KI & Arbeitspolitik-Zeitleiste

The Macroeconomic Effect of AI: Sizing the Software Engineering Channel (NBER WP 35793)

National Bureau of Economic Research

Blumenfeld, Hazell, Lian and Schaab measure how AI affects the economy through one channel: software engineering productivity. They build a forward-looking, real-time measure from financial markets, estimating each firm's stock-return sensitivity to an AI stock market index and how it varies with the share of firm payroll in software engineering, then use a model to map that relationship into productivity gains. The period studied is November 2022 to December 2025.

The Early Impacts of AI on Employment among Recent College Graduates (NBER WP 35796)

National Bureau of Economic Research

Fairlie and Wu use CPS microdata to produce what they describe as the first estimates of AI's effect on unemployment among recent college graduates in June, July and August 2026. The abstract says unemployment rates run especially high in summer months and that 2026 might be the first year with workplace AI use widespread enough to detect effects on this group. It notes there is no consensus on magnitude or timing.

Grab and OpenAI bring practical AI skills to Southeast Asia

OpenAI

OpenAI and Grab launched "GO Forward with AI", a regional programme to help 30,000 Grab partners across Southeast Asia build practical AI skills.

Canaries Dashboard: Employment in AI-exposed occupations slowed in August

ADP Research

ADP's monthly Canaries Dashboard reports that employment in AI-exposed occupations slowed in August 2026, with early-career workers continuing to lose ground.

Parallel cut research time and cost in half with GPT‑6 Astra

OpenAI

OpenAI customer note: GPT‑6 Astra allowed Parallel's agents to research and synthesize labor-market data in half the time and at half the cost compared with prior models. Summary is based on the feed excerpt only; the full article was not accessible at registration.

AI plays a role in weak labor market for college graduates

Federal Reserve Bank of Dallas

Dallas Fed researchers Samuel Dodini and Tucker Smith link Texas administrative records, Lightcast job postings, O*NET and Anthropic task data to measure how generative AI is reshaping outcomes for new college graduates. Graduates from AI-exposed majors were 1.7 percentage points less likely to find a Texas job within a year and earned 5% lower wages than peers in less-exposed fields. They were also 1.4 points more likely to enter graduate school, and undergraduate enrollment in exposed fields fell 4.8% between fall 2024 and fall 2025. The authors conclude that generative AI has reduced demand for roles whose tasks it completes more efficiently than humans.

How V7 gives AI agents institutional memory

OpenAI

A customer story published by OpenAI. According to OpenAI, V7 uses GPT-5.6 to turn a company's scattered files into context that AI agents can use, so the agents can complete complex work whose outputs are linked back to the underlying sources. OpenAI frames this as giving agents "institutional memory". The claims are the vendor's own and are summarized here from its post.

Expanding OpenAI Academy with new learning paths

OpenAI

An announcement published by OpenAI. According to OpenAI, OpenAI Academy is adding new learning paths for five audiences (employees, developers, leaders, educators and students) so that people can build practical AI skills and demonstrate them. This is the company's description of its own training offering; this entry summarizes the announcement only and does not assess the curricula or any outcomes.

Better Technology, Worse Motivation: Generative Artificial Intelligence’s Mediocrity Trap

Asian Development Bank (ADB)

This ADB paper examines how generative artificial intelligence (AI) affects both productivity and motivation in creative work. Summary based on the publication feed abstract; the paper itself was not read.

How Cooley is accelerating IPO work with ChatGPT

OpenAI

A customer story published by OpenAI. According to OpenAI, the law firm Cooley built "GO Public" with ChatGPT Work to bring intelligence to the IPO process, helping lawyers surface issues earlier and focus their judgment where it matters most.

Hex turns complex analysis into visual reports with GPT‑6 Astra

OpenAI

A customer story published by OpenAI. According to OpenAI, GPT-6 Astra helps Hex's data agents turn answers into interactive visualizations that employees are proud to share.

Distributional Intersectional Fairness in AI-Supported Job Matching

IAB (Institute for Employment Research)

An IAB paper analyses how machine learning methods change the distribution of outcomes between intersecting social groups, in the setting of AI-supported job matching. Summary based on the institute's one-sentence announcement; the paper itself was not read.

Workforce policy for the age of AI: Recommendations from the economic literature

Brookings Institution

Ben-Ishai and Thompson (MIT FutureTech), Brookings Economic Studies, 2026-09-15. Four findings from the economic literature: exposure is not commercial automation (models complete 3-4 hour tasks at 65% success as of end-2025; 10x task duration cuts performance 11%; firms would automate only 23% of automatable computer-vision tasks at current costs); the question is how AI changes the value of expertise (accountant vs family physician example); adoption requires knowing when to trust AI; reliability gains (failure halving ~2.5 years) will widen autonomous use. Five policy directions: prioritize displaced workers (displacement costs 2.8 years of prior earnings; low-wage workers -13% after six years) and high-productivity opportunities, domain-specific employer-partnered training, align programs with expertise shifts, expand apprenticeships (pay-for-performance, LEAP Act), federal wage insurance (TAA precedent). Authors concede training evidence is weak. Absolute mobility 90% (1940s cohort) to 50%.

Artificial intelligence adoption in Chinese enterprises: Productivity effects, workforce implications, and policy challenges

International Labour Organization

ILO Research Brief (Ernst with Renmin University co-authors, 2026-09-15, CC BY 4.0). 21 firm interviews + survey of 1,591 professionals. Reported gains: recruitment 30->13 days, 300-agent CS floor 6,000->15,000 issues/day, spec-reading team 10+->2-3, lighthouse factory +30%. 13 of 21 firms report no quantified gain. Survey: 56% inevitable, 47% net job creation, 39% expect income decline. Self-reported, purposive sample, no counterfactual.

Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews

arXiv (Jabarian & Henkel)

Preprint (arXiv 2607.28222v2, 2026-09-11) by Jabarian (CMU) and Henkel (Erasmus). Natural field experiment at PSG Global Solutions (Teleperformance subsidiary) in the Philippines: 70,884 entry-level customer-service applicants randomly assigned to human-recruiter or AI voice-agent interviews; humans made all hiring decisions. Offer rate 8.70% (human) vs 9.73% (AI), +12% relative; job starts +18%; retention +18-19% at 2-4 months; no productivity difference on three firm metrics. Mechanism: 'controlled variance' (guideline similarity 0.59 vs 0.43). 78% chose AI when given the choice, with negative sorting. Lead author became unpaid Chief Economist at the partner firm after data collection.

Traditional labor market data isn't keeping up with jobs in the 'frontier economy' (Lipson & Schwartz)

Brookings Metro

Five blind spots in NAICS/SOC/O*NET data for frontier jobs: supply chain nearly triples semiconductor postings; biopharma machine-maintenance workers 26K (2008) to 57K (2025) with no SOC code; nuclear technician postings +68% in 2025 vs BLS -8% projection later revised to +1%. Revelio Labs data.

Record of proceedings: Technical meeting on challenges and opportunities for promoting decent work, productivity and a just transition arising from AI in the manufacturing industry (TMDWAI/2026/9)

International Labour Organization

Record of the 13-17 April 2026 ILO tripartite technical meeting (115 participants, 54 countries) that unanimously adopted the first ILO conclusions on AI in manufacturing (TMDWAI/2026/8, to Governing Body Nov 2026). 11 of 17 paragraphs amended: 'when applicable' inserted into worker-consultation clause, guidelines recommendation dropped (employers + US), risk assessments 'encouraged', surveillance/algorithmic 0 mentions. Background report: 489-494M manufacturing jobs, 16.5% GDP; employer barriers cost 58% / skills 43% / regulation 19%.

The Challenger Report August 2026: Job Cuts Up 58%; Consumer Products, Food Lead

Challenger, Gray & Christmas

August 2026 US job cuts totaled 52,881 (+58% MoM, -38% YoY; quietest August since 2022). AI fell to the 4th-most cited reason (3,462 cuts, lowest since Dec 2025) after leading March-July; AI remains #1 YTD at 116,175 (~22% of 529,914 total cuts). Hiring plans rose 725% YoY to 12,325; YTD 119,825, strongest Jan-Aug since 2023.

Job postings show early signs of AI automation impact

Federal Reserve Bank of Dallas

Dallas Fed Economics article (Dodini & Smith, 2026-09-01) linking Lightcast job postings to Anthropic's task-based AI exposure index. Postings for more-exposed occupations fell ~5% by end-2023 and ~7-8% by 2025-26 relative to less-exposed occupations within the same industry (per 10pp exposure difference). Incumbent Texas firms cut postings 8-9% by early 2026 and shifted composition away from automatable tasks (-2pp, ~50% of mean). Aggregate Texas postings reduced ~1.8% (2024) and 2.6% (2025). Two-thirds of Texas firms use AI (TBOS May 2026, vs 40% two years earlier).

Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting (NBER WP 35720)

National Bureau of Economic Research

Pre-registered 3-month RCT (Autor + 6 Google co-authors): 133 patent lawyers at 11 US IP firms, 2:1 AI (Google InFlow) vs control, blinded expert scoring. AI raised drafting quality 0.34 SD (10d) / 0.38 SD (90d), juniors most; 10-min time saving on 112-min task. Unassisted redlining at 90 days: treated +0.32 SD, entirely among seniors (+0.45 SD); juniors ~0 on average with bifurcated distribution. 91/133 completed (32% attrition), 15/91 flagged possible AI use on no-AI task, results robust.

