Fewer layoffs, longer searches

The index tracks long-term unemployment among managers and professionals: people whose last job was in management, business, financial or professional occupations and who have been unemployed 27 weeks or more, as a share of that labor force. It stood at 162 in August 2026 (2019 = 100), close to its June reading of 170, the highest since 2021.

Two cautions shape how to read the index. First, single months are noisy: the margin of error on a monthly reading is roughly ±26 points, so August 2026 (162) is not statistically different from August 2025 (128) on its own. The eight-month comparison below is. Second, the 2019 base was the strongest labor market of the past decade. The index ran between 131 and 201 in 2015–2016, so today's level is high relative to the late 2010s, not unprecedented.

Employers added 29,000 jobs in September, below the 84,000 economists expected (per Axios). July was revised to a loss of 10,000 and August to +133,000, a combined downward revision of 60,000. Unemployment ticked up to 4.2% as more people joined the labor force. Over the first nine months of 2026 the economy added 612,000 jobs, versus 232,000 in the same months of 2025 on current data.

The problem for job seekers is not a wave of firings. Layoffs and discharges are near 1% of jobs. It is a slow market: few people quit, few seats open up, and employed people who switch jobs still win 1.4 points more in raises than those who stay, so displaced candidates compete with employed ones for a thin flow of openings.

Information and financial activities are where white-collar jobs are disappearing. Professional and business services grew by 123,000 over the year, though computer systems design lost 32,000. Federal employment is down 327,000 since December 2024.

Employers announced 43,281 job cuts in September, the fewest for a September since 2022, according to Challenger, Gray & Christmas. Year to date, announced cuts total 573,195, down 39% from 2025. Most of that decline reflects the 2025 federal workforce cuts falling away: excluding government, cuts are down 15%. Hiring plans were the lowest for any September since 2011.

Technology accounts for 165,925 announced cuts this year, 29% of the total and up 54% from 2025. TrueUp counts 190,933 people affected by tech layoffs in 2026 so far, against 245,953 in all of 2025. Notable September announcements include Uber (about 3,300, citing fewer management layers), Workday (about 500, restructuring) and Microsoft (about 500, Xbox).

What companies say about AI versus what they file

Employers cited AI as the reason for 120,136 announced cuts this year, about 21% of the total and the most-cited reason, per Challenger. Formal filings cite it less often. We searched every 2026 SEC Form 8-K that references Item 2.05, the item public companies use to report material restructuring costs, and read every AI passage in full.

AI is more prominent in how layoffs are announced (21% of announced cuts) than in formal restructuring filings (about 9% of filings), but both measures show AI as a real part of how companies describe restructuring in 2026. The comparison has three limits. First, the units differ, workers versus filings, so the two percentages are not directly comparable. Second, Form 8-K Item 2.05 requires companies to disclose a restructuring decision and its expected costs, not its reasons, so a filing that is silent about AI does not show AI played no role. Third, Challenger codes the reason employers give publicly, which may reflect how companies want a cut to be understood. Neither source is wrong; they measure different things, and the gap between them is worth tracking.

Long searches rose across most occupations, which points to a broad slowdown rather than one sector. Against that backdrop, computer and mathematical occupations stand out: 41% of their unemployed have been out 27 weeks or more, up from 28%, the only occupation where the rise is statistically significant.

What independent research says about AI, functions and seniority

Our tables show where long searches are concentrated; they do not show what caused them. Research that tracks AI exposure directly points the same way on two points: early-career workers and some tech occupations are most affected, and most of the effect is on hiring rather than firing.

• Seniority · Stanford Digital Economy Lab (Aug 2026)

  Employment of 22–25-year-olds in the most AI-exposed jobs is 19% below comparable peers, up from 15% a year earlier. Older workers show no comparable gap; declines are concentrated where AI automates tasks rather than assists.

• Seniority · Revelio Labs (Aug 2026)

  Compared with non-adopters, firms using AI grew senior roles by 32% but junior roles by only 6%.

• Job postings · Indeed Hiring Lab (Jul 2026)

  Senior postings rose 14.7% over the year to May 2026 while entry-level postings fell 7.5%.

• Job titles · Stanford; Anthropic

  Software developers and customer service representatives show the clearest early-career employment declines. AI usage data show the heaviest task coverage in data entry, software development, medical transcription and database architecture.

• Broad effect · Yale Budget Lab; Federal Reserve (Sep 2026)

  The occupational mix is not yet shifting in ways tied to AI. Fed Governor Barr saw "little evidence of significant displacement so far" but "some indications" of fewer entry-level jobs.

• Middle management · surveys

  41% of employees say their organization cut management layers (Korn Ferry survey, cited by Forbes, May 2026). There are no hard counts yet of manager headcount, so "flattening" remains a trend to watch.

The best independent research reaches a consistent and fairly narrow conclusion. AI is not yet causing broad job losses across the economy. Its clearest measurable effect so far is fewer young workers being hired into AI-exposed jobs. The open questions are about the future and about transitions: how quickly firms reorganize around AI, and how costly the move to the next job is for the people affected. Each item below is labeled as primary research (the authors' own data) or commentary, and estimates of exposure are kept separate from observed job changes.

