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 |
AI adoption is allowing professionals to blur and extend the boundaries of their corporate roles.
Reported by interview participants (qualitative evidence) from the 24 interviews at one large technology firm.
AI opacity, automation intensity, anthropomorphic and affective design features, and the degree of human-centered system design are determinant factors shaping users' psychological responses to human–AI collaboration.
Authors' synthesis from reviewed empirical and theoretical studies highlighting design and system characteristics associated with psychological outcomes.
The interdisciplinary literature identifies technostress, automation fatigue, cognitive overload, algorithmic anxiety, overtrust, and responsibility ambiguity as key phenomena arising from integration of AI systems and AI-enabled robots into collaborative human work environments.
Synthesis of interdisciplinary peer-reviewed studies (systematic review); topics extracted from reviewed papers as reported by the authors.
Artificial Intelligence (AI) has caused massive changes in nature of workplaces in healthcare sector.
Asserted in paper's introduction and supported by a scoping review (PRISMA-ScR) of 29 peer-reviewed empirical studies published 2020–2025.
The results of fsQCA demonstrate how the combination and roles of strategic resources (e.g. AI capabilities and decision-making agility) shift in response to varying organizational and environmental conditions.
fsQCA configurational analysis reported in paper showing multiple causal pathways and differing configurations of AI capabilities, decision-making agility, and contextual conditions associated with performance; based on the same survey of 251 firms.
Environmental dynamism and complexity differently moderate the relationship between decision-making agility and firm performance.
Reported moderation analyses in the PLS-SEM results indicating interaction effects of environmental dynamism and environmental complexity on the decision-making agility → performance path; based on survey of 251 firms.
The paper examines the macroeconomic impact of AI (drawing on the cited institutional projections) to understand sectoral and aggregate economic implications for Georgia.
Method: macroeconomic synthesis of external projections (Goldman Sachs, McKinsey, Penn Wharton, IMF) and application to Georgia; no reported experimental sample size.
The urban digital economy exerts a stronger effect than the rural digital economy in promoting servicization and inhibiting industrialization.
Heterogeneity analysis in the provincial panel (2013–2024) comparing urban versus rural digital-economy measures and their associations with changes in employment shares.
After 2017, industrial digitalization continued to strengthen servicization while suppressing industrialization.
Post-2017 analysis of provincial panel data (2013–2024) showing continued positive association of industrial digitalization with service employment and negative association with industrial employment after 2017.
After 2017, digital industrialization shifted toward promoting industrialization and restraining servicization.
Post-2017 subset analysis of provincial panel data (2013–2024) comparing the direction and magnitude of digital industrialization's association with industry and service employment shares before and after 2017.
The elevation of the 'digital economy' to a national strategy in 2017 constituted a critical turning point in the relationship between digital-economy development and labor-structure change.
Before-and-after (pre/post-2017) analysis using China's provincial panel data (2013–2024) showing a structural change in estimated effects around 2017.
The development of the digital economy generally promotes the servicization and deindustrialization of the labor structure.
Panel analysis using China's provincial data from 2013 to 2024 examining relationships between digital economy development and labor-structure indicators (servicization and industrial employment shares).
Benchmark-based evaluation can both overstate and understate deployed capability because it privileges tasks that can be precisely specified, automatically graded, easy to optimize for, and run with low budgets and short time horizons.
Analytical argument in the paper (theoretical/qualitative critique of benchmark methodology); supported by a survey of recent open-world evaluations (method description in paper), but no quantified cross-benchmark empirical study reported in the abstract.
Consumer decision-making is shifting from linear to nonlinear patterns under intelligent technologies.
Synthesis from the paper's systematic review and content analysis of literature (2010–2025); no sample size or primary empirical study reported in the summary.
AI adoption correlates with more-recent digital infrastructure—cloud computing and predictive analytics—rather than legacy on-premises IT or descriptive analytics.
Correlational analysis using variables from the Census Bureau survey that measure presence of cloud computing, predictive analytics, on-premises IT, and descriptive analytics; sample derived from ~28,500 establishments.
AI is less prevalent in simpler channels of automation overall, but AI is more prevalent on labour-substituting margins in lower-income settings and tends to augment labour in higher-income settings.
Task-level coding for technological channel and whether AI is involved, aggregated across 124 countries (2.33M task-country labels) and compared across income groups and labour margins (substitute vs augment).
Across countries, exposed tasks are skewed towards labour-substituting automation rather than labour-augmenting automation; low-income countries are disproportionately exposed to substitution, whereas middle-income countries are more heterogeneous.
Cross-country breakdown of exposed tasks by labour margin (substitution vs augmentation) using the task-country labels across 124 countries, with comparisons by income group.
Scaling helps but does not solve the accumulated-message effect (Anthropic models: Haiku -0.22 to Opus -0.17; OpenAI models: Nano -0.34 to GPT-5.2 -0.17).
Comparison of effect magnitudes (Cohen's d values) across model families and sizes reported in the experiments.
The accumulated-message effect concentrates on items where the model is genuinely uncertain at baseline (d = -0.34 for high-entropy items, vs d = -0.15 when the baseline is deterministic).
Subset analysis partitioning items by baseline model entropy/uncertainty; reported Cohen's d for high-entropy vs deterministic-baseline items (no separate sample counts reported in the abstract).
Models shift toward the conversation's prevailing polarity (accumulated message effect on LLM judgments, AMEL).
Experimental comparison where identical test items were presented either in isolation or following histories saturated with predominantly positive or negative evaluations, across the full dataset (75,898 API calls to 11 models). Reported effect: d = -0.17, p < 10^-46.
Much of the earlier provider spread came from end-to-end system behavior rather than planning alone.