인공지능(AI)는 일자리 파괴자 아닌 직무 재구성의 파트너… '인간 중심 인공지능(AI) 발전전략' 세워야

Ministry of Employment and Labor

Press release from Korea's Ministry of Employment and Labor announcing the Summer 2026 issue of "Employment Issues" (고용이슈), published by the Korea Employment Information Service. A survey of 2,297 establishments puts AI adoption at 28.6%, which the release calls an early stage; among adopting firms, 75.6% use generative AI. Among workers under 30, the 2025 employment index (2022 = 100) stood at 86.6 for the high-AI-exposure group against 92.5 for the zero-exposure group, a gap of 5.9 points, though the authors note the high-exposure decline began before ChatGPT. A separate survey of 305 incumbent workers reports an average self-rated skill gain from AI of 2.97 out of 5, highest for efficiency (3.40) and lowest for collaboration (2.37). The issue frames AI as a force that reshapes tasks rather than eliminating jobs and calls for a human-centered AI development strategy.

AI Transformation Requires Redesigning Work, Not Cutting Roles

Harvard Business Review

A Harvard Business Review article arguing that many companies are making AI-driven workforce cuts faster than the evidence warrants. It says early AI-driven layoffs have often failed to deliver expected returns, exposed gaps in organizational knowledge and created new demands for human oversight, and that some employers have reversed course. The proposed alternative starts from the work rather than headcount targets: break roles into tasks, identify where AI genuinely improves performance, and redesign processes around the right mix of human and technological capabilities.

The Pulse Beneath the Job Title: Monthly Readings of Requirements and Tasks from 750 Million Chinese Job Ads

arXiv (econ.GN)

Preprint analyzing 752.6M Chinese job ads (2022-2026, 5 platforms) into catalogs of 20,721 requirements and 44,479 tasks with monthly LLM-exposure readings. Key finding: AI-exposed occupations lose posting share (slope -1.33) far faster than their task categories decline (-0.83) — adjustment runs through which jobs are posted, not job content. Within-title exposure spreads up to 17%-48% by wage band.

Sophistication in GenAI Use: Field Evidence from a Large Firm

arXiv

Field study of 713,564 prompts from 3,925 KPMG back-office employees over 8 months of 2025. Sophistication rises with seniority and peaks in Strategy/Digital Innovation/PM; Accounting & Finance lowest. No improvement over time; formal training effects vanish after the training month.

Employment Projections: 2025-2035 (with new AI exposure categories data product)

U.S. Bureau of Labor Statistics

BLS projects +5.9M jobs 2025-35 (170.3M to 176.2M, +3.5% vs +10.9% in 2015-25) and debuts a four-category AI exposure classification (Low/Moderate/High/Very high) for 831 occupations, combining 3 theoretical measures (Felten et al., Eloundou et al., Eisfeldt et al.) with 2 observed telemetry sources (Anthropic Claude, Microsoft Copilot). Office/admin support -4.0% (-752,100, largest group decline); healthcare 37.0% of all new jobs; nurse practitioners +41.0% fastest.

Normative boundaries of AI in scientific work: Evidence from PhD researchers

arXiv (Angelini & Lyrvall)

Latent class analysis of 3,785 PhD students (Nature Graduate Survey 2025) finds 4 attitudinal profiles: division-of-labour 44%, status-quo 34%, all-purpose 16%, undecided 7%. AI accepted for literature tasks (60%/48%) but resisted for writing, data analysis and experiment design (33%/33%/36%). Weekly/daily AI use strongly predicts leaving the status-quo profile (-1.911, p<0.01).

AI adoption and the productivity promise: what workers report

European Central Bank

ECB Blog (Dias da Silva, Lebastard, Sondermann, 2026-08-26), cited in Lagarde's 2026-09-14 speech. ECB Consumer Expectations Survey (~20,000 respondents, 11 euro area countries): workers using AI at work 26% (2024), 41% (2025), 52% (2026), ~3 days/week. Highly educated 61% vs lower education 37%; younger +20pp. Median user saves 3 hours/week (7.7% of working time); only 48.8% use and save time, so economy-wide gain ~3.8%. Coding saves ~8 hrs/week but used by ~8%. Positive sentiment fell 43% to 41%. 41% of workers uninterested (34% of managers); ~half of firms plan AI training (SAFE).

Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring

arXiv

Bone, Stephany and del Rio-Chanona test ten LLMs used to screen job applicants, comparing base and post-trained versions. Post-trained models were 3.6% less likely to call back older applicants (in 8 of 10 models) and made far more correlated decisions. That consensus raised systemic exclusion rates from 5.6% to 17.3%, with intersectional rates of 12.2-21.7%, driven mainly by age-based bias.

What Work Does Generative AI Do? (Bick, Blandin, Deming, Schumacher)

National Bureau of Economic Research

First task-level genAI adoption indexes from a nationally representative US survey (RPS, 4 waves Aug 2025-May 2026). Adoption is widespread but shallow: >80% of occupations and >40% of tasks exceed 20% adoption, yet fewer than half of workers adopt within most tasks. Exposure scores explain 5-53% of occupation variation; chat-log measures over-classify generic tasks (correlations with survey 0.10-0.34).

Labour demand volumes by Standard Occupation Classification (SOC 2020), UK: January 2017 to July 2026

Office for National Statistics

ONS online job advert volumes by 4-digit SOC 2020 (Textkernel-collected, ONS de-duplicated). Total UK postings peaked at 13,780,433 in 2022 and fell to 8,312,308 in 2024 (-39.7%), 27.3% below 2019. Steepest declines cluster in graduate-entry digital/consulting roles; early education and childcare grew. Official statistics in development; Mar 2026 suppressed, several months partially imputed.

CentaurBench: Benchmarking LLM Capabilities on Augmenting vs. Automating Real-World Work Tasks

arXiv (UC Berkeley Haas, DIAL)

CentaurBench (arXiv:2608.18554v1, UC Berkeley Haas DIAL) separately benchmarks LLM automation vs augmentation across seven economically grounded tasks. Automation and augmentation rankings correlate only rho=0.48 (p=0.187, n=9 models); the augmentation winner differs from the automation winner in 5 of 7 tasks. The unaided GPT-3.5-Turbo worker outranked every assisted condition on operations research, tax preparation and travel planning. Only GPT-5-Mini beat the no-guidance baseline on average (mean rank 3.66 vs 3.79). Inter-judge agreement 71.0% across 6,265 comparisons (74.5% automation, 67.8% augmentation).

Canaries Dashboard: Employment in AI-exposed occupations rose slightly in July

ADP Research

July 2026 reading of the ADP Research / Stanford Digital Economy Lab Canaries Dashboard (Nela Richardson, 2026-08-19). Employment in high AI-exposure occupations rose 0.1% year over year, the first positive all-ages reading in the monthly series, while least-exposed occupations grew 1.1%. Workers aged 22-25: overall -1.3%, high-exposure -3% (34th consecutive monthly decline since October 2023), least-exposed flat. Ages 26-30: high-exposure -2.1%, least-exposed +1.8%. Payroll data covering 730+ occupations at tens of thousands of private U.S. employers.

[제2026-19호] 청년고용 위축, AI 탓인가? 변화하는 경력 사다리와 대응 과제

한국은행 (Bank of Korea)

BOK Issue Note 2026-19 (Oh Sam-il, Oh Young-sik): Korean youth (15-29) jobs fell 285,000 from June 2022 to June 2026, 268,000 (94.0%) in AI-high-exposure industries, while workers in their 50s gained 230,000 (75.2% in the same industries). Youth employment -31.4% information services, -27.4% publishing, -16.6% computer programming, -11.6% professional services. Automation-heavy industries show steeper youth declines; augmentation-heavy do not. Graduate youth unemployment 7.0% vs 5.4% since Nov 2022. Monthly youth outflow from high-exposure industries +32% (3.7k to 4.9k). Authors frame AI as an accelerant on top of experienced-hire preference, pandemic over-hiring and remote work, not the sole cause; prescribe career-ladder redesign.

Will AI take Filipino jobs? The answer depends on what we do now

International Labour Organization

Op-ed by Khalid Hassan, Director of the ILO Country Office for the Philippines. Entry point to the ILO February 2026 policy brief "Generative AI and Jobs in the Philippines" (Phu Huynh, PSA LFS Q1-2024 microdata): 27.7% / 12.7m of Philippine employment exposed to GenAI (highest in ASEAN), only 3.6% (1.7m) in Gradient 4, of which ~78% are clerical codes. IT-BPM employed 1.8m in 2024 (1.6m contact centres). ~2/3 of IBPAP member firms use AI vs 14.9% of Philippine firms overall. Fact-check: the 6.1% youth figure quoted in the op-ed is the World row of GET for Youth 2026 Table 2.2, not a Philippine figure; the Philippine youth Gradient-4 rate is 4.2% (217,200 jobs).

Stranded credentials: how a skill-signaling market absorbed generative AI

arXiv (Song Yao)

Audit of 444,698 Kaggle participations (2010-2026) across upload- and code-competition formats. Medals predict subsequent leaderboard performance almost entirely in the first year after being earned. Upload-competition medal stocks lost 82% of informativeness, but institutional stranding (format exit predating AI) explains half to three quarters of that loss. Official lifetime-count credential tiers discard 13-16% of medal information; a recency-weighted index built on pre-AI data alone outperforms them. Preprint, not peer reviewed.

Changing landscape of skills in the age of AI

ILO / IAG on TVET (ETF, Cedefop, Eurofound, EC, ILO, UNESCO)

Joint six-agency (IAG TVET) synthesis on how AI adoption shifts the variety and depth of skills demand: rising higher-order cognitive and socio-emotional skills, small AI workforce (0.3-5% of employment), AI literacy as a new foundational skill, and training supply lagging demand.

