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Inaugural edition of Revelio Labs' monthly AI Labor Market Tracker, built on online professional profiles, job postings, salaries, employee reviews and WARN notices. Five headline gauges: CS/IT enrollment supply -28% since 2022; demand for the most AI-exposed roles -42% vs least-exposed since Oct 2022; adopting-firm headcount +27% vs non-adopters; activity-mix dissimilarity +8.4pp yoy; 5.05 postings per hire. Early-career (22-25) employment in most-exposed occupations is down 13% relative to least-exposed since pre-ChatGPT — an independent replication of Brynjolfsson, Chandar & Chen (2025) on non-payroll data. Firm AI adoption reaches ~5.9% of eligible hiring firms; the AI-exposure wage premium has eroded from ~2% to roughly zero.

OpenAI Economic Research, 'Work at the Frontier: How AI is expanding what people do at work' (Chin & Richmond, Jul 27 2026). Random sample of 800,000+ work-related messages from individual ChatGPT accounts of US users whose occupations were linked via ChatGPT Business role data, across eight occupation groups (customer experience, design, engineering, finance, HR, legal, marketing, sales). Each message is classified to a single O*NET detailed work activity and compared with the sender's stated occupation. Headline: '16.8 percent of all work-related messages—and 43.5 percent of occupation-specific messages—concern tasks historically associated with another occupation'; 21.8% are within-occupation and 61.5% generic. Cross-occupation is a majority of occupation-specific messages in five of eight groups: customer experience 77%, design 75%, HR 69%, legal 56%, marketing 53%. Direction of travel differs from volume of borrowing: 'About 35.2 percent of messages sent by designers involve work usually associated with another occupation. But design tasks make up only 1.7 percent of the messages sent by workers in other fields.' Engineering is the inverse (18.5% borrowed in, 7.4% traveling out); marketing leads both directions (24.3% in, 8.9% out — 'the highest share in the sample'). Recurring borrowed tasks: calculating financial data and troubleshooting computer applications each rank top-three in all seven non-native occupation groups. Smaller workspaces show more crossover among typical-volume users: 18.9% at 2-5 seats vs 16.3% at 101+ seats. The report explicitly disclaims employment inference: 'This study maps AI-aided work; it does not estimate AI's effect on employment or productivity,' and the unit of analysis is 'a message, not an hour of work, a completed project, or a job.'

Stanford SIEPR policy brief reviewing observed AI labor market effects. Using IPUMS-CPS data, the unemployment rate for the top quintile of AI-exposed workers has risen by 0.77 percentage points since 2022, while the unemployment rate for the least-exposed workers rose slightly more, by 0.85 percentage points over the same period — a differential implying no measurable net AI displacement in aggregate US employment. Employment growth in coding-heavy occupations has slowed somewhat but remains positive, and online job postings for software developers have grown faster than for other occupations over the last year. Among firms that adopted enterprise AI, employment grew by 10 percent in the two years following adoption. In Census BTOS data only 5 percent of firms report any employment impact, with equal numbers reporting gains and losses. New-graduate unemployment reached 5.6 percent in early 2026, up 1.6 percentage points from three years earlier.

FRED has incorporated '137 data series about the adoption of generative artificial intelligence (AI) technology in the United States, reported by Alexander Bick, Adam Blandin, and David Deming' (FRED Announcements, Jul 24, 2026). The RPS GenAI module, fielded quarterly since August 2024, now publishes as official FRED series covering usage rates (overall, work, nonwork), time savings, and adoption compared with the PC and internet, with industry and occupation breakdowns. Latest FRED readings: work adoption 43.4% and overall adoption 57.9% of working-age adults in Q1 2026 (RPSGENAIUSAGESHAREWORK, RPSGENAIUSAGESHAREALL); GenAI-assisted work hours 6.3% in Q2 2026, up from 4.1% in Q4 2024 (RPSGENAIASSISTWRKHRSALL); reported time savings 2.2% of work hours in Q1 2026 (RPSGENAITSALL).

Google's AI & Economy ATLAS v1.0 (Iscenko, Strand, Chen, Imas, Manyika et al., Google/Google DeepMind, Jul 23 2026; reviewed by Diane Coyle and David Autor). 15M de-identified interactions across Gemini App, AI Mode, and Gemini API (Apr 6-19, 2026), mapped to 800+ occupations, 4,000 O*NET tasks, 300 ATUS activities, 150 countries, 140 languages. Work: 'AI adoption spans occupations covering just above 88% of US employment' (68% of detailed occupations) but 'penetration remains shallow' — 'AI is used for only 21% of total tasks in the median occupation with any AI use'; only 3% of occupations show usage for >75% of tasks. 'Attempts to automate tasks end-to-end represent less than 10% of AI conversations in non-routine cognitive work'; >25% for routine cognitive work. Non-routine cognitive tasks = 35% of O*NET universe but 65% of work interactions. Wages: 'a 1% increase in an occupation's median earnings is associated with a more than 2.5% increase in AI usage intensity'; Gemini-weighted median salary $82,919 vs $62,252 employment-weighted national median. Home: 86% of conversational use is non-work; government/civic queries over-represented ~20x vs time spent; household time-savings valued at $14.9B-$149B/yr (0.5-5% scenarios). Global: 1% GDP/capita ↑ → 0.9% usage ↑; English only ~1/3 of conversations across 143 languages.

July 22, 2026 monthly refresh of the Canaries Dashboard (ADP payroll sample, 4.6M workers, 730+ occupations), data through June 2026. All-ages employment in the most-exposed quintile: -0.2% YoY, +1.1%/yr annualized since ChatGPT vs +2.0%/yr least-exposed. Early-career (22-25) most-exposed: -3.5%/yr annualized, -4.3% past year. New gender analysis: early-career women in the most-exposed quintile contracting 4.5%/yr vs 2.5%/yr for men; 43.8% of early-career women work in the most-exposed occupation category vs 32.4% of men.

IBTimes (Pham Binh, Jul 20 2026) synthesis of US Census Business Formation Statistics + Bloomberg analysis. Headline: '~29,700 new employer businesses are expected to form each month nationwide over the next year, a 17% increase compared to 2025 estimates.' Professional services sector (legal, architectural, advertising) forming at '>5,000 companies per month, a 24% year-over-year increase.' Bloomberg reporting: 'Since the launch of ChatGPT in 2022, new business filings in professional services grew four times faster than in construction.' Torsten Slok (Apollo Global chief economist): 'We've never created as many businesses. It does tell you that AI is playing a very big role.'

Federal Reserve Board FEDS Note cataloguing publicly available indicators for tracking the generative AI buildout across three stages: capabilities and costs; firm investment and adoption; and productivity and labor. Notes that headline adoption figures do not reflect usage intensity, which surveys suggest remains shallow even where reported adoption is broad, and that the absence of a large aggregate signal as of 2026 would not necessarily invalidate future productivity impacts given that shallow adoption. Tracks sectoral productivity by AI exposure level, unemployment and labor force participation for workers aged 20-24 against prime-age workers, and layoffs, discharges and job openings in information-processing sectors as a white-collar displacement signal. Methodological reference rather than a source of new estimates.

FT opinion piece (mid-July 2026) synthesizing the PwC 2026 Global AI Jobs Barometer findings on entry-level work. Argues AI is not eliminating entry-level jobs but transforming them: newcomers previously learned by doing simple, repetitive tasks — the apprenticeship rung — but AI has automated that rung. What remains for entry-level workers now leans on judgment, idea generation, and interpersonal capability — historically senior skills. The piece frames the 'seniorisation' finding (entry-level roles 7x more likely to need senior skills; seniorised roles +35% since 2019 vs -10% for other entry-level roles) as a collapse of the traditional learning-by-doing path, not a jobs bloodbath.

Freelancers performing AI work on Upwork earn 34% more per hour than those not incorporating AI. GenAI/creative production contracts grew 90% YoY in contract starts but per-contract earnings fell 13% — a signal that 'lower-complexity AI execution may become less lucrative as it scales.' Freelancers doing more complex work with AI saw earnings rise 45% YoY; AI-augmented professional services grew 72% in volume with 22% earnings gains. Skilled freelancers now represent 38% of U.S. knowledge workers, up from 28% (a 10pp increase); 58% of full-time employees now consider freelancing, up from 36%.

On JPMorgan's Q2 2026 earnings call (July 14, 2026), CEO Jamie Dimon disclosed that AI has 'already reduced headcount by 30% to 40% in certain specific areas' — most affected workers redeployed internally rather than laid off. Dimon pushed back against expectations of dramatic margin gains: 'in a competitive capitalist world, everybody is going to use AI to serve their customers better,' and such improvements 'won't happen anytime soon.' CFO Jeremy Barnum warned AI token spending will grow 'at a non-trivial pace' in H2 2026. JPMorgan maintains ~1,000 active AI use cases.

Statement organized by Erik Brynjolfsson, Ajay Agrawal, Anton Korinek, and Tom Cunningham. Signed by 16 Nobel Laureates (including Michael Spence and Daron Acemoglu) and 200+ economists and AI researchers. Core thesis: AI 'may become radically more powerful over the next 10 years,' potentially producing an economic transformation 'larger than the Industrial Revolution' on 'a vastly shorter time frame,' with 'large-scale job displacement' as a primary risk. Calls for deeper research on AI's economic impacts and for building policies and institutions to ensure AI complements human capabilities. Korinek: 'Steam, electricity, and computers each gave societies decades to adapt; AI may give us only a few years.' Brynjolfsson: 'AI capabilities are advancing far faster than our understanding of economic implications.' Agrawal: 'Whether rapidly advancing AI broadly elevates living standards or concentrates wealth depends on choices we make today.' Cunningham: 'We are driving in the fog, and it is extraordinarily difficult to anticipate what will happen next.'

The Digital Education Council's AI in Higher Education Global Survey 2026 draws on 45,398 responses from 27,284 students and 18,114 faculty across 35 countries. 88% of students now use AI in their learning; 77% of faculty use it in their teaching, up 16 percentage points on 2025. 57% of students say assessments come with inadequate AI guidance; only 29% believe instructors are equipped to guide them on AI use; 31% of faculty feel meaningfully involved in institutional AI policy. Faculty intent to use AI in teaching dropped from 76% to 67% in US/Canada — the lowest globally — while other regions show 89-94% intent.

US Census Bureau Business Formation Statistics for June 2026 (released July 9, 2026). Total Business Applications (seasonally adjusted): 531,423 in June 2026, an increase of 1.1% compared to May 2026. Projected Business Formations (within 4 quarters), seasonally adjusted: 29,741 in June 2026, an increase of 0.7% compared to May 2026. BFS provides monthly, high-frequency information on new business applications and formations. The high-propensity applications series (HBA) tracks applications most likely to become employer businesses; definition was updated in Nov 2021 and applied retroactively.

University of Minnesota regents approved an operating budget eliminating 230 jobs through layoffs and attrition (cutting ~$44M). Johns Hopkins laid off ~110 employees concentrated in the Bloomberg School of Public Health, Carey Business School, and central administration — the university 'has lost hundreds of millions of dollars in federal funding since President Trump took office in January 2025.' The New School laid off 87 employees (19 professors, 68 staff). Southern Oregon University will shutter three academic programs and cut 66 positions (23 faculty, 43 staff).

NYT chief economics correspondent Casselman synthesizes the measurement problem: different data sources give contradictory answers on basic questions (how many companies use AI, which workers are most vulnerable, whether AI is helping or hurting employment, whether the productivity boom is real). Reports on Nathan Goldschlag's (Economic Innovation Group) new report documenting the measurement challenge; a bipartisan Senate bill (led by Sen. Mark Kelly, D-AZ, introduced June 2026) that would expand federal AI labor data collection; Yale Budget Lab's new monthly 'occupational churn' analysis as an early-warning system; Stanford DEL's ADP-based Canaries dashboard; and new Ramp/Revelio research finding companies using AI most intensely are adding jobs FASTER than laggards — the opposite direction from displacement narratives. Frames the current confusion as J-curve territory: companies still on the downward experimentation phase before productivity gains materialize. Notes federal statistical system is under stress from falling survey response rates and funding cuts; former BLS Commissioner Erika McEntarfer (fired by Trump last year) says $10M/yr would meaningfully expand the monthly labor market survey.

EIG applies five different measures of AI exposure (drawn from four different research papers) to test whether current labor market data shows an AI signature. The startling finding: which exposure measure you pick determines not just the SCALE but the DIRECTION of AI's estimated effect on employment. Under some measures AI appears to be hurting employment; under others it appears to be helping. Companion piece 'A New Threat to Economic Data' documents the deteriorating federal statistical system (falling response rates, funding cuts) that Casselman NYT (July 2) also foregrounds. Goldschlag is EIG's Director of Research and formerly Principal Economist at Census's Center for Economic Studies.

BLS Employment Situation, June 2026 (released July 2, 2026). Total nonfarm payroll employment changed little in June (+57,000), roughly in line with the average monthly change over the prior 12 months (+36,000). Unemployment rate 4.2% (dipped slightly). Health care +22,000 (below prior 12-month average of +38K); leisure and hospitality declined by 61,000 (weaker seasonal hiring). Prior months revised down: April -31K (to +148K), May -43K (to +129K); combined -74K. Average hourly earnings for private nonfarm payrolls +13 cents (0.3%), to $37.64. Wall Street had expected 115,000 jobs — actual well below.