Observed effects so far

• Stanford Digital Economy Lab · primary research · Aug 12, 2026

  Using ADP payroll records covering 3.5–5 million workers a month, Brynjolfsson, Chandar and Chen find employment of 22–25-year-olds in the most AI-exposed occupations is 19% below where it would be had it kept pace with less-exposed peers. The gap runs through reduced hiring of young workers rather than more separations, and workers 35 and older did not diverge. The authors describe these as descriptive patterns, not causal estimates, and say they "do not see widespread, economy-wide job displacement associated with AI." Their February 2026 follow-up found that part of the timing reflects factors other than AI, including interest rates.

• Harvard (Hosseini and Lichtinger) · primary research, working paper · Oct 2025

  Across 62 million workers at 285,000 firms, junior employment at firms that adopted generative AI fell 7.7% relative to non-adopters after six quarters, while senior employment did not fall. Again, the mechanism was slower hiring, not layoffs.

• Brookings and Yale Budget Lab · primary research · Oct 1, 2025

  Molly Kinder and co-authors found the mix of jobs across high-, medium- and low-AI-exposure occupations "remained remarkably steady" in the 33 months after ChatGPT's release, changing only marginally faster than in the early computer and internet eras. They caution that this method can miss damage concentrated in narrow occupations, and that a widening gap between younger and older graduates is consistent with, but not proof of, an AI effect.

• Oxford (Llanos-Paredes and Frey) · primary research, working paper · 2025

  In one occupation where the technology is mature, translation, each one-point increase in machine-translation use was associated with about 0.7 points slower growth in translator employment, roughly 28,000 translator jobs that would otherwise have been created in 2010–2023.

• Stanford, MIT and Harvard field studies · primary research

  Within a job, AI helps novices most: customer-support agents gained 14% in productivity on average and 34% for the least experienced (Brynjolfsson, Li and Raymond, QJE 2025). In a Procter & Gamble experiment with 776 professionals, one person working with AI matched the output quality of a two-person team without it (Dell'Acqua, Sadun, Lakhani and others, 2025). Read together with the hiring studies, the tension is clear: AI makes junior workers more productive, yet fewer of them are being hired.

Exposure, adaptability and who is at risk

• Brookings (Manning, Aguirre, Muro, Methkupally) · primary research · Jan 21, 2026

  Of 37.1 million workers in the most AI-exposed quarter of jobs, 26.5 million (about 70%) have above-median capacity to adapt, based on savings, transferable skills, local job density and age. About 6.1 million workers, 4.2% of the workforce, are both highly exposed and poorly positioned to adapt; 86% of them are women, concentrated in clerical and administrative roles. For most of NextChapter's audience of experienced professionals, the issue is therefore less whether they can transition than how long and how costly the transition is.

• Opportunity@Work (Byron Auguste) with Brookings · primary research · Apr 2, 2026

  15.6 million workers without four-year degrees ("STARs," Skilled Through Alternative Routes) hold highly AI-exposed jobs, including 11 million in "gateway" jobs that have historically led to better-paid work. Only 51% of the paths from gateway jobs to better-paid jobs avoid high exposure. Opportunity@Work's 2026 State of the Paper Ceiling report says STARs have regained 783,000 good jobs after losing access to 7.4 million in 2000–2020, and that 33 states have dropped degree requirements for many public jobs. These are advocacy-organization figures; partner-network data come from self-selected employers.

• Harvard Business School and Burning Glass Institute (Joseph Fuller and co-authors) · projection · 2025

  "The Expertise Upheaval" projects that generative AI will change the learning curves of about 50 million U.S. jobs, potentially making many entry-level roles obsolete while opening "mastery" roles to less-experienced workers. These are estimates from skills and job-posting data, not observed job losses.

• MIT (Autor and Thompson) · primary research · 2025

  Across 303 occupations from 1980 to 2018, automation that removes the expert parts of a job lowers wages but widens access, while automation that removes routine parts raises wages but narrows who can do the job. Which way AI cuts for a given white-collar role is an empirical question, and it is the right question for mid-career workers to ask about their own jobs.

The economy-wide picture and the policy response

• MIT (Acemoglu) · peer-reviewed model · 2025

  "The Simple Macroeconomics of AI" estimates AI will raise total factor productivity by no more than about 0.66% over ten years, and by less than 0.53% once harder-to-automate tasks are accounted for. This is a calibrated model, not observed data, and it sits at the cautious end of published estimates.

• MIT Stone Center on Inequality and Shaping the Future of Work · commentary · 2026

  Acemoglu, Autor and Johnson argue in a Hamilton Project paper (Feb 2026) that AI's labor-market effects depend on design choices, and that "more than six out of 10 workers in 2018 were employed in occupational specialties that did not yet exist in 1940." The center launched in November 2025.

• Stanford HAI AI Index 2026 · compilation · Apr 2026

  The Index's economy chapter compiles other organizations' data, including that one-third of organizations expect AI to reduce their workforce in the coming year (McKinsey survey) and that measured productivity gains range from 14–15% in customer support to 26% in software.

• New York FutureWorks Commission · policy · 2026

  Governor Hochul launched the commission on March 19, 2026 to advise on workers' economic security and to build ways to track AI's effect on New York jobs in real time. Its co-chairs are Tom Perez, Thasunda Brown Duckett and Molly Kinder, and recommendations are due by the end of December 2026. Separately, New York's layoff-notice system has asked employers since March 2025 whether automation contributed to layoffs; in its first year, none of more than 160 filers said yes (Hunton Andrews Kurth, May 2026). Whether that reflects reality or under-reporting is unknown.

