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
Filter claims →
Productivity
8807 claims
Filter claims →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filtered →
Skills & Training
3308 claims
Filter claims →
Inequality
2332 claims
Filter claims →
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
Remove filter
There is an urgent question of how humans can effectively supervise and control an economy operated by AI agents when this system may expand beyond the capacity of traditional governance.
Framed as a central research/policy concern in the paper's abstract; conceptual argument rather than empirical finding.
The Agent Economy raises new regulatory challenges concerning data privacy, security, ethics, and the risk of job displacement.
Stated in paper abstract as identified risks; based on literature synthesis and comparative policy analysis approach (method described), but no empirical incidence metrics reported.
We evaluate 36 models; the strongest, Claude Opus 4.7 under Claude Code, reaches only 45.9%.
Empirical evaluation reported by the authors: 36 models tested on JobBench; highest-performing model and its score (Claude Opus 4.7 under Claude Code achieves 45.9%).
AI-driven efficiency pressures in IT services may compress billable work and alter hiring and wage structures, raising transition risks even for technical workers.
Abstract cites high-reliability sector evidence (Reuters 2026a; Nasscom) to support this sector-specific claim; no sample size provided in abstract.
Labor-market segmentation and digital capability gaps in India create distributional vulnerabilities.
Abstract cites Indian official statistics and household/labor surveys (PLFS, HCES, MoSPI–NSO) and integrates sector evidence; no specific sample size reported in abstract.
Refined exposure measures imply widespread task transformation rather than uniform job destruction, with accelerated skill change as a central risk for vulnerable workers.
Abstract cites labor-market analyses and ILO (2025) as the basis for refined exposure measures and conclusions; no sample size stated in abstract.
Global frameworks warn that uneven readiness may produce a 'Next Great Divergence' between countries.
Cited global reports in abstract (UNDP 2025, WTO 2025, OECD 2026) which are summarized as issuing this warning; no primary data sample size reported in paper abstract.
Persistent adoption gaps among groups suggest unequal access to AI-enabled productivity.
Abstract references global reports (OECD, WEF, UNDP, WTO) and sector evidence indicating adoption gaps; no numerical sample size given.
AI may widen capability inequality—inequalities in access to knowledge, digital infrastructure, computational resources, and organizational adoption—thereby shaping income opportunities and socio-economic security for low-income groups.
Argument presented using the paper's socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) and official Indian statistics; no direct empirical sample from this paper reported.
These findings challenge the prevailing theory of skill-biased technological change.
Empirical observation that high-skill, high-exposure neighborhoods experienced wage stagnation post-2023 despite continued inflows of high-skilled workers, interpreted in contrast to predictions of skill-biased technological change.
Since 2023, high-exposure neighborhoods have experienced wage stagnation even as they continue to attract high-skilled workers (a 'high-skill trap').
Temporal analysis of job-posting wage signals in Beijing neighborhoods (2018--2024) using the GenAI Exposure Index to compare wage trajectories before and after 2023 between high- and low-exposure neighborhoods.
GenAI exposure is highly concentrated in the city's core districts, deepening the intra-urban AI divide.
Spatial analysis of a neighborhood-level GenAI Exposure Index constructed from 5 million Beijing job postings (2018--2024), where task-level assessments were aggregated across five leading large language models to measure exposure by neighborhood.
AI adoption contributes to labor market polarization and increases the risk of structural unemployment.
Authors' thematic synthesis of interdisciplinary studies reporting patterns of job polarization and macro/labor market risks associated with AI in manufacturing.
AI disproportionately affects routine and mid-skilled jobs.
Synthesis of literature (2010–2024) reported by the authors indicating disproportionate automation/AI exposure for routine and mid-skilled occupations.
AI adoption in manufacturing has critical implications for human labor, raising concerns about labor displacement.
Authors' systematic literature review (2010–2024) synthesizing interdisciplinary studies discussing labor impacts and displacement risks.
Simultaneously, there is a structural shortage of qualified personnel and a gap between the education system and the needs of the economy in Uzbekistan.
Synthesis of statistical data, industry reviews, and regulatory/legal document analysis presented in the paper (no primary survey/sample size reported).
The potential widening of the gender wage gap would operate through existing patterns of gender-based occupational sorting (i.e., because women are concentrated in occupations more exposed to generative AI).
