Evidence (16496 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 |
The convergence of the 2026 European Union Safe and Sustainable by Design (SSbD) framework, Corporate Sustainability Due Diligence Directive (CSDDD), and Carbon Border Adjustment Mechanism (CBAM) introduce a severe governance bottleneck for advanced semiconductor manufacturing facilities ("Smart Fabs").
Declarative claim in paper based on policy convergence analysis; no empirical dataset or sample size reported (conceptual/analytical argument).
Learning specialized simulator input languages can cost domain scientists hours to days.
Stated motivating claim in the paper (no experimental sample size or formal measurement reported in abstract).
In moderate scenarios, AI increases levelised cost of energy (LCOE) by 35 EUR/MWh in key hubs.
Model results for moderate scenarios indicating regional LCOE impacts; reported LCOE increase value for key hubs.
AI risks cumulative emissions overshoots of 67-181 MtCO2 between 2030 and 2050.
Same spatially explicit optimisation model of Europe across 21 AI growth scenarios; reported cumulative emissions overshoot range for 2030–2050.
Transformational leadership negatively moderates the relationship between AI application and employees' job insecurity, buffering employees' insecurity responses across varying levels of AI application.
Moderation analysis reported in the study using the same employee survey dataset (411 valid responses), indicating a statistically significant buffering (negative) moderating effect of transformational leadership on the AI–job insecurity relationship.
Self-efficacy negatively moderates the relationship between AI application and employees' job insecurity by strengthening the insecurity-reducing effect of moderate AI application and weakening the insecurity-enhancing effect of excessive application.
Moderation analysis on the same cross-sectional survey data (411 valid employee questionnaires), reporting a statistically significant negative (buffering) interaction of self-efficacy with AI application intensity on job insecurity.
In hyperscale cloud network infrastructure, traditional human-driven incident response cannot keep pace with the volume, velocity, and complexity of failures.
Stated as background/motivation in the paper; no quantitative data, sample size, or empirical comparison provided in the abstract.
Agents frequently overlook subtle yet critical details that are obvious to real human researchers.
Reported as a qualitative result/observation from the authors' experiments on AARRI-Bench; no numeric frequency or sample size provided in the excerpt.
Extensive experiments across frontier models and agentic systems reveal that even the best-performing configuration (Mini-SWE-Agent with Claude Opus 4.7) achieves only a 68.3% success rate on AARRI-Bench.
Empirical evaluation reported in the paper: experiments across multiple models/agentic systems; the excerpt reports the top configuration and its success rate. The excerpt does not state the number of tasks or sample size.
Despite their evolution from research assistants into autonomous research agents, these systems still exhibit significant limitations in field sensitivity, research ethics, and nuanced scientific judgment, and consequently remain unable to fully replace human researchers.
Asserted in the paper as a high-level observation and motivation; the excerpt does not provide quantified evidence or sample sizes for these limitations.
GenAI adoption may intensify informational imbalances in low-governance markets (asymmetric adverse effects).
Asymmetric effects observed in cross-market analyses and subgroup tests indicating worsening information asymmetries or related measures in low governance contexts.
In weaker governance environments, the benefits of GenAI adoption for institutional efficiency are limited.
Heterogeneity analysis / interaction models showing smaller or non-significant effects of GenAI on institutional efficiency in markets with low governance capacity.
Existing approaches to explainability are predominantly post-hoc, offering unstable, non-contestable accounts that have no formal relationship to the reasoning process that produced the output.
Critical literature/argumentative claim in the paper; presented as a conceptual critique rather than supported by empirical evaluation or systematic review data.
Opacity of such models in these settings is not merely inconvenient but institutionally and legally untenable.
Normative/legal argument presented in the paper (conceptual reasoning about institutional and legal requirements); no empirical legal-case analysis or quantified legal rulings provided.
Employees currently lack clear guidance on appropriate use of GenAI within organizations.
Background claim in paper motivating the study (statement that employees 'lack clear guidance on appropriate use').
Current research on AI-supported conflict techniques has focused predominantly on Devil's Advocate (DA) and has neglected Dialectical Inquiry (DI).
Literature review / gap statement in the paper pointing to relative emphasis on DA in prior research and lack of work on DI.
Other methods, such as variants of prediction-powered inference, do not have the 'do no harm' guarantee.
Comparative methodological claim in the paper (abstract)—likely supported by theoretical discussion and comparisons in the main text.
Gains from autonomous AI agents (reduced search costs and improved matching) are not automatic because the behavior of current AI agents introduces frictions that limit competitive outcomes.
Theoretical argument drawing on economic search theory and the paper's analysis of agent behavior as described in the abstract; no empirical details or sample sizes provided in abstract.
In that same limiting case, the social rate of profit tends to zero.
Limit-case implication from the theoretical model linking capital composition and absence of living labor to vanishing profit rate (model analysis; no empirical data).
In that same limiting case, surplus value tends to zero.
Limit-case implication of the model under the value-transfer assumption (theoretical derivation; no empirical backing).
In the limiting case where actual AGI adoption approaches complete substitution and new labor fields fail to compensate, living labor tends to zero.
Limit-case analysis within the theoretical model (as adoption → complete substitution and no compensatory new labor fields, the model's variables imply living labor → 0; no empirical data).
Deeper AGI adoption places downward pressure on the social rate of profit.
Analytical result from the political-economy model linking higher organic composition of capital and reduced living labor to a fall in the social rate of profit (theoretical derivation; no empirical sample).
Deeper AGI adoption compresses the source of surplus value.
Theoretical implication derived in the model under the value-transfer assumption: as living labor falls, the base generating surplus value narrows (model argument; no empirical data).
