps at the recessions of the early 1980s, 2001, 2008 and 2020, and it never climbs back. Mericle and Mei pointed to the same pattern in the "jobless recovery" after the dot-com bust, when GDP rebounded earlier than employment.

Industry by industry, Briggs found that "labor market displacement is more severe when the economic backdrop is less favorable." The unemployment hit from a technology-driven productivity gain ran 0.1 to 0.2 percentage points larger when the jobless rate was rising. Across 25 occupations past technology upended, employment fell an extra 4% over five years for every point national unemployment climbed.

How 6% became 9%

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%, using the same 15% productivity assumption. By June, on Goldman's Exchanges podcast, Briggs put the figure at about 9%, or 15 million workers over a decade. He first called them workers who would be "reallocated to new positions." Thursday's note calls them displaced.

He can square that with Goldman's U.S. team, which said last fall it was "skeptical of the boldest claims that rapid technological progress could lead to very high unemployment." Briggs says the displacement is temporary. "Even under our forecast for nine percent of workers being displaced, we'd still expect that the unemployment rate increase in any given year would be less than one percentage point," he said on the podcast.

Part of the answer is in what he counts. Thursday's note measures gross job losses, because the unemployment rate hides people. "Some workers that are displaced may find jobs relatively quickly," he writes, "while others may decide to exit the labor force."

Historically, each 1% gain in technology-driven productivity pushed unemployment up 0.3 to 0.4 points at its peak, fading to zero after two years, while raising cumulative gross job losses by 0.5 to 0.6 points. Multiply the top of that range by 15 and you get 9.

The occupation data shows how bad one line of work can get. Briggs looked at 25 jobs hit hard by past technology — photo processors, movie projectionists, proofreaders, file clerks, bank tellers. Employment in those jobs fell 25% on average in the five years after decline began, numbers he calls "likely upper bounds" for AI. Most of the shrinkage came from separations, not weak hiring: separation rates rose in 19 of the 25 occupations by 3.8 points on average, while hiring rates fell in 13 by 0.7 points. That's why Briggs says AI-related layoffs "should provide a good real-time guide" to disruption.

He also lists reasons 9% could be low. Displacement ran higher during the late-1990s/early-2000s productivity boom, "the period most widely viewed as comparable to today," and after unusually large productivity jumps. "This pattern suggests more scope for displacement during the AI transition," he wrote.

Why it's quiet now

"Given that our estimates point to a significant amount of technology-related job losses to come, why has the impact been limited so far?" Briggs asks. For now, the layoff gauge reads low: AI-attributed layoffs in Challenger's tracking have pulled back sharply from their spring peak, and job growth in the most AI-exposed industries has run about 10,000 below trend over the past three months, roughly offset by AI-related construction hiring.

Some prominent voices have softened too. Anthropic CEO Dario Amodei spent much of 2025 warning 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: "If you automate 90% of the job, then everyone does the 10% of the job. And the 10% kind of expands to be 100% of what people do and kind of 10xs their productivity." His caveat matches Briggs' thesis: "AI is moving faster than all these previous technologies. It's possible you get these weird behaviors and this big disruption."

Briggs' first answer is that it's early — only 23% of U.S. establishments have built AI into regular workflows. His second is the economy itself: "Given that the overall economy and labor market have remained resilient in 2026, companies may have had less incentive to focus on aggressively cutting labor costs." For practitioners, the practical takeaway is to watch layoff announcements and separations data when growth turns, not headline unemployment — that's where the AI transition will show up first.