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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22% of employment undergoes structural change (masking the net job gain).
Reported summary statistic from the paper's secondary quantitative analysis of international reports; no primary sample size stated.
Estimated coefficients for young workers are negative, in line with the existing literature, but they are small and statistically insignificant.
Reported coefficient signs and statistical significance levels in the paper's main estimations (negative point estimates for young workers; described as small and insignificant).
ABS adoption negatively affects high-status batters' BB/K (walks-to-strikeouts ratio) relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148); BB/K listed among impacted outcomes.
ABS adoption negatively affects high-status batters' strikeout rate (SO%) relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148); SO% reported among affected metrics.
ABS adoption negatively affects high-status batters' walk rate (BB%) relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148); BB% listed among impacted outcomes.
ABS adoption negatively affects high-status batters' IsoD relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148); IsoD reported among affected metrics.
ABS adoption negatively affects high-status batters' on-base percentage (OBP) relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148).
Monopoly production of AI restricts its deployment, slowing the transition and impact of AI.
Theoretical model comparing monopolistic AI producer behavior to competitive deployment; result is derived analytically. No empirical sample reported.
Wages of labor that is substituted for by AI decrease in both absolute and relative terms.
Analytical economic model / comparative statics predicting wage declines for labor substituted by AI. No empirical sample reported.
Public discourse still focuses heavily on job losses while paying less attention to the opportunities that AI creates.
Author's observation/argument in the paper (qualitative commentary comparing public discourse emphasis).
The report identifies 'AI washing,' a practice in which companies mention AI as justification for what are really financially motivated layoffs.
Identification/term introduced in the paper based on examples or synthesis of corporate reporting and layoff cases (as described).
Roughly 92 million jobs might face displacement by 2030.
Projection synthesized from cited external reports (WEF/PwC/MGI/Gartner/IMF) as reported in the paper.
The AI premium is absent in emerging markets, including China.
Geographic cross-sectional analysis indicating no significant AI premium in emerging market firms (explicitly mentioning China).
Unemployment, idle labour and weakening domestic demand are holding back growth.
Empirical results reported in the paper indicate a negative relationship between unemployment (and related indicators) and economic growth in the 27-country panel (2008–2020).
The existing international competency indices fail to capture the structural differentiation in AI-driven educational transformation across EU moderate innovator economies, rendering evidence-based policy design inadequate.
Stated as a motivating assertion in the paper; based on the author's critique of existing indices and the subsequent focused evaluation of selected EU moderate innovator economies (Visegrad and Baltic states). No specific quantitative comparison of indices is reported in the abstract.
AI poses environmental challenges.
The abstract lists environmental challenges as one of the potential trade-offs identified by the systematic review of 194 articles.
AI can contribute to widening inequality.
Abstract reports the review identifies widening inequality as a potential trade-off of AI, based on synthesis of 194 articles.
AI can give rise to job displacement.
The abstract states the review finds potential trade-offs including job displacement across the surveyed literature (194 articles).
Mediation analysis: AI adoption contracts employment in production and managerial positions.
Mediation models using occupational/role-level employment categories showing reductions in production and managerial headcounts associated with AI adoption.
AI adoption widens intra-firm pay disparities (increases pay inequality within firms).
Regression analyses showing divergent effects on employee vs. executive pay and explicit measures of intra-firm pay disparity in the panel data.
Oligopolistic capture of productivity gains is intelligible as an outcome of AI-driven assetisation (i.e., productivity gains are appropriated by a small number of firms).
Theoretical claim based on political economy argument about assetisation and market power; no empirical sample or quantitative evidence reported in the excerpt.
Labour markets for university-educated workers are where the explanatory limits of human capital theory are most consequentially exposed.
Theoretical critique supported by political economy / sociological reasoning (no empirical sample reported).
AI should be understood as a productive rentier asset whose returns derive from constructed scarcity and access control rather than from commodity exchange.
Conceptual/theoretical framing based on political economy and sociological analysis (argumentative, no empirical sample reported).
Experts in the study assign a 14% probability to 'rapid-progress' scenarios characterized by substantial GDP growth, declining labor force participation, and accelerating wealth inequality.
Result from the 2025 forecasting study of experts (69 economists + 52 AI experts), reporting a probability estimate (14%) for a named scenario with specified macroeconomic and labor-market features.
The effectiveness of prompt injection rapidly diminishes as more candidates inject, collapsing when manipulation becomes widespread.
Controlled experiments that vary the share of candidates performing prompt injection and observe changes in manipulation effectiveness; exact sample size not provided in the abstract.
The review identifies significant compliance challenges related to emerging regulations, including New York City Local Law 144, Illinois HB 3773, and the European Union AI Act.
Legal and policy scholarship included in the systematic review of 34 studies, which discuss regulatory requirements and compliance issues associated with AI-based recruitment.
AI-based recruitment systems frequently inherit demographic and historical biases embedded within training datasets, potentially leading to discriminatory hiring outcomes when adequate oversight mechanisms are absent.
Synthesis of findings across the systematic review of 34 studies reporting evidence of demographic/historical biases in training data and downstream discriminatory effects in hiring models.
There is a global disparity in data centre infrastructure (concentrations favouring some regions over others).
Analysis drawing on external data sources cited in the paper illustrating geographic distribution of data centre infrastructure.
Data workers in Kenya report direct employment by big tech corporations and exposure to graphic content.
Qualitative interviews / responses from data workers in Kenya collected and reported in the paper.
Hyper-datafication systematically redistributes labour risks and representational harms toward the Global South.