If AGI adoption outpaces the creation of new labor fields, deeper AGI adoption reduces the quantity of living labor.
Model-based theoretical result: comparative statics of adoption vs creation of new labor fields in the paper's framework (no empirical sample).
Even a perfect non-proprietary-data report would be capped at 3.83 by B's coverage (i.e., B imposes an upper bound on non-proprietary informed decision-quality).
Analytic upper-bound calculation based on B's measured coverage on the curated gold record (exact derivation not provided in abstract).
Interviews provide expanded analysis on existing skill gaps and lifelong learning needs among wind-energy professionals.
Qualitative interview data are reported to highlight skill gaps and lifelong learning needs; specific counts of interviewees not provided in the summary.
Operators have no standard way to tell an autonomous agent that a resource is off-limits: access controls either let the agent in (it has valid credentials) or hard-fail it (indistinguishable from any other client).
Analytical description/argument presented in the paper (problem statement); no empirical data reported for this claim.
Reporting on ethics, transparency and governance was inconsistent.
Reported synthesis result from the scoping review noting variability in reporting practices across included studies.
Formal theory use was limited, with only a small minority of studies explicitly drawing on established frameworks.
Authors' assessment of methodological/theoretical characteristics of the included empirical studies in the scoping review.
Fewer studies evaluated individual-facing developmental support, and sustained career outcomes were rarely measured.
Reported gap identified in the scoping review findings summarised in the abstract.
Empirical evidence on applications designed to support women’s career development remains limited.
Conclusion drawn from the scoping review: authors searched seven databases + backward/forward citation searching and synthesised identified empirical studies.
The framework reframes the education–employer gap as a structural failure in the pathway and outlines implications for universities, employers, accreditors, and policymakers.
Conceptual claim and implications drawn by the author(s) in the paper (stated in the abstract).
The architecture of the undergraduate degree is structurally incapable of replacing the informal post-degree apprenticeship system through curricular revision alone.
Argument presented in the paper, supported by the systematic review of eighteen peer-reviewed studies and labor-market analyses cited in the abstract.
The informal post-degree apprenticeship system that historically completed graduate formation no longer reliably exists.
Claim based on the paper's systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
Higher education has misdiagnosed the resulting challenge as curriculum misalignment—a content problem assumed to be solvable through revised syllabi, AI electives, and marginal expansions of experiential learning.
Argument presented in the paper, supported by the paper's systematic review of eighteen peer-reviewed studies and labor-market analyses (as described in the abstract).
Artificial intelligence and automation are restructuring early-career knowledge-work roles by compressing the entry-level functions through which graduates historically built portfolios, developed professional judgment, and earned professional credibility.
Statement supported in the paper by a systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
GenAI usage significantly decreased creativity-relevant skills.
Experiment with 82 participants reported in the paper; authors report a statistically significant decrease in measures of creativity-relevant skills for participants using GenAI.
GenAI usage significantly decreased domain-relevant skills.
Experiment with 82 participants reported in the paper; authors report a statistically significant reduction in measures of domain-relevant skills for the GenAI condition.
GenAI usage significantly decreased intrinsic task motivation.
Randomized experiment reported in the paper with 82 participants; authors report a statistically significant decrease in intrinsic task motivation for participants using GenAI.
AI cannot yet refute economic theory on its own.
Main conclusion: based on the experiments (models failed to autonomously find true errors) and caveats about data contamination, the author concludes models are not yet capable of independently refuting economic-theory papers.
No model located a true error without substantial human guidance.
Author reports that in the experiments none of the models identified a real error autonomously; successful identifications required substantial human guidance.
Other models (Gemini, Refine, Claude) fared worse than ChatGPT Pro at these tasks.
Reported qualitative performance differences across the four models on the 4 papers; other models did not match ChatGPT Pro's performance.
Private-market valuations are concentrated in a small number of firms.
Paper reports concentration of private-market AI valuations (distributional evidence across firms in private markets); exact counts or percentages not provided in the abstract.
Capital expenditure has accelerated faster than observed monetization in some layers of the AI stack.
Comparative analysis of capex trends vs monetization metrics presented in the paper (layered AI stack comparison); specific sample counts not provided in the abstract.
Without intentional, gender‑aware interventions in policy and design, the AI‑driven gig economy is more likely to entrench existing social and economic inequalities than to alleviate them.
Conclusion and social implications in the paper based on thematic synthesis across 48 studies and the feminist political economy analysis.
AI‑mediated platforms generate structural precarity and digital marginalization that disproportionately affect women.
The paper's thematic synthesis of 48 studies highlights structural precarity and digital marginalization as mechanisms that reproduce disadvantage for women.
Wage gaps are present in AI‑mediated platform work and contribute to unequal outcomes for women.
Reviewed literature synthesized in the paper repeatedly cites wage gaps as one mechanism producing gendered disadvantage; reported in Findings.
Algorithmic bias on AI‑mediated platforms contributes to gendered disadvantage in platform work.
The paper identifies algorithmic bias as a key mechanism in the thematic synthesis of the 48 studies; cited as reproducing or amplifying gender inequality.
AI‑enabled platforms reproduce and risk amplifying gender inequality through algorithmic bias, wage gaps, structural precarity, and digital marginalization.
Synthesis across the 48 reviewed studies identifying recurring mechanisms (algorithmic bias, wage gaps, precarity, digital marginalization) that disadvantage women; presented in Findings.
Entropy dissipation corresponds to organizational complexity, coordination frictions, energy constraints, regulatory uncertainty, talent mobility pressures, and opportunities to strengthen industrial absorption.
Definition/mapping provided in the paper as part of the HCLM framework; conceptual.