Sigue cómo gobiernos e instituciones de todo el mundo están respondiendo a la IA en el mercado laboral.
Datos desde enero de 2025
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.
International Labour Organization
ILO mixed-methods diagnostic (1,237 surveys + 29 IDIs, Phnom Penh) of taxi, tuk-tuk and delivery platform workers: 6.7-6.8 days/week and 11-12 hours/day worked; gross monthly earnings $581/$501/$477 but net earnings invert the ranking ($204 taxi vs $355 delivery); 79% say customer ratings very significantly affect earnings and 49% were penalized for refusing/cancelling orders, mostly via reduced job allocation; about half hold social security (vs 20.8% nationally) but mostly voluntary NSSF healthcare-only cover, while one third of delivery riders report work injuries.
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.
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.
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.
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).
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).
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.
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).
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).
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 (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.
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.
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."
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
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.
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.
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).
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
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%.
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%.
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).
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.
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.
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.
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.
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).
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.
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.
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.
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).
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.
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.
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.
ADP Research
ADP Research analysis (Nela Richardson and Tim Decker, 2026-04-21) of 25-year US employment composition trends. Professional services share grew from 14.9% (Jan 2000) to peak 17.6% (Jan 2022), declining to 16.8% (Jan 2025). Administrative/support roles fell from 47.5% (Jan 2020) to 39.5% (Jan 2025). Management/scientific consulting expanded from 10.1% (Jan 2020) to 17.3% (Jan 2025). Computer programming roles showed strongest 25-year growth. Data: BLS QCEW + ADP employment data + UMich consumer sentiment + Census retail sales + DOL unemployment claims. Affected occupations: legal services, accounting, architecture, engineering, design, advertising, computer programming, scientific research, management consulting, technical services.
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.
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.
arXiv
Evaluation framework for AI work-domain capabilities. Low blog value (framework paper, not labor market impact evidence).
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).
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).
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%.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
arXiv (Wharton)
Artículo Wharton arXiv: la trampa de despidos por IA — empresas corriendo a automatizar crean un problema de acción colectiva que podría ser contraproducente.
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.
Anthropic
Anthropic Economic Index marzo 2026: el 49% de los empleos usan IA para 25%+ de las tareas. Las curvas de aprendizaje muestran que los primeros adoptantes se adelantan.
Anthropic
Datos internos de Anthropic: los ingenieros usan IA para el 59% de su trabajo. Revela patrones de uso real vs estimaciones externas.
Stanford HAI + Indeed
Stanford HAI + Indeed: la contratación de IA se dispara mientras la no-IA se estanca, creando un mercado laboral de dos velocidades.
Brookings (Acemoglu, Autor, Johnson)
Acemoglu, Autor, Johnson (Brookings): tres economistas líderes argumentan que la trayectoria actual del desarrollo de IA perjudica a los trabajadores.
Stanford + Harvard
Estudio Stanford-Harvard: la IA no puede cerrar brechas de experiencia. Estudio de campo de 8 meses muestra que la IA amplifica las diferencias de habilidades.
Dallas Federal Reserve + HBR
Síntesis HBR + Dallas Fed: la IA simultáneamente ayuda y perjudica a los trabajadores por canales diferentes — productividad vs desplazamiento.
Harvard Business Review + IBM
IBM triplica contrataciones de nivel inicial mientras sus competidores recortan. Caso de estudio HBR sobre estrategia laboral IA contraria a la tendencia.
Dallas Federal Reserve
Datos del Dallas Fed: el empleo juvenil en tech ha disminuido desde inicios de 2022, meses antes del lanzamiento de ChatGPT.
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.
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.
Brookings Institution
Análisis de 33 meses de Brookings: no hay desempleo masivo por IA todavía, pero surgen señales de alerta en sectores específicos.
Andrej Karpathy / Fortune
Karpathy (ex-OpenAI) puntúa cada empleo de EE.UU. por exposición a IA. Los profesionales de cuello blanco muestran la mayor vulnerabilidad.
ILO
Índice refinado de la OIT en 138 países: las mujeres enfrentan 2x más riesgo de automatización debido a patrones de segregación ocupacional.
Brookings Institution
Brookings examina programas históricos de recapacitación y encuentra tasas de éxito limitadas para trabajadores desplazados.
ILO
Informe insignia de la OIT: desempleo global en 186M, la paradoja de la IA — ganancias de productividad sin recuperación del empleo. 408M carecen de trabajo adecuado.
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.
Brookings Institution
Metaanálisis de Brookings: la investigación sobre IA y empleo está en sus etapas más tempranas — la mayoría de las afirmaciones superan la evidencia.
Brookings Institution
Brookings identifica 6.1M de trabajadores estadounidenses con alta exposición a IA pero baja capacidad de adaptación — mapeo de vulnerabilidad estructural.
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.
Economic Innovation Group
Estudio EIG desafía la narrativa IA-jóvenes-empleos: la caída del empleo tiene causas no relacionadas con IA, incluyendo correcciones post-pandemia.