• RAISE US · program · launched Jun 25, 2026

  A new bipartisan nonprofit led by Gina Raimondo and Eric Holcomb, with more than $500 million in commitments from AI companies (including OpenAI's foundation, Anthropic, Microsoft and Amazon) and other employers, to pilot retraining, wage insurance for workers who take lower-paying jobs, and career navigation in four states. It has not yet published outcome data. (It is unrelated to New York's RAISE Act, an AI-safety law with no employment provisions.)

• Markle Foundation · status note

  Markle's skills-based hiring work, the Rework America Alliance, moved to Jobs for the Future in December 2023. We found no 2025–2026 Markle research on AI and jobs; we use Opportunity@Work and Jobs for the Future for current skills-based hiring evidence.

• MIT Martin Trust Center (Paul Cheek) · book · Aug 2026

  In No One Works Here (Wiley, 2026), Paul Cheek argues that firms will be reorganized around AI decision-making, and that hierarchies built around the limits of human communication become "structural debt." It is an argument, not a measurement, but it describes the organizational redesign that this report's Q3 addendum tracks through company filings and services firms.

What this adds up to: the measurable effect of AI today is concentrated at the entry point to white-collar careers and runs through hiring, not layoffs. Our own data show the other end of the problem, longer searches for experienced workers who lose jobs, but cannot attribute that to AI. Higher interest rates, the post-2022 tech correction and federal job cuts are all plausible contributors.

According to BLS, 1.94 million people had been unemployed 27 weeks or more in September, 27.1% of all unemployed, up from 23.6% a year earlier. Most of them have been out far longer than six months: about two in three have been looking for a year or more.

Age

Age shapes how long searches last more than whether people lose jobs. Among unemployed white-collar workers aged 55–64, 39% have been looking 27 weeks or more, up from 27% a year earlier, a statistically significant rise. Younger professionals lose jobs somewhat more often but find work faster. Across all workers, unemployment for ages 25–34 rose to 4.8% from 4.3% (significant), consistent with research showing fewer entry-level openings.

• Résumé gaps · Socio-Economic Review (2026)

  A meta-analysis of 28 résumé experiments finds gaps under six months do not hurt callbacks; penalties begin around 12 months and are larger for people who appear to have stopped looking.

• Scarring · Goldman Sachs (Apr 2026)

  Workers displaced by technology search about a month longer and see roughly 10 points less earnings growth over a decade; vocational training within three years of job loss is linked to better outcomes.

Workers 55 and older are less likely to lose a job than younger workers, but when they do, they are much less likely to get back to an equivalent one, and more of them leave the labor force entirely. Labor force participation for ages 55+ fell to 37.1% in January–August 2026, from 38.1% a year earlier and 40.2% in 2019.

What happens after a layoff

The Bureau of Labor Statistics' Displaced Worker Survey (January 2026, released August 27, 2026) counted 3.3 million long-tenured workers displaced in 2023–2025. By January 2026, 72.9% of those aged 25–54 were re-employed, against 57.3% of those 55–64 and 38.6% of those 65 and older. Across ages, only about 49% of workers who moved from one full-time job to another were earning as much as before, down from about 62% in the 2024 survey. For an experienced professional, the risk is less the layoff itself than the long road back to comparable pay.

• Age bias · AARP (2026)

  64% of workers 50+ have seen or experienced age discrimination at work and 22% felt pushed out. In AARP/NORC polling through summer 2026, two-thirds of adults 50+ think finding a new job would be hard, and 35% of those name age discrimination as the main barrier.

• Employers · SHRM (Oct 2025)

  93% of organizations have no formal program to recruit older workers, even though 88% of HR professionals rate older workers' performance as equal to or better than others'.

• Money pressure · AARP/NORC (2026)

  39% of working adults 50+ say covering basic expenses is their main reason for working, and nearly a third expect to retire later than planned, most often because of living costs.

• AI use by age · Gallup; AARP

  Among AI users, 64% of workers under 55 say it speeds up their work, against 54% of those 55 and older (Gallup, Oct 2026). 30% of adults 50+ use AI, up from 18% a year earlier (AARP, Dec 2025). The most common bias older workers report is the assumption that they lack tech skills (33%).

• Litigation · Mobley v. Workday

  The nationwide age-discrimination case over AI screening of applicants 40 and older remains in discovery; in June 2026 the court denied most of Workday's motion to dismiss, according to law-firm summaries.

Reading the data: falling participation among people 55+ partly reflects retirements of a large generation, and partly people who stop looking. The survey cannot separate the two cleanly, which is why we report participation next to long-term unemployment and re-employment rates.

Applications per job have roughly doubled since 2022, so each application is far less likely to get a response. Candidates use AI to apply faster, employers use AI to screen faster, and trust on both sides has fallen.

• How many applications it takes

  Among Huntr users, two-thirds of offers came within 50 applications. Interview rates fall as volume rises: 9.3% per application for people sending 11–20, 2.6% for people sending 100 or more. In that sample, seekers with 20+ years of experience had the highest interview rate (9.2%).

• Recruiters are overloaded

  Greenhouse counts 746 applications per recruiter per year in 2025, up from 146 in 2022, while recruiters per organization fell to 5 and time to fill rose to 57 days.

• AI on both sides

  In Greenhouse's November 2025 survey (U.S. subset: 1,200 job seekers, 665 hiring professionals), 70% of hiring managers said AI leads to faster, better decisions, but only 8% of job seekers called it fair. 41% of U.S. job seekers said they had put hidden instructions in résumés to get past AI filters. Only 21% of recruiters were very confident their AI does not reject qualified candidates.

