Evidence (6444 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
20058 claims
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Productivity
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Governance
16099 claims
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Human-AI Collaboration
16034 claims
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Innovation
10501 claims
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Org Design
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Labor Markets
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Skills & Training
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Inequality
4148 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 | 1820 | 479 | 278 | 1820 | 4588 |
| Organizational Efficiency | 2711 | 616 | 401 | 173 | 3922 |
| Governance & Regulation | 2075 | 886 | 459 | 246 | 3714 |
| Technology Adoption Rate | 1467 | 530 | 258 | 206 | 2488 |
| Decision Quality | 1281 | 496 | 289 | 152 | 2228 |
| Output Quality | 1227 | 447 | 207 | 138 | 2025 |
| AI Safety & Ethics | 634 | 754 | 207 | 83 | 1688 |
| Research Productivity | 826 | 241 | 114 | 422 | 1624 |
| Firm Productivity | 1052 | 154 | 163 | 66 | 1441 |
| Task Allocation | 685 | 211 | 331 | 99 | 1335 |
| Market Structure | 433 | 423 | 242 | 46 | 1150 |
| Innovation Output | 639 | 91 | 105 | 34 | 871 |
| Task Completion Time | 476 | 113 | 43 | 36 | 672 |
| Firm Revenue | 445 | 126 | 58 | 25 | 656 |
| Skill Acquisition | 364 | 119 | 109 | 34 | 626 |
| Consumer Welfare | 288 | 167 | 104 | 31 | 592 |
| Employment Level | 214 | 140 | 174 | 50 | 582 |
| Error Rate | 230 | 251 | 35 | 16 | 535 |
| Fiscal & Macroeconomic | 268 | 136 | 71 | 50 | 532 |
| Inequality Measures | 100 | 307 | 96 | 12 | 515 |
| Worker Satisfaction | 221 | 173 | 60 | 30 | 484 |
| Automation Exposure | 155 | 138 | 65 | 36 | 398 |
| Regulatory Compliance | 171 | 120 | 30 | 13 | 335 |
| Developer Productivity | 222 | 58 | 27 | 13 | 321 |
| Team Performance | 188 | 56 | 50 | 24 | 320 |
| Wages & Compensation | 146 | 104 | 46 | 16 | 312 |
| Training Effectiveness | 207 | 41 | 21 | 26 | 298 |
| Job Displacement | 23 | 153 | 52 | 4 | 232 |
| Hiring & Recruitment | 102 | 57 | 30 | 11 | 202 |
| Skill Obsolescence | 16 | 102 | 24 | 6 | 148 |
| Creative Output | 71 | 42 | 23 | 6 | 143 |
| Social Protection | 57 | 30 | 11 | 3 | 101 |
| Labor Share of Income | 29 | 42 | 24 | 2 | 97 |
| Worker Turnover | 43 | 29 | 6 | 4 | 82 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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Movement toward digital work is pathway-dependent rather than uniform across career transitions.
The study constructs consecutive same-person job transitions and calculates changes in job-title digitalization scores between source and destination jobs, summarizing these by occupational groups.
Data ownership and algorithmic control influence who receives economic rents from AI-driven value creation.
Qualitative case studies and literature synthesis focused on data rights, data dividends, ownership, and algorithmic governance.
Automation causes employment disruption in some occupations, while the net employment effect varies by sector, skill composition, and institutional context.
Synthesis of empirical studies within the review, with heterogeneity reported across sectors, worker skill compositions, and institutional settings.
Short-run disruption includes job churn and wage compression for affected groups, while long-run outcomes depend on reskilling, capital re-allocation, and institutions.
Asserted in the supplied example contribution; no longitudinal employment, wage, or reskilling evidence is provided.
Regions with higher human capital and adoption capacity capture more productivity gains, while disadvantaged regions face stagnation.
Presented as a regional heterogeneity claim; no regional panel, productivity measure, or comparative estimate is supplied.
