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).
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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 |
Labor Markets
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This project studies how much labor is required to manage capital across those waves by tracking a simple productivity measure: assets under management per employee.
Stated research design: longitudinal tracking of assets under management (AUM) per employee as the primary productivity measure; described in the paper's methods/summary. No numeric sample size provided in the excerpt.
Financial firms have gone through three major technological waves: computerization in the 1980s and 1990s, the rise of indexing and passive investing in the 2000s and 2010s, and the AI and automation wave from roughly 2015 to the present.
Author's historical categorization stated in the paper's introduction/summary (time periods specified). No sample or empirical test reported in the excerpt.
This study proposes a framework for evaluating platform ecosystems by their long-term effects on human capital formation and institutional resilience.
Methodological contribution claimed by the paper (development of an evaluative framework); presented as part of the paper's contributions rather than an empirical finding.
Our findings highlight the importance of additional research and progress on economic measurement related to AI.
Authors' concluding statement/recommendation based on their results and measurement challenges discussed in the paper.
We use tools to indirectly estimate the impact of AI via the lens of BEA’s industry accounts.
Methodological description in the paper: authors apply indirect estimation methods using BEA industry accounts to infer AI's economic impact.
Currently, there is not a line item in the U.S. national accounts that can be used to identify and measure the economic impact of artificial intelligence (AI).
Statement by authors about the state of U.S. national accounts (BEA) and absence of a specific national-accounts line item for AI.
In both popular and academic press, concerns are often expressed that AI threatens not only people’s livelihoods but also the meaning they derive from their work.
Observational/literature-commentary claim made in the paper's abstract; references to discourse in popular and academic press (no empirical study or sample reported).
The analysis uses causal discovery methods and integrates scenario-based outcomes, communication analysis, and questionnaire measures.
Paper abstract states that causal discovery analysis was used and that it integrates scenario outcomes, communication analysis, and questionnaire measures.
The study examines user Extraversion and Agreeableness alongside AI design characteristics including Adaptability, Expertise, and chain-of-thought Transparency.
Variables listed in the abstract as the human personality traits and AI design characteristics analyzed.
The study compares two interaction scenario categories: (1) hiring negotiations between human job candidates and AI hiring agents; and (2) human-AI transactions in which AI agents may conceal information to maximize internal goals.
Explicit description of the two scenario categories in the paper abstract; method: experimental / simulation scenarios.
The study includes a parallel human subjects experiment involving 290 human participants.
Statement in paper abstract reporting a human-subjects experiment with 290 participants.
The study uses a purely simulated dataset comprising 2,000 simulations.
Statement in paper abstract describing a simulated dataset of 2,000 simulations; method: simulation experiments.
A randomly sampled coalition of equal size remains largely ineffective at increasing platform spending / wages.
Theoretical comparison in the model between targeted coalitions and randomly sampled coalitions of the same size; analytical results showing limited impact for random coalitions.
The article examines the socioeconomic implications of AI-driven automation through the lens of political economy and labor sociology.
Methodological statement in the paper indicating theoretical framing and disciplinary approaches; no empirical sample reported in the abstract.
The review is a focused qualitative evidence synthesis and the proposed governance model is an evidence-informed conceptual framework that warrants future empirical validation.
Authors' explicit framing of the review approach and caveat calling for empirical validation of the proposed model.
Given the focused Title/Abstract/Keywords query and the small, heterogeneous corpus, the findings are interpreted as a scoped evidence map rather than an exhaustive census of all AI-and-work research.
Authors' explicit limitation statement referencing the search strategy (title/abstract/keywords focus), small number of included studies (n=19), and heterogeneity of studies.
Nineteen studies met the eligibility criteria and were analyzed using qualitative thematic synthesis.
Reported result of the screening/eligibility process in the review: final included sample = 19 peer-reviewed articles; analysis method stated as qualitative thematic synthesis.
We conducted a systematic review guided by PRISMA 2020, searching Scopus and Web of Science (Title/Abstract/Keywords) for English-language journal articles published between 2015 and 2025.
Methods reported in the paper: PRISMA 2020-guided systematic review; databases searched explicitly named (Scopus, Web of Science); query fields (Title/Abstract/Keywords); language and date restrictions stated (English, 2015–2025).
The paper analyses the complex interactive relationships among job seekers, recruitment platforms, and enterprises on the basis of the classic theory of incomplete information games.