• Fake jobs, fake candidates

  69% of job seekers said they had seen fake job postings (Greenhouse). Gartner forecasts that one in four candidate profiles worldwide will be fake by 2028; that is a prediction, not a measurement.

• Employer adoption · SHRM (Apr 2026)

  39% of organizations use AI in HR, rising to 60% of those with 5,000+ employees; recruiting is the most common use.

• Law and litigation

  Illinois began regulating AI in hiring decisions on January 1, 2026. California's rules on automated decision systems (October 2025) hold employers responsible for their vendors' tools. Colorado replaced its AI Act with a narrower law effective January 2027. In Mobley v. Workday, a nationwide age-discrimination case over AI screening of applicants 40 and older, about 14,000 people reportedly opted in; the case is in discovery. The EEOC withdrew its AI hiring guidance in January 2025.

AI skills now carry a measurable pay premium, and demand for them has spread well beyond tech. Universities are responding mainly with free certificates for alumni, while public retraining money is not yet reaching professionals.

• Pay premium · Lightcast (Jul 2025)

  U.S. postings that list AI skills offer 28% higher pay, about $18,000 a year; 51% are outside IT and computer science. Demand for AI skills grew 66% a year in HR, 50% in marketing and 40% in finance.

• Pay premium · PwC (Jun 2026, global)

  Workers with AI skills earn a 62% wage premium on average, up from 57%. PwC measures worker wages and Lightcast posted salaries, so the two are not directly comparable.

• Fastest-growing skill · McKinsey (Nov 2025)

  Demand for "AI fluency" in postings grew almost sevenfold in two years.

• Rising skills · LinkedIn; WEF

  LinkedIn's 2026 U.S. list of fastest-growing skills (as reported by EdTech Innovation Hub) is led by AI implementation, workflow automation and AI business strategy. The World Economic Forum expects about 39% of core skills to change by 2030.

The mid-career and senior skills gap

The skills gap for experienced professionals is less about technical knowledge than about hands-on use of AI and the training to build it. Employers increasingly screen for it; few are paying to close it.

• Usage by role · Gallup (Oct 2026)

  37% of managers use AI regularly, against 25% of individual contributors. Workers whose managers support AI are 1.7 times as likely to use it frequently (May 2026).

• Training gap · BCG (Jun 2026, 14 countries)

  88% of workers think they will need major new skills within five years; only 36% say they get enough training.

• Training hours · ATD (2025)

  Formal learning fell to 13.7 hours per employee in 2024, from 17.4 the year before.

• Hiring screens · Patriot Software (Jul 2026, small businesses)

  40% of small employers rejected a candidate in the past year for weak AI skills: 65% of Gen Z employers and 26% of Gen X employers. Experienced candidates are increasingly interviewed by younger managers who expect AI fluency.

• Entry-level spillover · NACE (2026)

  13.3% of entry-level postings now ask for AI skills, nearly three times the fall 2025 level, a sign of how quickly the expectation is spreading.

• Who gets trained · WEF; Urban Institute

  Of every 100 workers, 59 will need training by 2030 and 11 are unlikely to receive it (WEF). 92% of workers 50+ want to learn new skills, but only 10% have taken AI training in their field (Urban Institute).

• Does retraining pay? · Workcred; Chicago Fed

  Older trainees in federal workforce programs mostly choose short credentials, and industry certifications do best, but most still earn less than before their job loss. Earlier research found a year of community college raised older workers' earnings 8–10%. There is little recent rigorous evidence on mid-career AI retraining.

Universities and retraining

• Alumni view · NACM (2024)

  In a survey of 9,000+ alumni at 34 institutions, 49% rated their school highly on career preparation and 23% on its ongoing investment in their careers.

• Adult learners

  First-time college students aged 25+ fell 15.5% from fall 2024 to fall 2025, as reported by Inside Higher Ed.

• Workforce Pell

  Short-term Pell grants for 8–15-week programs began July 1, 2026, and bachelor's degree holders can qualify. Programs approved so far are in trades and health care, not white-collar retraining.

• WIOA and funding

  H.R. 8210 (A Stronger Workforce for America Act) passed committee on a party-line vote; the House FY2027 Labor-HHS bill would cut Labor Department training by about $3.3–3.7 billion while raising the Dislocated Worker National Reserve to $326 million. Labor Department guidance (TEGL 15-25) made about $50 million in "Rapid Reskill" grants available for workers affected by AI.

• Does retraining work? · Anthropic (Aug 2026)

  A review of 56 U.S. randomized trials finds training raises employment 2–3 points and earnings about $1,000 a year, at roughly $13,000 per participant; sector programs built with employers do several times better.

• State partnerships · California (Jul 2026)

  "AI-Ready California" offers free AI-literacy micro-credentials through San Diego State, including to unemployment-insurance claimants, and the state now tracks jobless claims in AI-exposed occupations monthly.

AI is reshaping which jobs grow, not yet how many there are. Postings that mention AI doubled in a year, to 6.7% of all U.S. postings, while the government's new ten-year projections expect declines concentrated in clerical, customer-service and routine programming work.

Source: BLS Employment Projections 2025–35 (August 27, 2026). Total employment is projected to grow 3.5%, to 176.2 million. BLS expects AI-powered automation to reduce office and administrative support employment by 4.0% (−752,100 jobs) and cites AI in projected losses in sales.

• AI demand · Indeed Hiring Lab

  Postings that mention AI reached 6.74% of U.S. postings on August 31, 2026, up from 3.44% a year earlier and 1.70% in 2019 (NextChapter calculation from Indeed's published tracker data).