Qualitative responses from data workers in Kenya describing labour conditions and exposure; analysis of language data representation; external data on global data centre infrastructure and geography.
Hyper-datafication drives substantial and growing environmental costs.
Quantitative analysis of dataset growth and estimated storage-related energy consumption and carbon footprint across the analysed Hugging Face datasets (≈550k); modelled storage and emissions impacts.
Türkiye is one of the most fragile regimes due to its weak regulatory capacity, high algorithmic discipline, and lack of transparency.
Regime assessment in the comparative analysis component of the paper that evaluates Türkiye's regulatory capacity and algorithmic governance characteristics.
Regime positioning reveals that despite the EU's partial regulatory capacity, it cannot fully close the collective rights gap.
Comparative normative analysis of EU regulatory frameworks relative to collective algorithmic rights dimensions (paper's regime positioning assessment).
Individual-centered regulatory frameworks (GDPR, AI Act, CCPA, LGPD, etc.) are limited in their understanding of the collective operating logic of algorithmic governance.
Normative comparative analysis of existing regulations across the EU, US, Latin America, Asia, and Türkiye as reported in the paper (conceptual/legal analysis rather than empirical measurement).
Algorithmic governance under a data-driven, predictive, and dynamic authority architecture is creating structural transformations that exceed the institutional capacity of the existing individual rights paradigm.
Conceptual argument presented in the paper; theoretical analysis of algorithmic governance and its impacts on institutional frameworks (no empirical sample reported).
This convergence has the potential to lower wages on entry-level thinking jobs.
Theoretical/empirical implication drawn from observed reduction in productivity differences; presented as a potential consequence rather than an established empirical result in the abstract.
Early evidence indicates AI is reducing the productivity difference between beginner and expert employees.
Reported 'early evidence' from the paper's empirical analysis (difference-in-differences on freelance platforms) indicating convergence in productivity between novices and experts; no numeric effect estimates given in the abstract.
These results demonstrate how people's decision-making processes can be insufficient for overseeing AI in high-stakes domains.
Synthesis/interpretation of experimental findings (longer viewing when no AI, small increases in selection probability with more time for non-recommended candidates, IAT effects) to argue that human decision processes may not adequately supervise biased AI in high-stakes settings. This is an interpretive/concluding claim based on the experiment; not a direct empirical measure. Sample size not stated in the excerpt.
In manual jobs, AI compresses the returns to undereducation as tasks become more skill-intensive.
Occupation-specific heterogeneity analysis using CLDS and city AI diffusion showing reductions in the undereducation wage premium within manual-occupation subsamples under higher AI diffusion.
AI diffusion slightly lowers the wage premium for undereducated workers.
Interaction effects from fixed-effects models using CLDS and city AI diffusion indicators showing a small reduction in undereducation-related wage premium with higher AI diffusion.
Overeducation leads to a significant wage penalty.
Microdata from the China Labor-force Dynamics Survey (CLDS) 2014–2018; cohort-based measure of educational mismatch; estimated using extensive fixed-effects models comparing wages by educational mismatch status.
AI serves as a financial risk factor for platform-based illustrators by increasing price pressures, enhancing market transparency, and increasing exposure to revenue volatility.
Author interpretation based on the statistical finding of a significant association between AI and income plus theoretical/accounting discussion; no additional quantified causal mechanism presented in the reported results.
In the Sakha Republic (Yakutia), factors shaping last-mile costs and platform dependence include territorial scale, low population density, concentration of demand in Yakutsk, seasonal navigation and northern supply constraints.
Regional empirical analysis focused on the Sakha Republic (Yakutia) considering territorial scale, population density, demand concentration, seasonal navigation and supply chains as presented in the paper.
Common mechanisms through which food delivery platforms form market power include network effects, economies of scale and scope, data control, algorithmic management and ecosystem lock-in.
Comparative case analysis of major Chinese platforms (Meituan, Ele.me/Taobao Instant Commerce, JD Waimai), supported by statistical data review and academic literature on platform markets.
Traditional indicators of market share, price and commission do not sufficiently reflect the influence of platforms that control data, algorithms, access rules, ratings and couriers’ work practices.
Conceptual argument and comparative case analysis drawing on the study's qualitative review of platform governance (Meituan, Ele.me/Taobao Instant Commerce, JD Waimai), supplemented by literature and regulatory/legal acts analysis.
Critical post-work thought posits that not only certain jobs, but also jobs in general, are disappearing.
Statement summarizing the position of a body of theoretical work ('critical post-work thought') as described by the author; this is a characterization of a viewpoint rather than an empirical finding.
The majority of extant studies focus exclusively on the 'technical' aspect of new technologies replacing labour, thereby ignoring their social dimension and consequently falling into the trap of technology fetishism.
Claim about the literature based on the paper's review and critique of existing studies; no citation counts or systematic review methodology described in the excerpt.
Policy asymmetries, digital literacy gaps, and regional inequalities deepen digital divides and impede inclusive development.
Policy analysis and comparative case studies documenting how policy differences, literacy, and regional disparities affect digital inclusion; China used as a focal example. No quantitative sample sizes or causal estimates given in summary.
Agriculture remains digitally marginalized due to infrastructural and institutional deficits.
Comparative case studies and sectoral data showing lower digital adoption in agriculture; qualitative policy analysis identifies infrastructure and institutional shortcomings. No sample size or quantified adoption metrics provided in summary.
Fertility is strongly countercyclical and almost perfectly negatively correlated with hours worked in the model, placing household time allocation at the center of the mechanism.
Model-simulated correlations and business-cycle dynamics showing fertility and hours worked time series and their correlation.