21SEP

Goldman: early job loss cuts pay for a decade

3 min read

16:45UTC

Goldman's 40-year historical analysis reveals that workers displaced between 25 and 35 never fully recover their earnings trajectory.

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EconomicDeveloping

Key takeaway

Workers displaced at 25 to 35 lose a decade of earnings growth they never recover.

Goldman Sachs' 40-year historical analysis, published 6 April, found that workers aged 25 to 35 who are displaced early in their careers face real earnings growing 10 percentage points less than never-displaced workers over the following decade ¹. Gen Z recovers faster due to occupational mobility and AI literacy, but the immediate unemployment gap is widening.

The scarring dimension compounds the pipeline blockage documented elsewhere. A study of 62 million resumes found AI-adopting firms cut entry-level postings by 15% while senior roles held flat . The Dallas Fed confirmed the losses concentrate among workers under 25 through collapsed job-finding rates . Workers who lose positions at the start of their careers face a decade of depressed earnings; workers who cannot find entry-level positions at all face an even longer shadow.

Law school applications surged 33% year-on-year and MBA applications rose 7%, mirroring the 2008 recession's credential flight ². But ChatGPT already passes the bar exam. Gen Z workers fleeing entry-level AI disruption are accumulating $200,000 or more in debt for credentials in professions that AI is simultaneously transforming.

Deep Analysis

In plain English

Losing a job in your late twenties or early thirties is not just a bad month. Goldman Sachs looked back at 40 years of data and found something troubling: workers displaced early in their careers never fully catch up. Ten years after the displacement, workers who lost jobs between 25 and 35 still earn roughly 10 percentage points less in real terms than peers who were not displaced. That is a permanent reduction in lifetime income, not a temporary setback. For younger workers specifically, Gen Z, the picture is mixed. They recover faster than older displaced workers because they are more mobile and more comfortable with AI tools. But the gap is widening now, in the immediate term, because entry-level positions are the first to be cut and the slowest to be replaced.

Deep Analysis

Root Causes

The 10-percentage-point earnings gap arises from three compounding mechanisms that Goldman's 40-year dataset captures. First, early-career displacement interrupts the accumulation of firm-specific and role-specific human capital at the period of highest growth rate.

Second, displaced workers re-enter the labour market in lower-paying roles, and this entry point anchors subsequent wage negotiations. Third, displacement signals to future employers impair hiring prospects independently of actual skill levels.

For the current AI cohort, a fourth mechanism is visible: law school applications surged 33% year-on-year as displaced or threatened workers flee into credential programmes. But ChatGPT already passes the bar exam, and AI is simultaneously transforming the legal profession. Workers accumulating $200,000 or more in debt for legal credentials face the scarring process twice: once in the role AI displaced them from, and again if the credential they acquired is itself disrupted.

What could happen next?

Consequence
Workers aged 25-35 displaced during the current AI transition face a decade of real earnings 10 percentage points below never-displaced peers, converting a labour market shock into a generational earnings crisis.
Risk
Law and business school credential flight among AI-displaced workers is creating a second-order scarring risk: debt accumulation for credentials in professions AI is simultaneously transforming.

Source Landscape

This story draws on mixed-leaning sources from United States

United States

LeftRight

Primary parallel: The 1981-82 US recession displaced roughly 3.5 million manufacturing workers, many aged 25-40. Lawrence Katz and Alan Krueger's subsequent research found earnings gaps of 15-30% persisting 20 years later among displaced steelworkers, attributable to the destruction of sector-specific human capital that could not be transferred to new industries. The AI-era displacement is broader in occupational scope but the scarring mechanism is structurally analogous.

Counter-parallel: The dot-com bust of 2000-2003 displaced large numbers of technology workers aged 25-35. Follow-up studies found their earnings gaps largely closed within 7-10 years, because their technical skills remained in demand across expanding adjacent sectors. The question is whether AI displacement resembles the 1981-82 pattern (sector-specific capital destruction) or the 2000-2003 pattern (transferable skill set in continued demand elsewhere).