• Rising roles · LinkedIn (Jan 2026)

  AI engineers and AI consultants top LinkedIn's 2026 U.S. Jobs on the Rise; data annotators (#4), independent consultants (#7) and founders (#9) also made the list, as reported by Allwork.Space.

• Programmers vs. developers

  BLS projects computer programmers to shrink while software developers grow: the work of writing routine code is being automated, while designing and integrating systems is not. Fortune, citing a Washington Post analysis of BLS survey data, reports programmer employment at its lowest since 1980.

• Skill demands · PwC (Jun 2026, global)

  AI-specialist roles are growing 69% a year against 9% for all jobs, and entry-level roles in AI-exposed fields are 7 times more likely to ask for senior-level skills.

• Global view · WEF (2025)

  Employers expect 170 million jobs created and 92 million displaced worldwide by 2030; cashiers, administrative assistants and graphic designers are among the fastest-declining roles.

Americans are filing to start businesses at near-record rates, and AI is making it cheaper to launch one. But the share of workers who are self-employed has not risen, so the boom in filings has not yet become a broad shift of displaced workers into running their own firms.

• AI lowers the cost of starting · Gusto (May 2026)

  60% of founders who started businesses in 2025 used AI to launch, up from 21% in 2023. Financial stability (51%) now outranks autonomy (46%) as the main reason for starting.

• Smaller founding teams · Carta; Stripe

  Solo founders were 36.3% of new startups in the first half of 2025, up from 23.7% in 2019 (Carta). 42% of companies formed through Stripe Atlas in 2025 were AI-focused, and 20% had a paying customer within 30 days.

• Business AI adoption · Census Bureau

  Between 17% and 20% of businesses reported using AI from December 2025 to May 2026, but under 20% of firms with four or fewer employees, against 37% of firms with 250 or more.

• Independent work · MBO Partners (2025)

  72.9 million Americans did independent work, 27.6 million of them full time, and 74% of independents used generative AI.

• Funding · SBA

  The SBA's 7(a) and 504 programs lent a record $44.8 billion in fiscal 2025, including $5.6 billion to startups.

For displaced professionals: consulting, fractional and founder roles are growing options, and AI tools lower the cost of trying one. The data so far show more people filing to start businesses, not more people earning a living from self-employment, so a bridge business works best alongside a search, not as an assumed replacement for salary. No rigorous 2025–2026 study yet tracks how many laid-off professionals start businesses.

Over the year to August 2026, job growth was fastest in South Carolina, New Mexico and Louisiana (+1.6% each), and Texas added the most jobs (+159,400). The District of Columbia was the only place with a statistically significant decline (−27,000, −3.6%), driven by government job losses.

For white-collar work the split is sharp. Professional and business services jobs grew about 3% in Texas and North Carolina but fell in Virginia (−1.8%), DC (−2.7%), California and Massachusetts. Financial-activities jobs fell in California, Washington, Massachusetts, Virginia and Maryland. Unemployment rates are highest in DC (5.7%), California, Connecticut and Oregon (5.1% each) and Michigan (5.0%), and lowest in South Dakota (2.0%) and North Dakota (2.2%). Connecticut (+1.0 point) and Oklahoma (+0.9) had the largest over-the-year increases; New Jersey (−1.2) and Ohio (−1.1, a record low of 3.3%) improved most.

In the 12 months to August 2026, 39.3% of unemployment insurance claimants exhausted their benefits. Claimants drew benefits for 15.8 weeks on average, at $493 a week. These are completed benefit spells; the 24.8-week average in Exhibit 4 is for searches still in progress, so the two measure different things. Together they suggest many long-term job seekers are past the end of their benefits.

• Uneven access

  State recipiency rates ranged from 8% to 55% of the unemployed in 2025 (Minneapolis Fed). Florida and North Carolina cap benefits at 12 weeks. Virginia raised its maximum weekly benefit to $478 and Iowa to $790 in July 2026.

• Severance

  Some states delay or offset benefits during paid severance; executives should check their state's rules before filing.

• Paid family leave

  Delaware and Minnesota began paying benefits January 1, 2026, and Maine on May 1; Minnesota received about 100,000 applications in six months. Maryland's start is delayed to 2028. Returnship programs continue at large employers, but we found no reliable 2026 count.

• AI layoff disclosure

  California's SB 951 takes effect January 1, 2027. A federal bill, the AI-Related Job Impacts Clarity Act, would require large employers to report AI-related layoffs to the Labor Department.

• Funding timeline

  A federal stopgap keeps workforce programs funded through December 11, 2026.

The two groups outside the white-collar core are moving in opposite directions. New college graduates face the weakest entry-level market in years, and their unemployment advantage over peers without degrees has narrowed. Skilled trades are short of workers, wages are rising faster than average, and AI exposure is low.

New college graduates

The degree still pays, but less of a cushion than it used to. Young graduates' unemployment rate is 1.5 points above 2019 while young high-school graduates' rose 0.8 points, so the gap between them has narrowed from 3.0 to 2.2 points. The Cleveland Fed finds young graduates' job-finding rates have fallen to roughly match high-school graduates', the narrowest gap since the late 1970s (as reported by Fox Business).

• Official data · NY Fed; BLS

  Recent graduates aged 22–27 had 5.6% unemployment and 42% underemployment in Q2 2026. Unemployment for all 20–24-year-olds fell to 8.0% in September from 9.2% a year earlier (BLS, seasonally adjusted).