AI substitutes for routine cognitive and manual tasks, shifting worker duties toward nonroutinized, interpersonal, and creative tasks.
Presented as a task-based displacement claim; no task-level dataset or estimates are supplied.
Net employment effects are modest short-run losses, with potential long-run gains if complementary skill investment and policy support occur.
Asserted in the supplied example contribution; the text provides no employment panel, identification strategy results, or quantified estimates.
High-skill cognitive tasks and complementary occupations gain earnings, while routine tasks and low-skill occupations face displacement and wage pressure.
Asserted in the supplied example contribution; no occupational employment or wage data are presented.
AI-driven automation accelerates occupational task reallocation, raising productivity but producing uneven wage effects.
Asserted in the supplied example contribution; no underlying paper, dataset, sample, or statistical analysis is provided.
The strength of psychological barriers to enterprise AI adoption differs by enterprise size, industry type, and employees' prior AI experience.
Multi-group comparison analyses examined heterogeneity across enterprise and employee subgroups in a three-enterprise survey.
For middle managers, AI has both positive and negative effects: it supports data analysis and managerial decision-making while creating concerns about automation of some managerial responsibilities.
Cross-study synthesis of findings differentiated by organizational level.
Incorporating constitutive dynamics into matching theory implies that worker preferences and suitability may be endogenous and shaped by organizational experience rather than fully stable and observable in advance.
Theoretical implication for labor-market matching and market-design models; no equilibrium model or empirical estimate is presented.
Correspondence-based fit research is most compatible with surveys, alignment metrics, dyadic or market-matching measures, and causal estimation, while constitutive-fit research is more compatible with qualitative methods, longitudinal process tracing, and analysis of interpretation and affect.
Methodological implications derived from the distinct epistemological assumptions of the two ontologies.
The two ontologies imply different process explanations for organizational outcomes: correspondence models emphasize trait alignment leading to outcomes such as satisfaction and retention, whereas constitutive models emphasize interpretive work leading to meaningfulness and adjustment.
The article develops separate processual explanations for each ontology; the claim is conceptual rather than an estimate from observed data.
Person–organization fit research rests on two distinct ontologies: a correspondence ontology that treats fit as alignment between person and organization attributes, and a constitutive ontology that treats fit as an enacted and interpretive accomplishment.
Conceptual and theoretical synthesis distinguishing two underlying ontologies; no new empirical data are reported.
In the authors' illustrative macroeconomic exercise, the currently automatable share implies negligible aggregate effects of around 0.1 percentage points per year, Wave 1 implies about 0.9 percentage points, Wave 2 around 4 percentage points, and Wave 3 more than 20 percentage points in annual productivity and price effects.
Order-of-magnitude calculation assuming displaced employment is fully automated over roughly ten years at an even rate, with displaced labor redeployed; the authors explicitly state that these are not forecasts.
Occupations made automatable by Wave 1 show a slight employment decline of about 1%, while occupations first made automatable at Wave 2 or later show flat or rising employment.
Employment-weighted US OEWS changes for 2023–24 and 2024–25, grouped by the wave in which occupations first meet all nine AI capability requirements.
The paper argues that age influences employment-transition outcomes indirectly through digital literacy and access to training opportunities rather than acting as a deterministic factor.
The claim is stated in the abstract and conceptual framework; the supplied text does not report age-stratified estimates or a formal moderation analysis.
The paper argues that adaptive capacity is the core mediator of employment divergence following AI-related employment shocks.
This is the study's stated interpretive conclusion, derived from questionnaire evidence and grounded-theory-informed coding involving frontline workers and managers; no formal mediation analysis is reported.
The management responses associate AI-related employment change with distributional consequences, institutional governance and organisational responsibility, not only productivity.
Coding of 159 usable Q23 management responses identified social fairness, industry regulation, data privacy, human–AI collaboration, skills training and employee welfare as recurring categories.
Frontline workers expressed both fear of unemployment and expectations that AI could create new employment opportunities.