Developing a strong workforce requires collaboration

Federal Reserve Bank of Dallas

Dallas Fed SVP Roberto Coronado on AI impact for jobs not requiring bachelor degrees: administrative and IT roles greatly affected, truck drivers and auto mechanics less immediate change; short-term credentials add about $5,000/yr for high school or associate holders vs $2,600 for bachelor holders (Census-controlled).

An evidence review of worker retraining (Anthropic Institute Working Paper 2026-01)

The Anthropic Institute (arXiv 2609.07011)

Roodman & Massenkoff (Anthropic Institute WP 2026-01, arXiv 2609.07011) meta-analyze 146 impact estimates from 56 US randomized job-training trials since 1973: employment +1.7pp (years 3-5), pre-tax earnings +$791/yr per person offered, cost $13,598/participant, narrow B/C 1.44, government recoups ~3/4 of outlay. JTPA/WIA/Job Corps small or null. Sector programs (Project QUEST, Per Scholas, Year Up): earnings +$3,000-4,000/yr family average, $5-10K at top programs, B/C 7.18, but accept ~20% (Per Scholas 7.1%) of applicants and have proven hard to replicate.

World Development Report 2026: The Promise of Artificial Intelligence

World Bank

The World Bank's annual flagship report assesses AI's potential for developing economies through an adopt/adapt/advance framework. Key findings: 14.2% of high-income-country jobs at generative-AI automation risk vs 4.5% in low- and middle-income countries, while productivity-gain potential is nearly equal (18.7% vs 16.2%); middle-income countries reached 50% of ChatGPT global traffic within six months of launch; "small AI" field evidence includes +40% daily diabetic eye screenings in Bangladesh, ~1 year of additional learning at ~US$5/student via SMS math tutoring in Ghana, and up to US$560 farmer savings from AI monsoon forecasts in Telangana.

Occupational Convergence or Divergence? Mapping Labor Market Structural Shifts Driven by AI Penetration

arXiv (Hilman & Koltai)

Analysis of LinkedIn/Indeed/Glassdoor postings (Dec 2025-May 2026, 10 countries) finds 75-85% of AI-related vacancies concentrated in STEM occupations; health/education, services, production/logistics, and public service each below 5%. AI skill demand concentrates at entry level, acting as a precondition for occupational access. Bifurcated pattern: convergence within the STEM core, divergence from the rest — consistent across Global North and South.

Use of generative artificial intelligence tools among Canadian workers, March 2026

Statistics Canada

First results from a new recurring Labour Force Survey supplement on AI use at work (reference period March 2026, workers aged 15-69 in the ten provinces). 35.9% of workers used generative AI tools for work in the previous 12 months; 41.6% used at least one AI or automation technology. Adoption sorted by an exposure-by-complementarity taxonomy: high exposure/high complementarity 53.8%, high exposure/low complementarity 45.9%, low exposure 14.2%. Industry ranged from professional, scientific and technical services 65.6% to accommodation and food services 16.3%. Within high-complementarity occupations, workers aged 15-24 used AI least (39.1%) versus 56.9% for ages 25-54. Depth of use is shallow: among users, 63.5% some but not most tasks, 24.9% almost no tasks, 8.0% most, 3.6% almost all. Among the 64.1% of non-users, 56.0% said it was not applicable to their work and only 5.8% cited a skills gap.

How Houston finds workers in an AI boom

Federal Reserve Bank of Dallas

Dallas Fed President Logan Houston visit: AI chatbot pilot matching job seekers to employers by skillset; GE Vernova Houston Learning Center turbine training, high schooler to field engineer in 7-8 years; Houston payrolls +2.1% annualized over 3 months ending May 2026.

Unbundling jobs: Measuring the value of tasks in an AI economy

ADP Research

ADP Research with Stanford Digital Economy Lab (Richardson, Wang) linked 5M+ ADP client job postings to 9M+ workers in payroll data; analytic sample ~7,000 workers in selected IT jobs 2019-2025, 20,000+ worker-year observations across 600+ employers. Postings mapped to 25 O*NET Intermediate Work Activities; wages regressed on task indicators controlling for age, gender, year and firm effects. 8 activities associated with higher wages (incl. advising others on design/use of technologies, designing databases), 6 with lower wages (incl. diagnosing system or equipment problems, monitoring operation of computer or information technologies). 5 tasks showed reduced compensation in 2023-2025 vs 2019-2022, including "develop models of systems, processes, or products" which otherwise ranks high-value. No effect sizes, standard errors or significance tests published. Conclusion: AI's biggest and most immediate effect is on tasks, not entire occupations.

AI and the global economy: implications for central banks (BIS Bulletin No 130)

Bank for International Settlements

BIS Bulletin 130 finds the AI boom is driving a large, increasingly debt-financed investment surge (~1% of GDP in the most exposed economies), with an uncertain and uneven productivity payoff. Labor displacement is limited so far, with early substitution signals in call centres and business centres; widespread wait-and-see adoption is consistent with a low hiring, low firing dynamic. AI blurs cyclical signals, complicating monetary policy.

Who Takes the Hit? The Uneven Impacts of Generative AI on Labor Demand Across Countries

World Bank

WDR 2026 background paper analyzing 555 million Lightcast job postings across 84 countries (2021Q1-2025Q2). In high-income countries, postings for above-median GenAI-vulnerability occupations fell 5.8% relative to less substitutable occupations after ChatGPT; in middle- and low-income countries the impact is small and statistically insignificant. Displacement is larger with greater GenAI adoption, schooling, English proficiency, and digital services trade specialization.

Economic Futures Research Fund: Research Agenda

Anthropic

Anthropic published the research agenda for its $200M Economic Futures Research Fund: grants of $5-30M (minimum $1M) to universities, research institutes, and nonprofits for large-scale RCTs across five areas — firm-level AI impact, transition support, income support modernization, pre-displacement worker equity, and public investment evidence.

Canaries Dashboard: Employment in AI-exposed occupations contracted in June

ADP Research

June 2026 reading of the ADP/Stanford Digital Economy Lab Canaries Dashboard. Employment in high AI-exposure occupations fell 0.2% year over year while least-exposed occupations grew 0.6%. For workers aged 22-25 high-exposure employment fell 4.3%, a 33rd consecutive monthly decline since October 2023; least-exposed employment for that age band was flat. For ages 26-30 the figures were -2.6% and +0.9%. Based on payroll files of tens of thousands of private US employers, ~4 million workers, 730+ occupations.

Artificial intelligence in UK businesses: 2023 to 2026

Office for National Statistics

ONS official statistics in development. UK business AI adoption rose from ~12% (Sept 2023) to ~35% (June 2026) among 10+ employee businesses, but average AI technologies per adopter moved only 1.4 to 1.6 and just 10% describe use as extensive. Role impacts concentrate in creative/design and administrative/clerical work; ~7% of medium-sized businesses report headcount decrease. BICS Wave 159, 5-28 June 2026, 26.7% response rate.

The Last Ten Per Cent: How task-level AI exposure fits into a jobs forecast

Economic Innovation Group

Joshua Gans (Rotman/U. Toronto) reframes the "AI automates 90% of a job" claim as an economic question: whether the remaining 10% expands (focus effects, expertise recalibration, org redesign, demand elasticity) or shrinks headcount is indeterminate. Argues task-level exposure matters more than job-title automation, and that exposure/applicability scores are starting points, not employment forecasts — converting them requires investigating expertise thresholds, job bundling, market demand, and work intensity that payroll data obscures.

The Impact of Artificial Intelligence on Israel's Labor Market (Selected Issues Paper 2026/061)

International Monetary Fund

IMF Selected Issues Paper applying the exposure-complementarity framework to Israel: 23.1% of workers hold high-exposure, low-complementarity jobs (displacement risk), 32.8% are in low-exposure occupations, and most workers stand to benefit. Highest risk: business/administration professionals, sales workers, ICT specialists. Policy conclusion: comprehensive lifelong learning strategy.

Aggregate Gains from AI and Their Distribution: Global Evidence from Usage Data (WP 2026/147)

IMF

First multi-wave cross-country estimate of aggregate AI gains from usage data: $2.7T/year labor cost equivalent (3.4% of 86-country GDP), with an AI concentration index showing gains tilt to high-wage occupations everywhere — near-total concentration in low-income countries.

Generative AI and labour markets in ASEAN: Significant exposure, limited disruption, uneven preparedness

ILO

ILO brief (8 July 2026) estimating that 22.9% of ASEAN employment (~80 million workers) is in occupations with more than minimal generative AI exposure, while only 3.3% (11.7m) sit in the highest exposure band. Employment in exposed occupations grew from 66m (2017) to 80m (2025), showing no widespread disruption. Women 4.8% vs men 2.3% in the top band. Adoption is concentrated in large firms and technology-intensive occupations, with Singapore MOM data showing 71.5% of firms had not begun AI adoption. Concludes outcomes depend on preparedness and policy choices.

인공지능(AI) 시대 청년 일자리, 직업훈련 확대로 돌파구 찾는다

Ministry of Employment and Labor

Press release from Korea's Ministry of Employment and Labor on a July 8, 2026 roundtable in Jongno, Seoul, where Minister Kim Young-hoon met trainees and graduates of an AI vocational training program run by KG ICT under K-Digital Training (KDT). The ministry describes newly created KDT "AI Campus" specialized courses and the launch of "AI Worker" training as its response to youth employment pressure in the AI era. KG ICT's courses include a physical-AI manufacturing AX campus track that began in June 2026 and an AI battery campus track starting in October 2026; the release cites an application ratio of 3 to 1.