• By major · NY Fed (2024 data)

  Computer engineering (7.8%) and computer science (7.0%) are among the five majors with the highest unemployment, yet they still have low underemployment and median early-career pay near $87,000–$90,000. Criminal justice and performing arts graduates are most often underemployed (66% and 64%). Figures as reported by Research.com.

• Hiring and pay · NACE (2026)

  Employers planned to hire 5.6% more class-of-2026 graduates and 45% rated the market "fair." Projected starting salaries: computer science $81,535 (+6.9%), engineering $81,198, business $68,873.

• Entry-level squeeze · Stanford; Anthropic; PwC

  Employment of 22–25-year-olds in the most AI-exposed jobs is about 19% below less-exposed peers (Stanford, Aug 2026). Job starts in AI-exposed occupations for that age group fell about 14% after ChatGPT launched (Anthropic). Entry-level roles in AI-exposed fields are 7 times more likely to ask for senior-level skills (PwC).

• AI readiness · Handshake; NACE; ZipRecruiter

  85% of 2026 seniors use AI, but only 28% say their programs meaningfully built it in, while 13.3% of entry-level postings already ask for AI skills (NACE). Fewer than 30% of 2026 graduates received substantial AI training (ZipRecruiter survey). 62% of seniors are pessimistic about the market, up from 46% two years ago (Handshake).

• What helps · ZipRecruiter; Strada

  82% of 2026 graduates with work experience found jobs, against 41% without. Graduates whose first job requires a degree are 3.5 times as likely to be in a college-level job ten years later (Strada), which is why the first job matters so much.

Blue-collar and skilled trades

• Shortages · ABC; HBI

  Construction needs about 349,000 new workers in 2026 and 456,000 in 2027, mostly to replace retirees; about one in five electricians is over 55 (Associated Builders and Contractors, Jan 2026). The skilled-labor shortage costs homebuilding about $10.8 billion a year (Home Builders Institute).

• Data centers · BLS; IBEW/NECA

  BLS projects electricians to grow 9% over 2025–35, with about 72,700 openings a year, citing AI and data centers. Electrical work is 45–70% of the cost of building a data center, and union apprenticeship applications rose from about 70,000 to 120,000 between 2022 and 2024 (Fortune, citing IBEW and NECA).

• Training pipeline · DOL; National Student Clearinghouse

  Registered apprentices number about 700,000, the first year-over-year dip in a decade (Bloomberg Law, citing DOL data). Enrollment at vocational-focused community colleges rose 2.8% in spring 2026, and undergraduate certificate programs grew fastest (+10.2%) while computer science enrollment fell.

• Automation · IFR (Jun 2026)

  U.S. industrial robot installations rose 11% to 38,000 in 2025, led by food and non-manufacturing industries rather than autos.

• AI exposure · Anthropic (Mar 2026)

  Construction, installation and repair, and transportation occupations show the lowest observed AI exposure; about 30% of workers have none.

What it means for professionals: trades are not a realistic pivot for most experienced office workers, but the contrast explains why headline job numbers look steadier than white-collar workers feel. Adjacent roles do exist where office skills meet the building boom: project management, estimating, procurement, safety and operations in construction, energy and data-center firms.

The BLS jobs report is released at 8:30 a.m. Eastern, usually on the first Friday of the month. This report publishes within two business days after it; the White-Collar Index is updated when the Census Bureau posts that month's microdata, usually one to two weeks later.

Frontier signals: language and expectations

• "Super Intelligence" in federal usage · Sep 29, 2026

  Executive Order 14434 directs federal agencies to use "Super Intelligence" (SI) in place of "Artificial Intelligence" in their own documents. For now SI carries the same legal definition as AI (15 U.S.C. 9401(3)), and the President's science adviser has 60 days to propose a statutory definition. The order has no employment provisions. A voluntary "White House Accord on Super Intelligence" signed the same day by leaders of major AI companies commits them to safety controls and audits. This report continues to use "AI," the term in its data sources, and notes the federal terminology where relevant. The renaming is a change of terminology; it does not mean federal agencies have found that current systems are smarter than humans, which is what AI labs usually mean by "superintelligence."

• Recursive self-improvement (RSI)

  RSI refers to AI systems that design and develop their own successors. Anthropic reported that, as of May 2026, more than 80% of the code merged into its codebase was written by its own model, and has called for the option to slow or pause frontier development if needed; OpenAI was reported in September 2026 to have reached its goal of an automated AI "research intern." METR estimates the length of tasks AI can complete has been doubling roughly every six to seven months, but cautions that this does not mean tasks of that length can be delegated reliably. Skeptics, including Princeton's Sayash Kapoor, find AI agents still weak at original research. We include RSI not as a forecast but because it is the main argument for why AI's labor-market effects could accelerate faster than past technologies'.

The September edition of each quarter (March, June, September, December) carries a quarterly addendum. It uses data that only make sense over a full quarter or a year, such as company filings, business surveys and corporate results, and it asks a question the monthly figures cannot answer: are firms changing how they are organized, and is that showing up in hiring? Figures here are labeled as source-reported or as NextChapter calculations. Several Q3 government releases (GDP, the Employment Cost Index and productivity) come out in late October and early November and will be added in the next edition.

