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AI: Jobs, Power & Money
23APR

AI job losses concentrate in under-25s

4 min read
14:51UTC

The Federal Reserve Bank of Dallas finds the jobs vanishing from AI-exposed industries belong overwhelmingly to workers who never held them — entry-level positions that simply stopped being posted.

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EconomicAssessed
Key takeaway

AI is eliminating the career ladder's bottom rung through hiring suppression, making youth displacement statistically invisible.

The Federal Reserve Bank of Dallas found employment down approximately 1% in the top 10% of AI-exposed industries while the broader economy continued to add jobs 1. The decline landed mostly on workers younger than 25. The mechanism is not mass termination but collapsed job-finding rates — positions that stopped being advertised rather than workers who were dismissed.

A separate Dallas Fed paper provides the structural explanation 2. It distinguishes between "codified knowledge" — textbook procedures, the kind of work that can be documented and therefore automated — and "tacit knowledge", the hands-on expertise built through years of practice that AI cannot readily replicate. Entry-level workers possess mostly the former. Returns to experience are rising in AI-exposed occupations: seasoned workers are gaining pay rises in the same sectors where doors are closing for new entrants.

The pattern has independent corroboration from multiple directions. Year-to-date hiring fell 56% compared with the same period in 2025 , with UBS chief economist Arend Kapteyn attributing record-low white-collar turnover partly to "AI fear." Nonfarm payrolls dropped by 92,000 in February against a consensus estimate of +50,000 . Anthropic's own research, by Maxim Massenkoff and Peter McCrory, found no systematic unemployment increase among heavily exposed occupations since late 2022 but identified "suggestive evidence" of slowing hiring of younger workers 3 — a finding the Dallas Fed data NOW independently confirms. ServiceNow CEO Bill McDermott's projection that college graduate unemployment could reach the "mid-30s" within a couple of years 4 may be hyperbolic, but the directional trend in the Dallas Fed's data does not contradict the underlying concern.

The long-term risk is structural. If companies stop bringing in junior workers, the pipeline that produces the experienced professionals AI currently complements dries up within a generation. The labour market is not shedding workers in a visible, politically legible way — it is quietly narrowing the entrance. Aggregate employment figures do not register a crisis that is happening in who gets hired rather than who gets fired, which means the policy responses NOW taking shape in Washington — the Warner-Rounds commission , Sanders's proposed robot tax, California's SB 951 — are calibrated to displacement through firing, not displacement through the slow closure of the entry-level door. The Dallas Fed's data suggests the actual mechanism may already be outrunning the policy framework designed to address it.

Deep Analysis

In plain English

Economists at the Dallas Federal Reserve studied industries most exposed to AI — software development, financial services, data analysis, office administration. They found that overall employment in those sectors fell roughly 1% even while the broader economy was adding jobs. But companies were not firing people in large numbers. Instead, they simply stopped hiring young workers. Graduates and under-25s could not get their foot in the door. This matters because it will not appear in standard unemployment figures — those workers are not classified as unemployed, they are simply not yet employed.

Deep Analysis
Synthesis

The 'collapsed job-finding rate' mechanism is the most fiscally dangerous form of AI displacement: it generates no unemployment claims, no severance costs, no union triggers, and no identifiable individual stories for press coverage. It is systematically invisible to every standard labour metric. Connected to the Brookings finding that approximately 75% of federal tax revenue derives from labour taxation (Event 17), quiet youth hiring suppression represents a slow fiscal haemorrhage with no political alarm mechanism — the deficit impact will only become measurable after cohort scarring is already advanced and partially irreversible.

Root Causes

Large language models are most capable at precisely the tasks historically assigned to entry-level workers: drafting, summarising, basic research synthesis, data entry, and routine analysis. Prior automation waves displaced specific tasks but created adjacent entry-level roles requiring new skills. LLMs remove the training function of junior roles — the mechanism through which tacit knowledge and professional judgement are acquired — not merely the labour cost. This structurally distinguishes the current wave from every prior automation event on which long-run optimism about job creation is empirically based.

What could happen next?
  • Consequence

    Youth hiring suppression will not appear in headline unemployment data for years, systematically reducing political urgency until cohort scarring has compounded to the point of irreversibility.

