Evidence (7560 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
Filtered →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
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 |
Human Ai Collab
Remove filter
Trust in AI should be conceptualized as a socio-technical, team-level mechanism (trust calibration) that mediates between AI design/enablers and downstream collaboration and performance, rather than an individual-level stable attitude.
Theoretical synthesis combining findings from the thematic analysis of 40 interviews with socio-technical systems theory (STS) and adaptive structuration theory (AST) to propose an initial and revised conceptual model linking enablers → trust-calibration practices → collaboration dynamics → performance.
Five enablers support effective trust calibration: transparency/explainability, clear role definitions, good user experience (UX), supportive cultural norms, and timely system feedback.
Synthesized from recurring themes in the interview data (N=40) where respondents identified these factors as facilitating appropriate reliance on AI in project settings; coded and aggregated through thematic analysis.
Performance and reward structures must be redesigned to value oversight, hypothesis testing, escalation and governance behaviours that mitigate model risk but may not immediately increase output.
Managerial recommendation derived from the framework and organizational reward literature; no empirical evaluation provided.
Firms need new metrics to decompose value created by humans, AI, and their interaction (to distinguish complementarities versus substitution).
Analytic implication derived from the framework and literature on productivity measurement; presented as a recommendation for empirical work rather than tested evidence.
Symbiarchic leadership is a practical, HR‑oriented framework for leading integrated human–AI “cyber teams,” specifying four linked leadership practices that make AI a co‑actor in knowledge work while preserving human judgement, accountability and organizational legitimacy.
Paper's central proposition based on theoretical synthesis of academic literature on human–AI collaboration, hybrid teams and digital‑era leadership plus illustrative practitioner examples; no original empirical data or experiments.
Regulators should anticipate new forms of intangible capital and data monopolies arising from sensory models and consider standards for data interoperability, public datasets/models, and workforce retraining.
Policy recommendation based on foresight and literature on data governance and platform regulation; no empirical regulatory impact analysis provided.
Economics of AI in food must incorporate non-price metrics (perceptual quality, cultural fit) and design ways to monetize and protect sensory intellectual property (trade secrets, data governance).
Normative policy and methodological recommendation derived from literature synthesis and conceptual analysis; not validated with empirical economic valuation studies.
Interdisciplinary approaches (cognitive science, behavioral economics, design thinking) are necessary to capture the social, perceptual, and cultural dimensions of food experience.
Normative argument supported by literature synthesis across relevant disciplines; no experimental comparison of mono- vs interdisciplinary approaches provided.
Treating food as a soft-matter system centered on rheology provides a bridge from molecular/structural properties to macroscopic sensory experience.
Conceptual and theoretical argument grounded in soft-matter science and rheology literature; interdisciplinary literature synthesis; no new empirical data or experiments reported.
Firms can differentiate via domain expertise and partnerships with ecological institutions, and funders should prioritize interdisciplinary teams, long‑term monitoring projects, and data infrastructure to unlock high social returns.
Strategic-implications recommendation drawn from the collection's examples of successful partnerships and long-term data needs (policy/strategy recommendation from synthesis).
AI advances that improve monitoring and policy implementation generate positive externalities because biodiversity and ecosystem services are public goods, reinforcing the case for subsidized or open‑source solutions.
Externalities/public-goods argument linking technical potential in the collection to economic characteristics of biodiversity (theoretical economic argument supported by examples of public-benefit applications).
Regulation and procurement by public agencies could shape the sector through standards for ecological AI tools and requirements for transparency and ecological validation.
Paper's governance analysis suggesting roles for public procurement and standards based on the conservation-applications focus in the collection (policy inference).
Effective uptake of ecological AI requires mechanisms to align incentives across academics, conservation practitioners, and policymakers (grants, contracts, data‑sharing platforms).
Policy-and-governance prescription in the paper derived from barriers and enablers observed across the collection (normative recommendation grounded in cross-paper synthesis).
There are economies of scale in data curation and annotation: shared ecological datasets and labeling infrastructure reduce marginal costs for new models.
Production-and-cost-structure claim derived from discussion of shared datasets and annotation infrastructure in the collection (economic argument tied to observed practices).
Techniques and tools developed for ecology (robust models for noisy, imbalanced, spatio‑temporal data) can spill over to other domains and improve overall AI productivity.
Knowledge-spillovers assertion in the paper based on methodological advances reported in the collection and their potential transferability (theoretical extrapolation).
Markets for public‑interest AI may expand, with value accruing to conservation agencies, NGOs, and funders rather than purely commercial customers.
Paper's economic implication noting the client base and value capture patterns implied by conservation-focused applications (interpretation of demand and beneficiaries).