International comparisons show AI effect on productivity

Federal Reserve Bank of Dallas

US productivity grew 2.4% annualized since 2024Q1 (vs 1.6% pre-pandemic); the three most AI-exposed sectors (information, finance/insurance, professional & technical services) grew 3.7% vs 1.7% for the rest, and account for 40% of US productivity gains with only 16% of hours worked. In the EU, where the same exposure index applies, there is no correlation between sector AI exposure and productivity growth. The Anthropic AI Usage Index (US 3.69, EU-27 1.85, Estonia 3.05, Poland 1.41) explains the gap: the exposure-productivity slope strengthens with national AI usage. Correlation, not causation.

Skills England Annual Skills Report 2026

Skills England (GOV.UK)

Skills England's first Annual Skills Report (6 July 2026) estimates 70% of UK workers are in AI-exposed occupations, with highest exposure among professional, analytical and higher-paid cognitive roles. Graduate online job adverts fell 45% in 2025 and entry-level adverts 25%; AI-exposed firms cut junior employment by 5.8%. AI-skilled workers earn a 56% wage premium. Priority occupations (22.9% of the workforce, 7.6M) are projected to add 1.8M jobs by 2035, led by care workers, software developers and IT analysts. The core constraint is skills: business training spend fell 9% (£49.4bn to £44.8bn, 2019-2024), and the government targets upskilling 10M workers by 2030 via TechFirst (£187M) and AI Skills Boost.

Integrating AI in TVET: A practical guide for institutions

UNESCO-UNEVOC

UNESCO-UNEVOC guide (Yang Congkun, Wu Wenxi, Hannes Tegelbeckers; with Shenzhen Polytechnic University and Otto von Guericke University Magdeburg), published 2026-07-03, launched by webinar 2026-07-06, English only. Builds on Beijing Consensus 2019, Ethics of AI Recommendation 2021, GenAI Guidance 2023, AI Competency Frameworks 2024. Five principles (human agency, equity, pedagogical purposefulness, transparency, accountability/data protection) with a procurement rule: if a tool fails any one, do not proceed. Four ecosystem dimensions and a four-phase integration pathway (diagnosis, early adoption incl. automated marking, pedagogical deepening, ecosystem integration). Seven institutional domains; assessment tasks classified AI-excluded/permitted/integrated/enabled; AI-resilient methods; safety-critical occupations keep hands-on competency and human oversight. Launch deck opening figures: 62% of youth used AI daily in 2025 vs 30% formally taught; <10% of institutions follow formal guidance. Full PDF (unesdoc) not readable this week; post based on landing/launch pages, agenda and 33-slide launch deck.

June 2026 Layoff Report: Cuts Cool to 45,849, Down 53% from May, AI Leads Reasons for Fourth Consecutive Month

Challenger, Gray & Christmas

U.S. employers announced 45,849 job cuts in June 2026, down 53% from May. AI was cited in 14,029 cuts (31%), the fourth consecutive month AI led all reasons. YTD 2026 AI-attributed cuts reached 101,743 (~23% of total), versus an estimated 54,836 (5%) for all of 2025. Total YTD cuts were 443,604, down 40% from H1 2025 (744,308). Technology led sectors at 139,156 YTD (up 83% YoY), roughly a third of all cuts. Hiring plans YTD were 91,405, up 10% YoY.

Getting to all-of-the-above: A framework of solutions for AI's coming impacts on work and workers

Brookings Metro

Xavier de Souza Briggs argues there is no silver-bullet policy for AI job disruption and sorts solutions into four families — brakes (human-in-the-loop rules, automation impact assessments), steers (incentives for pro-worker AI design), buffers (modernized UI, wage insurance, right-to-retraining), and shifts (shorter workweeks, wealth-sharing, tax rebalancing). Key finding: "the great mismatch" — AI exposure is inversely correlated with union membership across 1,000+ occupations. Cites private-sector unionization just under 6% (BLS), 55% of business leaders and 68% of investors expecting less entry-level hiring, Meta's 700 + 8,000 AI-pivot layoffs, and state action in Illinois, California, New Jersey and New York. Warns that "training or retraining alone does not produce good jobs."

Annual Economic Report 2026, Chapter I: Progress and peril

Bank for International Settlements

BIS AER 2026 Ch I finds task-level AI time savings of 20-50%, aggregate productivity lift under 1%, US high-exposure sectors showing productivity gains at the expense of lower employment growth, rising earnings-call signals of labour-input reduction, and warns AI investment (>$1tn hyperscaler capex 2025-26) resembles historic booms with sustainability risk.

Anthropic Economic Index report: Cadences

Anthropic

Anthropic Economic Index June 2026 report "Cadences" analyzing how AI usage mirrors human rhythms — workweeks, daily schedules, key dates. Based on privacy-preserving telemetry (Apr 10–Jun 10, 2026) plus a linked survey of ~9,700 respondents. Key findings: personal use rises from ~35% weekday to ~50% weekend; tax queries spiked 8x on April 14; 93% of conversations produce artifacts; higher-wage occupations (marketing managers, programmers) use 2.5x more tokens and 1.34x output per turn; over 35% expect AI to handle most tasks within 12 months yet workers delegating most are the most optimistic (86% productivity gains, only 10% fear job loss); early-career workers carry the most concern (1/3 see junior job-loss probability >60%); computer/math roles 30% of respondents vs 4% of US employment; physical occupations underrepresented. Underlying data: BLS OEWS (May 2025), World Bank WDI, UN WPP.

What separates firms that use AI intensively from firms that don't?

European Central Bank

ECB SAFE survey round 37 (Oct-Dec 2025, 5,000+ euro area firms): 70%+ of firms use AI but only 7% intensively. Intensive users are smaller, younger, ICT/professional-services firms driven by growth not cost-cutting; 84% have completed AI investments and allocate ~20% of total investment to AI. Top barriers: skills shortage (40%) and finance.

Human Capital, AI, and Labor Commoditization

arXiv

Study of 49,610 Upwork workers and 2.26M contracts (2021Q1-2026Q1) finds generative AI reduced the importance of human capital signals (credentials, experience, reputation) by 7.8% in AI-exposed categories. Contract volume fell ~7%, demand shifted to lower-cost workers (3.2%->7.9% share), price weight rose (1.1%->1.8%), and the high vs low human-capital demand gap narrowed (10.3%->6.2%). Average AI exposure across 102 subcategories was 0.252; highest Legal Translation (0.80), lowest Photography (0.02).

Workplace artificial intelligence use: A profile of sociodemographic and job characteristics

Statistics Canada

StatCan survey: generative AI use among Canadian workers nearly doubled from 17% to 30% in under a year (Sep 2024–Jul 2025), concentrated in degree-holding analytical and knowledge-sector roles.

PwC 2026 Global AI Jobs Barometer: Two futures for jobs in an AI era

PwC

PwC analyzed 1B+ job ads across 27 countries. AI-exposed firms grew headcount 52% vs 36% and wages 24% vs 17% since 2018. AI skills wage premium hit 62% (up from 57%). Two-track market: professionalised jobs grow 2x faster with 42% faster wage growth than democratised jobs. AI-exposed entry-level roles up 35% since 2019 while others fell 10% ("seniorization"); junior AI roles 7x more likely to require senior skills. AI-skill jobs grow 8x faster (69% vs 9%). Tech/media/telecom led at 11%.

Agentic coding and persistent returns to expertise

Anthropic

Analysis of ~400,000 Claude Code sessions (Oct 2025-Apr 2026, ~235,000 users): users make ~70% of planning but only ~20% of execution decisions. Verified success 15% (novice) vs 28-33% (intermediate/expert); troubled-session recovery 4% vs 15%. Software occupations 34% vs other occupations 29%. Debugging share fell 33%->19%; average task value +27%.

Entry-level jobs support: AI bootcamps and tech training as government supports young people into the jobs of the future

UK Government (DSIT & DWP)

The UK launched a £20m Early Careers Jobs Alliance (co-chaired by the Prospect union, with partners including Microsoft, Accenture, BAE Systems and JD Sports) alongside the TechFirst programme reaching 400,000 students, an £820m Youth Guarantee supporting almost 1 million young people via 350,000 placements and 360+ youth hubs, and a phased AI bootcamps rollout (North West summer 2026, nationwide 2027-2028). National target: 10 million UK workers AI-upskilled by 2030 (1.7m courses completed).

Texas Business Outlook Surveys: Special Questions (AI), May 2026

Federal Reserve Bank of Dallas

Survey of 313 Texas executives (May 12-20, 2026). 66.2% of firms use AI (flat vs Dec 2025). Among 203 AI-using firms, 76.4% report no impact on their need for workers; only 10.4% report any decrease. But 25.7% expect a decrease over the next few years (29.3% among current users) - a ~2.5x gap between realized and expected cuts. 71.4% report higher productivity for AI-using employees, yet only 7.4% report higher wage growth for them.

Climbing the employment ladder tough when bottom rung is broken

Federal Reserve Bank of Dallas

Dallas Fed economists find youth (under-25) employment in highly AI-exposed occupations fell ~13% since 2022. The decline comes not from layoffs but from a falling job-finding rate — young workers cannot get hired in the first place. Firms pull back on entry-level recruitment while competing aggressively for proven, experienced talent, because AI now handles much codified (textbook) knowledge while tacit (experience-based) knowledge stays scarce. Computer systems design wages rose 16.7% since fall 2022 vs 7.5% nationally, evidence of a widening experience premium.

Generative AI and the Reorganization of Labor Demand

arXiv

Wang, Wei & Wang (2026) measure AI exposure dynamically at the job-posting level across the U.S. economy using a two-stage LLM pipeline. Decomposing the decline in AI exposure: 52% comes from hiring reallocation, 39.5% from within-job task redesign; observable job characteristics explain ~90% of exposure changes (Oaxaca-Blinder). Senior roles adjust earlier via reallocation; junior roles via a broader mix of reallocation, redesign, and their interaction.