US Census Bureau BTOS 2026 AI Supplement (reference period Nov 17, 2025 - Feb 8, 2026; released 2026). Full biannual supplement replacing the biweekly two-week question with a six-month lookback plus new modules on task substitution, wage/employment effects, and planned adoption. Headline (Q1, 2-week): 17.9% of US businesses used AI in any function in the past two weeks (SE 0.13%). Use case incidence (6-month): sales/marketing 14.3%, strategy 12.4%, IT 11.4%, R&D 11.2%, PR/comms 9.3%. Task substitution: 10.1% used AI to perform a task previously done by an employee; 43.7% to supplement/enhance an employee task; 10.6% to introduce a new task. Of firms that substituted, 70.9% substituted 'a small number' of tasks, 22.0% moderate, 7.1% large. Employment effect: 95.7% report no change in total employment from AI use; 2.3% increased, 2.0% decreased. Generative AI specifically: 20.8% of firms report employees using GenAI at work in past six months; of those, 85.4% used for writing/editing, 49.9% for information search, 44.6% for translation/analysis, 34.7% for paperwork, 30.0% for new-project development. Sector leaders (Q1 two-week): Information 37.6%, Professional/Scientific/Technical 34.2%, Education 30.7%, Finance/Insurance 30.4%, Real Estate 23.5%. Six-month outlook: 21.6% expect to be using AI, with sales/marketing (62.8%) and strategy (57.4%) leading.

Bloomberg (Boesler & Prakash, Jul 2 2026): 'A decline in payrolls in the financial-activities and information sectors — where AI adoption rates have been fastest — has accelerated in 2026, to 28,000 per month on average based on government data.' 'The weakness stands out against an otherwise robust labor market that created more than 113,000 jobs monthly this year through May.' Challenger data: 'almost 102,000 announced job cuts attributed to AI so far this year.' 'Overall, the tech sector accounted for a third of all layoffs announced in 2026.' Finance workforce: 'Office and administrative support occupations — including customer service representatives, bank tellers and insurance claims processors — account for about a quarter of employment in financial activities.' California Policy Lab: 'Finance and insurance had the highest concentration of unemployment claims in the state coming from workers in highly AI-exposed occupations.'

First study to link observed firm-level AI spending to workforce outcomes at scale. Tracked AI spending across 21,599 US firms via Ramp expense-management data, matched to Revelio Labs employment records. Companies that invest heavily in AI grew headcount 10% over the two years following adoption; entry-level headcount grew 12%. Gains are ENTIRELY driven by high-intensity adopters — low-intensity adopters see no statistically significant change. Direction is opposite to the displacement narrative: heavy AI adopters ADD jobs faster than laggards. Caveats: AI adopters are already larger, more engineering-intensive, more likely to be venture-backed, and faster-growing than non-adopters; effect may partly reflect selection into adoption rather than causal effect of adoption.

Chandar — coauthor of the Brynjolfsson-Chandar-Chen 'Canaries' paper — explains why he was one of 5 of 16 economists on a WSJ panel to predict AI would cause net job loss (others: Acemoglu, Henderson, Restrepo, Wolfers; 8 said no change, 2 said net growth). All 16 agreed AI would boost productivity. The 5 net-loss economists also unanimously said AI would replace rather than complement workers and would reduce demand for white-collar jobs. Chandar's argument: his net-job-loss prediction is not a 'jobs bloodbath' story — he expects AI to make people rich enough (via capital income or transfers) that the income effect dominates the substitution effect, lowering labor force participation in the long run (~50yr horizon). He explicitly cites Kinder's 'messy middle' framing as the short-to-medium-run risk. Useful disambiguation of what economists mean by 'net job loss' — long-run LFP decline driven by post-scarcity income, not displacement-driven destitution.

Creative Boom's flagship 2026 survey of 882 creative professionals (UK/US-weighted; 43% with 10+ years experience): Nearly 47% of self-employed creatives earn less than £30,000 a year (vs UK median full-time salary of £39,039). 69% experienced burnout in past 12 months — mid-career 77%, early-career 74%, studio founders 59%. 50% feel less financially secure than a year ago; 38% considering a job change; 7.5% planning to leave the industry. 86% use AI tools in their work but only 10% believe AI's impact on the industry is positive; 58% describe it as mixed, 28% straightforwardly negative. 'Creatives aren't refusing to use AI; they're adopting it because they feel they have to.'

Anthropic's June 2026 Economic Index 'Cadences' report combines anonymized Claude usage patterns with a survey of ~9,700 Claude users. Headline findings: about half of surveyed users report AI can already handle 50% or more of their work tasks; 4% say Claude could perform their entire job today; more than one-third expect AI to do most or nearly all of their work tasks within 12 months. The report also documents a widening 'cadence' gap between experienced and newcomer Claude users — experienced users automate substantially more of their work. Biggest automation increases occur in business sales, automated trading, and routine market-research tasks, flagging those roles as near-term automation candidates.

A bipartisan consortium called RAISE US launches with a 'people strategy' for the AI era — led by former Commerce Secretary Gina Raimondo (CEO) and former Indiana Gov. Eric Holcomb. Founding employers include Amazon, Microsoft, Bank of America, and Eli Lilly; OpenAI and Anthropic are involved; MIT economist David Autor sits on the advisory board. The group has raised $500M+ (about half its multiyear goal) and will initially work with Arkansas, Maryland, Utah, and Connecticut. Mandate goes beyond retraining: revisiting unemployment insurance so displaced workers can keep benefits while starting AI-enabled businesses, and developing corporate incentives for employers to retain and reskill rather than lay off.

Difference-in-differences study on 49,610 Upwork workers and 2.26M contracts (2021Q1–2026Q1) around the release of ChatGPT. Uses text embeddings of worker profiles and Shapley values to quantify the predictive importance of human capital signals (self-presentation, credentials, reputation) and price. In max AI-exposed categories vs. unexposed, contract volume fell ~7.0% post-ChatGPT (9.6% late period, 2025Q2–2026Q1); the combined importance of human capital signals fell 7.8% (10.1% late) while price importance rose 1.1% (1.8% late). Demand premium for high-human-capital workers compressed by 6.2% (10.3% late), and demand reallocated toward lower-priced workers by 3.2% (7.9% late). Authors interpret as empirical evidence of AI-driven labor commoditization — clients view differently-skilled workers as more substitutable when AI compresses output quality.

US Census Bureau Business Trends and Outlook Survey (BTOS) biweekly release Jun 18, 2026. As of the May 3, 2026 reference date: national AI use rate = 19.8% of responding businesses. Between Dec 2025 and May 2026 the national AI use rate hovered between 17% and 20%, with 20-23% of firms expecting to use AI in the next six months. Sector breakdown: Information 39.7%; Finance & Insurance 33.9%; Retail Trade ~14%. Sample: approximately 1.2 million businesses with biweekly data collection.

Anthropic Economic Research (Hitzig, Massenkoff, Lyubich, Zhang, Heller, McCrory — Jun 16, 2026). Analysis of ~400,000 Claude Code sessions from ~235,000 users between Oct 2025 and Apr 2026. Verified success rates: 'Novice: 15%; Intermediate/Expert: 28-33%.' Partial success rates: 77% (novice) vs 91-92% (intermediate/expert). Abandonment when troubled: novice 19% vs intermediate+ 5-7%. 'Every one of the ten largest occupations in our dataset lands within seven points of software engineers' (29-34% verified success). 'The estimated value of the average session rose by 27% between October and April.' Division of labor: 'people make about 70% of the planning decisions but only 20% of the execution decisions.' Work mix: code writing/fixing/testing 56%, ops 17%, planning/exploration 14%, analysis/prose 13%. Novice: ~5 actions, 600 words output/prompt; Expert: ~12 actions, 3,200 words/prompt.

Yale Budget Lab's preferred econometric strategy finds no strong evidence of AI's impact on aggregate US employment or unemployment 33 months post-ChatGPT. Measures of AI usage show no detectable connection to changes in employment or unemployment. The labor market in early 2026 features low layoffs but also low hiring, with payroll growth ~20,000 net new jobs per month and unemployment 4.3% in March 2026. Authors caution that historically technological disruption unfolds over decades, not months — AI is likely to leave its mark eventually, but the macro signature is not yet visible. A companion to the lab's March 2026 CPS update.

PwC 2026 Global AI Jobs Barometer (Atkinson & Brown, Jun 15, 2026). Analyzed 1+ billion job advertisements across 27 countries and territories, including 2.4M entry-level US jobs. Key findings: entry-level roles most exposed to AI are 'seven times more likely to require traditionally senior-level skills'; job openings for 'seniorised' entry-level roles grew 35% since 2019 while other entry-level roles declined 10%. AI-skills wage premium reached 62% (up from 57% in the 2025 barometer), ranging from 16% (government) to 118% (consumer markets). AI-skill jobs growing 69% vs 9% for the total jobs market — 'almost twice as high as 2024.' Companies most exposed to AI: 52% headcount growth vs 36% (least exposed); wage growth 24% vs 17%; productivity 34% vs 24% (2018-2025), with the top-20% 'super-stars' at 163% productivity gain. 'Professionalised' roles growing twice as fast with 42% faster salary increases. Technology/media/telecom: 11% AI job share; health: <1%.

The 114th International Labour Conference adopted Convention C193 on Decent Work in the Platform Economy on June 12, 2026 — the first legally binding international treaty dedicated entirely to regulating the digital platform economy. Attendance: 5,700+ delegates from 187 ILO Member States. The Convention extends fundamental rights (freedom of association, collective bargaining, protection from discrimination and forced labor), occupational safety and health, adequate remuneration, data protection, and algorithmic decision-review mechanisms to platform workers. It establishes global protections for more than 150 million workers who earn their living through digital labour platforms.

An 18,000-word fictional five-year scenario (June 2026) by European AI researchers warning the EU risks becoming AI-dependent on the US or China by 2031. Puts Europe at 5% of global AI compute against ~80% for the US in the scenario. Three identified 2025 misjudgements drive the trajectory: underestimating AI's pace, underestimating its scope of change, and overestimating Europe's catch-up capacity. Scenario beats include a 2027 ransomware wave (open-source frontier model) hollowing out European cybersecurity, US and Chinese firms acquiring distressed European carmakers and machine-tool makers, and conversion of factory floors to robot production. Narrative form (two protagonists in Brussels and Silicon Valley), not policy paper. Positioned in the Yelizarova Economic Futures Map at 'concentrated gains, strong replacement.'

many economists are more concerned about a different, larger group of white-collar workers: customer service representatives, bookkeepers, payroll clerks and human resources specialists who fly under the radar but collectively account for tens of millions of jobs.

Monthly-updated dashboard built on anonymized ADP payroll data covering 4.6 million workers across 730+ occupations. April 2026 reading: most AI-exposed occupations contracted 0.2% YoY, least-exposed grew 0.1% YoY — a small but consistent gap. For workers ages 22-25 in highly AI-exposed occupations, employment is now shrinking at 3.8% per year, with the early-career decline sharpening from -2.8% to April 2024 to over -4% per year since. Builds on Brynjolfsson, Chandar, Chen (2025) 'Canaries in the Coal Mine.' Live update of the underlying employment story behind the 'overall not yet, early-career already' pattern.

EDUCAUSE's 2026 Workforce Report examines how higher-education technology and data teams are adapting to AI, financial pressures, and shifting institutional priorities. The report draws on survey and focus-group data to trace how external forces reshape institutional strategies, roles, critical competencies, and skills. Central themes: AI strategy, rising workloads, and expanding team responsibilities are reshaping the workforce. The report is designed for higher-ed leaders, managers, and professionals navigating workforce change driven by AI, financial pressures, and evolving institutional needs.

Amazon unveiled next-generation Proteus, an autonomous mobile robot that takes commands in conversational language, at its 'Delivering the Future' event in London. The original Proteus, deployed since 2022, is now in 25 U.S. fulfillment centers, with the new version rolling out in Europe in H1 2027. Amazon has eliminated 30,000 corporate positions since October as it prioritizes AI initiatives. Amazon UK/Ireland VP John Boumphrey told CNBC: 'our experience of robots is that it's driven up employment rather than the reverse.'

US Tech Sector Cut 38,242 Jobs in May, AI Most Cited Reason for Layoffs

SAG-AFTRA members ratified a four-year deal with the AMPTP on June 4, 2026 — 91.42% approved, 8.58% opposed, 19.25% turnout. AI protections: 'synthetic AI-generated performers may only be used when they provide significant additional value to a project'; studios must have 'an articulable business reason' to scan a performer for a digital replica; minimum payment rates and residuals apply to independently created digital replicas; digital replicas cannot be used to circumvent strike participation. Minimum wage increases 3% annually; health plan contribution rises 1% July 1. Pension plan merger targeted for Jan 1, 2028. Deal effective July 1, 2026 through June 30, 2030.

occupations characterized by low exposure to precarity had a significantly higher mean LLM exposure [mean 0.386, 95% confidence interval (CI) 0.356–0.417]

AI-driven investing has grown steadily since the early 2010s and is concentrated among hedge funds. Average AI labor intensity is 1.17% of job postings at SEC-registered investment advisers. AI hedge funds peaked at ~2.7% of all hedge funds in 2023.