There is growing demand for specialized AI tools tailored to ecology and conservation (niche models, annotated data services, integrated monitoring platforms).
Market-and-demand-shifts analysis in the paper drawing on the collection's focus and implied needs from practitioners (projected demand based on reviewed trends).
Papers prioritize ecological relevance, generalizability across sites and taxa, and usefulness for decision‑making rather than solely optimizing task accuracy or benchmark scores.
Evaluation-emphasis statements in the paper summarizing evaluation criteria used in the collection (synthesis of reported evaluation practices).
Research can improve both fundamental ecological understanding and applied conservation while also helping translate scientific insights into policy, provided it balances technical innovation with ecological relevance and meaningful cross‑disciplinary collaboration.
Main-finding synthesis of outcomes reported across the collection (examples of empirical insight and translational work cited in the review; claim is an overall conclusion).
Genuine collaboration between ecologists and computer scientists is essential to produce tools that are scientifically useful and policy‑relevant.
Interdisciplinarity claim supported by the paper's summary and recommended practice across the collection (normative conclusion drawn from cross-paper patterns).
Papers in the collection aim to push AI methodology forward while addressing core ecological questions, not just demonstrating technical feasibility.
Characterization of the papers as 'dual advancement' in the collection (methodological papers alongside empirical ecological applications cited in the review).
The study discovers a three-dimensional model for measuring performance, including AI Tool Mastery, Collaborative Work Quality, and Human-AI Synergy to measure hybrid skills developed through human-machine collaboration.
Model development derived from systematic analysis of the collected data (5,000 LinkedIn job adverts and 2,000 Indeed salary records, 2022–2024) and theorizing about dimensions needed to capture hybrid human-AI skills; the paper reports these three dimensions as its measurement model.
AI-trained staff are rewarded with a 17.7% overall premium for their wages.
Analysis of 2,000 Indeed salary data records from 2022–2024, comparing salaries for roles or incumbents identified as having AI training/skills versus those without.
The need for AI skills has grown at a rate of 376% since the release of ChatGPT.
Temporal comparison within the dataset of LinkedIn job adverts from 2022–2024 (5,000 adverts), comparing pre- and post-ChatGPT frequencies of AI-skill mentions to compute growth rate.
AI skills are especially needed in 27.8% of knowledge workers' jobs.
Systematic analysis of 5,000 LinkedIn job adverts collected between 2022–2024, where job postings were coded for AI-skill requirements, yielding the reported percentage.
Dynamic feedback loops create reinforcing organisational learning cycles.
Theoretical assertion from the paper's synthesis indicating learning dynamics as part of the model; described conceptually without empirical quantification in the abstract.
Complementarity–trust interaction determines optimal performance when high capability utilisation combines with appropriate trust levels.
Mechanistic claim from the TCM‑CI derived via systematic review/synthesis of existing studies; no primary experimental or field sample reported in the abstract to validate this interaction effect.
Calibrated trust maximises collective intelligence by balancing appropriate reliance with necessary oversight.
Core mechanism asserted by the paper based on synthesis of prior research in human–AI interaction and trust literature; presented as a conceptual mechanism rather than tested empirically in the abstract.
The Trust–Complementarity Model of Collective Intelligence (TCM‑CI) explains how calibrated trust and complementary capability utilisation drive superior organisational performance.
Theoretical model proposed by the authors derived from systematic literature synthesis (conceptual/modeling contribution); abstract does not report empirical validation or sample size.
Quantitatively, AI-adopting firms raise aggregate value-added total factor productivity by approximately 1.51% in a representative post-adoption year.
Aggregate TFP decomposition/aggregation based on estimated firm-level treatment effects and value-added weights (methodological details in paper); the 1.51% figure is the reported quantitative estimate for a representative post-adoption year.
AI functions as an innovation-enabling intangible investment that supports productivity growth.
Synthesis of empirical findings: increased patenting and patent quality, increased R&D (but not capex), improved productivity and market value; evidence derived from the firm's adoption-timing measure and stacked diff-in-diff estimates.
AI adoption enhances knowledge recombination (increased recombination across technologies).
Increases in measures such as patent originality, generality, and technological distance interpreted as evidence of enhanced knowledge recombination; estimated with the stacked diff-in-diff design.
Evidence on mechanisms indicates AI improves firm-level efficiency.
Mechanism tests reported in the paper linking AI adoption to improved efficiency metrics (e.g., productivity measures) using the same empirical strategy; specific metrics and sample size not provided in the abstract.
The effects of AI adoption on innovation outcomes are stronger for firms with a more focused business scope.