The adoption of AI by industrial sectors

PIIE

PIIE analysis by Hufbauer & Zhang finds AI adoption is skill-biased: high-wage service sectors (information, finance, professional services) lead with 30%+ firm adoption while retail/hospitality/food services lag. Large firms (250+) tripled adoption from ~4% to ~12% (2023-2025); large-firm employment share rose 37.6% (2000) to 42.3% (2025). Modest positive correlation between compensation and AI adoption. Labor displacement effects deemed premature/inconclusive.

How ChatGPT adoption broadened in early 2026

OpenAI Signals Research

OpenAI Signals Q1 2026 update documents ChatGPT consumer adoption broadening across age (35+ gaining share), gender (feminine-name users now majority), and geography (Dominican Republic +9, Haiti +9, Japan +8, Mexico +6, Tanzania +6, Brazil/Costa Rica/Myanmar/Papua New Guinea +5 in per-capita messaging rank between Q4 2025 and Q1 2026).

April 2026 Job Cuts Report: Cuts Rise 38% From March, YTD Cuts Down 50%

Challenger, Gray & Christmas

April 2026: 83,387 US job cuts (+38% MoM, -21% YoY). AI #1 reason at 21,490 cuts (26%), second straight month. YTD AI cuts 49,135 (16% of all 2026 cuts, up from 13%). YTD total 300,749 (-50% YoY). Technology led industries with 33,361 cuts in April (85,411 YTD, +33%). Hiring announcements down 69% MoM to 10,049.

Artificial Intelligence in German establishments: One in four has adopted generative AI (IAB-Kurzbericht 08/2026)

IAB (Institute for Employment Research)

IAB-Kurzbericht 08/2026 (Friedrich & Kagerl) reports that generative AI adoption among German establishments rose nearly fivefold from 5% (2023) to 24% (2025), based on the ~15,000-establishment IAB-Betriebspanel. Adoption is 48% at firms with 200+ employees vs 21% at firms under 10; leading sectors are information & communication (59%) and finance & insurance (50%), while construction, hospitality, transport and raw materials stay below 15%. 90% of adopters use off-the-shelf tools; only 16% train purchased models and 6% build their own. About half invested financially, 25%+ offer AI training, and 20% have workplace AI rules.

AI growth acceleration versus distributional fairness

Brookings Institution

Brookings synthesis arguing AI-driven growth acceleration is plausible but not guaranteed. Diffusion is highly uneven across sectors, firms, countries, demographic groups. Relative employment declines for early-career workers in AI-exposed occupations identified using high-frequency payroll data. Five-pillar policy recommendation including complementary capabilities investment, strategic procurement, transition tools, measurement standards, broad-based asset ownership.

What Jobs Can AI Learn? Measuring Exposure by Reinforcement Learning

arXiv

Tomei & Klein Teeselink introduce the RL Feasibility Index (RLFI), scoring all 17,951 O*NET tasks across 894 occupations on whether reinforcement learning could learn them. A physical-feasibility gate zeroes 40.7% of tasks. RLFI correlates 0.88 overall with Eloundou LLM exposure but only 0.15 among digitally feasible tasks — diverging on which reachable jobs AI can learn. High RLFI/low LLM-exposure: power plant operators, railroad conductors, aircraft cargo handling supervisors. Low RLFI/high LLM-exposure: musicians, physicians, natural sciences managers. RL exposure is hump-shaped by wage (peaks upper-middle, +12.2pt per log-salary point) and inverted-U by seniority (mid-career peak). Post-ChatGPT DiD: +1SD RL exposure ~ -2.9% job openings (p=0.085).

Evaluating the Impact of AI on the Labor Market: March 2026 CPS Update

Yale Budget Lab

March 2026 CPS update finds no meaningful shift from December across most categories. Occupational dissimilarity, industry dissimilarity, and exposure/usage metrics all remain flat or continue along existing trends. Notable uptick in occupational mix dissimilarity between older and younger college graduates (high end of historical range). Anthropic February 2026 usage data shows observed usage more associated with automation than augmentation, consistent with prior November 2025 release. Conclusion: anxiety widespread but data suggests AI labor market impact remains largely speculative — picture reflects stability, not major disruption at economy-wide level.

Today at Work 2026, Issue 1: AI Powers Into the Workplace

ADP Research

ADP 2025 Global Workforce Survey (payroll data for 25M+ US workers + 600,000+ workers across 34 countries) finds 50% of workers use AI at least a few times weekly and 20% almost daily. Daily AI users report higher full engagement (30% vs 14% non-users; 19% baseline) and lower overload (11% vs 23%), yet are 4x more likely to feel less productive than capable — a productivity paradox the authors attribute to workers perceiving they personally did less. Young workers and men lead adoption; early-career workers aged 22-25 in AI-exposed fields (software development, customer support) face employment-growth concerns and are less optimistic about job security.

From Clerks to Agentic AI: How Will Technology Transform the Labor Market in Finance?

arXiv

Yu & Li (April 2026) track 40 years of financial labor productivity across three regimes: computerization (1.36 AUM/employee coefficient), indexing (2.42), and agentic AI era (3.39 — 149% higher than baseline). AI exposure correlates +0.5843 with AUM/employee but -0.0535 with revenue/employee, indicating volume-over-yield strategy. Mid-layer coordination roles face greatest pressure.

Workers exposure to AI: What indicators tell us and what they don't

ILO

ILO Research Brief warning that AI exposure indicators (3.3% global highest exposure, 34% HIC vs 11% LIC, female 4.7% vs male 2.4%) measure technical task overlap with current AI capabilities — not predictions of job displacement.

AI Is Probably Not (Yet) the Reason for Labor Market Weakening

The Budget Lab at Yale

Yale Budget Lab March 2026 CPS analysis: US unemployment rose to 4.3% but AI exposure/automation/augmentation metrics show no relationship to employment changes. Immigration slowdown, not AI, is the primary driver of labor market weakening. Dissimilarity index flat since ChatGPT 2022; recent graduates (20-24) show no growing divergence from older workers (25-34).

The AI Jobs Transition Framework: Mapping AI's Near-Term Impact on Jobs

OpenAI

OpenAI economist Alex Martin Richmond maps 921 US occupations covering 99.7% of US employment using a 4-dimension framework: technical capability, human necessity (regulatory/relational/physical), demand elasticity, and ChatGPT consumer usage data (H2 2025). Findings: 18% face higher short-term automation risk (legal, education, office/administrative); 46% likely see less change; 24% may see employment decline as task composition shifts; 12% could grow because of AI. Insulated roles include teachers, nurses, lawyers due to regulatory, relational and physical necessity. ChatGPT usage 3x higher in high-risk jobs.

WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain

arXiv

Evaluation framework for AI work-domain capabilities. Low blog value (framework paper, not labor market impact evidence).

ChatGPT Usage and Adoption Patterns at Work

OpenAI

OpenAI April 2026 workplace report: 28% of US workers use ChatGPT (up from 8% in 2024), Fortune 500 adoption 93%, 7M+ Enterprise/Team seats. Four core tasks: writing, research, programming, analysis. Adoption split sharply between IT/finance (deep) and healthcare/construction/transportation (lagging).

Inside the AI Index: 12 Takeaways from the 2026 Report

Stanford HAI

Annual AI Index 2026 report highlights: software developer employment (22-25) down ~20% since 2024, 14-26% productivity gains in customer support/SW dev, 73% expert vs 23% public optimism gap, US ranks 24th in AI adoption (28.3%), AI researcher US inflow down 89% since 2017, GenAI consumer surplus $172B, 53% global usage, physician note-writing time reduced 83%.

AI Impact on Labor: Double-Edged Sword

Morgan Stanley

Morgan Stanley analysis: AI added at most 0.1 percentage point to overall US unemployment rate. Gen AI impact is double-edged — same technology that automates tasks also augments labor and raises productivity.

Is your job safe?

ADP Research

ADP Research Global Workforce Survey of 39,000+ workers found only 25% globally and 28% in the U.S. feel their jobs are safe. Age breakdown: 18-26: 26%, 27-39 & 40-54: 30%, 55-64: 23%. Three contributing factors: geopolitical concerns, AI impact, ongoing inflation. Workers feeling secure are 6× more likely to be fully engaged and 3.3× more likely to report high productivity. Authored by Nela Richardson Ph.D. April 7 2026.

AI Eliminating 16,000 U.S. Jobs Per Month

Goldman Sachs

Goldman Sachs economist Elsie Peng finds AI substituting 25K jobs/month while augmenting 9K, net loss 16K. Gen Z hardest hit with 3.3pp wage gap widening. Insurance clerks, bill collectors at highest substitution risk.

Challenger Report: March Cuts Rise 25% From February, AI Leads Reasons

Challenger, Gray & Christmas

March 2026: 60,620 total job cuts, AI led all reasons with 15,341 cuts (25%). Q1 YTD: 217,362 total, 27,645 AI (13%). Cumulative AI cuts since 2023: 107,094. Tech sector Q1 up 40%, Healthcare record Q1 high.

How AI may reshape career pathways to better jobs

Brookings Institution

Analysis of 70M STARs (non-degree workers): 15.6M in high AI-exposed roles, 3.5M at dual risk (high exposure + low adaptive capacity). Nearly half of career advancement pathways from Gateway to Destination occupations are highly AI-exposed. Florida metros most affected. Uses Anthropic observed exposure measure.

The 2026 AI Index Report

Stanford HAI

Stanford HAI's 2026 AI Index documents accelerating AI capability, investment and adoption, and highlights early labor-market signals including employment declines among younger workers in AI-exposed entry-level occupations.