At 16,000 net job losses monthly and a 10-percentage-point lifetime earnings gap for those displaced at 25-35, the aggregate long-run earnings loss is substantial. A rough calculation: 16,000 workers per month, half of whom fall in the 25-35 cohort, multiplied by the present value of a decade of 10% lower earnings, implies a cumulative human-capital destruction running well into the tens of billions of dollars annually at current displacement rates.

The NBER Manning and Aguirre working paper (January 2026) finds 6.1 million US workers face both high AI exposure and low adaptive capacity. Of these, 86% are women, concentrated in clerical and administrative roles. Goldman's scarring data, combined with the NBER vulnerability concentration, suggests the earnings gap will fall disproportionately on women in administrative roles in small metros and state capitals.

Consensus view: Lawrence Katz at Harvard and the Economic Policy Institute's Heidi Shierholz assess the Goldman scarring data as consistent with the 'career capital' literature: early-career displacement interrupts skill accumulation at the period of highest marginal return, creating compounding earnings gaps that conventional retraining programmes do not close.

Counter-view: The OECD's Mathilde Pak and NBER's Sangmin Aum argue that the Gen Z recovery differential Goldman identifies, faster recovery through occupational mobility and AI literacy, suggests the scarring effect is not deterministic for younger cohorts; workers who acquire AI-complementary skills within three years of displacement show measurably better earnings trajectories than the historical baseline.

Key tension: Whether AI-era displacement produces a new scarring pattern for adaptable younger workers, or whether the 40-year historical average continues to apply regardless of the specific technology driving the disruption.

Sources:Fortune (reporting Goldman Sachs research)·Fortune (reporting Goldman Sachs research)

Mentions:OECD →ChatGPT →Prima →Dallas →Goldman Sachs →National Bureau of Economic Research →

First Reported In

Update #5 · The model they won't release

Fortune (reporting Goldman Sachs research)· 10 Apr 2026

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Causes and effects

This Event

Goldman: early job loss cuts pay for a decade

The scarring data transforms the displacement debate from a cyclical jobs story to a generational earnings crisis concentrated on workers entering the labour market during the AI transition.

Led to

Meta codes its own org chart

Goldman's 40-year analysis of early-career displacement informed the framing of Meta's restructuring as a corporate morphology change rather than a cost cut.

Occurred 19 Apr 2026

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Different Perspectives

Salesforce, Synopsys and TD Bank Group

Salesforce, Synopsys and TD Bank Group each filed quarterly disclosures in late August booking restructuring charges, or none at all, without naming AI as a cause. Their silence matters because Challenger's tracker shows AI as a stated reason fell to fourth place in August even as the year-to-date AI-cut total still leads at 116,175.

Singapore, South Korea, Taiwan and Indonesia

Singapore launched its Skills and Workforce Development Agency on 16 September, giving citizens six months of free premium AI tools, while South Korea ring-fenced its AI tax windfall in a new Future Response Fund. Taiwan kept funding its AI build past NT$190bn and Indonesia rewired vocational training around AI literacy, betting state-built skills beat a market-led adjustment.

ver.di, CGT Fonction Publique and CCOO

Germany's ver.di banked a 3.3% pay rise on 1 September and opened talks on a Tarifvertrag Transformation covering dismissal bans and reskilling, while France's CGT rejected Paris's AI negotiating timetable the same week. Spain's CCOO went further on 21 September, proposing to tax companies by the jobs they generate rather than wait for the next bargaining round.

BIS General Manager and Federal Reserve governors

The BIS's General Manager said on 10 September that AI displacement remains limited, even as the BIS's own survey found nearly 80% of firms plan to automate roles. Two Federal Reserve governors made the same point in July, arguing the labour-market data does not yet show a mass-firing event.

Bank of Canada, ONS and ECB

The Bank of Canada found the job-finding gap between AI-exposed and unexposed occupations widened from 2.2 to 13.9 percentage points since 2015-19, while separations barely moved. That framing, a hiring freeze rather than a firing wave, is echoed by the ECB's finding that euro-area AI use hit 52% of workers in 2026, concentrated among the university-educated.

Office for National Statistics

Deferred its Transformed Labour Force Survey beyond November 2027 and disclosed a May 2026 telephone-collection failure. The ONS carries no AI-attribution layer at all, so Britain sits outside this month's cohort of measuring states by its own admission.