Methodological description in abstract stating the use of incomplete information game theory to model interactions among stakeholders.
Mainstream recruitment algorithms are taken as the core research object and the multidimensional specific manifestations and internal generation mechanisms of group prejudices in algorithm screening are systematically investigated.
Methodological claim in the paper describing the study's scope and analytic focus (systematic investigation of manifestations and internal mechanisms); no empirical detail provided in abstract.
Existing academic research focuses primarily on macrolevel governance paths of algorithmic discrimination, with relatively insufficient in-depth exploration of the microlevel game logic of job seekers and the construction of systematic adaptation strategies.
Paper's literature review/positioning statement claiming a gap in the literature (macro focus vs. microlevel adaptation under-explored); no systematic literature-mapping statistics provided in abstract.
This paper treats pilots of supply chain innovation and application as a quasi-natural experiment and employs a difference-in-differences method to identify causal effects of supply chain digitalization.
Methodological description in the paper: sample of A-share listed companies (Shanghai and Shenzhen) 2013–2022; DID estimation using policy pilots as exogenous variation.
The LLM fallacy is situated within existing literature on automation bias, cognitive offloading, and human–AI collaboration, but is distinguished as a form of attributional distortion specific to AI-mediated workflows.
Conceptual positioning and literature synthesis in the paper; claim is analytic rather than empirically tested in the abstract.
Less attention has been given to how LLM usage reshapes users' perceptions of their own capabilities.
Literature gap claim from the paper's review of prior research on model reliability, hallucination, and trust calibration; no quantitative synthesis or meta-analysis reported.
The global onset of Industry 4.0 and Artificial Intelligence (AI) necessitates a re-evaluation of employment forecasts for Nagpur's medium enterprises.
Interpretive/prescriptive claim based on the paper's framing of technological change (Industry 4.0/AI) and implications for employment forecasting; no empirical sample size or quantitative backing provided in the excerpt.
Medium-scale industries in zones like Butibori and Hingna have traditionally been labor-intensive.
Descriptive statement in the paper about the nature of current industries in Nagpur/MIDC; no sample size or quantitative data reported in the excerpt.
The full model, including all 11 analytical tabs, is made publicly available to facilitate replication and independent sensitivity testing.
Paper states that the full model and all 11 analytical tabs are publicly available.
A sensitivity analysis shows that the high-skill capture rate and the pace of friction decay are the two parameters with the greatest influence on the aggregate result.
Paper reports results of a sensitivity analysis identifying parameter importance; explicitly names high-skill capture rate and friction decay pace as most influential.
AI coverage scores are sourced from Massenkoff and McCrory (2026) and mapped to NAICS industries using employment-weighted averages derived from BLS Occupational Employment and Wage Statistics data for 2023.
Citation to Massenkoff and McCrory (2026) for theoretical LLM task coverage across SOC groups and explicit statement that mapping used employment-weighted averages from BLS OES 2023.
The core formula multiplies six inputs: base GDP, labor share, AI coverage, productivity gain percentage, adjusted adoption rate, and a skill-weighted capture rate.
Model specification in the paper describing the multiplicative core formula and listing the six inputs.
The paper provides statistics on the agreement rates between different measures of AI exposure.
Descriptive/statistical comparison of multiple AI-exposure measures (e.g., different O*NET-based metrics) reporting agreement rates.
The authors validate their industry-level control variable by examining historical examples of occupations that experienced either occupation-specific or industry-level shocks.
Validation exercise using historical case studies/examples comparing known occupation-specific and industry-level shocks to assess the control variable's performance.
There is a significant research gap in comparative understanding of generative AI's impact across developed and developing economies; differences in infrastructure, labour markets, and skill distributions may lead to uneven outcomes.
Review observation that the included literature lacks sufficient comparative studies across country-development contexts (explicitly noted as a gap in the paper).
This systematic literature review synthesised findings from 40 empirical and conceptual studies published between 2020 and 2025 using the PRISMA framework (search across Google Scholar and Dimensions.ai), yielding 3,252 database records plus 8 hand-searched studies, of which 40 met the inclusion criteria.
PRISMA-style structured literature search reported in the paper: database search (Google Scholar, Dimensions.ai) returning 3,252 records, 8 hand-searched records, 40 studies meeting inclusion.
All four models converge to similar skill profiles (3.6-point spread), suggesting that text-based automation feasibility may be more skill-dependent than model-dependent.