AI and the US Labour Market: Effects on Employment Growth

European Central Bank

ECB Economic Bulletin (Issue 4/2026) finds US occupations at high AI-substitution risk declined more than 4% from 2019-2025 while low-risk occupations grew 13%, a ~15pp employment wedge that widened fastest after ChatGPT's late-2022 launch. Low-risk share rose 23%->25% of employment, high-risk fell 35%->33%. No significant wage-growth effect detected. Difference-in-differences with sector fixed effects.

Crashing Waves vs. Rising Tides: Preliminary Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks

MIT / arXiv

MIT researchers evaluated 3,000+ text-based O*NET tasks through 17,000+ worker assessments. Found "rising tides" pattern: AI task success climbing from ~50% (2024-Q2) to ~65% (2025-Q3), projected 80-95% by 2029. Little evidence of sudden "crashing waves" displacement. Adoption timelines expected to substantially lag capability gains.

2026 AI Index Report: Economy Chapter

Stanford HAI

Economy chapter of 2026 AI Index. Key findings: software developers aged 22-25 employment fell nearly 20% from 2024; ~1/3 of surveyed organizations anticipate workforce reductions over coming year; expected decreases concentrated in service operations, supply chain, and software engineering; productivity gains measured at 14-15% (customer support) and 26% (software development). AI labor effects concentrated in hiring pipelines and youngest workers in exposed occupations.

Forecasting the Economic Effects of AI

National Bureau of Economic Research

Karger, Kuusela, Tetlock et al. (15 co-authors) ran a structured forecasting exercise across 5 groups (academic economists, AI company employees, policy researchers, superforecasters, general public). Median GDP growth forecast through 2050: 2.5% (vs CBO baseline 2.0%/1.7%). Under rapid AI scenario: GDP ~4%/year, labor force participation drops 62.7% to 55%, ~10M jobs displaced by AI specifically. Main driver of disagreement: not AI capability timelines, but what high-capability AI does to the economy. Policy preferences: experts favor targeted retraining + portable benefits; general public prefers UBI + federal job guarantees.

Economics of Human and AI Collaboration: When is Partial Automation More Attractive Than Full Automation?

arXiv

MIT/IBM working paper develops a unified framework for optimal task automation. Convex AI accuracy cost curve makes partial automation the cost-minimizing equilibrium. 11% of computer-vision-exposed labor compensation captured at firm level; share rises sharply under economy-wide deployment. Calibrated with O*NET task data, 3,778 domain expert surveys, GPT-4o decompositions.

Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption

arXiv

Introduces Agentic Task Exposure (ATE) score framework. Analyzes 236 info-intensive occupations across 5 US tech hubs. 93.2% cross moderate-risk threshold by 2030 in Tier 1 regions. High-risk: credit analysts, judges, sustainability specialists (ATE 0.43-0.47). 17 emerging occupational categories in AI governance. Authors: Gupta & Kumar.

New ILO-World Bank paper highlights uneven global impact of generative AI on jobs

ILO / World Bank

Joint ILO-World Bank background study for WDR 2026 analyzing GenAI labor market exposure across 135 countries. Finds uneven impact: advanced economies face higher exposure in clerical/professional roles; developing economies risk faster disruption than productivity gains due to digital gaps. Women and youth disproportionately vulnerable.

The AI Layoff Trap: Automation Arms Race

arXiv (Wharton)

Wharton-arXiv-Paper: Die KI-Entlassungsfalle — Unternehmen im Automatisierungswettlauf erzeugen ein Kollektivhandlungsproblem, das nach hinten losgehen könnte.

A people-first vision for the future of work in the age of AI

Brookings Institution

Policy vision by Friedler, Booth, Schrank, Helper proposing worker-centered AI policies: minimum staffing laws in care sectors, tripartite institutions, participatory AI design. Cites 50%+ Americans fear AI displacement (Reuters/Ipsos). Occupations mentioned: teachers, nurses, social workers, software engineers, manufacturing, utility, guest room attendants.

Economic Index March 2026: Learning Curves

Anthropic

Anthropic Economic Index März 2026: 49 % der Jobs nutzen KI für 25 %+ der Aufgaben. Lernkurven zeigen, dass Frühnutzer davonziehen.

How AI Is Transforming Work at Anthropic (Internal Data)

Anthropic

Interne Anthropic-Daten: Ingenieure nutzen KI für 59 % ihrer Arbeit. Enthüllt tatsächliche Nutzungsmuster im Vergleich zu externen Schätzungen.

Building Pro-Worker AI

Brookings (Acemoglu, Autor, Johnson)

Acemoglu, Autor, Johnson (Brookings): Drei führende Ökonomen argumentieren, der aktuelle KI-Entwicklungspfad schade den Arbeitnehmern.

AI Hiring Is Booming While Everything Else Stalls

Stanford HAI + Indeed

Stanford HAI + Indeed-Daten: KI-Einstellungen steigen rapide, während Nicht-KI-Einstellungen stagnieren — ein Zwei-Klassen-Arbeitsmarkt entsteht.

Gen AI Wont Make Your Employees Experts

Stanford + Harvard

Stanford-Harvard-Studie: KI kann Erfahrungslücken nicht überbrücken. Eine 8-monatige Feldstudie zeigt, dass KI bestehende Kompetenzunterschiede verstärkt.

AI and the Entry-Level Job

Harvard Business Review + IBM

IBM verdreifacht die Einstellung von Berufseinsteigern, während Konkurrenten kürzen. HBR-Fallstudie zu einer konträren KI-Personalstrategie.

How AI Is Changing the Labor Market

Dallas Federal Reserve + HBR

HBR + Dallas Fed Synthese: KI hilft und schadet Arbeitnehmern gleichzeitig über verschiedene Kanäle — Produktivität vs. Verdrängung.

Disruption without dividend? How the digital divide and task differences split GenAI's global impact

International Labour Organization

ILO Working Paper 166 examines generative AI's uneven labor-market effects across 135 countries. Advanced economies face higher automation exposure (30-32% of employment), but developing nations face a paradox: workers vulnerable to displacement already have internet connectivity, while those positioned to gain from AI-driven productivity often lack reliable digital infrastructure. The paper warns that "disruptive effects may materialize before benefits" in some developing economies, underscoring gaps between AI's technological potential and actual implementation capacity.

Equipping workers with insights about compensation

OpenAI + University of Michigan

ChatGPT wage usage study: In Jan-Feb 2026, US consumer ChatGPT users sent ~3M messages/day about wages, compensation, or earnings. Workers use ChatGPT to navigate wage info gaps, especially in labor markets with high uncertainty. Joint study with University of Michigan on how Americans engage with AI for wage information. Released 2026-03-17.

AI and Young Workers: Employment Effects

Dallas Federal Reserve

Dallas-Fed-Daten zeigen, dass die Jugendbeschäftigung im Techsektor seit Anfang 2022 rückläufig ist — Monate vor dem ChatGPT-Start.

New Data Show No AI Jobs Apocalypse — For Now

Brookings Institution

Brookings-Analyse über 33 Monate: Noch keine Massenarbeitslosigkeit durch KI, aber erste Warnsignale in bestimmten Sektoren.

Karpathy AI Job Exposure Score for Every US Job

Andrej Karpathy / Fortune

Karpathy (ex-OpenAI) bewertet jeden US-Beruf nach KI-Exposition. Angestellte Fachkräfte zeigen die höchste Verwundbarkeit.

Research on AI and the Labor Market Is Still in the First Inning

Brookings Institution

Brookings-Metaanalyse argumentiert, dass die KI-Arbeitsmarktforschung noch in den Anfängen steckt — die meisten Behauptungen gehen über die Evidenz hinaus.

AI Labor Displacement and the Limits of Worker Retraining

Brookings Institution

Brookings untersucht historische Umschulungsprogramme und stellt begrenzte Erfolgsquoten für verdrängte Arbeitnehmer fest.

World Employment and Social Outlook: Trends 2026

ILO

ILO-Leitbericht: Globale Arbeitslosigkeit bei 186 Mio., KI-Paradoxon mit Produktivitätsgewinnen ohne Beschäftigungserholung. 408 Mio. ohne angemessene Arbeit.

Evaluating the Impact of AI on the Labor Market: January/February CPS Update

Yale Budget Lab

33 months after ChatGPT release, AI exposure metrics, occupational dissimilarity, and industry dissimilarity all flat or within historical ranges. Recent vs older college graduate dissimilarity in 30-33% band since Jan 2021. Anthropic Feb 2026 usage data tilts toward automation rather than augmentation. Direct quote: exposure/automation/augmentation measures show no sign of being related to changes in employment or unemployment.

Generative AI and Jobs: Refined Global Index of Occupational Exposure

ILO

Verfeinerter ILO-Index über 138 Länder: Frauen tragen ein doppelt so hohes Automatisierungsrisiko aufgrund beruflicher Segregationsmuster.

Measuring US Workers Capacity to Adapt to AI-Driven Job Displacement

Brookings Institution

Brookings identifiziert 6,1 Mio. US-Arbeitnehmer mit hoher KI-Exposition aber geringer Anpassungsfähigkeit — strukturelle Verwundbarkeitskartierung.

Labor market impacts of AI: A new measure and early evidence

Anthropic Economic Research

Anthropic introduces "observed exposure" — a new metric measuring AI's actual deployment in real workflows. Computer Programmers top at 75% observed exposure; full Computer & Math category sits at 33% (vs 90% theoretical). 30% of workers have zero observed exposure. New hires aged 22-25 in high-exposure fields drop ~0.5pp.

AI and Young-Adult Jobs: The Real Mystery

Economic Innovation Group

EIG-Studie hinterfragt die KI-Jugend-Jobs-Erzählung: Der Beschäftigungsrückgang hat nicht-KI-bezogene Ursachen, darunter Post-Pandemie-Korrekturen.