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.
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.
Anthropic
Anthropic India Brief: 2a mayor base de usuarios de IA del mundo pero 101o en adopción per cápita — la brecha entre escala y profundidad.
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.
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.
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.
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.
Challenger, Gray & Christmas
Challenger reporta 12,304 recortes de empleo por IA en ene-feb 2026, los planes de contratación cayeron 56% interanual.
Bank for International Settlements (BIS Review)
Harvard Business Review
Estudio de campo HBR de 8 meses: la IA aumenta la intensidad del trabajo en vez de reducir la carga — la paradoja de la productividad.
Yale Budget Lab
Skeptical analysis of AI productivity boom claims. Productivity data is too noisy and lagged to confirm GenAI productivity gains.
ADP Research
ADP Research January 2026 payroll panel: job-changers 6.4% YoY wage growth vs job-stayers 4.5% (1.9pp premium, smallest since 2020). New-hire median base pay jumped from $18 to $19/hour after 18-month plateau, driven by construction and financial services. Average weekly hours 1 hour below pre-pandemic, near 7-year low. ~45% of workers now part-time (<35 hrs/wk), +6pp vs 2019. Sector switcher premium: construction +6.6pp, mining +5.6pp, leisure/hospitality negative. Authored by Nela Richardson Ph.D. and Liv Wang Feb 17 2026.
Brookings Institution
Estudio Brookings del mercado freelance: los freelancers altamente calificados son los más golpeados por la IA, contradiciendo la narrativa de desplazamiento de baja calificación.
The Budget Lab at Yale
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.
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.
arXiv (Payrolls to Prompts)
Estudio arXiv: las empresas gastan solo 3 centavos en IA por cada dólar recortado de presupuestos freelance — la sustitución es incompleta.
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.
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
Harvard Business Review
Análisis HBR: los CEOs despiden basándose en el potencial de la IA, no en su desempeño demostrado. Solo 1 de cada 50 inversiones en IA es transformadora.
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.
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
Anthropic Economic Index: la ganancia real de productividad de la IA es 1.0%, no 1.8% — la mayoría del uso es aumento, no automatización.
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.
University of Pittsburgh + RAND
Estudio Pittsburgh/RAND de 10.5M perfiles de LinkedIn: los empleos expuestos a IA se deterioraron 8-10 meses antes del lanzamiento de ChatGPT.
World Economic Forum
The WEF Future of Jobs Report 2026 surveys employers worldwide on how AI and automation will reshape roles by 2030, projecting large-scale displacement alongside even larger job creation and a major shift in required skills.
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.
NBER
Agrawal/McHale/Oettl: AI as productivity tool vs task replacer, wage inequality depends on skill distribution
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.
Eurofound
This Eurofound policy brief assesses whether job quality across EU Member States is improving and whether workplace standards are converging or diverging. It analyzes key indicators on fairness, health and safety, work-life balance, skills for the digital transition, and the role of social dialogue and collective bargaining, alongside disparities in social-protection access and persistent gender gaps. The comparative cross-country evidence is intended to inform current policy debates, including development of the EU Quality Jobs Act.
한국은행
Korea has 57K AI specialists but 6% wage premium (vs US 25%), 16% brain drain, 30% firms lack AI job definitions.
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).
PwC
Barómetro PwC: los empleos expuestos a IA muestran 4x más productividad y una prima salarial del 56% sobre los no expuestos.
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.
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.
한국은행
BOK Issue Note 2025-36: 57K AI workforce with 58% holding graduate degrees, 69% large firms plan to expand AI hiring.
Bureau of Labor Statistics
El BLS incorpora por primera vez impactos de la IA en sus proyecciones oficiales de empleo 2024-34 — cambio metodológico.
McKinsey Global Institute
MGI report: $2.9T US value by 2030, 57% work hours automatable, 40% jobs highly automatable, 70%+ skills transferable
한국은행
Youth employment declined 2.11M over 3 years with 98.6% in AI-exposed sectors. Junior workers with codified knowledge most vulnerable.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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
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.
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.
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).
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.
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
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.
Stanford HAI
Stanford HAI AI Index 2025: análisis comparativo integral entre países sobre tasas de adopción de IA y tendencias de desempleo.
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.
NBER
Deming/Ong/Summers: Historical labor disruption analysis, STEM jobs 50%+ surge since 2010, AI as potential GPT
Las instituciones y organizaciones que monitoreamos continuamente en busca de investigaciones sobre IA y mercado laboral. Actualmente rastreamos 55 fuentes activas.
Cada fuente lleva un nivel de fiabilidad (S es el más alto) y un dominio tiene que estar en esta lista antes de que un artículo pueda citarlo: la base de datos rechaza cualquier otro. Añadir una fuente es una decisión que tomamos, no un paso automático, y para los candidatos que la canalización propone por su cuenta registramos esa decisión.