Q19 coding found 16 responses (10.70%) expressing fear of unemployment and 16 responses (10.70%) identifying new employment opportunities.
The most frequently reported frontline-worker concerns about AI were the need for skills training, the need for policy support and labour-market reshuffling, each reported by 22 of 150 respondents (14.70%).
Frequency distribution of eight recurring categories in 150 usable responses to Q19.
AI-related workplace change simultaneously produces efficiency gains or improved work organisation and displacement-related effects for low-skilled workers.
Thematic coding of frontline-worker responses identified workflow optimisation, reduced repetitive tasks and reduced overtime alongside partial job substitution and income decline.
Among the surveyed UK low-skilled workers, AI was experienced primarily as task restructuring and transformation rather than the immediate elimination of entire jobs.
Inductive coding of 150 usable frontline-worker responses to Q14 identified workflow optimisation, reduction of repetitive tasks, task simplification and partial job substitution as recurring categories.
The shift-share decomposition indicates that exposed tasks lose ground mainly through changes in which occupations are posted, while the task mix within surviving occupations remains broadly flat.
Shift-share decomposition of posting-share and within-occupation task-composition changes.
Highly skilled workers who complement AI are likely to receive a skill premium, whereas medium-skilled workers displaced by AI may experience stagnant or declining incomes if they do not successfully retrain.
The paper explains the claim through skill scarcity, complementarity, and displacement mechanisms and refers to micro-level studies and an IMF working paper, but reports no original estimates.
AI contributes to employment polarization by increasing the relative demand for high-skilled and low-skilled labor while reducing demand for medium-skilled labor.
The paper provides a conceptual task-based explanation: AI is relatively advantaged in routine cognitive and physical tasks, while non-routine abstract and manual tasks remain more dependent on human labor; it cites prior empirical research.
nDCG@K also produced a borderline finding in the example audit, despite the score-delta, top-K-retention, and merit-aware rate-gap metrics remaining within tolerance.
The paper compares ranking-quality results with score, retention, and merit-aware fairness metrics.
Mean absolute rank change produced borderline audit findings in the example corpus, including a finding on the neutral baseline configuration.
The paper reports the results of its rank-stability metric in the illustrative audit.
The World Economic Forum projects a net global gain of 78 million jobs by 2030, alongside 92 million job losses in more automatable categories, with software- and AI-related roles among the fastest-growing.
World Economic Forum Future of Jobs projection cited by the report; this is a forecast rather than an observed causal estimate.
Employment among developers aged 22–25 fell nearly 20% from its late-2022 peak between 2021 and mid-2025, while employment among more experienced developers grew by approximately 6–12%.
Payroll-based labor-market research covering 2021–2025; the paper explicitly characterizes these labor-market findings as correlational rather than causal.
Google's DORA survey found that individual developer effectiveness increased by 17%, while delivery stability declined by nearly 10%.
Industry-wide developer survey conducted by Google's DORA team.
An industry survey reported approximately 46% time savings from AI on routine software-development tasks, but less than 10% savings on complex work.
McKinsey industry survey of approximately 4,500 developers.
The sign and magnitude of digitalization's effect on aggregate inequality are not universal because they depend on the dominant channel and the inequality measure selected.
Analytical characterization and comparative-static or simulation experiments in a heterogeneous-agent growth model evaluate multiple economy-wide inequality statistics.
Within-skill-group inequality can move independently of between-skill-group inequality, so skilled–unskilled wage gaps do not fully characterize the distributional effects of digitalization.
The model distinguishes worker ability levels and separately analyzes between-group and within-group inequality measures.
Digitalization-induced changes in labor-time supply—including flexibility, intensity, and hours—can affect earned income and contribute to distributional differences.
The model explicitly includes a supply-side labor-time modulation mechanism and evaluates its implications for earned income and inequality.
Digital tools that raise labor productivity can change relative wages across workers or tasks and thereby affect wage inequality.