Mechanistic interpretation supported by the combination of descriptive occupational sorting evidence from Swedish administrative data and results from the partial-equilibrium simulations incorporating predicted AI exposure and task complementarity.
Mechanical partial-equilibrium simulations indicate that generative AI may widen the gender wage gap.
Counterfactual simulations (mechanical partial-equilibrium) based on hypothesized deviations from the 2021 occupational and wage distribution, incorporating predicted AI exposure and task complementarity; applied to Swedish context.
Women are overrepresented in occupations predicted to be more affected by generative AI (using pre-ChatGPT occupational sorting).
Descriptive analysis of Swedish administrative data characterizing occupational gender composition before the release of ChatGPT and mapping occupations to predicted exposure to generative AI.
Even SOTA coding agents (Codex with GPT-5.4 and Claude Code with Opus 4.6) succeed on only 2/7 distributed key-value-store specifications.
Empirical evaluation reported in the paper comparing two SOTA coding agents on a suite of 7 distributed key-value-store specifications; success counted as meeting the specification.
A complementary Oaxaca–Blinder decomposition shows that shifts in occupational composition account for about 90% of the exposure change attributable to observable job characteristics.
Oaxaca–Blinder decomposition reported in the paper attributing ~90% of exposure change (among the portion explained by observable job characteristics) to occupational composition shifts.
Within-job redesign accounts for 39.5% of the aggregate decline in generative-AI exposure and becomes increasingly important over time.
Same decomposition as above reported in the paper (result: within-job redesign = 39.5% of aggregate decline; authors note its increasing importance).
Hiring reallocation explains the largest share of the aggregate decline in generative-AI exposure, accounting for 52% on average.
Decomposition of changes in aggregate exposure into two margins (reallocation across jobs and within-job redesign) reported in the paper (result: hiring reallocation = 52% of aggregate decline).
The de-coring and skill-demand changes are concentrated among low entry-threshold, small firms.
Abstract statement reporting heterogeneity: concentration of observed patterns among firms characterized as small and with low entry thresholds.
Both displacement and augmentation exposure are associated with a de-coring pattern: a shallower and more dispersed skill portfolio with within-category importance diverging from share movements.
Empirical description in abstract that both forms of exposure correlate with changes in portfolio depth and dispersion, and with divergence between within-category importance and category shares.
Displacement exposure is negatively associated with the routine cognitive skill share.
Empirical result stated in abstract: negative association between displacement exposure and routine cognitive share, identified using within-firm variation and the constructed exposure measures.
Regulatory uncertainty and the absence of explicit legislation on digital data and artificial intelligence may leave the economic potential of these technologies unexplored while increasing market concentration, inequality, and the risk of personal information misuse.
Argued implications from the paper's theoretical model and comparative legal discussion; no empirical testing or quantified analysis provided.
The measurement bias understates substitution effects more than it understates augmentation effects.
Analytical argument and empirical evidence showing directional bias from measurement error that causes estimated substitution (labor displacement) effects to be more severely understated than augmentation (complementarity) effects.
Reweighting platform-based exposure measures to Bureau of Labor Statistics workforce shares attenuates estimates by 42 to 93 percent.
Reweighting exercise where exposure scores built from platform logs are reweighted to match BLS workforce shares and resulting employment estimates are compared; reported attenuation range of 42–93%.
Low-wage workers on platforms perform supporting tasks—such as data annotation and content moderation—that underpin technological infrastructures.
Empirical grounding drawn from cited ethnographic, sociological and anthropological studies and mapping exercises discussed in the paper documenting the kinds of work performed on microtask platforms.
Artificial intelligence (AI) systems depend on invisible labor performed on microtask platforms.
Claim based on synthesis of sociological and anthropological studies cited in the paper mapping production networks and documenting microtask platform work (e.g., data labeling, content moderation) that supports AI.
Socio-technical imaginaries that forecast the displacement of humans from production accompany the technological developments of the Fourth Industrial Revolution.
Conceptual claim supported by literature review and theoretical framing in the paper describing historical and contemporary narratives around automation and the Fourth Industrial Revolution.
Emerging evidence indicates that algorithms often inherit and amplify the historical biases present in training data.
Literature claim in paper referencing 'emerging evidence' and empirical studies (2024–2026) — specific studies, methods, and sample sizes not included in excerpt.