Heterogeneity analysis by firms' business scope (more focused vs. less focused) within the stacked diff-in-diff framework; outcome assessed on innovation measures such as patenting and quality.
Post-adoption patents span more technologically distant classes (greater technological distance / broader technological scope).
Patent-class based measures of technological distance and class-spanning applied to patents from adopter firms versus nonadopters in the diff-in-diff design.
Post-adoption patents exhibit greater originality and greater generality.
Patent-level measures of originality and generality (standard patent metrics) estimated in the stacked diff-in-diff framework comparing adopters to nonadopters.
After AI adoption, firms have a higher share of 'exploitative' patents that build on the firm's existing technologies.
Classification of patents as exploitative (building on firm’s prior technologies) and comparison across adopters and nonadopters using the staggered adoption diff-in-diff design.
AI-powered developer tools (often based on large language models) aim to automate routine tasks and make secure software development more accessible and efficient.
Framing/assumption in the paper's introduction (general description of such tools' intended purpose; not directly measured in this experiment).
Organizations increasingly adopt AI-powered development tools to boost productivity and reduce reliance on limited human expertise, especially in security-critical software development.
Background/contextual claim stated in the paper to motivate the study (general trend claim; likely supported by prior literature but not by the study's experimental data described here).
Cross-talk between distributed systems and LLM-team research yields rich practical insights.
Conclusion drawn by the authors based on their mapping and findings (qualitative claim supported by the paper's arguments and examples; excerpt lacks concrete metrics).
There is recent and increasing interest in forming teams of LLMs (LLM teams).
Claim made in the paper asserting increased interest and deployment at scale; supported in the paper by literature/contextual citations and reported deployments (specific numbers or studies not provided in the excerpt).
Both stable individual differences and moment-to-moment fluctuations in perspective-taking influence AI response quality.
Analyses reported in the paper linking both trait-level (stable) and state-level (moment-to-moment) measures of perspective-taking to variation in AI response quality across the benchmark dataset; assessed via the Bayesian IRT model and supplementary within-subject analyses.
Theory of Mind (the capacity to infer and adapt to others' mental states) emerges as a key predictor of synergy.
Statistical association reported between participants' Theory of Mind measures and the estimated synergy (improvement in performance with AI), based on analysis of the benchmark dataset (n = 667) within the Bayesian IRT framework.
Experiments on simulated and real-world data show that humans assisted by the adaptive AI ensemble achieve significantly higher performance than humans assisted by single AI models trained either for independent AI performance or for human-AI team performance.
Empirical experiments reported in the paper on both simulated datasets and real-world data; the abstract states results are statistically significant but does not provide sample sizes, datasets, or statistical details in the excerpt.
An adaptive AI ensemble that toggles between two specialist models (an aligned model and a complementary model) using a Rational Routing Shortcut mechanism overcomes the complementarity–alignment limitation of single-model approaches.
Methodological contribution described in the paper; includes the design of the ensemble and the Rational Routing Shortcut; theoretical guarantees of near-optimality are claimed in the paper (proofs referenced but not shown in the excerpt).
The findings provide valuable insights for entrepreneurs, policymakers, and academic institutions to implement adaptive strategies for sustainable and inclusive entrepreneurial growth in the era of artificial intelligence.
Authors' implications/conclusions based on the study results (n=350; statistical analyses) recommending adaptive strategies targeted at stakeholders.
AI functions as a strategic enabler that reshapes entrepreneurial practices, labour dynamics, and innovation strategies.
Conclusion drawn from the study's quantitative findings (survey of 350, regression/SEM results) that linked AI adoption to changes in opportunity recognition, labour substitution, and innovation processes.
AI-driven innovation processes accelerated product development, improved operational efficiency, and supported experimentation, thereby strengthening entrepreneurial performance.
Survey data from 350 AI-adopting SMEs analyzed with regression and SEM showing positive associations between AI adoption and measures of product development speed, operational efficiency, experimentation, and overall entrepreneurial performance.
AI facilitated labour substitution by automating repetitive tasks, allowing human resources to focus on creative and analytical roles.
Responses from the same sample (n=350) of AI-adopting SME entrepreneurs/managers; descriptive statistics and inferential analyses (regression/SEM) linking AI adoption to increased automation and role reallocation.
AI adoption significantly enhanced opportunity recognition by enabling entrepreneurs to identify emerging market trends, assess risks, and make informed strategic decisions.
Quantitative survey of 350 entrepreneurs and managers of SMEs who had adopted AI; relationships tested using regression analysis and structural equation modelling (SEM) reported a significant positive effect of AI adoption on opportunity recognition.