The theoretical model incorporates a demand-side labor-productivity channel that raises productivity for tasks or workers and examines its effects on relative wages.
Automation changes the composition of labor demand by substituting for some tasks, with consequences for economic growth and wage inequality.
The model explicitly includes automation as one of three digitalization mechanisms and traces its effects on labor demand, growth, and distributional outcomes.
Digitalization can either increase or decrease wage inequality, depending on the inequality metric used and the relative strength of its underlying channels.
Theoretical dynamic growth/general-equilibrium model with heterogeneous workers, endogenous occupational choice, and digitalization operating through automation, productivity, and labor-time modulation; comparative-static or simulation analysis considers multiple inequality measures.
Validation work can require substantial expertise and time because fluent AI output may contain substantive errors, yet validation is often treated by the market as undifferentiated work that commands a lower price.
The paper supports the claim with the machine-translation case and cited studies on AI-generated content and overreliance [3, 6, 7, 13, 16]. The paper does not report a sample size or quantitative estimate for this synthesis.
Freelancers use generative AI to structure their learning and explore unfamiliar skills, but generally stop short of trusting it as their primary teacher because of inconsistency, weak contextual understanding, and the need to verify its output.
The claim is based on empirical studies of freelance knowledge workers' upskilling practices [9]. No sample size or quantitative effect estimate is reported in the paper.
The feasibility and welfare effects of share compensation depend on firm size, share liquidity and valuation, bargaining and negotiation mechanisms, corporate governance, legal and regulatory constraints, and transaction costs.
The paper acknowledges implementation limitations and institutional frictions rather than testing them empirically.
In the paper's central numerical parameterization, low- and medium-autonomy uses make augmentation uniquely optimal, a high-autonomy use lies in the coordination region near the risk-dominance boundary, and still greater autonomy makes automation dominant.
Numerical illustration calibrated using professional-services revenue-to-payroll ratios, local-employment multipliers, operating margins, and task-exposure estimates translated through an explicit realization rate.
In the vanishing-friction limit, human augmentation is selected when the static tipping point is below one-half, while automation is selected when the tipping point is above one-half.
Risk-dominance implication stated in the aggregate-shock analysis, conditional on the Burdzy et al. fast-revision assumptions.
The interval supporting both automation and augmentation paths widens when firms place more weight on the market that later revisers will create, and switching costs narrow the interval.
Closed-form Corollary 1 under the fixed-wage linear-payoff benchmark. Equation (32) gives the overlap width and equation (33) gives the condition for it to be positive.
With forward-looking firms and staggered opportunities to revise production plans, the same inherited employment structure can support either an automation cascade or an augmentation recovery, depending on firms' expectations about later adopters.
Proposition 3 derives two perfect-foresight paths using discounted integrals of the relative payoff G(x), with conditions for an all-automation path and an all-augmentation path. The overlap is nonempty under sufficiently small switching costs.
Under the paper's production and complementarity conditions, the economy can have both a high-employment human-augmented equilibrium and a low-employment automated equilibrium, with a unique unstable interior threshold separating them.
Proposition 1 analytically classifies equilibria when κ > 0 and condition (12) holds. In the coordination region GA < 0 < GH, both endpoint equilibria exist and the interior equilibrium x* is unique and unstable under myopic adjustment.
The net employment effect of AI adoption depends on the balance between task displacement and the creation of new tasks.
Conceptual economic interpretation in the paper's implications section; no direct employment estimate is reported.
AI adoption in banking may shift labor demand toward skilled workers, potentially increasing wages for data- and AI-related roles while compressing demand and possibly wages for routine positions.
Economic interpretation and implications presented in the supplied text; no quantitative wage data or estimated effect sizes are reported.
AI adoption in banking is associated with skill polarization: demand for routine, low-skill tasks declines while demand increases for high-skill technical roles such as data analysts, AI engineers, and cybersecurity specialists.
Qualitative synthesis of the literature's reported labor impacts; the supplied text does not report a causal identification strategy or quantitative labor-market estimates.