Stanford Digital Economy Lab Research Note #1: AI Economic Indicators June 2026 Update (Brynjolfsson, director). Three linked dashboards: (1) Canaries — 5-year balanced ADP payroll sample of 25,000 firms, 4.6M workers, 730+ occupations. Across all ages, most-exposed occupations growing 1.1%/yr vs 2.0%/yr for least-exposed since ChatGPT. For early-career workers (22-25, 7.4% of sample), most-exposed occupations contracting 3.8%/yr vs +2.0%/yr for least-exposed. The AUTOMATION ratio (using Anthropic Economic Index) correlates with employment declines; the augmentation ratio does not. Software developers and customer service reps show substantial early-career declines; home health aides grow. (2) Takeoff Tracker — 12 aggregate US indicators of AI-driven takeoff. As of May 2026: 7 show no evidence, 3 mild, 2 strong. Capital share = strong evidence (persistent upward trend); TFP growth = neutral (no break from recent levels); IP equipment share of private nonresidential equipment = mild (recovering to early-2000s levels). 'No decisive evidence of takeoff.' (3) Adoption Monitor — individual work adoption trending up in most surveys but reversing in some recent workplace data; firm adoption widespread and US-led; robotics and autonomous vehicles show largest current-vs-expected adoption gaps.

Doctorow's book-length argument that AI is mostly deployed not to automate work but to put workers into 'reverse centaur' configurations — humans serving as helpers to machines at inhuman pace (delivery drivers, warehouse pickers, AI-supervised coders). Argues the $16T+ AI investment thesis only makes sense if AI replaces vast swathes of the wage-earning workforce, and that workers, not 'AI-enjoyers,' are the political constituency for resisting that path. Companion talk delivered at University of Washington Neuroscience-AI-Society lecture series. Positioned in the Yelizarova Economic Futures Map at 'concentrated gains, strong augmentation' (augmentation in the cynical sense — humans augmenting machines, not the reverse).

Goldman Sachs revised its US labor displacement forecast upward from 6-7% to ~9% (about 15 million workers) over the next decade. The change reflects a new methodology measuring total flow of workers leaving jobs due to AI-driven productivity gains, rather than steady-state unemployed count. Briggs estimates each 1% increase in technology-driven productivity yields a 0.5-0.6% increase in the job-destruction rate over the following two years. Goldman maintains the long-run benefits outweigh the disruption (US churns 25-35M jobs annually, and AI will generate new employment), and that unemployment rises less than 1 percentage point at peak under standard adoption timing. As of mid-2026, Goldman estimates AI is erasing ~16,000 net jobs per month, concentrated in entry-level and administrative roles.

Connecticut SB 5 requires employers filing WARN Act notices to disclose whether layoffs are related to AI (effective Oct 2026), and requires notice to employees/applicants when automated systems are a substantial factor in hiring, promotion, discipline, or discharge (effective Oct 2027).

In the last three months of 2025, call centers fielded 150,000 questions. Three-quarters of the time, A.I. was able to provide the right answer to straightforward questions. The agent then reviews and if necessary, modifies and refines the answer with the caller.

Stripe Atlas (Jesse Carey, May 28 2026). Analysis of thousands of solo-founded Atlas startups incorporated 2022-2023 with 2+ years of revenue data. Headline: solo founders account for '63% of C corps formed so far in the second quarter of 2026 — an all-time high.' Revenue split: median solo-founder revenue -23% YoY in 2025; top-decile +19%. Top decile earns 61x median (vs 34x four years prior). Top-decile founders sold into 10 countries in month 1 (vs 3 for median) and 40 non-US countries by month 24. International revenue share: 51% top-decile vs 2% median. Top-decile month-one retention 29% vs 8% middle-decile. AI angle: 'Top-decile founders approximately twice as likely building AI-native companies'; AI-native startups generated nearly 2x revenue of non-AI at month 24. Top founders 20-26pp more likely to use recurring-billing models.

Anthropic Economic Research (Lyttelton, Massenkoff, Wilmers — May 27, 2026). Survey of 1,260 quantitative social scientists. '81% of respondents said yes' to using generative AI to aid research. '20% of respondents use coding agents' regularly (weekly). '86% of users reporting Claude Code use.' Demographic disparities: 'those with typically male names have adopted coding agents at more than twice the rate of respondents with typically female names.' Career stage: 'Just over a quarter of doctoral students and postdocs use coding agents' vs tenured professors at less than half that rate. University prestige: 'Researchers at top universities are 40% more likely than others to use coding agents.' Task usage: '97% of coding agent users and 77% of other AI users report using it to generate code.' Productivity effects over 6 months: users show approximately 'a quarter of a paper more' in project starts and 'around a half of a working paper more' in working papers, but 'no evidence that coding agent users are submitting more new papers to journals.' '88% of respondents were above a 5' on 10-point AI productivity scale.

Goldman Sachs's economists estimate that, over the next decade, A.I. may automate 25 percent of current work hours. If our estimate proves correct, A.I. won't eliminate 25 percent of jobs. What's more likely is that people will find more productive ways to spend their time. American companies destroy and create between 25 million and 35 million jobs annually.

Stripe Economics (Ernie Tedeschi, May 19 2026). New business applications rising worldwide with US divergence between total applications and 'high-propensity' employment-generating filings. Stripe Atlas data: 'startups have accelerated since 2023, and especially in the first quarter of 2026' — 'overwhelming[ly]' driven by solo founders across both AI and non-AI ventures. SaaS market context: 'Over 30 days in early 2026, the software sector shed roughly $1 trillion in market capitalization,' but weekly transactions for the 100 largest non-AI SaaS companies on Stripe showed 'a brief dip followed by a swift recovery' — the SaaSpocalypse was expectations-driven, not economic-activity-driven. Historical framing: post-electrification (1882), inflation-adjusted output per worker grew just 0.5%/yr for three decades before productivity 'more than doubled' in the decade after 1917; PCs showed 'gains everywhere except the productivity statistics' (Solow 1987) with visible productivity acceleration mid-1990s.

Lambert & Schindler (2026, SSRN 6787638) find declines in the junior hiring share in AI-exposed jobs, replicating the Canaries pattern — BUT show these patterns can be explained by exposure to remote work, not AI per se. A key counter-evidence paper cited in Stanford DEL Research Note #1 as one of the candidate confounds in the current debate over whether the entry-level employment decline is causally attributable to generative AI adoption.

While job postings show a relative decline in vacancies in occupations with greater exposure to AI, that divergence began before the release of ChatGPT in late 2022. These patterns make it difficult to attribute the recent slowdown in entry-level hiring to AI alone.

Gusto 2026 New Business Formation Report (May 2026, 6th annual). '60% of new business owners used AI to help launch their business in 2025' — nearly 3x the 21% in 2023. Among AI-using founders: 75% used it to develop business ideas, 53% for administrative/legal tasks, 51% for setting up operations. Industry adoption: Professional Services 56% (highest); Goods-Producing 43%; Community Services 36%. Generational: 71% of Gen Z founders vs 42% of Boomers used AI. Growth linkage: 49% of AI-using new businesses plan headcount growth in 2026 vs 41% of non-AI-using — 'AI adoption is associated with growth rather than job displacement.' Gusto also reports Gen Z entrepreneurs outnumber Boomers in new business starts for the first time.

Analysis of 29,585 PR lifecycles across 5 major AI coding tools using an Initiator x Approver taxonomy. Collaborator workflows are >=96% agent-initiated, yet terminal merge authority remains almost exclusively human, with agent-classified approvers confined to a small fraction of PRs.

despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%

FactSet S&P 500 Q1 2026 earnings-season update (published May 8, 2026 with 89% of S&P 500 having reported actual Q1 results). AI citations: 'about 65% of S&P 500 earnings calls have cited the term AI so far,' slightly below the prior quarter's 68% (which was the highest percentage going back at least five years). AI remains substantially elevated vs historical levels. Overall Q1 2026 earnings performance: 84% of reporting companies beat EPS estimates; aggregate earnings 18.2% above estimates. Co-trending terms: 'Middle East' and 'oil' both at 5-year highs alongside AI.

Synthetic differences-in-differences (SDID) comparing AI-exposed (top tercile) vs. a synthetic comparison group of unexposed (bottom tercile) occupations through 2026Q1. Finds 'no clear evidence of AI effects on the labor market' for employment shares or real hourly wages. Unemployment in latest quarter ~0.5pp higher for AI-exposed (more for 16-34 subsample) but statistically insignificant. Demographic differences make naive comparisons unreliable: AI-exposed occupations are 55.1% women and 57.3% BA+ vs. unexposed 32.7% and 10.3%. Treatment date 2022Q4 (ChatGPT release). Caveats noted: LLMs improve over time, exposure metrics may misclassify, CPS underpowered for 22-27 cohort.

Synthesis of micro-level firm evidence finds generative AI already embedded in everyday business practice. Productivity gains in the near term come from task reallocation and upskilling rather than headcount reduction, with high-skill workers who effectively augment their output capturing a widening wage premium. The brief cautions that this benign short-run picture may mask longer-run structural displacement.

AI-as-augmentation out-mentions AI-as-substitution on earnings calls by ~8:1. Software Development jobs (both by count, and a percent of the overall job market) have been increasing since the beginning of 2025. The aggregate effects of AI on employment are "basically null" per recent academic research, with some evidence of reallocation between jobs and tasks.

responses to a recent survey suggest that most forecasters are quite optimistic about AI's potential but are assuming only a slow pace of AI adoption through 2030

Major AI industry figures (Altman, Jensen Huang, Andreessen) are pivoting from displacement-focused messaging to augmentation-focused messaging amid deteriorating public opinion on AI. The new pitch: AI will create new tasks in the short term (task creation, Jevons Paradox), and in the long term humans will be paid for the 'relational sector' where the human element is the product itself. Noah Smith cautiously endorses the new pitch as better PR and potentially self-fulfilling for research direction, while noting it may be partly competitive positioning by OpenAI against Anthropic.

Analysis of public communications from five major AI labs finds a consistent narrative framing domain experts as interchangeable data suppliers rather than irreplaceable professionals. The reframing creates a cheap-expertise gig economy with downward pressure on credentialed knowledge-worker wage premiums and freelancer rates.

WIOA rarely supports worker resilience to automation, with 45% of all WIOA participants returning to their prior industry of work, and 27% staying in the same occupation. Successful outcomes driven mostly by wage gains, possibly due to catch-up mean reversion, rather than changes in occupation.

Construct a new index based on reinforcement learning (RL) covering every occupation in the U.S. economy. Scoring all 17,951 O*NET tasks across 894 occupations on eight dimensions of RL-training feasibility. A difference-in-differences analysis finds that a one-SD increase in RL exposure is associated with a 2.9% decline in job openings after ChatGPT's release.

Strada Institute surveyed 1,498 US executives and senior talent leaders (Mar 3-22, 2026). 46% of employers that have at least explored AI say it increased entry-level hiring in 2025 vs 13% decrease (4-to-1); 2.7x more expect AI to raise than cut entry-level hiring in 2026. 92% are engaging with AI in some way; AI literacy is ranked the least important entry-level skill.

Reweighting to Bureau of Labor Statistics workforce shares attenuates estimates by 42 to 93 percent.

Kinder predicts a long, hard 'messy middle' between today's mostly intact labor market and any post-AGI abundance. In that period, most jobs survive but losses concentrate in some of the best-paid, most coveted jobs — cognitive computer work in offices and professional sectors, which are exactly the roles that have grown the most as a share of the US labor force over the last 50 years. Frames these concentrated losses as politically explosive. Her rule of thumb: 'if you can do your job locked in a closet with a computer, you're probably in trouble.' Positioned in the Yelizarova Economic Futures Map at 'concentrated gains, strong replacement.'

Generative AI Adoption Tracker (Bick, Blandin, Deming), May 2026 update: '45.2% of the employed respondents used genAI for work.' Based on the Real-Time Population Survey (RPS), a nationally representative online labor market survey of US adults 18-64 running continuously since 2020. Tracker visualizes findings from 8 combined survey waves encompassing approximately 40,000 respondents, weighted to be nationally representative.

Oliver Wyman finds '63% of healthcare organizations have already integrated AI-powered automation into their revenue cycle workflows.' Studies show 'up to nearly 46% reductions in coding time for complex cases' and clinical accuracy 'hit 90% or higher in specific clinical domains.' 80% of health systems are actively exploring, piloting, or implementing generative AI tools for RCM — a 38-percentage-point increase over less than two years. The survey encompassed over 200 decision-makers and 90 end users across US provider organizations. AI addresses long-standing friction points such as documentation burden and coding variability.

Official Atlas announcement: Q1 2026 incorporations up 130% year-over-year, 100,000 all-time incorporations; all Delaware incorporations up 38% year-over-year. Population-gated to overlay: Atlas counts confounded by platform market-share growth (~25% of Delaware C corps).

When we originally released GDPVal, which was just a few months ago, none of the models were yet on par with human experts. Months later, we have over an 80 percent win rate compared to human professionals.

Replicating the dominant rubric with three frontier models on identical tasks, we find a 3.6-fold divergence in mean exposure with agreement as low as 57%.

The research highlights a growing divergence between workers who can complement AI tools and those whose skills are substituted by them, amplifying within-occupation wage inequality.

