KI & Arbeitspolitik-Zeitleiste

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.

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.

The Rise – and Rise – of Knowledge Work

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.

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.

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).

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%.

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 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.

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.

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.

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.

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.

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.

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.

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.

Building Pro-Worker AI

Brookings (Acemoglu, Autor, Johnson)

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

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.

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.

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.

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.

AI Labor Displacement and the Limits of Worker Retraining

Brookings Institution

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

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.

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.

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.

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.

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.

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.

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, 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.

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.

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.

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.

Pay trends to watch in 2026

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.

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

The Budget Lab at Yale

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.

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.

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.

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

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.

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.

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.

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.

[제2025-36호] AI 전문인력 현황과 수급 불균형: 규모, 임금, 이동성 분석

한국은행 (Bank of Korea)

BOK Issue Note 2025-36 (December 2025) by BOK Employment Research Team. Korean AI specialists totaled approximately 57,000 in 2024. Wage premium ~6% in 2024 with recent acceleration. About 16% (11,000 workers) employed overseas. Composition: 64% engineering majors, 58% master/PhD degrees. Data source: LinkedIn online profiles covering 1.1M Korean workers and over 10M job histories. Demonstrates AI talent expansion across industries while highlighting supply-demand imbalance and brain drain pressure in Korean AI labor market.

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.

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.

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).

Rapid Spread of AI and Productivity Effects: Based on 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

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.

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.

Technological Disruption in the Labor Market

NBER

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