Content filtering (blocking searches for Gaza War and Tulsa race massacre).
Documented cases of content filtering cited/synthesized in the paper (specific blocked search topics reported).
AI cataloguing failures (26% F1 accuracy for subject headings).
Empirical studies of AI accuracy in cataloguing synthesized by the paper (reported F1 accuracy for subject heading assignment).
Selective displacement from AI is concentrated among older and lower-mobility workers.
Explicit claim in chapter summary, stated to be traced from labour market data and emerging workplace evidence (no numeric breakdown in excerpt).
The tech industry claims that its products, business models, and methods of resource extraction are unprecedented and fall outside any existing legal framework.
Descriptive claim about prevailing industry discourse referenced by the authors. (Citations or examples of industry statements not included in the excerpt.)
Exploitative working conditions violate workers' rights.
Legal assessment based on documents and the authors' interpretation of rights under applicable law (GDPR and labour rights frameworks). (Specific legal rulings or counts not provided in the excerpt.)
The results of this approach provide legally grounded evidence of the structural disadvantages faced by content moderators in the Global South, whose exploitative working conditions violate workers' rights.
Documents obtained via GDPR requests (employment contracts, NDAs, etc.) and legal interpretation are used as evidence to support claims of structural disadvantage and rights violations. (Specific documents and counts not provided in the excerpt.)
There are limits to technology‑led growth strategies in labor‑abundant contexts; such strategies do not reliably deliver inclusive employment gains.
Argument based on synthesis of theory and comparative field evidence demonstrating weak employment outcomes from technology‑led growth in labor‑abundant settings (no quantitative effect sizes reported).
Digital media play a significant role in shaping youth mobilization and political unrest in migrants' countries of origin.
Empirical observations and regional field evidence reported in the paper linking digital media use to youth mobilization and political outcomes (qualitative/comparative evidence; no numeric sample size provided).
Developing countries face macroeconomic vulnerabilities because of dependence on remittances, which are exposed by automation-driven changes in migrant labor demand.
Analytical linkage developed in the paper supported by comparative field evidence and macroeconomic reasoning; remittance dependence highlighted as a vulnerability (no quantitative estimates or sample sizes reported).
Technology adoption in core industries in advanced economies is linked with labor displacement, rising youth unemployment, and urban labor saturation in South Asia and North Africa.
Geographically grounded framework combined with comparative regional field evidence focused on South Asia and North Africa (qualitative/comparative field data referenced; no numeric sample sizes provided).
AI adoption and accelerating automation amplify employment precarity in labor‑surplus economies.
Conceptual synthesis grounded in economic geography and labor economics, supported by comparative field evidence cited for labor‑surplus contexts (no quantitative sample size reported).
Automation functions as a transnational shock that contracts demand for migrant labor in advanced economies.
Theoretical argument drawing on economic geography, labor economics, and development studies; comparative/regional field evidence referenced in the paper (no numerical sample size reported).
Rather than restoring stability, this cycle intensifies anxiety, undermines mastery, and erodes professional confidence.
Theoretical claim about psychological outcomes from the conceptual reskilling loop; paper provides argumentation but no empirical measurements.
Based on Job Demands–Resources (JD-R) theory and Conservation of Resources (COR) theory, the paper conceptualizes an AI-induced reskilling loop in which ongoing technological change leads to skill erosion, continuous reskilling demands, cognitive and emotional depletion, and reinforced learning as a defensive response to perceived obsolescence.
Theoretical model/loop derived from applying JD-R and COR frameworks; no empirical test or sample reported in the paper.
The paper introduces the concept of 'reskilling fatigue' to explain the human consequences of persistent skill volatility among Established Knowledge Professionals (EKPs).
Conceptual/theoretical contribution presented by the authors; definition and argumentation rather than empirical validation.
Continuous reskilling is widely promoted as a solution to AI-driven disruption, but little attention has been paid to its cumulative psychological costs.
Argument from literature review/observation in the paper; no empirical measurement or sample reported in the paper.
Unless labour law evolves to address digitally mediated control and platform-based asymmetry, the gig economy risks normalising exploitative labour conditions under the guise of innovation and flexibility.
Predictive/theoretical claim based on the paper's synthesis of platform practices, legal gaps, and normative concerns; argued through comparative analysis and conceptual reasoning rather than quantitative forecasting.