A job is a bundle of tasks. The real question is not whether AI can perform one component of the bundle. It is whether that component can be separated from the rest at low cost... In 2013, a study by Carl Frey and Michael Osborne put the probability that accountants and auditors would be automated at 94 percent. A decade later, the US Bureau of Labor Statistics counts 1.6 million accountants and auditors employed, median pay of $81,680, and projects the occupation to grow another 5 percent through 2034... the argument that 'half of entry-level white-collar jobs be gone in five years' confuses task automation with the extinction of jobs.

Between 2011-2023, 18% of US workers were employed in jobs introduced since 1970. The wage premium is four times larger for new work associated with technological change than for other types of new work. The labor share has declined 10% in the US since the early 2000s.

Hundreds of millions of people use artificial intelligence (AI) for writing assistance.

Survey of 80,508 Claude.ai users (personal accounts). One fifth voiced concern about economic displacement. Perceived job threat correlated with observed exposure: for every 10pp increase in exposure, perceived threat increased by 1.3pp; top-quartile exposure workers mentioned worry 3x as often as bottom-quartile. Early-career respondents much more likely to express concern than senior workers. Mean productivity rating 5.1/7 ('substantially more productive'); 3% reported negative or neutral impacts, 42% no clear indication. 48% of users mentioning productivity cited scope (new tasks), 40% speed. Management occupations (mostly entrepreneurs) and computer/math groups showed largest gains; scientific and legal professions the mildest. 10% of respondents naming a beneficiary said employers/clients capture the surplus; only 60% of early-career workers said they personally benefited vs 80% of senior professionals. U-shaped relationship between reported speedup and perceived job threat: both those slowed and those sped up most are more anxious.

Three years after generative AI reached the market, there is no detectable employment effect for the most exposed occupations on UK data, regardless of which exposure metric is used. The estimates are noisy, the confidence intervals are wide, and neither measure produces a statistically distinguishable effect on aggregate employment.

18% of firms used AI in a business function, rising to 32% on an employment-weighted basis. In 23% (41%, employment-weighted) of firms, workers use AI in work-related tasks. Most users (66%) rely on AI solely to augment tasks, while AI-related employment decreases are rare, occurring in only 2% of firms.

U.S. labor productivity growth has accelerated, rising from 1.3% per year in the pre-pandemic expansion (2013–2019) to 2.2% in the post-pandemic period (2019–2025). Three technology-exposed groups — white-collar services, retail trade, and advanced manufacturing — are posting 3.2% to 3.9% annualized productivity growth. The rest of the private economy is at 0.1%. The pressure is likely to emerge first in entry-level white-collar work, where the same industries posting the strongest productivity gains are also showing weaker demand for junior labor.

Our proprietary survey of AI integration efforts finds that only 11% of management teams expect AI to replace existing software subscriptions. Far more expect the move to AI-centric architectures to reduce maintenance spending on legacy infrastructure, save on consulting fees, and increase the productivity of existing workers, reducing future headcount needs. Figure 4: Internal headcount cited as AI spending offset by 26% of respondents — the largest single category; IT services/consultants cited by 22%.

Workers initially employed in occupations that later decline by at least 25% demonstrate 0.4 lower future years of work, although this employment difference is mostly explained by other individual traits. These workers, conditional on controls, experience a 4.7% reduction in future cumulative earnings relative to starting earnings, akin to losing one year's worth of earnings over 2007–2024.

Metaculus community forecasts for 2030 and 2035: overall US employment -1.9%/-3.4%; most vulnerable AI-exposed occupations -11.4%/-17.2%; software developers -15.1%/-22.3%; financial specialists -8.1%/-15.3%; services sales -11%/-14%; lawyers -5.4%/-9.6%; designers -4%/-8.4%; K-12 teachers -1.3%/+1.3%; overall median wage -0.6%/+1.4%; percent workers using AI daily 52.5%/70.9%; new-grad unemployment ~10%/12%.

All 921 occupations (147.9M jobs) sort into four categories: 18% are at a higher short-term automation risk, 46% are less likely to experience near-term change, 12% could grow because of AI, and 24% may see declining employment as their task composition shifts but remaining jobs will still need workers. ChatGPT is used about 3x more in the kinds of jobs our framework identifies as most at risk of automation. Capability overhang by archetype: high-automation-risk jobs show 23.8% realized vs 90.0% theoretical exposure (66.2pp gap); jobs that grow with AI 22.7% vs 72.4% (49.7pp); reorganize 14.9% vs 67.1% (52.3pp); less immediate change 6.4% vs 27.4% (21.0pp). Since 2024Q1, unemployment rose most in jobs we predict to have less immediate change (+0.6pp) vs +0.3pp in higher-automation-risk, reorganize, and grow-with-AI groups — underscoring that exposure alone is a weak predictor of immediate labor market pressure.

What A.I. company executives are saying about their products — that they might lead to human extinction and almost certainly will lead to large-scale permanent disemployment — is so obviously 'bad messaging' that I would really urge people to consider that it's not a 'message' at all.

Regression-adjusted employment of early career workers in the most AI-exposed quintile of industry-state cells declined by 12% over the 10 quarters following the introduction of ChatGPT, even as employment in less-exposed industries has remained stable. I find that hires of these early career workers declined immediately by 9% in comparison with those in less exposed industries, and that they have not recovered over time.

The addition of the March 2026 CPS and the introduction of Anthropic's February usage metrics do not suggest any substantial changes. Occupational dissimilarity, industry dissimilarity, and our exposure and usage metrics all remain flat, lie within historical ranges, or continue along the trends they were already exhibiting. Currently, measures of exposure, automation, and augmentation show no sign of being related to changes in employment or unemployment.

Imas argues AI will trigger a post-commodity economy where spending shifts toward the relational sector (care, hospitality, craft, education) whose value is inseparable from human provenance. Evidence: Starbucks rolling back automation after it hurt satisfaction; experimental finding that human-made art gains 44% from exclusivity vs only 21% for AI-generated art.

Among currently employed respondents, 39 percent report that they are either using AI tools in their current job or have used AI tools in their jobs in the last twelve months. College graduates are more than twice as likely to have used AI tools at work in the past twelve months as those without a college degree (58.7 percent versus 22.9 percent). AI adoption rises from 15.9 percent among workers earning under $50,000 to 66.3 percent among those earning over $200,000 annually. Around 38 percent of employed respondents said that having training in how to use AI tools is important to them, yet only 15.9 percent report that their employer currently offers any AI training. Around 62 percent of all respondents believe the unemployment rate will increase over the next twelve months due to AI.

28% of employed U.S. adults use AI a few times a week or more; 13% use daily (up from 10% in 2024). 41% of employees report their organization has integrated AI tools. 23% in AI-adopting orgs report workforce reductions vs. 16% in non-adopting. Survey of 23,717 employed U.S. adults, margin of error +/-0.9pp.

Employment among software developers aged 22–25 has plummeted nearly 20% since 2024, even as their older colleagues' headcount grows. The pattern repeats in other jobs with higher levels of AI exposure, like customer service. Meanwhile, firm surveys indicate executives expect this trend to accelerate, with planned headcount reductions outpacing recent cuts. Generative AI reached 53% population adoption within three years, faster than the personal computer or the internet... the U.S. ranks 24th at 28.3%. Across multiple hospital systems, physicians reported up to 83% less time spent writing notes and significant reductions in burnout.

Long-lasting impacts: 10 years after a job loss, technology-displaced workers' real earnings were 10 percentage points below that of non-displaced workers. Short-run impacts: It can take one month longer for technology-displaced workers to find a new job; and their inflation-adjusted earnings take bigger hits (more than 3%) versus other workers (negligible effect). Recessions worsen outcomes: The effects of technology-related displacements are amplified (by three weeks of additional unemployment and a 5-percentage-point likelihood of subsequent joblessness). Goldman Sachs previously estimated that 6% to 7% of US workers (about 11 million people) could have their jobs displaced by AI.

I was somewhat surprised that the gap between sort of coding in general, which as we point out had something like 94% theoretical exposure, but then based on actual adoption, it was closer to 30% of the tasks across all the jobs in that pocket of the economy.

Here, through a series of randomized controlled trials on human-AI interactions (N = 1, 222), we provide causal evidence for two key consequences of AI assistance: reduced persistence and impairment of unassisted performance. Although AI assistance improves performance in the short-term, people perform significantly worse without AI and are more likely to give up.

Our analysis implies that AI substitution has reduced monthly payroll growth by roughly 25k and raised the unemployment rate by 0.16 percentage points over the past year, while augmentation has added about 9k to monthly payroll growth and lowered the unemployment rate by 0.06pp. This implies a net drag of 16k per month on payroll growth and a 0.1pp boost to the unemployment rate. These negative effects fall largely on less experienced workers, widening the entry-level-to-experienced wage gap by 1.3% and the unemployment rate gap by 0.6pp from their pre-pandemic averages.

AI will definitely eliminate some jobs, while it enhances others. Huge increase in AI-driven capital spending and construction by the five hyperscalers. In 2025, this number was $450 billion, and in 2026, it will be approximately $725 billion. There is a possibility that AI deployment will move faster than workforce adaptation to new job creation.

New research by Goldman Sachs economists finds that AI is already a measurable drag on the U.S. job market—erasing roughly 16,000 net jobs per month over the past year, with the pain falling hardest on Gen Z and entry-level workers. Goldman's breakdown shows AI substitution wiped out roughly 25,000 jobs per month in the past year, while augmentation added back about 9,000. The wage gap has similarly deteriorated, with Goldman's regression analysis estimating that a one standard-deviation increase in AI substitution exposure widens the entry-level-to-experienced wage gap by roughly 3.3 percentage points.

Total nonfarm payrolls rose by 571K in March 2026 to 157,775K.

Information sector employment at 2,772K, +3K month-over-month.

Unemployment rate 4.3% in March 2026, down 0.1 percentage points.

Most still do not see much evidence that A.I. is disrupting the job market. But they are starting to take seriously the possibility that it could someday soon. If it does, they are worried that policymakers are not ready to respond.

Autonomous coding agents are generating code at an unprecedented scale, with OpenAI Codex alone creating over 400,000 pull requests (PRs) in two months. CRA-only PRs achieve a 45.20% merge rate, 23.17 percentage points lower than human-only PRs (68.37%).

Adoption stood at about 18 percent of firms at the end of 2025. Prior to the question revision, the adoption rate had grown by 68 percent (3.9 percentage points) over the prior year but decelerated in Q2 2025. Over 20 percent of firms expect to use AI in the first half of 2026. The right panel of figure 2 shows that work-related GenAI adoption reported in the RPS stands at about 41 percent of the workforce, and non-work-related usage at about 50 percent of the population as of the latest survey in November 2025. The SBU estimates an employment-weighted firm AI adoption rate of around 78 percent and an LLM adoption rate of about 54 percent.

Of America's ~70M STARs (workers skilled through alternative routes, no four-year degree): 15.6M work in roles in the top quartile of AI exposure (43% of all top-quartile workers); 11M of those are in Gateway occupations — the stepping-stone roles connecting entry-level to higher-wage work — with 6 Gateway occupations alone accounting for ~8M of them. STARs are 62.3% of all Gateway-occupation workers. Across Destination occupations, 12.9M workers (~1/3) are highly exposed, including sales reps, accountants, financial managers. Only 51% of Gateway-to-Destination career pathways AVOID high AI exposure. 3.5M STARs are both highly exposed AND have low adaptive capacity (67% of all such workers). 23M STARs have low adaptive capacity overall (68% of all such workers). Highest pathway-exposure metros: Palm Bay FL (35.5%), Cape Coral FL (34.7%), Jacksonville (33%), Albany NY (32.8%), Harrisburg (32.6%), Providence (30.1%). 73% of US workers live and work in the same county, so disruption — and remediation — will be place-specific. Uses Anthropic's observed-exposure measure on Opportunity@Work pathway taxonomy.

U.S.-based employers announced 60,620 job cuts in March, according to Challenger, up 25% from 48,307 cuts announced in February. AI was the leading reason for cutting jobs, cited in 25% of announcements, followed by closings, restructuring and economic conditions.

NBER Working Paper 35046. Survey of 69 economists, 52 AI industry/policy professionals, 38 superforecasters, and 401 general public on AI's economic effects. Unconditional median economist forecasts: GDP growth 2.5% for 2025–2029, LFPR 61.0% for 2030 (vs 62.6% Jan 2025), 58.3% for 2050. Only 14.0% mean probability assigned to a 'rapid' AI progress scenario by 2030. Conditional on rapid scenario: GDP growth 3.5%, LFPR falling to 55.0% by 2050 (~10M lost jobs attributable to AI), wealth inequality reaching 80.0% held by top 10% by 2050, work hours AI-assisted rising from 3.35% (2024) to 10.1% (2030 unconditional) or 24.2% (2030 rapid). Variance decomposition finds expert disagreement driven primarily by different beliefs about economic effects of highly capable AI systems, not by disagreement about AI capability progress. Economists support retraining (71.8%) over job guarantees (13.7%) or UBI (37.4%); general public supports both.

GenAI reshapes wage inequality through a productivity-scarcity race: productivity gains widen wage disparities while reduced entry barriers compress them. Net effect is a modest increase in inequality.

