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Home Papers Evidence Explore Trends Syntheses Digests About 🎲 Workforce Futures
Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review. How this is built →

Evidence (4004 claims)

Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.

The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).

Browse by theme

Nine broad, paper-level topics. Click one to filter the claims below.

Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category

Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.

Outcome Positive Negative Mixed Null Total
Other 870 233 116 1066 2363
Governance & Regulation 976 451 218 133 1809
Organizational Efficiency 949 224 144 88 1416
Technology Adoption Rate 764 287 141 122 1325
Research Productivity 501 152 74 362 1101
Output Quality 542 216 69 69 896
Decision Quality 387 198 94 54 740
Firm Productivity 513 67 101 27 714
AI Safety & Ethics 249 303 73 36 667
Market Structure 190 192 134 27 548
Task Allocation 243 77 91 36 452
Innovation Output 291 33 55 20 401
Skill Acquisition 206 72 65 21 364
Employment Level 133 63 115 22 335
Fiscal & Macroeconomic 153 79 52 32 323
Task Completion Time 206 37 12 15 272
Firm Revenue 179 52 29 5 266
Consumer Welfare 130 76 47 13 266
Inequality Measures 48 137 51 6 242
Worker Satisfaction 101 81 25 13 220
Error Rate 84 110 11 5 210
Wages & Compensation 98 47 30 10 185
Regulatory Compliance 88 73 17 7 185
Automation Exposure 66 64 33 16 182
Team Performance 105 29 30 11 176
Training Effectiveness 109 22 14 21 168
Developer Productivity 114 21 14 8 158
Job Displacement 12 90 24 1 127
Hiring & Recruitment 57 9 9 5 80
Skill Obsolescence 6 56 9 1 72
Social Protection 43 17 8 2 70
Creative Output 35 21 9 4 70
Labor Share of Income 18 21 17 1 57
Worker Turnover 15 16 — 4 35
Industry — — — 1 1
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Labor Markets Remove filter
Guerreiro et al. (2022) characterize optimal Mirrleesian tax system with automation and find that robot taxes should be transitional—high when incumbent workers cannot retrain, converging to zero as new cohorts adjust skill investments.
Citation reported in the paper summarizing Guerreiro et al. (2022)'s theoretical result on transitional robot taxes.
high neutral NBER WORKING PAPER SERIES optimal robot tax path over time
If labor becomes economically redundant, the policy focus shifts from steering innovation to redesigning public finance and redistribution (e.g., new tax instruments, redistribution mechanisms).
Theoretical scenario analysis in the paper with references to related works (Korinek and Juelfs 2024; Korinek and Lockwood 2026).
high neutral NBER WORKING PAPER SERIES policy priority shift (steering -> public finance/redistribution)
We critically compare LLM-generated rulings against 10,000 real-world court judgments from China Judgments Online (CJOL).
Dataset statement: the paper compares model outputs to a corpus of 10,000 CJOL labor dispute judgments.
high neutral LLM Safety in Judicial AI: A Stress Test of Social Media Inf... agreement / deviation between LLM-generated rulings and CJOL judgments
We introduce a novel stress test that evaluates LLM-generated labor dispute outcomes by injecting social media sentiment as an external pressure.
Methodological description in the paper: a designed stress test where social media sentiment is used to perturb LLM outputs for labor dispute cases.
high neutral LLM Safety in Judicial AI: A Stress Test of Social Media Inf... sensitivity of LLM-generated labor dispute outcomes to injected social media sen...
Economic evaluations of GLAI should account for end-to-end risk externalities (error propagation, institutional trust, rights impacts), not only short-term productivity gains.
Methodological recommendation grounded in conceptual synthesis of technical, behavioral, and legal risks; normative argument rather than empirical result.
high neutral Why Avoid Generative Legal AI Systems? Hallucination, Overre... comprehensiveness of economic evaluations (inclusion of externalities vs. narrow...
Generative Legal AI (GLAI) systems are built on token-prediction (LLM) architectures rather than formal legal-reasoning architectures.
Conceptual and technical analysis in the paper distinguishing GLAI from other legal-tech; literature synthesis on common LLM architectures. No original empirical dataset or sample size—qualitative/technical review.
high neutral Why Avoid Generative Legal AI Systems? Hallucination, Overre... underlying model architecture type (token-prediction vs. formal-reasoning)
Productivity effects at the aggregate (economy-wide) level are delayed relative to firm-level gains.
Cross-study synthesis noting temporal lags between observed firm-level productivity improvements and measurable aggregate effects in the literature included in the SLR.
high null result Artificial Intelligence and the Digital Economy: Impact on E... timing of aggregate productivity effects
The review followed the PRISMA protocol and synthesized 78 peer-reviewed studies and institutional reports published between 2015 and 2025.
Systematic Literature Review using PRISMA protocol; sample of 78 peer-reviewed studies and institutional reports (2015–2025) as described in the paper.
high null result Artificial Intelligence and the Digital Economy: Impact on E... review_coverage
Additional analyses reveal no statistically significant heterogeneity in the AI exposure — working-hours relationship by employment type, flexible working arrangements, labour union membership or part-time employment status.
Heterogeneity/subgroup analyses reported in the paper comparing effect estimates across groups defined by employment type, flexible work, union membership, and part-time status (no significant differences found).