Comparison across 4 LLMs (LLaMA 3.3 70B, Mistral Large, Qwen 2.5 72B, Gemini 2.5 Flash) with reported 3.6-point spread in skill-profile SAFI scores.
The study employed a mixed-methods approach: a quantitative survey of 150 leading Nigerian firms across finance, tech, and manufacturing, complemented by qualitative analysis of government policy and workforce interviews.
Methodological statement in the paper explicitly describing sample and methods (quantitative survey n=150; qualitative policy and interviews).
Acemoglu (2025) argues that near-term aggregate productivity gains from AI may be quite modest.
Citation to Acemoglu (2025) viewpoint noted in the introduction.
Despite substantial expected AI progress, most respondents do not forecast major departures from recent macroeconomic baselines, citing factors like historical base rates, adoption lags, demographic headwinds, policy responses, and infrastructure bottlenecks.
Qualitative summary of respondents' reasoning accompanying their unconditional forecasts (Key Findings and 1.2 description of survey elicitation).
Conventional microeconomic models often treat interactions between algorithmic platforms and workers as static principal-agent problems.
Literature statement in paper (conceptual framing / literature review); no empirical sample reported.
We find little evidence of crashing waves (in contrast to recent work by METR).
Analysis of the >3,000 tasks and >17,000 evaluations which reportedly do not show abrupt, concentrated surges in AI capability on small sets of tasks.
The evaluation is based on more than 17,000 evaluations by workers from these jobs.
Reported sample of >17,000 human evaluations of model outputs.
We test for these effects in preliminary evidence from an ongoing evaluation of AI capabilities across over 3,000 broad-based tasks derived from the U.S. Department of Labor O*NET categorization that are text-based and thus LLM-addressable.
Empirical study design reporting an ongoing evaluation covering >3,000 text-based tasks mapped from O*NET.
We introduce the Agentic Task Exposure (ATE) score, a composite measure computed algorithmically from O*NET task data using calibrated adoption parameters (not a regression estimate), incorporating AI capability scores, workflow coverage factors, and logistic adoption velocity.
Methodological description in the paper; algorithmic construction from O*NET task data with specified calibrated adoption parameters and components (AI capability scores, workflow coverage, logistic adoption).
The research documents a transition in the literature (2013–2025) from early 'risk-of-automation' evaluations toward task-based and firm-level econometric models.
Literature review/synthesis across the 2013–2025 body of research as described in the paper.
Metode penelitian yang digunakan adalah penelitian hukum normatif dengan pendekatan perundang-undangan, konseptual, dan komparatif, didukung oleh analisis literatur dari jurnal nasional terindeks SINTA dan jurnal internasional bereputasi.
Pernyataan metode yang jelas tercantum dalam abstrak/metodologi makalah.
Penelitian menilai kecukupan perlindungan hukum yang tersedia bagi pekerja terdampak PHK akibat adopsi AI.
Pernyataan tujuan penelitian dan pendekatan analitis (normatif, komparatif) yang didukung oleh tinjauan literatur pada jurnal-jurnal terpilih.
Penelitian ini bertujuan menganalisis bagaimana Undang-Undang Cipta Kerja dan peraturan turunannya mengklasifikasikan dan menjustifikasi Pemutusan Hubungan Kerja (PHK) akibat adopsi AI.
Pernyataan tujuan penelitian yang tercantum di bagian metodologi/pendahuluan; pendekatan peraturan-perundang-undangan dalam penelitian hukum normatif.
Data construction: The authors treat Wikipedia technology pages as distinct technologies and trace them across patents and job postings from 1976 to 2007, using technical bigrams to identify technologies in texts.
Description of dataset construction building on Kalyani et al. (2025) in Section 2; methodological description of linking Wikipedia pages, patent text, and job postings.
Proposition 1: With a constant pace of technology creation (m(b)=m), the model admits a unique balanced growth path (BGP) along which real wages and output grow at rate g, the skill premium remains constant and is independent of m.
Analytical result (proposition) proved in the paper's model appendix under model assumptions.
The modal technology in the top 1% densest locations (e.g., New York, San Francisco) is 34 years old, while the modal technology in the bottom 50% lowest-density locations is 48 years old, indicating sizable diffusion gaps.
Empirical measurement from the text-based technology dataset tracking vintage of technologies across locations; reported modal ages by location density percentile.