Headcount reduction was the largest outcome in 45% of the deployments, but alternatives (hiring avoided, redeployment, no reduction) accounted for 55%. Agentic implementations showed 71% median productivity gains versus 40% for high-automation. 90-95% of food delivery customer service interactions fully automated by AI agent.

If progress continues, we can expect systems to be capable of carrying out projects that currently take people months. This shift will reshape how organizations run, how knowledge is created, and how people find meaning and opportunity.

Using realized worker-firm matches data for 138 million U.S. workers, Azar et al find the arrival of ChatGPT reduced average wages of employees at firms that can substitute AI for labor by 4.5% relative to firms that cannot.

Johnston & Makridis (2026, SSRN 6460358) use US data through 2024 and find that exposure to AI is associated with SECTOR-LEVEL INCREASES in employment, not declines. Cited in Stanford DEL Research Note #1 as a key counter to the Brynjolfsson-Chandar-Chen Canaries finding of early-career declines — same period, different unit of analysis, opposite sign at the sector level. Illustrates the current dataset-and-methodology dependence of the AI-labor debate.

Healthcare leaders describe RCM as 'an arms race, and payers are definitely ahead' — larger organizations with deeper pockets outpace providers on AI investment. Payers use AI to scale claims and automate denials at higher speed; providers use AI to improve accuracy in documentation, coding, and denial prevention. Cleveland Clinic sees more than 15% of claims initially denied, requiring lengthy appeals to bring the figure below 2%. 'Revenue cycle teams are entering a new era shaped by AI, intelligent tools and emerging roles.' At systems like Carle Health, workforce constraints compound the technological gap, requiring them to leverage technology wherever possible.

In a rapid AI progress scenario, economists forecast a drop in labor force participation from its 2025 baseline of 62.6% to 59.1% in 2030 and 55% in 2050, with roughly half of this decline—equivalent to 10 million jobs—attributable to AI.

The activities that comprise transaction costs—learning prices, negotiating terms, writing contracts, and monitoring compliance—are precisely the types of tasks that AI agents can potentially perform at very low marginal cost. Once agents can indeed execute these functions effectively and cheaply, we will see significant shifts in the traditional make-or-buy boundaries that define firm organization and market structure.

The paper maps both the opportunities GenAI creates and the structural challenges it poses to workers across skill levels. It underscores the uneven distribution of risk, with routine cognitive roles facing the greatest exposure.

Despite faster growth, treated firms do not scale inputs proportionally. Their demand for external capital investment falls by 39.5% relative to the control group, while their demand for labor remains unchanged.

The paper emphasizes that white-collar and knowledge-work roles are now squarely in the displacement frame, departing from earlier automation waves that primarily affected manual labor.

92% of AI applications map to only 6.8% of 39,603 classified work activities. AI apps grew 6x from 2022-2024 but activity coverage expanded only 1.2x. 75% of AI market value concentrated in software/information tasks. 58% of AI apps target 'Create information' activities.

'There is no evidence thus far that industries with higher levels of AI adoption are posting fewer jobs.' 5-40% of firms have adopted AI across various surveys. 45.9% of workers reported LLM adoption at work in June/July 2025 (up from 30.1% in Dec 2024). In 2025, only 5.5% of firms had AI-related job postings. Industry-level models showed generally positive but statistically insignificant coefficients; firm-level analysis revealed 'precisely-estimated null effects' on job postings. Data period: Sept 2023 – Nov 2025 using Lightcast job postings and Census BTOS (1.2M businesses surveyed). Analysis is 'explicitly backward-looking' and does not forecast future impacts.

If AI dramatically boosts the productivity of high-paying knowledge work while leaving lower-paid service occupations largely unaffected, between-occupation inequality could widen even as within-task inequality narrows.

In 2022, there was essentially no relationship between a metro area's share of college graduates and where its unemployment rate stood relative to its own history. The correlation was -0.01. Three years later, that correlation is 0.26 — modest but meaningful across more than 300 metropolitan areas.

Industries with high volumes of structured, repetitive tasks face the sharpest near-term employment contractions.

Industry-wide vulnerability to job displacement is approximately 6%... 9.3 million jobs are vulnerable to job loss due to AI under our median adoption path, with a plausible range of 2.7 to 19.5 million

About 49% of jobs have seen at least a quarter of their tasks performed using Claude. High-tenure users have a 10% higher success rate. Early adopters with high-skill tasks have more successful interactions, identifying a channel through which skill-biased transformation may already be unfolding.

Two jobs with identical exposure scores can have completely opposite displacement risks depending on whether their tasks are complements, whether demand for their output is elastic or inelastic, and the incentives of the firm to invest in automation. The workers at greatest risk are not necessarily those with the highest average exposure, but those whose jobs are built around a small number of core tasks that AI can automate.

In weak-bundle occupations, AI automates some tasks and narrows the boundary of the job, leading to the standard task-substitution channel. In strong-bundle occupations where tasks are not independently reallocable, AI improves performance inside the job, but does not remove the human from the bundle.

After controlling for industry-level shocks we find that coder employment growth has been 3 percent lower since the introduction of ChatGPT. Cumulating over the roughly 3 years since November 2022 and using 5.735 million coder jobs as the base value, the implication is that roughly 500,000 additional coder jobs would have existed in the absence of large-scale LLM use.

The addition of the January and February 2026 CPS and the introduction of Anthropic's 'Observed Exposure' metrics do not suggest any substantial changes. Occupational dissimilarity, industry dissimilarity, and exposure and usage metrics all remain flat, lie within historical ranges, or continue along pre-existing trends.

6-7% of workers will be displaced during that transition period. In the US, AI can potentially automate tasks that account for 25% of all work hours. Goldman Sachs Research expects to see a 0.6 percentage point increase in the unemployment rate over a decade.

average individual wages are roughly unchanged for incumbents of occupations with no AI exposure, rise by around 4% at moderate exposure, and drop by as much as 35% for workers in the most exposed occupations

AI professionals earn $215,000, on average, in San Jose, CA — the highest in the country. But, with living costs 13% above average, that premium narrows. For comparison, in Dallas–Fort Worth, TX, $128,000 salaries stretch further with costs just 3% above average.

A lot of these claims are premature. Many of the people making them have an interest in selling a product. Right now I would describe the labor market as more of a wait-and-see environment. You cannot automate everything tomorrow because we simply don't have enough computing power to do it.

An event study documents an accelerating decline in employment of 22–25-year-olds in high-AI-exposure occupations, reaching 5.5 per cent by early 2025 relative to less exposed occupations within the same employers, while employment of workers over 50 rose by 1.3 per cent.

Two points of general agreement stand out: There's no measurable evidence so far that AI is putting Americans as a whole out of work, economists say. And while the victims of past workplace automation were mostly factory and trade workers, it's white collar jobs that are first in line for AI shake-ups today.

New business applications remain elevated but 'high propensity to hire' applications are in decline. SMBs are rapidly increasing tech spend (including AI) while payroll spend is flat or declining — consistent with AI-native solopreneurs substituting software for labor.

Survey of ~750 CFOs finds AI adoption widespread (58.5% in 2025, 85.4% expected 2026). Implied revenue-based labor productivity gains of 0.6% in 2025, 1.8% expected 2026, with largest gains in finance (>2%). Aggregate employment decline <0.4% in 2026 (~502K workers). Routine clerical roles declining; skilled-technical rising. Productivity paradox: reported gains 3x larger than implied revenue-based gains.

Reviewed more than 800 academic papers related to AI and K-12 education (Repository now over 1,100 papers) and identified only 20 high-quality causal studies that rigorously examine how AI tools affect students or educators. 'Student performance often improves with access to AI tools, but once removed, results are mixed.' No high-quality causal studies of student AI use conducted in U.S. K-12 classrooms. AI tools designed with pedagogical guardrails show more promising outcomes than general-purpose chatbots.

The evidence on how AI is affecting the labor market today is inconclusive, and claims about harmful impacts on particular groups of workers are premature. Initial evidence suggests that transitional disruption from AI to date is not outpacing recent technological changes.

I'm skeptical that simply slotting AI into human-shaped jobs will have the results people seem to expect... as long as we are in that regime, I expect disappointing productivity gains and relatively little real displacement.

89% of the respondents said they have no plans to cut staff or lay off workers because of AI. Just 3% said they have already cut staff, while 7% said they have plans to reduce their workforce within the next year.

AMIE's differential diagnosis included the final diagnosis in 90% of cases, with 75% top-3 accuracy. Blinded assessment suggested similar overall DDx and Mx plan quality between AMIE and PCPs. Human safety supervisors did not need to intervene to stop any consultations.

The U.S. Army's 18th Airborne Corps, using software from data company Palantir Technologies in a continuing string of exercises dubbed Scarlet Dragon, matched its own record from Iraq as the military's most efficient targeting operation ever. Thanks to AI, the corps achieved that with only 20 people, compared with more than 2,000 staffers employed in Iraq.

We find no systematic increase in unemployment for highly exposed workers since late 2022, though we find suggestive evidence that hiring of younger workers has slowed in exposed occupations

new grad hiring in large tech is down over 50% since 2019. For the first time in over decades, recent college grads have a higher unemployment rate than the national average. In addition, the 'underemployment rate' for recent graduates has risen to 42.5% (Q4 2025, New York Federal Reserve).

So far in 2026, AI has been cited for 12,304 job cut announcements, or 8% of job cut plans. In 2025, companies referenced AI for 54,836 announced layoff plans, 5% of total cuts during the year.

Are we finally seeing AI in the productivity data? A big upward revision to earlier data and strong Q4 bring us 2.2% above CBO's pre-pandemic forecast. Annual rates: 1 year: 2.8%, 2 years: 2.5%, 6 years: 2.2%

3% workforce reduction amidst record profits; investment banking revenue up 47%.

Companies with frequent AI use are about 4% more likely to take on additional staff. Two-thirds of the 3,500 firms that took part in the survey reported that their employees use AI.

In 2025, Ramp shipped 500+ features, reached $1B revenue, and did it all with 25 PMs.

According to the American Community Survey, 7.2 percent of early-career college graduates who majored in CS were unemployed in 2024—well above the 4.5 percent rate for all early-career graduates.

In spite of the current displacement narrative – job postings for software engineers are rising rapidly, up 11% YoY. There is little evidence of AI disruption in labor market data as of today.

The survey shows institutional adoption is accelerating, with 66% of respondents reporting their institution is currently leveraging AI, an increase from 49% year over year. Eighty-eight percent of respondents say they expect institutional AI use to increase over the next two years.

While we still do not find a meaningful relationship between productivity and AI adoption at the economywide level, companies that quantified productivity impacts of AI on specific tasks reported a median productivity gain of around 30%.

Project APE — Autonomous Policy Evaluation... has produced 204 papers — with 60 added in a single week... These are fully automated papers, like a version 1.0, with no human iteration whatsoever.

AI capex – roughly $650bn this year and potentially ~$2tn over three years – materially alters the equilibrium.

Today, AI apps have crossed 1 billion weekly active users and ChatGPT alone accounts for 900M of that.

Analysis of 4.5M+ non-prisoner federal civil cases (FY2005-FY2026) and 46M PACER docket entries. Pro se filing share rose from ~11% steady-state (FY2005-FY2022) to 16.8% in FY2025, with pro se case counts nearly doubling (23,210 pre-AI avg → 41,490 in FY2025). Rise concentrated in 'simple' NOS categories (civil rights, consumer credit, foreclosure); absent in patent/securities. Plaintiff-side only — defendant pro se counts fell. Pro se docket entries per court up 158% vs pre-AI mean by 2025Q2; per case up 38% (16.9 → 23.3). Represented entries per case also up 23% (18.2 → 22.5). Case durations and disposition mix unchanged. Pangram AI-text detector applied to 1,600 random complaints (200/yr, 2019-2026): AI-text share rose monotonically from 0.1% pre-AI (1 of 800 false-positive baseline) to 1.0% (2023), 3.5% (2024), 10.5% (2025), 18.0% (early 2026).

in 2026 we find that 43% of US workers use AI for their job compared with 32% among European workers in our surveys.

Across all benchmarks, examples collectively cover a limited 56.5% of the domain taxonomy but a substantially broader 85.4% of the skill taxonomy.

We first analyze 43 benchmarks and 72,342 tasks, measuring their alignment with human employment and capital allocation across all 1,016 real-world occupations in the U.S. labor market.

45% of leaders are willing to pay 11% to 15% more for strong AI skills

AI models successfully complete tasks that take humans approximately 3-4 hours with about a 50% success rate, increasing to about 65% by 2025-Q3. Most text-based tasks projected to reach 80-95% AI success by 2029. Based on 17,205 expert evaluations across 3,000+ O*NET tasks and 40+ LLMs.

Unemployment level 7,239K in March 2026.

As technology devalues labor, optimal policy shifts from steering innovation toward redistribution.

In particular, firm-size-and-sector-weighted aggregate employment is expected to decline by less than 0.4% due to AI in 2026. [...] little evidence of near-term aggregate employment declines due to AI. [...] Over the next three years, the share of routine clerical employment is expected to decline by more than 2 percentage points (mostly among large firms), with partially offsetting increases in skilled technical roles. [...] The mean reported increase in labor productivity attributable to AI investment was 1.8% in 2025 and is expected to reach 3.0% in 2026. Survey of nearly 750 corporate executives.