high null result Artificial Intelligence Exposure and Working Hours: Evidence... weekly working hours (heterogeneity of effects)
No statistically significant differences in working-hour trends by AI exposure are observed in 2020 and 2021, consistent with the parallel-trends assumption.
Pre-treatment (pre-2022) event-study estimates reported in the analysis showing no significant differences in 2020–2021.
high null result Artificial Intelligence Exposure and Working Hours: Evidence... weekly working hours (pre-treatment differences)
This study constructs a Korean AI Industry Exposure Index.
Methodological description in the paper: the authors report building an industry-level index measuring AI exposure for Korean industries (used in the subsequent empirical analysis).
high null result Artificial Intelligence Exposure and Working Hours: Evidence... AI exposure (Korean AI Industry Exposure Index)
Results are robust to state-by-year and industry-by-year fixed effects.
Robustness checks reported in paper that include state-by-year and industry-by-year fixed effects with results stated to hold.
high null result AI, Output, and Employment robustness of estimated effects to alternative fixed-effects specifications
Where AI can perform tasks independently, we find no significant employment effect.
Heterogeneous DiD estimates showing null (statistically non-significant) employment coefficients for occupations/industries where AI can perform tasks independently.
high null result AI, Output, and Employment employment (in occupations/industries with independent AI exposure)
We examine aggregate effects using administrative data covering essentially all U.S. employers in a difference-in-differences design exploiting occupational AI exposure across industries and states.
Statement in paper describing data and empirical strategy: administrative data covering essentially all U.S. employers; difference-in-differences design exploiting occupational AI exposure variation across industries and states.
high null result AI, Output, and Employment data_coverage_and_design (administrative data, DiD)
This suggests the apparent post-2022 decline reflects, at least in part, pre-existing secular trends rather than a clean AI-period break.
Interpretation based on the backdating exercise and the pattern of estimates (larger absolute estimates for backdated dates), leading the authors to infer pre-existing trends contributed to observed post-2022 declines.
high null result Labor Market Consequences of Generative AI: Early Evidence f... interpretation of trend vs causal break in employment outcomes
We find no robust response across other age cohorts or on incumbent labor-market outcomes.
Paper reports null/insignificant findings across other age cohorts and for incumbent labor-market outcomes using the same empirical approaches.
high null result Labor Market Consequences of Generative AI: Early Evidence f... employment and other incumbent labor-market outcomes across age cohorts
We find no robust evidence of employment displacement among young workers in highly AI-exposed occupations.
Estimation results from the cited DiD and supplementary designs applied to population-wide Norwegian registers; paper states no robust displacement for young workers in highly AI-exposed occupations.
high null result Labor Market Consequences of Generative AI: Early Evidence f... employment (displacement) among young workers in highly AI-exposed occupations
We provide population-wide evidence from Norwegian administrative registers, 2015 through March 2025.
Population-wide Norwegian administrative registers covering 2015–March 2025, as stated in the paper.
high null result Labor Market Consequences of Generative AI: Early Evidence f... data coverage / sample scope
The study uses difference-in-differences linear regressions on 2023 and 2024 KBO season data to identify the causal impact of ABS adoption by player status.
Methods statement in paper: difference-in-differences linear regressions; sample sizes reported as n = 148 batters and n = 112 pitchers.
high null result Technology adoption and bias in officiating: automated Ball-... methodological approach (DiD estimation)
High-status pitchers' performance remains unaffected by ABS adoption.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for pitchers (n = 112); paper reports no detectable change for high-status pitchers.
high null result Technology adoption and bias in officiating: automated Ball-... Pitcher performance (aggregate; specific metrics not listed in summary)
The Korea Baseball Organization (KBO) officially implemented the Automated Ball-Strike System (ABS) in 2024.
Paper statement of policy change and use of 2023 and 2024 KBO season data; presented as factual background to the natural experiment.
high null result Technology adoption and bias in officiating: automated Ball-... ABS implementation / adoption
Wages of labor that is used only in final goods production and is not displaced by AI increase in line with overall GDP.
Analytical economic model result indicating proportional wage growth for final-goods-only labor relative to GDP. No empirical sample reported.
high null result The Economic Benefits and Costs of AI and Policies to Mitiga... wages of final-goods-only labor
The paper synthesizes evidence drawing on reports from the World Economic Forum, PwC, McKinsey Global Institute, Gartner, and the International Monetary Fund.
Literature/report synthesis explicitly described in the paper (citation list to those organizations).