The quarter combined fewer announced layoffs with very little hiring. AI's share of stated layoff reasons fell in Q3 (14%, against 21% for the year), and so did the share of formal restructuring filings citing AI. One quarter is too short to call that a trend. The productivity and labor-share figures are economy-wide and say nothing on their own about AI, but they are the measures to watch if AI begins to raise output faster than employment.

What firms expected versus what happened

Business surveys let us compare what firms said they would do with what they later did. Four patterns stand out. Firms expect sales to grow three to four times faster than employment. Small firms keep saying they plan to hire, but their actual employment is shrinking. AI adoption has run ahead of expectations. AI-related layoffs have run well behind them.

The pattern fits a cautious, low-hire market more than a wave of AI substitution: firms expect to grow without adding much headcount, and only a small minority report that AI has reduced their need for workers so far. A larger minority expect it to in the next few years. That gap between expectation and outcome is exactly what this section will track each quarter.

What company filings say about AI and restructuring

We read every 2026 Form 8-K that references Item 2.05, the item public companies use to report material restructuring costs, and coded each AI mention by what role AI plays, following the evidence levels in Appendix B. A mention in a risk factor is not evidence that AI caused a layoff; a company's statement that its plan is designed around AI is a company's stated rationale (evidence Level 1), not proof of effect.

Two things stand out. First, when companies do cite AI in a restructuring filing, they split between AI changing how work is done (8 companies) and cutting elsewhere to fund AI (6 companies). The second is a reallocation story, not a substitution story. Second, nine in ten restructuring filings say nothing about AI. Filings are required to disclose the decision and its costs, not its reasons, so silence does not show that AI played no part.

Revenue per employee at large white-collar employers

Can large companies grow without adding staff? We compiled revenue and year-end headcount from the annual reports (10-Ks) of the 30 largest U.S.-listed companies by revenue in white-collar-intensive industries (software and internet, banking, insurance and managed care, financial services and professional services), comparing each company's most recent fiscal year with the year three years earlier. Twenty-seven reported comparable headcount in both years.

The pattern is striking, but it is not evidence that AI replaced workers. Under the evidence levels in Appendix B, this is at most Level 2 for any company that also attributes gains to AI. Higher interest rates lifted bank revenues; health insurers raised premiums; Meta, Alphabet and Oracle were correcting pandemic-era overhiring; and acquisitions change both lines. What it does show is that the largest white-collar employers are generating far more revenue per worker than three years ago without adding people, which is consistent with the low hiring rates in Chapter 02 and the "growth without hiring" expectations above. We will update this sample annually and add operating income per employee and payroll as a share of revenue where companies disclose them.

The labor-market services economy

The question: as job searches get longer and screening becomes automated, are workers and employers spending more on help navigating the labor market, such as recruiting, staffing, executive search, outplacement, coaching and job-search tools? The evidence says total spending is not rising, but its mix is shifting.

Staffing remains in a cyclical slump, and freelance marketplaces are shrinking, possibly because AI substitutes for some gig work. Growth is in executive search, permanent placement and paid job-board pricing. Application volume is the clearest sign of a navigation problem: Ashby, a recruiting-software company, reports applications per hire have roughly tripled since 2021 to more than 300 (vendor data). Two cautions apply. Most evidence on outplacement and coaching demand comes from the vendors themselves, and LinkedIn reports that applications per applicant fell 24% over the year to March 2026. We found no independent data on the size of the outplacement or career-coaching markets.

Remaking the organization: forward-deployed engineers and AI services firms

A new kind of company is being built specifically to redesign other companies' workflows around AI. This matters for white-collar workers because it moves AI from tools individuals choose to use toward the organization-wide redesign described in Appendix B's adoption stages, which is the stage most likely to change headcount.

• AI labs' services ventures · 2026

  Anthropic, Blackstone, Hellman & Friedman and Goldman Sachs, with other investors, formed an enterprise AI services company (announced May 4, 2026; named Ode with Anthropic in July) that places engineers inside client companies. OpenAI formed a deployment company backed by TPG and other investors, with McKinsey, Bain and Capgemini as consulting partners, and acquired the London consultancy Tomoro. The engineers in these roles are called "forward-deployed engineers"; Fortune reported postings for the title rose more than tenfold in January–August 2026 from a year earlier.

• AI roll-ups of services firms · 2025–2026

  Thrive Holdings, backed by Thrive Capital with an OpenAI stake, has acquired more than 50 accounting firms and about 20 IT services firms (as reported by The Next Web) and automates their work with AI; it raised $2 billion in August 2026. Long Lake, backed by General Catalyst, has made about 40 acquisitions and agreed in May 2026 to buy Amex Global Business Travel. These models target exactly the back-office and professional-services work that employs many white-collar workers.

• Consultancies · 2025–2026

  Accenture took an $865 million restructuring charge in September 2025; CEO Julie Sweet said the firm was "exiting people so we can get more of the skills in we need." It reported about 814,000 people at the end of fiscal 2026. Bloomberg reported McKinsey plans to cut about 10% of non-client-facing roles. PwC cut 5,600 staff in the year to June 2025 and dropped its pledge to add 100,000 people. Deloitte U.S. replaced its analyst-to-manager titles with new job families in June 2026. Consulting pyramids, traditionally built on large junior cohorts, are the clearest test of the junior-compression hypothesis in the Future Research Agenda.