In the last two weeks, did this business use Artificial Intelligence (AI) in any of its business functions? Yes: 17.5%, No: 73.1%, Do not know: 9.4% (Cycle 202604, ref period Jan 26–Feb 8, 2026). First biweekly reading (Cycle 202524, Nov 2025): Yes: 17.3%.

AI's labor market impact still small — only 5-10K/month drag on net job growth. Only 2½% of jobs exposed to automation today. Baseline forecast: 6-7% of workers displaced (range 3-14%), lowering annual hiring by 1M jobs and raising unemployment ~½pp. US creates 30M gross new jobs/year; does not anticipate job apocalypse. Impact so far largely confined to tech sector but expects it to grow materially.

METR retracts original 19% slowdown finding, citing severe selection bias. Follow-up shows -18% speedup for original devs (CI: -38% to +9%), -4% for new recruits (CI: -15% to +9%). Authors state data gives 'unreliable signal' and are redesigning the study.

Employment in the computer systems design and related services sector has declined 5 percent. AI exposure associated with 0.28pp wage growth reduction for low-experience-premium jobs but 0.2pp increase for high-experience-premium occupations.

AI's impact depends on whether it automates or augments. For zero-experience-premium occupations, increased AI exposure is associated with -0.28pp reduction in wage growth. Wages in AI-exposed jobs not uniformly declining.

Identifies five categories of labor-affecting technology: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating. Only new task-creating technologies are unambiguously pro-worker because they expand the range of valuable work humans can do and raise the value of human expertise; the other four can lift productivity while reducing the scarcity value of expertise. Cites Schneider Electric AI tool for field electricians that cuts average maintenance-report completion time roughly in half as augmentation evidence. As reported in MIT Sloan Ideas Made to Matter, June 17, 2026.

U.S. healthcare avoided $258B in admin costs. 50%+ of health plans and 25% of provider organizations use AI tools in admin workflows.

AI exposure metrics broadly agree with each other, but that they disagree with each other more on highly exposed occupations. The key point of disagreement between different AI exposure metrics is in the magnitude of exposure, not whether an occupation is exposed.

GPT-scored AI exposure for 342 BLS occupations on a 0-10 scale, validated against 6 academic metrics (Pearson 0.878 with Yale PCA, 0.885 with OpenAI). Employment-weighted industry exposure scores for 186 4-digit NAICS industries.

37% of tech professionals say their role has been 'redefined or restructured due to gen AI in the past two years.' 80% of respondents save at least one hour per day using generative AI. AI literacy job postings growing 70%+ year over year.

OpenAI average stock-based compensation reached $1.5M per employee in 2025. Research scientist total compensation ranges from $763K to $1.44M. The company raised $6.6B in October 2024 at a $157B valuation.

Full-year 2025 revenue of $430.9M, up 10.1% YoY. Active buyers down 13.6% in 2025. Spend per buyer reached $342, rising 13% YoY. Writing, translation, and simple programming categories declining ~20% due to AI substitution. High-value projects over $1,000 grew 23%. 2026 revenue guidance: $380M-$420M, a decline of 3-12% YoY.

Only 20% of customer service leaders have actually reduced agent staffing due to AI, yet 80%+ expect to reduce headcount over the next 18 months. Nearly 80% plan to transition agents into new roles. Survey of 321 CS leaders (Sep-Oct 2025).

AI adoption causally increases labour productivity levels by 4% on average in the EU, with no evidence of reduced employment in the short run. Wage gains may accrue disproportionately to highly skilled workers.

Governor Barr's speech at the NY Association for Business Economics (Feb 17, 2026): 'AI could contribute between 0.3 and 0.9 of a percentage point to annual total factor productivity growth over the next decade.' '17 percent of businesses in the U.S. Census Business Trends and Outlook Survey report using AI in their business functions; about 30 percent of businesses with more than 250 employees report using AI.' 'Early-career workers in occupations highly exposed to AI — such as software developers and customer service representatives — have experienced a decline in employment relative to other early-career workers in less exposed fields.' A NY Fed survey found firms 'plan to retrain their existing workforce' rather than pursue significant layoffs.

Customer service could see as much as 75% of interactions automated by 2026. AI-driven platforms could deliver primary investment advice to nearly 80% of retail investors by 2027. Over 85% of software developers now use AI coding assistants, delivering productivity gains of up to 60%.

69% of firms across US, UK, Germany, and Australia currently use some AI technology (US: 78%). LLM text generation most common (41%). Expected to rise to 75% in 3 years. Larger, more productive, higher-paying firms more likely to adopt.

Survey of ~6,000 executives across US, UK, Germany, Australia. 90%+ report no employment impact from AI over the past 3 years. Firms expect AI to reduce employment by 0.7% over the next 3 years (US: -1.2%). Employees, by contrast, expect +0.5% job creation.

Looking ahead three years, executives predict a 1.4 percent productivity boost and 0.8 percent output increase alongside a 0.7 percent employment reduction—while employees forecast a 0.5 percent employment increase.

68% (331 out of 485) of S&P 500 earnings calls in Q4 2025 cited AI — a new 10-year high. Financials and IT had highest counts (67 each). IT (94%), Financials (91%), Communication Services (89%) had highest sector percentages.

Revenue grew 2% year over year to $787.8 million for full year 2025. GSV per active client was $5,129 in Q4 2025, an increase of 7% year over year. AI-related skills grew by 109% year over year. Active clients were 785,000 as of December 31, 2025.

Job openings fell to their lowest level since December 2017. Professional & business services openings down 21.8% (-284,000). Financial activities openings down 25.1% (-86,000). Healthcare openings down 10.8% (-152,000) since October.

Survey respondents said that AI adoption had led to elimination of 11% of jobs and an additional 12% were left unfilled. This is partially offset by 18% new hires, resulting in a 4% net job loss globally.

Cutting ~20,000 jobs (8% of workforce) by 2026 in restructuring tied to automation; broader plans for 60,000 total reductions including Banamex IPO.

By 2027, Gartner predicts that half of companies that attributed headcount reduction to AI will rehire staff to perform similar functions, but under different job titles. Despite speculation that AI will drastically reduce customer service headcount, Gartner's report found that only a fifth of customer service leaders have actually reduced agent staffing due to AI. Gartner's report polled 321 customer service and support leaders in October 2025.

35.9% of US workers used generative AI by December 2025; adoption is accelerating but remains unevenly distributed across industries and skill levels.

No measurable change in job openings or aggregate employment in AI-exposed occupations; adjustment happening through task reallocation, not broad displacement.

Pressure concentrated in entry-level segments of highly exposed occupations; adjustment happening at the margin through task reallocation.

Small but statistically significant positive wage effects found across AI-exposed occupations; 35.9% of US workers used generative AI by December 2025.

a 10 percent increase in the minimum wage increases robot adoption by roughly 8 percent relative to the mean

In a randomized experiment with 1,174 adults ages 25-45, AI access closed approximately three-quarters of the education-based productivity gap: higher-education participants outperform lower-education participants by 0.548 standard deviations without AI; with AI, this gap falls to 0.139 standard deviations.

The United States has seen 50% growth in AI-related postings over the past year, with AI now appearing in 2.5% of all job postings.

Without AI, higher-education participants outperformed lower-education participants by 0.548 standard deviations. With AI access, this gap fell to 0.139 standard deviations—closing about 75 percent of the baseline productivity difference.

Synthesizes firm-level survey data from the US, UK, Germany, and Australia measuring AI's realized and expected impacts on sales per employee and employment. Data collected Feb-Apr 2025 and Nov 2025-Jan 2026.

Job openings at 6,882K in February 2026, gradual decline from 2024 peaks.

UPS has automated 127 facilities to date. Package volume through automated facilities was 57% in 2023, projected to reach 68% by end of 2026. 24 additional facilities are slated for automation in 2026. CEO Carol Tomé stated 'the cost per piece in automated buildings is 28% lower than in conventional, non-automated buildings.' UPS uses pick-and-place systems for small package sorting, Pickle Robots to unload trucks, and autonomous guided vehicles.

AI use impairs conceptual understanding, code reading, and debugging abilities. The AI group averaged 50% on the quiz, compared to 67% in the hand-coding group.

36.8% of individuals across OECD used generative AI in 2025. 20.2% of firms used AI in 2025 (up from 8.7% in 2023). 41.1% of employed individuals used generative AI. 52.0% of large firms vs 17.4% small.

I've been warning about this risk for over a year; now, the people building the technology are putting timelines on it.

6.1 million U.S. workers face both high AI exposure and low adaptive capacity. Medical secretaries and administrative assistants (831,000 workers) stand out as one of the largest occupations in this high-risk category. About 86% of these workers are women.

Administration comprises 25% of all healthcare costs, making it a key target for automation. Organizations are using AI for documentation, coding, scheduling. Ambient documentation frees up to four hours of clinician time.

Only about 43% of US workers reported regularly using AI at work last year

AI is redistributing economic value away from routine cognitive tasks toward judgment, coordination, and specialized knowledge.

In het derde kwartaal van 2025 werkten ruim dertien procent minder jongeren in de meest vatbare beroepsgroepen dan in het vierde kwartaal van 2022, terwijl de werkgelegenheid in andere beroepsgroepen juist drie procent steeg.

6.1 million workers in AI-exposed roles lack adaptive capacity; 86% are women. Vulnerability concentrated in college towns and state capitals in Mountain West and Midwest.

Of the 37.1 million workers in the top quartile of AI exposure, 26.5 million are in occupations that also have above-median adaptive capacity. 6.1 million workers (4.2%) face high exposure + low adaptive capacity; 86% are women.

raising average wages by 21 percent

A one-standard-deviation increase in Generative AI exposure is associated with an 8% decline in job postings and a 0.6% decline in the hourly wage rate.

AI skills significantly increase interview invitation probabilities by approximately 8 to 15 percentage points, compared with candidates without such skills

Computer/mathematical tasks account for ~33% of all Claude.ai conversations and ~50% of API traffic, indicating concentrated AI impact on tech-adjacent roles.

49% of jobs now use AI for at least 25% of their tasks (up from 36% in early 2025); widespread adoption could boost US labor productivity by 1.0-1.8 percentage points annually.

49% of jobs now use AI for at least 25% of their tasks (up from 36% in early 2025); augmentation (52%) has overtaken automation (45%) as primary use pattern.

Education task exposure jumped from 11% to 49% — a 4.5x increase driven by multimodal AI, advanced reasoning, and agentic systems. Based on reassessment of 18,000 tasks across 1,000 O*NET occupations.

Unemployment risk in AI-exposed occupations rose beginning early 2022; graduates with AI-exposed curricula have higher first-job pay and shorter job searches post-ChatGPT.

Unemployment risk in AI-exposed occupations rose beginning early 2022; graduates with AI-exposed curricula have higher first-job pay and shorter job searches post-ChatGPT.

95% of generative AI projects have failed to deliver measurable returns (citing MIT). PwC: 56% of CEOs said AI failed to boost revenue or lower costs. Layoffs are anticipatory, not performance-driven.

Around 40% of global employment is exposed to AI; in advanced economies 60%. Demand for new skills is rising with 10% of jobs in advanced economies requiring at least one new skill.

Emerging evidence that generative AI adoption is reducing entry-level hiring, especially where tasks are automatable rather than complementary to humans.

Employment 3.6% lower in AI-vulnerable occupations in regions with high AI-skill demand after 5 years; geographic divergence driven by skill concentration.

Job postings with 4+ new skills pay up to 15% more (UK) and 8.5% more (US); middle-skill roles being squeezed while high-skill workers gain.

95% of customer interactions projected to be AI-powered by 2028. Current AI resolution rates at leading companies exceed 55% without human escalation.

By 2040, output is only 4% higher than it would have been without the growth acceleration, and by 2060 the gain is still only 19%. A key reason for the slow acceleration is the prominence of 'weak links' (an elasticity of substitution among tasks less than one).

In the AI group, screen readings fell from 109,692 to 61,248—a 44 percent workload reduction—while screen-detected cancers rose from 262 to 338, a 29 percent increase in detection.

New book releases tripled over this period, but average quality declined... the authors estimate a potential steady-state consumer surplus gain of 25–50 percent from market expansion.

Retail trade projected to decline 1.2% as automation and e-commerce reduce sales occupations. Mining/extraction declining 1.6% driven by robotics and drones. Total employment projected to grow 3.1% (2024-34), much slower than prior decade.

Trinity Health is laying off 10.5% of its revenue cycle management staff across 15 states and 92 hospitals. Non-patient-facing revenue cycle functions will be transitioned to an external partner.

Nearly 3 in 4 (74%) companies plan to deploy agentic AI within two years. 34% are already starting to use AI to deeply transform their businesses. 84% of companies have not redesigned jobs around AI capabilities.

71% of organizations regularly use generative AI in at least one business function (up from 65% in early 2024). 92% plan to invest in generative AI over next three years. 66% report productivity gains.

The high demand for new IT skills may not necessarily translate into a one-for-one rise in demand for IT and AI specialists, especially as many IT tasks may progressively be automated by AI.