high null result AI-Driven Workforce Transformation: Displacement, Opportunit... sources and scope of evidence used in the paper
The AI premium is not present for loadings on casual use or open-weight (open-source) model use.
Decomposition analysis showing null or weaker relation between AI premium and loadings on casual usage metrics and open-weight/open-source model consumption.
high null result AI Premium AI premium related to casual or open-weight model use
We construct a high-frequency AI Factor from growth in tokens, dollars, and users, estimate firm-level AI Betas from stock return comovement, and characterize the AI Premium.
Methodological claim based on constructing a factor (AI Factor) using metrics of tokens, dollars, and users; estimating firm-level betas via stock return comovement.
high null result AI Premium AI Factor, firm-level AI Betas, characterization of AI Premium
The analysis uses 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption.
Descriptive statement about the dataset used: OpenRouter licensed proprietary dataset; 380 trillion tokens; >400 LLMs; coverage ≈2% of global monthly AI token consumption.
high null result AI Premium scale and coverage of AI consumption data (tokens, model count, % global consump...
New generation panel data methods were applied, taking into account cross-sectional dependence and heterogeneity across countries.
Methodological description in the paper indicating use of advanced panel techniques that account for cross-sectional dependence (common shocks/spillovers) and heterogeneity (institutional/structural differences).
high null result AI Readiness, Renewable Energy, and Industrial Development: ... methodological approach (panel estimation accounting for cross-sectional depende...
This study examines the determinants of economic growth in the 27 countries with the highest GDP for the period 2008–2020.
Study sample and period explicitly stated in the paper: the 27 highest-GDP countries, years 2008–2020.
high null result AI Readiness, Renewable Energy, and Industrial Development: ... economic growth (country-level)
The study employed a simplified multiple criteria assessment methodology based on global and regional expert evaluations of education quality determining knowledge and innovation development.
Methodological statement in the paper describing the approach used to analyze the Visegrad and Baltic states over 2022–2025; implies use of expert evaluations and multiple-criteria assessment.
high null result AI-Education and Innovation Competitiveness: EU Moderate Inn... methodology used to evaluate AI-driven educational transformation
This study systematically reviewed 194 peer-reviewed articles published between 2011 and 2025.
Statement in the paper's abstract describing a systematic review of 194 peer-reviewed articles (2011–2025).
high null result Artificial Intelligence and Economic Development: A Systemat... number_of_studies_reviewed
We detect no negative spillovers on contact rates or exit-to-job rates for unemployed German or other immigrant job seekers, finding no evidence of resource reallocation or displacement.
Placebo/spillover analyses comparing contact rates and exit-to-job rates for unemployed German and other immigrant job seekers in the same public employment service offices before and after program rollout using administrative panel data and difference-in-differences methods.
high null result Refugee labor market integration at scale: Evidence from Ger... contact rates and exit-to-job rates for unemployed German and other immigrant jo...
Moderation analysis: Regional AI development and supportive policies have limited impact on the effect of AI adoption on gender composition.
Moderation models interacting AI adoption with regional policy/industry measures when predicting male-to-female employment ratios; reported limited/modest moderation effects.
high null result Creative disruption or destructive inequality? Firm-level ev... male-to-female employment ratio conditional on regional policy/industry context
Scholars remain divided on AI’s implications for the future of work, with debate centred on what AI can do to jobs rather than on the economic regime shaping how it is deployed and who appropriates its returns.
Literature review / conceptual observation by the author(s); argumentative claim based on survey of scholarly debate in political economy and sociology (no empirical sample reported).
high null result From human capital to asset ownership: AI as rentier asset orientation of scholarly debate (focus on capability vs economic regime)
A limitation of the study is that using occupation-level data prevents capturing within-occupation wage heterogeneity.
Paper statement of research limitations indicating occupation-level data as a constraint to measuring within-occupation variation.
high null result Artificial intelligence exposure and occupational wages: Evi... ability to capture within-occupation wage heterogeneity
The AI exposure index reflects potential exposure to AI technologies rather than actual firm-level AI adoption.
Paper explicit limitation: the AI exposure index measures potential exposure and is not a direct measure of firm-level adoption.
high null result Artificial intelligence exposure and occupational wages: Evi... measurement scope of the AI exposure index
The study uses occupation-level data for 671 occupations combining wage information with an AI exposure index.
Methods description: occupation-level dataset of 671 occupations and an AI exposure index constructed/used by the authors.
high null result Artificial intelligence exposure and occupational wages: Evi... data coverage (occupational wages and AI exposure index)
Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate algorithmic hiring systems.