    Medium term · Assessed
  • Risk

    If the Japan 'employment ice age' parallel holds, the current under-25 cohort faces permanently lower lifetime earnings and elevated welfare dependency regardless of subsequent macroeconomic recovery.

    Long term · Suggested
  • Meaning

    The 'not-hiring' mechanism severs the entry-level training function through which tacit knowledge is acquired — the very knowledge Dallas Fed identifies as AI-resistant — creating a self-reinforcing cycle of reduced human capability in AI-exposed sectors.

    Medium term · Assessed
  • Risk

    Suppressed entry-level hiring reduces payroll tax receipts without generating unemployment claims, creating a fiscal gap that is invisible to standard budget forecasting models and incompatible with the Brookings labour-tax-dependency analysis.

    Short term · Suggested
Sources:Federal Reserve Bank of Dallas·Fortune
Mentions:Federal Reserve →Ukraine weapons export ban (2022) →United States →Bureau of Labor Statistics →AI fear →Mike Rounds →robot tax →Bank of England →Senator Mark Warner →Bernie Sanders →ServiceNow →AI Job Displacement →Anthropic →Brookings Institution →US Treasury →Bill McDermott →Arend Kapteyn →Maxim Massenkoff →Peter McCrory →
First Reported In

Update #2 · 45,000 tech layoffs, half may be reversed

Federal Reserve Bank of Dallas· 22 Mar 2026
Read original →
Causes and effects
This Event
AI job losses concentrate in under-25s
The Dallas Fed isolates a displacement mechanism invisible in aggregate statistics — entry-level positions vanishing from AI-exposed industries through hiring freezes rather than terminations. If sustained, this closes the pipeline that produces the experienced workers AI currently complements.
Led to
Strong March payrolls mask tech decline
Dallas Fed finding that employment losses concentrate among under-25s through collapsed job-finding rates prefigures the reemployment time extension.
Occurred 3 Apr 2026
Read story →
AI hollows out entry-level career paths
Dallas Fed finding on collapsed job-finding rates for under-25s is confirmed and extended by SSRN seniority trap finding across 62 million resumes.
Occurred 29 Mar 2026
Read story →
Different Perspectives
UK financial regulators (BoE FPC / FCA)
UK financial regulators (BoE FPC / FCA)
The Bank of England's April FPC directive on agentic AI in payments was scoped around one frontier model; AISI confirmed a second model cleared the same 32-step threshold on 1 May. The supervisory architecture is one model behind the capability it was built to contain.
Indian IT sector workers (TCS, Infosys, Wipro)
Indian IT sector workers (TCS, Infosys, Wipro)
TCS posted its first annual revenue decline in the modern era, Infosys shed 8,400 workers in a quarter, and Wipro hit its zero-fresher target. Western Big Tech's AI automation is cannibalising the offshored-services model that employs roughly five million Indian IT workers.
Chinese workers (Hangzhou and Beijing plaintiffs)
Chinese workers (Hangzhou and Beijing plaintiffs)
Workers Zhou and Liu won cases that established a two-court doctrinal chain: AI adoption is the employer's deliberate strategy, placing the cost of displacement on the employer rather than the worker. Any Chinese employee facing AI-driven dismissal now has a citable legal route that American, British, and European counterparts do not.
Chinese government, courts, and domestic employers
Chinese government, courts, and domestic employers
The Hangzhou rulings were released on Workers' Day eve alongside the Ministry of Human Resources' recognition of 42 new AI occupations. Domestic firms now face mandatory retraining obligations; the Orgvue estimate of 8-14 months added to displacement timelines will feature in employer compliance briefings throughout 2026.
EU regulators and European Parliament
EU regulators and European Parliament
The second Digital Omnibus trilogue collapsed without agreement on 28 April; the third is scheduled for 13 May with the binding employer AI-literacy obligation still contested. Brussels is arguing over a non-binding encouragement clause while Beijing's courts have already bound employers.
US legislators (Warner, Rounds, Hawley, Sanders)
US legislators (Warner, Rounds, Hawley, Sanders)
Warner and Rounds produced the Economy of the Future Commission Act, the most concrete federal vehicle still moving, endorsed by the companies it would notionally regulate. The Sanders-AOC moratorium was killed by Democratic senators; the Hawley-Warner disclosure bill remains in committee with no floor date.