The six largest US banks counted 1.09 million employees at end-December 2025 — 10,600 fewer than a year earlier and the lowest headcount total since 2021. Wells Fargo's headcount declined ~25% since Q2 2020, ending 2025 with ~205,000 employees. Citigroup CFO Mark Mason said the bank has made headway on its plan to trim 20,000 jobs by end of 2026 and 'expects a further decline in headcount in 2026 and subsequent years.' Bank of America CEO Brian Moynihan: 'the No. 1 thing...is work the headcount through operational excellence and applications of new technologies, including AI.'

There is no evidence that job postings for junior roles within occupations most exposed to AI have declined more than postings for senior positions. Postings for both levels of seniority have been falling roughly in parallel since their peak in Spring 2022, with the decline in junior positions stabilizing faster.

Nearly 40% of AI time savings are lost to rework, including correcting errors, rewriting content, and verifying outputs from one-size-fits-all AI tools. Only 14% of employees consistently get clear, positive net outcomes from AI.

CEO Brian Moynihan: 'We have 18,000 people on the company's payroll who code, and we've — using the AI techniques, we've taken 30% out of the coding part of the stream of introducing a new product or service or change that saved us about 2,000 people.'

94% of higher education workers use AI tools for work, but only 54% are aware of institutional AI policies. 67% identified six or more urgent AI-related risks, including job loss. Survey of 1,960 staff and faculty across 1,800+ institutions.

19% of respondents said they are actively pursuing different careers (7%) or considering changing plans in the near future (12%) due to AI-driven transformation.

FinThrive's 2026 RCM Investment Priorities Report: 76% of RCM leaders cite automation as their #1 initiative; 56% identify automation and AI as their most significant investment area. Top AI deployment areas: prior authorization (73%), denials/underpayment management (67%), clinical documentation/coding (60%). More than 70% plan to reduce reliance on third-party RCM vendors; nearly 60% will consolidate RCM vendors within three years. 85% report changing strategic investment in RCM technology in response to cybersecurity/clearinghouse disruptions. CEO Hemant Goel: 'The winners will be organizations that move from a siloed set of point solutions to an AI-enabled platform approach.'

BLS projects a further 6% decline in programmer roles through 2034. Computer programmer employment (routine coding roles) fell ~27.5% in roughly two years following ChatGPT's release — one of the largest two-year drops in any occupation tracked by BLS. Software developer employment remained flat.

Employers cited AI as a direct cause for 55,000 job cuts in 2025, concentrated in customer service, data entry, and back-office operations.

The global market value of industrial robot installations has reached an all-time high of US$16.7 billion. IFR names five 2026 trends: (1) AI and Autonomy in Robotics, (2) Robots gain versatility as IT meets OT, (3) Humanoids to prove reliability and efficiency, (4) Safety and Security, (5) Robots as allies in tackling labor gaps. Humanoid robots need to match high industrial requirements towards cycle times, energy consumption and maintenance costs. Employers around the world are struggling to find people with the specialized skills required.

automation can depress demand and push AI-driven growth toward the lower end of current forecasts. [...] the conditions needed for growth to actually turn negative are likely too unrealistic to hold in practice

Employment share of young workers (20-24) in most AI-exposed occupations fell from 16.4% to 15.5%; decline driven by fewer people transitioning into employment, not layoffs.

If fully translated to unemployment, AI-driven employment declines among young workers would explain only 0.1 percentage point rise in aggregate unemployment.

Employment share of young workers (20-24) in most AI-exposed occupations fell from 16.4% to 15.5% between November 2022 and September 2025.

Employment share for AI-exposed occupations fell from 16.4% (Nov 2022) to 15.5% (Sep 2025). Workers age 22-25 in most AI-exposed occupations experienced 13% employment decline since 2022, driven by fewer workforce entrants rather than layoffs.

AI/ML engineers median total compensation reached $185K in 2025, with top-tier researchers commanding $500K-$1M+. The premium over general software roles widened to 35%.

Widely-used exposure indices, which aggregate task-level automation risk using linear formulas, will overstate displacement when tasks are complements. Labour income can rise under partial automation because automation scales the value of remaining bottleneck tasks.

35.9% of US workers used generative AI by December 2025; small positive wage effects, no significant decline in job openings.

AI adoption led to moderate productivity increases — an 8.5 percent increase in coding activity and 8.7 percent faster task completion — with no measurable quality declines. These productivity gains did not translate into increased output, changes in task composition, or effects on employment.

Perceived 'identity fit' — whether a new skill feels compatible with a person's sense of self — dominated re-skilling decisions, often outweighing beliefs about wages or employer demand.

Freelance marketplace spend collapsed from 0.66% to 0.14% of GDP after ChatGPT launch.

Pixiv illustrator uploads down 10.1% post-AI; commercial creators saw -14.3% decline.

EU firms see +4% productivity from AI adoption; no employment decline detected.

Gender gap in AI adoption: women adopt less; 5.3 percentage point gap in UK data.

Combined headcount fell to 1.09 million — lowest since 2021, down ~10,600 from prior year, largest annual reduction since 2016.

AI adoption for production rose from 4.6% (early 2024) to 10% (Sep 2025). Broader measure (any business function): 17.3% (Nov 2025). 78.4% annualized growth rate. 80.9% of non-adopters cite inapplicability.

falling labor share accounted for 16% of U.S. real wage growth between 1954 and 2019

A Deloitte survey reveals how AI and human collaboration can help close talent gaps, speed upskilling, and transfer knowledge as demographic changes reshape the workforce.

Each year of AI model progress reduced task time by 8%. Access to any AI model increased Earnings Per Minute by 81.3%. Continued scaling could boost US productivity by ~20% over the next decade.

Challenger, Gray & Christmas reports AI has been cited as the reason for 54,694 planned layoffs in 2025. Overall job cuts topped 1 million in 2025.

No economy-wide employment disruption detected since ChatGPT's release, but this masks occupation-specific impacts for early-career cognitive workers.

42% of 2025 Atlas C corp founders building AI startups (up from 33% in 2024); 22% of LLCs AI (up from 5% Jan 2023); 44% of AI startups building agents; 23,000 incorporations in 2025; Atlas Delaware C corps +41% YoY avg past 6 months; 20% land first paying customer within 30 days.

AI was the most-cited topic in S&P 500 earnings calls in 2025. Companies mentioning AI in workforce context outperformed peers by 8.2 percentage points on average.

46% of US workers reported using AI tools at work in Q4 2025, up from 33% in Q1 2025. Adoption driven by white-collar professional and technical roles.

AI chatbots had no statistically significant impact on hours worked or wages earned for lawyers. Law graduate employment in 2024 reached 93.4% — the highest rate on record.

Despite early signs that AI is beginning to affect entry-level workers, labor statistics have yet to show lawyers being displaced. Law graduate employment in 2024 reached 93.4% — the highest rate on record.

Experian Health's 2026 RCM predictions: More than one-third of providers report denial rates of 10% or higher; '69% of those using AI have experienced a reduction in denials.' '53% anticipate that AI will be widely adopted but will still require oversight.' Early AI adoption focuses on high-volume front-end tasks like eligibility, scheduling, and registration where AI 'can save time, reduce errors and free staff to focus on more complex work.' Data privacy/security is the top adoption barrier; more than 40% of providers cite accuracy as a sticking point. Up to 700 rural hospitals, nursing homes, and clinics may face closure.

Mountain region leads US AI adoption at 42%, followed by Pacific at 39%. Southeast lags at 22%. Regional adoption gaps correlate with widening wage divergence.

AI adoption in core business functions ranges from 1.9% in Japan to 6.1% in the United States in 2024. Among SMEs using generative AI, only 29% report using it in their core activities. Annual labour productivity growth stemming from AI could range from 0.4 to 1.3 percentage points for the US over the next decade. Around a quarter of OECD workers are exposed to generative AI. 50% of SMEs report employees lack the skills to use generative AI.

Since January 2023, salaries at the top quartile of the wage distribution have grown by more than 30%. Over the same period, salaries at the bottom quartile have risen by only around 10%.

54,694 job cuts cited AI as a reason in 2025, concentrated in tech, finance, and customer service sectors.

78% of organizations report using AI in at least one business function. Enterprise AI spending grew 45% YoY globally.

Education support roles face moderate displacement risk while core teaching remains protected. Administrative and content creation roles most affected by AI adoption.

40% of employers globally expect to reduce headcount as AI automates tasks. Net displacement estimates range from 5-14% of current roles by 2030.

200,000+ European banking jobs could vanish by 2030 — roughly 10% of workforce at 35 major European lenders (2.12M staff). Efficiency gains of 30% quoted by banks.

306 S&P 500 earnings calls cited AI in Q3 2025 (then-record), significantly above the 5-year average of 136 and 10-year average of 86. IT (95%) and Communication Services (95%) had highest sector percentages.

In 2025, 19.95% of EU enterprises with 10 or more employees used at least one of the eight AI technologies covered (text mining, speech recognition, natural language generation, image recognition, machine learning, AI-based workflow automation, autonomous machines/vehicles, and AI-based decision systems). Data extracted December 2025 from Eurostat dataset isoc_eb_ai. The Brookings FCAI dialogue briefing cites this figure as the EU diffusion benchmark.

Following its $13.3B acquisition of Interpublic Group (completed Nov 26, 2025), Omnicom announced 4,000 direct job cuts and retired three storied creative networks: DDB, FCB, and MullenLowe. Total headcount of the enlarged group will be about 105,000 — a reduction of about 18% versus the 128,000 across Omnicom and IPG at end of 2024. DDB and MullenLowe are being folded into TBWA; FCB into BBDO. Creative structure now built around three global networks: BBDO, McCann, and TBWA. Most redundancies fall during December 2025. John Wren is CEO of the combined holding company.

AI can already replace 11.7% of the U.S. workforce, worth $1.2 trillion in wages. Exposure is distributed across all 50 states, not confined to coastal tech hubs. South Dakota, NC, Utah show higher values than California.

57% of US work hours are technically automatable. Physical tasks comprise 50%+ of hours for 40% of the US workforce. Robot unit costs ($150-500K) must fall to $20-50K for mass physical automation adoption. At least 14% of employees globally may need career changes by 2030.

Currently demonstrated technologies could automate activities accounting for about 57 percent of US work hours today. AI agents could perform tasks occupying 44 percent of US work hours, while robots could handle another 13 percent. Roles with the highest potential for automation make up about 40 percent of total jobs.

By 2026, 20% of organizations will use AI to flatten their organizational structure, eliminating more than half of current middle management positions in those organizations.

75% of knowledge workers now use AI tools. Adoption in enterprises with 500+ employees reached 60%, up from 40% a year prior.

Industries more compatible with AI experienced an increase of over 10% in firm formation following ChatGPT's release. New firms are more likely to survive, grow faster, and attract more educated workers.

Median annual wage for all occupations: $49,500. AI-exposed administrative and clerical wages grew 1.1% vs. 3.8% for non-AI-exposed occupations.

Record 306 S&P 500 companies (61%) cited AI in Q3 2025 earnings calls — a new 10-year high, surpassing the prior record of 292. 95% of IT and Communications sectors mentioned AI. AI-citing companies outperformed 13.9% vs. 5.7%.

LLMs disrupted labor market signaling on Freelancer.com coding jobs (2.7M applications, 61K posts). Employer WTP for 1 SD signal increase fell from $25.67 to $14.85 (42% decline). Counterfactual no-signaling equilibrium: 5% avg wage decline, 1.5% hiring reduction, 4% worker surplus loss, top-quintile hired 19% less, bottom-quintile hired 14% more. 14% of post-LLM apps used AI writing tool.

Early-career workers (ages 22-25) in AI-exposed occupations experienced 16% relative employment declines, controlling for firm-level shocks. By September 2025, employment for software developers aged 22-25 declined nearly 20% compared to its peak in late 2022.

We find little difference in annual salary trends by age or exposure quintile, suggesting possible wage stickiness. Labor market adjustments are visible in employment more than in compensation.

Workers aged 22 to 25 have experienced a 6% decline in employment from late 2022 to September 2025 in the most AI-exposed occupations, compared to a 6-9% increase for older workers.

Overall employment continues to grow robustly, but employment growth for young workers has been stagnant since late 2022. Declining employment in AI-exposed jobs drives stagnant overall employment growth for 22- to 25-year-olds.

UnitedHealth deployed over 1,000 AI applications across insurance, care delivery, and pharmacy. AI chatbots handled over 65 million customer calls in 2024. Invested $1.5B in AI targeting ~$1B in annual operating cost savings.

Service teams estimate 30% of cases are currently handled by AI. By 2027, as AI agents - or digital labor - gain momentum, they project that figure will reach 50%. Data is sourced from a double-anonymous survey of 6,500 service professionals and decision makers conducted from April 25 through June 6, 2025.

Nearly half of all respondents (49%) use AI daily for client work. 69% don't feel fully prepared for an AI-driven creative industry. 1 in 3 expect design roles to transform this year. Survey of 1,780 global creatives.

Software output increased by 39% after the AI agent became the platform's default code generation mode. Quality did not deteriorate. Senior developers benefit most from agents; junior developers benefit most from autocomplete.

Claude speeds up individual tasks by about 80%. Extrapolating suggests 1.8% annual US productivity growth over the next decade.

32% of companies expect AI to reduce workforce by 3%+ within next year. Median 17% of respondents report workforce declines within functions from AI use. 88% of organizations now use AI in at least one business function.