Background claim stated in the paper's abstract, likely supported by literature or observation in full text; not empirically quantified in the abstract.
high null result Prompt Injection in Automated Résumé Screening with Large La... use/adoption of LLMs for applicant screening and implied incentives for manipula...
Code and resources are publicly available at: https://github.com/preetb1199/Prompt_Injection_ACL26
Repository link provided in the paper's abstract.
high null result Prompt Injection in Automated Résumé Screening with Large La... availability of code/resources
Prompt injection is defined as subtle self-promotional text that introduces no new qualifications but is designed to influence LLM evaluations.
Definition provided in the paper (abstract).
high null result Prompt Injection in Automated Résumé Screening with Large La... definition/construct (no empirical outcome)
A systematic review of 34 peer-reviewed studies spanning computer science, organizational psychology, human resource management, and legal scholarship was conducted.
Methodological statement in the paper describing the study design: a systematic review and the explicit count of 34 reviewed studies.
high null result Predictive Talent Acquisition: AI Governance and Enterprise ... coverage of literature (number and disciplinary distribution of studies reviewed...
We analyse approximately 550,000 datasets from the Hugging Face Hub.
Empirical analysis reported in the paper of datasets indexed on the Hugging Face Hub; sample size given as ~550,000 datasets.
high null result How Hyper-Datafication Impacts the Sustainability Costs in F... dataset count / dataset growth (Hugging Face Hub)
The study examines regime variations of algorithmic governance across five dimensions covering the European Union, the United States, Latin America, Asia, and Türkiye.
Descriptive/method statement in the paper: normative comparative analysis across specified geopolitical/regime cases.
high null result COLLECTIVE ALGORITHMIC RIGHTS: A NEW RIGHTS ARCHITECTURE FOR... scope and comparative coverage across five regions
The study uses microdata from the China Labor-force Dynamics Survey (CLDS) 2014–2018 combined with city-level indicators of AI diffusion and a cohort-based measure of educational mismatch, estimated with extensive fixed-effects models.
Study design and data description reported in the paper abstract/introduction.
high null result Technological diffusion, skill reconfiguration and wage adju... research design / data sources (not an outcome)
General life satisfaction remains unaffected by AI diffusion.
Auxiliary/sensitivity analyses using life-satisfaction measures available in CLDS; reported null effect of city-level AI diffusion on general life satisfaction.
high null result Technological diffusion, skill reconfiguration and wage adju... general life satisfaction
AI diffusion is not significantly associated with individual wages.
City-level indicators of AI diffusion linked to CLDS microdata; estimated main effects of AI diffusion on individual wages in fixed-effects models found no significant association.
high null result Technological diffusion, skill reconfiguration and wage adju... wages
The study used an explanatory quantitative approach with simple random sampling of 385 illustrators from the Artist's Base community and analyzed relationships using simple linear regression.
Study methods statement: sample = 385 illustrators, sampling = simple random sampling within Artist's Base community, analysis = simple linear regression (explanatory quantitative approach).
high null result The Influence of Artificial Intelligence on Revenue Performa... methodology / study design
The argument that automation is leading to a general decline in employment opportunities is not supported by actual facts and trends; rather, it is a product of the pervasive influence of technological fetishism.
Author's evaluative conclusion based on the reviewed theoretical and empirical studies; the excerpt provides no specific datasets or statistical tests supporting this rebuttal.
high null result New Technologies and Increase in Employment general trend in employment opportunities in relation to automation
A conceptual framework is developed showing how digital infrastructure and institutional support mediate sectoral transformation.
Paper presents a conceptual framework (theoretical/modeling component) derived from empirical findings and policy analysis; this is descriptive rather than a quantified empirical result.
high null result How to Utilize New Technologies to Improve Productivity role of digital infrastructure and institutions in mediating transformation
Artificial intelligence is taking on advising functions and automating both the production of student work and employer-side candidate screening.
Statement in the essay (perspective/argumentative piece). The claim is supported as a conceptual observation drawing on literature on AI adoption; no empirical sample or quantified measurement reported.
high null result Vouching towards Bethlehem: what colleges and universities o... degree of automation of advising, student work production, and candidate screeni...
The paper's contribution is theoretical: it reframes the AI productivity debate beyond automation anxiety by linking technological change, income distribution and effective demand in a single analytical framework.
Author-stated contribution and framing in the conceptual review (description of scope and aim).
high null result Artificial Intelligence, Labour Income and Effective Demand:... conceptual framing of the AI productivity debate (qualitative contribution)