Reverse gear: how AI is bringing vocational occupations back

Cedefop

Cedefop analysis of EU online job advertisements shows VET (vocational education and training) occupations' share fell from 36% (2019-mid 2022) to 33% (mid 2022 trough), then recovered above 36% by early 2025 — coinciding with the emergence of generative AI in late 2022. Declining categories: software developers, sales & marketing, customer info workers, database specialists. Growing categories: engineering technicians, machinery mechanics, construction trades, transport workers.

Generative AI, Occupational Segregation and Gender Equality in the World of Work

ILO

This ILO study examines how generative AI intersects with occupational gender segregation, concluding that women are roughly twice as likely as men to be in highly exposed clerical roles, which could widen labor-market inequalities without targeted policy.

Generative AI and Jobs: A Refined Global Index of Occupational Exposure (Research Brief)

ILO

This ILO research brief presents a refined global index of occupational exposure to generative AI, finding that clerical and administrative jobs are most exposed and that women face higher exposure than men because of their concentration in such roles.

Tracking the Impact of AI on the Labor Market

Yale Budget Lab

The Yale Budget Lab tracks AI's labor-market impact using current population data and finds that occupational exposure has stayed broadly stable since the launch of ChatGPT, with no clear sign of large aggregate disruption to employment yet.

Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI

NBER

Danish admin data + adoption surveys show rapid AI chatbot adoption and reported productivity gains, but precise null effects on earnings and recorded hours at worker/workplace level (within 2%) two years after ChatGPT launch. Employers reorganized tasks rather than reducing workforce.

India Brief: Economic Index

Anthropic

Anthropic Indien-Brief: Weltweit zweitgrößte KI-Nutzerbasis, aber Platz 101 bei der Pro-Kopf-Adoption — die Kluft zwischen Umfang und Tiefe.

AI, Productivity, and the Workforce: Evidence from Corporate Executives

NBER

Survey of 750 corporate executives. Over half invested in AI. Little evidence of near-term aggregate employment declines. Larger companies anticipate reductions, smaller firms expect gains. Productivity paradox: executives perceive larger gains than data shows.

February 2026 Job Cuts Report

Challenger, Gray & Christmas

Challenger meldet 12.304 KI-bedingte Stellenstreichungen im Januar/Februar 2026, während die Einstellungspläne um 56 % gegenüber dem Vorjahr zurückgingen.

Fed's Barr Names 3 AI Scenarios — Including 'Essentially Unemployable'

Bank for International Settlements (BIS Review)

AI Doesnt Reduce Work — It Intensifies It

Harvard Business Review

HBR-Feldstudie über 8 Monate: KI erhöht die Arbeitsintensität statt die Belastung zu reduzieren — das Produktivitätsparadoxon.

An AI Productivity Boom? Don't Count Your (Productivity Data) Chickens

Yale Budget Lab

Skeptical analysis of AI productivity boom claims. Productivity data is too noisy and lagged to confirm GenAI productivity gains.

Is Generative AI a Job Killer? Evidence From the Freelance Market

Brookings Institution

Brookings-Freelancer-Studie: Hochqualifizierte Freiberufler sind am stärksten von KI betroffen — entgegen der Annahme, Geringqualifizierte würden zuerst verdrängt.

Yale Budget Lab: AI Exposure Hasn't Moved Workforce Distribution Since ChatGPT

The Budget Lab at Yale

Payrolls to Prompts: Firms Replacing Labor With AI

arXiv (Payrolls to Prompts)

arXiv-Studie: Unternehmen geben nur 3 Cent für KI pro Dollar aus, den sie bei Freelance-Budgets kürzen — die Substitution ist unvollständig.

Building Pro-Worker AI

The Hamilton Project (Brookings)

Acemoglu, Autor and Johnson argue that AI should be steered toward complementing workers rather than replacing them, identifying barriers such as misaligned incentives and a pro-automation bias, and proposing nine policy measures to make AI more pro-worker.

Firm Data on AI

NBER

Survey of 6,000 executives across US/UK/Germany/Australia finds 69% use AI but 90% report zero employment/productivity impact. Executives predict -0.7% employment next 3 years; employees expect +0.5% growth.

Graph-Based Analysis of AI-Driven Labor Market Transitions

arXiv

Analyzes 9,978 Egyptian job postings using knowledge graphs. Finds 20.9% of jobs face high automation risk, but only 24.4% of at-risk workers have viable transition pathways. 75.6% face structural barriers requiring comprehensive reskilling, not incremental upskilling. Process-oriented skills appear in 15.6% of feasible transitions.

Companies Are Laying Off Workers Because of AIs Potential, Not Performance

Harvard Business Review

HBR-Analyse: CEOs entlassen Mitarbeiter aufgrund von KI-Potenzial, nicht nachgewiesener Leistung. Nur 1 von 50 KI-Investitionen ist transformativ.

How AI is changing early careers: A view from entry-level workers

PwC + World Economic Forum

PwC+WEF joint survey of 9,394 entry-level workers across 48 economies. 47% curious about AI, 38% excited, 29% worried. 76% say job security is most important factor, but only 53% feel confident. Presented at Davos 2026.

[제2026-3호] '쉬었음' 청년층의 특징 및 평가: 미취업 유형별 비교 분석 (Characteristics and Assessment of 'Resting' Youth)

Bank of Korea

Analysis of Korean youth in "resting" status: 6.3%p higher probability for non-college youth, reservation wage 31M KRW (not high), AI cited as labor market deterioration factor, debunks "high standards" narrative

Looking for the Ladder: Is AI Impacting Entry-Level Jobs?

Economic Innovation Group

In this EIG working paper, Zanna Iscenko and Fabien Curto Millet (Google economics team) analyze whether AI is eroding entry-level employment. They find job postings declined more in AI-exposed occupations, but the inflection point began in 2022, months before ChatGPT's public release. The timing aligns better with the macroeconomic shift of rising interest rates than with the launch of large language models, challenging the dominant narrative that AI is the primary driver of weakening entry-level hiring.

Anthropic Economic Index: January 2026

Anthropic

Anthropic Economic Index: Der tatsächliche KI-Produktivitätsgewinn beträgt 1,0 %, nicht die berichteten 1,8 % — die meiste Nutzung ist Augmentation, nicht Automatisierung.

Payrolls to Prompts: Firm-Level Evidence on the Substitution of Labor for AI

arXiv

Ryan Stevens (Ramp applied science) analyzes firm-level spending on Ramp expense management platform (2021 Q3 - 2025 Q3). Top-exposed firms substitute $1 in labor for only $0.03 in AI spend (33x cost savings); mid-exposed firms at $0.30. Online labor market spending fell from 0.66% (2021 Q4) to 0.14% (2025 Q3); AI spending rose from 0% to 2.85% over same period. 50%+ of firms reduced online labor spending to 0%. Limitation: covers online freelance markets only, no occupation-level decomposition.

AI-Exposed Jobs Deteriorated Before ChatGPT

University of Pittsburgh + RAND

Pittsburgh/RAND-Studie mit 10,5 Mio. LinkedIn-Profilen: KI-exponierte Berufe verschlechterten sich 8–10 Monate vor dem ChatGPT-Start.

AI is reshaping entry-level hiring while lifting pay for experienced workers

Federal Reserve Bank of Dallas

Dallas Fed economists examine employment and wage trends across more than 200 occupations since ChatGPT's late-2022 release. They find AI is simultaneously reducing employment in the most exposed industries (computer systems design down roughly 5%) while pushing wages higher for experienced workers in those same fields. Entry-level workers face the steepest challenge because AI can replicate codified textbook knowledge but not the tacit knowledge built through years of experience, so the labor-market impact hinges on whether AI automates or augments tasks.

The end of work feels near. How do people perceive the impact of digital technologies and automation?

IAB / Labour Economics

Survey experiment with 5,147 US/German workers (2019, YouGov). Over 50% expect automation to raise aggregate unemployment; ~90% expect unequal impacts; but under 30% fear for their own job (correlation between the two below 0.1). Randomized provision of scientific information (Graetz & Michaels 2018: robots did not significantly reduce overall employment) cut aggregate-unemployment fear by ~0.15 SD, persisting 4 weeks later (n=2,225 follow-up). Effects heterogeneous by prior beliefs, with opposing shifts in policy demand. Pre-generative-AI data.

Enhancing Worker Productivity Without Automating Tasks: A Different Approach to AI and the Task-Based Model

NBER

Agrawal/McHale/Oettl: AI as productivity tool vs task replacer, wage inequality depends on skill distribution

AI 인재 5.7만 명 시대, 왜 기업은 사람이 없다고 할까?

한국은행

Korea has 57K AI specialists but 6% wage premium (vs US 25%), 16% brain drain, 30% firms lack AI job definitions.

Preparing for 2040: four AI-powered scenarios for the future of continuing skills development

Cedefop

Cedefop foresight report naming AI the most influential yet unpredictable force shaping continuing skills development (CSD) by 2040, presenting four scenarios (A opportunities / B polarisation / C slow adoption / D dystopia) and cross-cutting red flags. Paired with the 2024 European AI skills survey (42% need AI skills vs 15% trained = 27pp gap).

AI Jobs Barometer 2025

PwC

PwC-Barometer: KI-exponierte Berufe zeigen 4-fache Produktivitätsgewinne und 56 % Lohnprämie gegenüber nicht-exponierten Berufen.

Stanford AI Experts Predict What Will Happen in 2026

Stanford HAI

2026 predictions: emergence of high-frequency AI economic dashboards tracking task/occupation-level productivity, displacement, new roles. Shift from speculation to careful measurement. Round-up prediction article, not primary research.

AI 전문인력 현황과 수급 불균형: 규모, 임금, 이동성 분석

한국은행

BOK Issue Note 2025-36: 57K AI workforce with 58% holding graduate degrees, 69% large firms plan to expand AI hiring.