Computer graphic artist postings down 33% in 2025, photographers and writers down 28%, journalists down 22%, against an 8% overall market decline. Analysis of 180M global job postings from company websites using 650 normalized job titles.

the median expert predicted 2% growth in white-collar jobs between January 2025 and December 2030, compared to 6.8% pre-existing trend; one in four experts foreseeing net job losses

Snapshot of the GenAIAdoptionTracker.com dashboard at the time of ingestion (Nov 2025). Used here for the Feb 2025 (33.5%) and May 2025 (35.5%) quarterly waves — both estimated from the tracker chart before those results were published in the working paper. Later quarterly waves are captured in separate source entries (bick-blandin-deming-wp-2025 for Aug/Nov 2025; bick-deming-mind-the-gap-2026 for Feb 2026; genai-adoption-tracker-may-2026 for May 2026). The underlying RPS runs continuously and updates each quarter.

Study of 11 occupations in Denmark: essentially zero effects on earnings and hours worked; confidence intervals rule out effects larger than 2%. AI adoption linked to ~4% occupational switching.

88% of organizations use AI in ≥1 function (up from 78%). Gen AI adoption at 71%. But only 7% fully scaled, and just 6% report EBIT impact ≥5%. 62% experimenting with AI agents.

Survey of 2,500 C-suite executives finds 90% report no significant net employment impact from AI adoption as of Q3 2025, though task reallocation is widespread.

Job postings for high-AI-substitution occupations fell 12% relative to low-substitution roles post-ChatGPT; effect grew from 6% in year one to 18% by year three. Based on 285 million Lightcast job postings.

Plans to reduce approximately 20% of full-time workforce by 2028, citing AI capabilities in operations and compliance.

23% of employed workers used generative AI at work weekly as of late 2024. Share of work hours with GenAI increased from 4.1% (Nov 2024) to 5.7% (Aug 2025). GenAI may have increased aggregate labor productivity by up to 1.3%.

Health insurance companies deploying AI to combat rising costs driven by hospital use of AI for billing and coding.

AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%. 240 real freelance projects across game dev, design, architecture, data analysis, and animation.

Financial institutions could see 14% headcount reduction over next 3 years (worst of any category surveyed); only 11% of clients actively cutting employees due to AI currently.

Brynjolfsson (Stanford DEL) & Richardson (ADP chief economist), 'How Representative Is ADP Employment Data?' — Oct 30, 2025. Methodology companion to the Canaries line of work. ADP covers 'more than 26 million U.S. workers at more than 500,000 employers' — '1 in 6 workers in the country and 1 in 5 workers in the private sector.' QCEW private-sector benchmark is 131.3M workers. ADP overrepresents manufacturing (9% vs QCEW 3.5%) and underrepresents construction (4.6% vs 6.1%); ADP is 'more representative of employment for small and medium employers than the BLS survey' (32.4% of active ADP is <50 workers vs QCEW 43.9%). Establishments lean larger and concentrate in manufacturing and the Northeast; growth bias exists because 'employers that outsource payroll services are likely to be more profitable than those that don't.' ADP re-weights by industry, state, and employer size to correct. Jan-Jun 2025 revision comparison: ADP net revision +2K jobs vs BLS -488K, with final convergence to 14K difference — evidence of ADP's greater month-to-month stability. Overall conclusion: 'ADP's National Employment Report ... is overall representative of U.S. employment.'

Amazon plans to cut 14K corporate jobs (10% of white-collar workforce). UPS reduced management by 14K. Target cut 1,800 corporate roles. Nearly 2M Americans unemployed 27+ weeks.

Whether AI destroys jobs or transforms them, young people still need fundamental supports: skill-building, career navigation, and mentorship.

Less than 1% of announced layoffs in the first half of 2025 are attributable to productivity gains from AI; most (79%) have been unrelated to AI.

Amazon crossed 1 million robots deployed globally by mid-2025. Internal documents reviewed by The New York Times show Amazon's robotics team has an 'ultimate goal to automate 75 percent of the company's operations.' Executives told Amazon's board that robotic automation could prevent the need to hire more than 600,000 new workers as sales are expected to double by 2033. By 2027, Amazon's automation team expects to avoid hiring over 160,000 U.S. workers it would normally need, saving 'about 30 cents on every item it packs and delivers.'

University administrative roles are 'ripe for GenAI' according to sector leaders. Ant Bagshaw, deputy chief executive of the Australian Public Policy Institute, writes that AI adoption in universities means 'the net result is likely fewer jobs' and characterizes cost reduction as 'the only place that real and sustained savings can be made.' Framing positions retraining as more compassionate than sustaining roles machines can perform: 'it is more humane to help colleagues into new roles now than to sustain jobs which machines can do better and cheaper.' 'Reports, agendas, policies and minutes...are all ripe for GenAI to work on.'

Gartner projects 20-30% of customer service agents will be replaced by generative AI by end of 2026. AI adoption in CS jumped from 46% to 61% in one year.

Workers with AI skills now command a 56% wage premium over non-AI peers, up from 25% just one year prior. AI roles median compensation reaches $157K.

Analysis finds no significant macro-level job displacement from AI as of late 2025, though structural shifts are emerging in specific sectors.

Operations and support staff to fall by at least 10% over next five years despite growing business volumes.

AI-skilled workers command a 56% wage premium, up from 25% in prior year. AI roles median compensation at $157K vs. $49,500 all-occupation median.

Large-scale field experiments find treatment effects 0-16.3% on sales, mapping directly into TFP improvements.

Indeed analysis finds 26% of jobs face radical transformation from AI. Paralegals face 80% automation risk by 2026. Legal research and contract review most affected.

Deloitte's 2026 Global Insurance Outlook: '90% of insurance executives surveyed agree on the urgency of reinventing the employee value proposition to reflect human-machine collaboration; only 25% of respondents have taken tangible action to elevate human skills.' The outlook emphasizes the growing importance of agentic AI: 'Gen AI and Agentic AI already shaping the next stage of development in the insurance industry.' Carriers like Allianz, AXA, and AIG are working to unlock agentic capabilities in areas like claims processing. Insurers are shifting from 'What can AI do?' to 'How do we make AI work at scale?'

The overall effect of industrial robots on wages is close to zero and insignificant. 53 papers, 2,143 estimations.

Survey of 180 Fortune 100 executives and 12,000 knowledge workers. 96% of organizations not seeing dramatic improvements in efficiency, innovation, or work quality from AI. Costing Fortune 500 ~$98B/year in lost returns. Workers report feeling 33% more productive individually, but organizational metrics flat.

Nationally representative survey of 25,000+ working-age adults finds generative AI adoption reached 55.9% overall and 40.7% at work by November 2025, outpacing PC and internet adoption at comparable points post-launch.

BofA's Erica AI assistant surpassed 2 billion client interactions with a 98% resolution rate. Now handles 56% of all customer inquiries without human escalation.

78,000 tech sector job losses attributed to AI in the first half of 2025, with QA testing, junior development, and IT support roles most affected.

AI chatbots for customer service increased sales by 16%; AI-generated product descriptions increased sales by 2.05% in field experiments.

No economy-wide employment disruption tied to AI since ChatGPT's debut. Measures of AI exposure show no clear relationship with changes in employment or unemployment.

Despite fears of imminent AI jobs apocalypse, the overall labor market shows more continuity than disruption since ChatGPT's launch.

At least 50% of tasks are automated in only 7.3% of education and library occupations — the lowest of any occupational group. Survey of 20,262 U.S. workers fielded March-April 2025.

542,000 industrial robots installed in 2024 — more than double the number 10 years ago. 4,664,000 robots operational worldwide. US installations at 34,200 units. Global market value $16.5 billion.

15% of US jobs — about 23 million total — are at high risk of displacement due to automation, concentrated in manufacturing, transportation, and food preparation.

US manufacturing lost 78,000 jobs over the past year with 12,000 cuts in August alone. Manufacturing employment at lowest level since onset of COVID-19 pandemic.

Among organizations with extensive agentic AI adoption, 45% expect reductions in middle management layers.

About half of respondent firms in the Philadelphia Fed's Third District are using generative AI. Survey conducted late October 2025.

Fairwork's 2025 Cloudwork Report evaluates 16 global cloudwork platforms — ranging from freelance marketplaces to data annotation services — against five principles of fair work: pay, conditions, contracts, management, and representation. Findings show both encouraging improvements and persistent gaps in platform accountability and worker protections for 'millions of workers powering the digital and AI-driven economy,' highlighting 'the urgent need for stronger governance, transparency, and rights.' Most cloudwork platforms are not meeting minimum standards of fair pay, conditions, contracts, management, and representation.

In sharp contrast to the past two centuries, AI will shift relative demand toward occupations with lower education, lower wages, and a greater share of male workers.

39% of US adults used AI by August 2024. Small-firm adoption pace continues rising. Firms with 250+ employees at 11% usage; smallest firms (1-4 employees) at 7%.

Survey of 1,150 US workers: 40% received AI-generated 'workslop' in the past month; each instance cost ~2 hours to deal with. 18% of AI users admitted sending low-quality AI output. Estimated cost: $186/worker/month or ~$9M/year for a 10,000-person org.

Freelance marketplace spend fell from 0.66% to 0.14% of total business spend (Q4 2021 to Q3 2025); substitution ratio: $1 reduced freelance spend → $0.03 in AI spend.

Census Bureau rigorous measure finds 9.7% of firms actively using AI, while broader surveys report 78%. Adoption gap driven by definitional differences in what counts as AI use.

Only ~25% of Information sector businesses using AI; ~2.5% in Accommodation/Food — adoption patterns driven more by workforce composition than income.

Productivity gains 20-60% in controlled RCTs, 15-30% in field experiments; AI exposure measures converge toward high-wage jobs being most exposed.

18% of CFOs have already eliminated roles due to AI. Of those, 88% of cuts were in accounting, 38% in FP&A, 33% in treasury. Finance teams are the earliest white-collar AI displacement.

Revere Health eliminated 177 positions (billing, coding, accounts receivable) after partnering with IKS Health for AI-driven claims processing and automated billing. Largest documented healthcare admin AI layoff.

Peer-reviewed study finds AI can now automate substantial portions of creative work. Freelance writing gigs dropped 30% post-ChatGPT launch.

By July 2025, 18 billion messages were being sent each week by 700 million users. 81% of work-related messages involve obtaining information or making decisions, not task automation.

AI usage heavily concentrated by income and geography globally; within US, workforce composition matters more than income for adoption patterns.

In about 40% of employment in exposed occupations, at least 50% of tasks will be replaceable. Average labor-cost savings ~25% from current tools, potentially 40% as systems improve.

Roughly 10% of US work affected in the short run; AI will increase productivity and GDP by 1.5% by 2035. Jobs that AI can completely replace saw 0.75% employment fall 2021-2024.

50% are with agents, 50% are with humans. From 9,000 heads to about 5,000 because I need less heads. Support costs reduced by 17%.

In jobs with high AI exposure, employment for 22- to 25-year-olds fell 6% between late 2022 and July 2025. Software developers saw a 20% early-career decline. Employment among workers 30 and older grew 6-13%.

Software developer employment projected to grow 17.9% (2023-2033). Despite AI exposure, this occupation is unlikely to experience employment decline because robust software needs are expected to support continued demand.

IT unemployment rate rose from 3.9% to 5.7% between late 2024 and mid-2025, the highest level since 2021, driven by AI-related restructuring.

67% of providers believe AI can improve claims processing, but only 14% have implemented AI tools. Among adopters, 69% report reduced denials. Survey of 250 providers.

Analysis of 62M U.S. resumes: junior employment declined sharply at AI-adopting firms while senior employment was largely unchanged — described as 'seniority-biased technological change.'

Sufficiently advanced AI could dramatically raise growth rates and lower labor share, breaking Kaldor Facts. Wages may rise or fall depending on returns to scale and direction of technical change.

37% of software development tasks show AI involvement. Writing code is 18% of actual AI usage in software development; most use is for understanding, debugging, and reviewing code.

The framework yields several predictions: larger average firm size, greater industry concentration, and reduced local managerial autonomy. Transformative AI sharply expands what counts as codifiable local knowledge. In the absence of active countermeasures, transformative AI may lead to significantly more centralization of decision-making.

Global banks expected to shed 200,000 roles over 3-5 years (~3% headcount drop). Citigroup: 54% of banking jobs have high automation potential. AI could lift bank pre-tax profits 12-17% by 2027.

Audit of 500 AI vendors finds surveillance pay spreading across healthcare, customer service, and logistics.

37.4% of US workers used AI on the job in the past week. Adoption highest among college-educated (52%) and workers earning $100K+ (58%). Survey of 25,000 workers.

Companies hiring AI talent face a 30% salary premium as demand far outstrips supply, with experts warning that the cost of delayed AI hiring will only increase over time.

Unemployment rate for 20- to 30-year-olds in tech rose nearly 3% since early 2024. Entry-level job postings in the US diminished about 35% since January 2023. Goldman Sachs predicts AI will displace about 6-7% of the total workforce.

70% of providers and 80% of payers now have an AI strategy in place or in development. RCM is the top AI use case, with ambient documentation at ~20% full rollout and ~40% in pilot. Nearly half of provider executives said revenue cycle management was a top three IT investment priority. Survey of 228 US healthcare provider and payer executives.