Reemployment after job loss: the biennial check

The BLS Displaced Worker Survey, run every two years in January, measures what happens after a layoff. Of 3.3 million long-tenured workers displaced in 2023–2025, 66.1% were reemployed by January 2026 and about 49% of those earned as much as or more than before, so roughly half took a pay cut. After the 2009–2011 layoffs, only 56% were reemployed and about a third took cuts of 20% or more. Today's displaced workers are faring better than after the Great Recession, but reemployment is not recovery: half of those who found work earn less than they did. Reemployment rates for workers 55–64 are lower (see Chapter 07).

Every major wave of automation has eventually created more work than it destroyed. Every wave has also imposed large, long-lasting costs on the specific workers and places whose tasks lost value. History is a guide to the shape of an adjustment, not a forecast of AI's effects.

New work does appear, but not for the same people

About 60% of U.S. employment in 2018 was in job titles that did not exist in 1940 (Autor, Chin, Salomons and Seegmiller, Quarterly Journal of Economics, 2024). The same research finds that the job-destroying effect of automation was more than twice as strong after 1980 as in 1940–1980, while new work increasingly appeared in high-paid professional and low-paid service jobs rather than in the middle. Earlier waves of computerization hollowed out routine clerical and production work and polarized employment toward the top and bottom (Autor, Levy and Murnane, 2003; Goos, Manning and Salomons, 2014).

The costs of displacement are large and long-lasting

• Earnings after a mass layoff

  Long-tenured workers lose about 25% of annual earnings for many years after a mass layoff (Jacobson, LaLonde and Sullivan, 1993). Over 20 years, the loss averages 1.4 years of prior earnings when the layoff happens while unemployment is under 6%, and 2.8 years when it is above 8% (Davis and von Wachter, 2011). When you lose a job matters almost as much as whether you lose it.

• Industrial robots

  Each additional robot per thousand workers reduced the local employment-to-population ratio by about 0.2 points and wages by about 0.42% in 1990–2007 (Acemoglu and Restrepo, Journal of Political Economy, 2020).

• The China trade shock

  Import competition cost an estimated 2.0–2.4 million U.S. jobs in 1999–2011, including spillovers (Acemoglu, Autor, Dorn, Hanson and Price, 2016). Most of the adjustment in hard-hit areas came through people leaving work rather than moving away, and the damage persisted long after the shock (Autor, Dorn and Hanson, 2021).

• Engels' pause

  During Britain's early industrial revolution, output per worker rose 46% in 1780–1840 while real wages rose 12%. Wages caught up only in the following decades (Allen, 2009). Carl Benedikt Frey's The Technology Trap (2019) argues that such lags are the norm when technology replaces rather than complements workers.

• Bank tellers

  ATMs cut tellers per branch from 20 to 13 in 1988–2004, but banks opened 43% more branches, so teller jobs held up (Bessen). Two decades later, mobile banking did what ATMs did not: BLS now projects teller employment to fall 13% by 2035. The same technology can complement a job for years before substituting for it.

Predictions have often been wrong in both directions

Frey and Osborne's 2013 estimate that "about 47 per cent of total US employment is at risk" of computerization was widely read as a forecast of job loss, which the authors did not intend. Task-based estimates put the share closer to 9% (OECD, 2016), and the predicted wave did not arrive in the following decade. Exposure estimates for AI today, such as MIT's "Iceberg Index" finding 11.7% of U.S. wage value technically exposed, measure what AI could do, not what employers will do. Those are different questions.

Lesson for this report: the history most relevant to white-collar workers today is not whether total employment survives (it has every time) but the duration and cost of transitions for the people whose tasks lose value. That is what our long-term unemployment measures track.

Remote work solves geography but not occupational transition. It has leveled off at about a fifth of all workers and a third of managers and professionals. Fully remote jobs are scarce and draw a heavily outsized share of applicants, and the evidence suggests they are also more exposed to hiring abroad. Each of these links is a hypothesis to test, not an established cause of longer searches.

• Competition for remote roles

  When 9% of postings draw 37% of applications, a remote search is a far more crowded search. That is consistent with, but does not prove, longer searches for professionals who restrict themselves to remote work.

• Offshoring of remote-friendly work · Revelio Labs

  From 2019 to 2024, U.S. firms' overseas headcount grew 32% against 16.7% at home, and roles suited to remote work grew 42% faster abroad, while roles that are not remote-friendly grew at similar rates in both. This is vendor analysis of online profiles, not official data. Richard Baldwin's "globotics" thesis predicts exactly this combination of remote work and overseas "telemigrants."

• Return-to-office mandates

  The Flex Index found 34% of U.S. companies required full-time office attendance in Q3 2025, with required office days up 12% since early 2024 but actual attendance up only 1–3%.

What we can and cannot say: remote work widens the pool of competitors for each remote job, at home and abroad. We have no evidence yet linking remote work to longer white-collar unemployment spells. The CPS telework questions make that testable in future editions.

Some of the most important questions about AI and white-collar work cannot yet be measured rigorously. Rather than estimate them to make the report feel complete, we list them here with the data and validation each requires. No number appears for any of these measures until it meets its publication threshold.

Framework: four stages of AI adoption (a hypothesis, not a finding)

We organize this agenda around a hypothesis: AI's effect on jobs may depend less on whether workers use AI and more on whether companies redesign work around it. Stage 1, individual augmentation: AI helps a worker do the job. Stage 2, task substitution: AI performs part of the job. Stage 3, workflow automation: AI performs an entire workflow. Stage 4, organizational redesign: the company changes its structure. Most survey evidence today describes stages 1 and 2. The forward-deployed engineering firms and AI roll-ups described in the Q3 addendum are explicitly selling stages 3 and 4.