Beyond Automation: Redesigning Jobs with LLMs to Enhance Productivity

arXiv

UK Civil Service study analyzing 193,497 job vacancies and 1,542,411 tasks for AI exposure. Finds productivity gains, not just displacement. Authors propose redesign framework: automate, optimize, reallocate.

Incorporating AI Impacts in BLS Employment Projections 2024-34

Bureau of Labor Statistics

Das BLS bezieht erstmals KI-Auswirkungen in die offiziellen Beschäftigungsprognosen 2024–34 ein — ein methodischer Wendepunkt.

Agents, Robots, and Us: Skill Partnerships in the Age of AI

McKinsey Global Institute

MGI report: $2.9T US value by 2030, 57% work hours automatable, 40% jobs highly automatable, 70%+ skills transferable

AI 확산 초기, 청년고용은 왜 감소하는가?

한국은행

Youth employment declined 2.11M over 3 years with 98.6% in AI-exposed sectors. Junior workers with codified knowledge most vulnerable.

The State of AI: Global Survey 2025

McKinsey & Company (QuantumBlack)

McKinsey's QuantumBlack surveyed 1,993 participants across 105 nations (June-July 2025) on enterprise AI adoption. Some 88% of respondents report regular AI use in at least one business function, up from 78% a year earlier, though most enterprises remain in experimenting or piloting stages and only about a third have begun scaling. Sixty-two percent are at least experimenting with AI agents and 23% are scaling agentic systems, yet just 39% report enterprise-level EBIT impact. The highest-value performers redesign workflows and pursue growth and innovation, not efficiency alone.

AI 확산과 청년고용 위축: 연공편향(seniority-biased) 기술변화를 중심으로 [BOK 이슈노트 제2025-30호]

한국은행 (Bank of Korea)

BOK Issue Note No. 2025-30. In Korea over the past 3 years, youth (ages 15-29) employment in high-AI-exposure sectors fell by 211,000 jobs, of which 208,000 (98.6%) occurred specifically in AI-high-exposure sectors. Senior (50+) employment grew by 209,000 in same period, with 146,000 in AI-exposed sectors. AI diffusion exhibits 'seniority-biased technological change' — entry-level (codifiable, routinized) jobs replaced by AI, while jobs requiring tacit knowledge and social skills show complementarity. Korea GenAI adoption rate 63.5% (work-use 51.8%) — roughly 2x US, 8x faster diffusion than internet adoption.

AI and Demographic Changes

Yale Budget Lab

Martha Gimbel analysis: AI exposure intersects with workforce aging and immigration. Some aging-heavy occupations (legal secretaries, admin assistants) show higher AI exposure; passenger attendants show minimal applicability. AI exposure concentrates in occupations with lower shares of foreign-born workers. Commentary piece, no numerical breakdowns in article body.

GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

OpenAI

GDPval benchmark: 1,320 tasks across 44 knowledge-work occupations in 9 top-GDP sectors (Real estate, Government, Manufacturing, Professional services, Health care, Finance, Retail, Wholesale, Information). Top models (GPT-5, Claude Opus 4.1) rated as good as or better than humans in ~50% of tasks. 100x faster and cheaper than experts. Occupations include: software developers, lawyers, registered nurses, mechanical engineers, accountants, financial analysts, customer service reps, pharmacists, project management specialists, police supervisors, producers/directors, and others. Based on O*NET + BLS May 2024 wage data.

Remote Labor Index: Measuring AI Automation of Remote Work

arXiv

Introduces a Remote Labor Index to measure AI automation capability across remote-work tasks. Key finding: AI capabilities on remote-work benchmarks are rising rapidly but remain below human baseline for complex multi-step tasks. Relevant for occupations dominated by remote knowledge work.

Evaluating the Impact of AI on the Labor Market: Current State of Affairs

The Budget Lab at Yale

This Budget Lab at Yale analysis evaluates whether AI is measurably reshaping the US labor market, using occupational and industry dissimilarity metrics alongside AI-exposure measures. The data show no substantial acceleration in the rate of change in labor-market composition since ChatGPT's introduction; dissimilarity and exposure metrics remain flat or follow prior trends. The authors conclude the picture reflects stability rather than major economy-wide disruption, while cautioning that better data is needed and committing to regular updates of the analysis.

AI and jobs: A review of theory, estimates, and evidence

arXiv

Meta-review of AI exposure and employment literature. Synthesizes theoretical frameworks, empirical estimates, and emerging evidence across 2023-2025. Tests consistency of exposure scores (Felten, Webb, Pizzinelli) with observed hiring patterns.

How People Use ChatGPT (NBER Working Paper 34255)

OpenAI / NBER

Aaron Chatterji, Thomas Cunningham, David J. Deming, Zoe Hitzig, Christopher Ong, Carl Yan Shan, Kevin Wadman. NBER WP 34255 (Sep 2025). ChatGPT adopted by ~10% of world adult population by Jul 2025; gender gap narrowing; higher growth in lower-income countries. Non-work messages grew 53% -> 70%+. 75% of conversations focus on practical guidance, information seeking, and writing. 49% of messages are Asking mode (advisor) showing decision-support value especially in knowledge-intensive jobs. Privacy-preserving automated classifier on de-identified messages. Companion to OpenAI compensation paper (#64) and GDPval (#65).

Yes, AI is affecting employment. Here's the data.

ADP Research

ADP Research examines how generative AI is reshaping employment across worker age groups, drawing on Stanford Digital Economy Lab findings. Employment for workers aged 22-25 fell about 6% between late 2022 and July 2025 in AI-exposed fields like software development and customer support, while older workers in the same roles saw employment gains of 6-13%. The analysis distinguishes AI tools that automate tasks from those that augment human work, concluding that early-career professionals in easily automatable roles face the greatest vulnerability.

[제2025-22호] AI의 빠른 확산과 생산성 효과: 가계조사를 바탕으로 (Rapid Spread of AI and Its Productivity Effects: Evidence from a Household Survey)

Bank of Korea

63.5% of Korean workers use GenAI (51.8% for work), 3.8% work time reduction, 1.0% productivity gain, autonomous robot collaboration 11%->27%, equalizing effect for junior workers

Our Gen AI Transition: Implications for Work and Skills

Jobs and Skills Australia

JSA Gen AI Capacity Study Overarching Report. First national whole-of-labour-market study by Australian government. 358 occupations analyzed using ANZSCO + two-axis (augmentability + automatability) framework. Key findings: 79% low risk, 21% meaningful exposure (routine clerical concentrated).

Our Gen AI Transition: Industry Exposures & Adoption (Industry Data)

Jobs and Skills Australia

A companion dataset to Jobs and Skills Australia's Generative AI Capacity Study (14 August 2025), this analysis presents AI exposure and adoption patterns at the industry level across the Australian economy. It complements the occupation-level data by showing which sectors face the greatest AI exposure and where adoption is concentrated, supporting a whole-of-labour-market view of generative AI's potential, its impact to date, and what is needed to support Australia's digital and AI transition.

Our Gen AI Transition: Exposures, Adaptation, Dynamism (Occupation Data)

Jobs and Skills Australia

Part of Jobs and Skills Australia's Generative AI Capacity Study (released 14 August 2025), this dataset maps AI exposure by occupation using ANZSCO v1.3 unit-level classifications. It measures how jobs are changing in response to AI, including the extent of task adaptation, worker mobility between roles, and overall occupational dynamism. The findings show augmentation generally outweighs automation, with higher automation potential concentrated in routine roles and women-dominated occupations, while entry-level positions show no evidence of widespread displacement in Australia yet.

AI and Jobs: The Final Word (Until the Next One)

Economic Innovation Group

EIG researchers Sarah Eckhardt and Nathan Goldschlag examine whether AI is currently driving meaningful job losses across the US labor market, testing five different AI-exposure measures against unemployment, labor-force participation, occupational switching, and industry employment. They find no substantial employment losses among AI-exposed workers; highly exposed workers actually show lower unemployment than less-exposed peers. The authors conclude AI adoption is accelerating (~9% of businesses) but real economic diffusion remains modest, with most firms reporting neutral or positive employment effects.

Advancing AI Capabilities and Evolving Labor Outcomes

arXiv

Empirical analysis linking AI capability advancement to labor market outcomes. Higher AI exposure associated with reduced employment, higher unemployment rates, and shorter work hours. Evidence consistent with 2025 AI-related hiring slowdowns and layoffs.

OECD Employment Outlook 2025

OECD

The OECD Employment Outlook 2025 reviews labor-market conditions across member countries and examines how AI adoption is affecting jobs, wages and skill demand, finding uneven exposure across occupations and limited net employment effects so far.

AI Index Report 2025

Stanford HAI

Stanford HAI AI Index 2025: Umfassende länderübergreifende Analyse der KI-Adoptionsraten im Vergleich zu Arbeitslosigkeitstrends.

Augmenting or Automating Labor? The Dual Impact of AI on Jobs and Wages

arXiv

Studies 2015-2022 US data using instrumental variables. Finds automation AI negatively impacts new work, employment, and wages in low-skilled occupations, while augmentation AI fosters new work and raises wages for high-skilled occupations. Concludes AI may accelerate existing wage inequality.

The Future of Jobs Report 2025

World Economic Forum

The WEF Future of Jobs Report 2025 surveys more than 1,000 employers worldwide on how AI and automation will reshape roles by 2030, projecting 92 million jobs displaced alongside 170 million created and a shift in 39% of workers' core skills.

Technological Disruption in the Labor Market

NBER

Deming/Ong/Summers: Historical labor disruption analysis, STEM jobs 50%+ surge since 2010, AI as potential GPT