Inference from the contrast between the cross-provider championship (end-to-end) where provider differences were observed and the planner bakeoff (standardized execution) where planners were near-equal.
Resource (digital talent) agglomeration should remain at a moderate level and achieve coordinated development, because excessive concentration can reduce the growth benefits (implied by the inverted-U finding).
Policy implication drawn from the paper’s finding of an inverted-U relationship between talent agglomeration, industrial digitalization, and regional economic growth (normative recommendation based on empirical nonlinear result).
For the country as a whole and for the eastern, central, and western regions, there is a deviation from the conjugate (coordinated) state between digital talent agglomeration and industrial digitalization.
Subsample/regional analysis across China’s regions (national and by eastern/central/western regions) reported in the paper indicating lack of positive coordination between talent agglomeration and industrial digitalization in these areas. Exact methodology and sample sizes by region not provided in the excerpt.
The relation among digital talent agglomeration, industrial digitalization, and regional economic growth follows an inverted-U shape (consistent with the Williamson hypothesis).
Systematic empirical examination of China's provincial regions using regional-level empirical analysis (paper reports an econometric test of nonlinear/quadratic relationships between digital talent agglomeration, industrial digitalization, and regional economic growth). Sample size (number of provinces/observations) not stated in the excerpt.
Acceleration in the Generate/Take Action phase translates into durable performance only when Analyze/Prioritize is de-biased by individuals and teams, and Measure/Review converts results into reusable knowledge with appropriate inference discipline.
Thematic conclusions from the 17 interviews and cross-case analysis (Gioia methodology) identifying conditional relationships across stages of the seven-stage growth pipeline.
GenAI enables small teams to expand capacity while creating new dependencies and coordination logics.
Empirical finding from 17 interviews indicating both expanded capacity and emergent dependencies/coordination needs.
GenAI drives structural recomposition across four domains: shifting roles, AI-embedded workflows, evolving capability expectations, and leaner work architectures.
Empirical finding from thematic analysis of 17 expert interviews reported in the results.
High-information AI improves short-run (immediate) performance without reducing post-AI outcomes on average in the experiments, but effects are heterogeneous across participants.
Experimental condition with high-information AI in the controlled logical reasoning task showing improved short-run performance and no average reduction in post-AI outcomes; heterogeneity in effects reported (sample size not provided in abstract).
Comparative analysis reveals significant institutional differences between EU and Ukrainian legal systems that are relevant to regulatory stability, the cost of innovation, data accessibility, the balance of market power, and guarantees for consumers and employees.
Qualitative comparative examination of institutional and cultural/procedural differences between EU and Ukraine as presented in the paper (method: comparative approach; no quantitative metrics provided).
Most Ukrainian laws relevant to the digital economy are based on existing legal structures and systems, and Ukraine currently lacks a unified regulatory system specifically designed for artificial intelligence.
Comparative analysis of Ukrainian and EU legal frameworks as described in the paper (method: comparative approach; legal document review referenced qualitatively).
Digitalisation is making data and algorithmic systems increasingly important economic resources, thereby changing the way markets operate, how labour is organised, how productivity is measured and how income is distributed.
Conceptual analysis and theoretical model developed via literature synthesis and comparative approach (no empirical sample reported).
The paper formalizes the non-classical measurement error, deriving probability limits and partial-identification bounds for employment elasticities.
Theoretical/mathematical derivations presented in the paper that model the non-classical measurement error structure and derive probability limits and partial-identification bounds for elasticities.
Within-vendor consumer-versus-enterprise channels produce estimates that disagree in sign.
Within-vendor comparison of exposure measures constructed from consumer-facing versus enterprise-facing conversation channels; reported that resulting estimates (e.g., employment effects) have opposite signs.
Holding outcome, sample, controls, and estimator fixed while varying only the platform input changes the post-ChatGPT employment coefficient by a factor of 1.9.
Empirical robustness exercise where the authors keep outcome, sample, controls, and estimator constant and vary only the platform input (different conversation-log sources) and report change in estimated post-ChatGPT employment coefficient multiplicatively by 1.9.
AI platform conversation-log exposure scores partly measure the platform user base rather than the underlying workforce.
Comparative empirical analysis using AI platform conversation logs to construct occupation exposure scores; authors compare exposure measures across platforms and show variation attributable to platform user composition rather than labor-force composition.
Interpretability, trust calibration, and interface design matter, but they cover only part of what determines whether human-AI combination works.
Authors' argumentative claim based on their analysis and mapping of broader factors; presented as an evaluative conclusion rather than an empirical estimate.
Through case studies and architectural illustrations, the paper highlights both the innovation potential and governance challenges posed by agentic systems.
Case studies and architectural illustrations cited in the abstract as the basis for highlighting benefits and challenges. No numeric evaluation provided in the abstract.
The integration of artificial intelligence (AI) agents into payment systems signals a profound shift in the architecture of financial transactions.
Conceptual and technical analysis presented in the paper (argumentative claim in abstract). No empirical sample or quantitative data reported in the abstract.
Meta-analytic evidence shows moderate but heterogeneous effects of agentic/code-generation tools on productivity.
Reference to meta-analytic synthesis across studies reported in the paper (meta-analytic details not provided in abstract).
The benchmark therefore assigns value to coordination only when the corresponding performance, provenance, or representation claim is supported by explicit comparators.
Concluding statement in the paper tying value of coordinated AI agents to evidence from explicit baseline/comparator evaluations across performance, provenance, and representation dimensions.
For molecular sonification, the gain is representational rather than predictive.
Reported outcome for molecular structure to music task indicating improvements in representation/sonification quality but not in predictive performance.
AI changes the traditional relationship between learning and performance: in AI-intensive environments, learning must be supported by systems that coordinate knowledge and build intelligence rather than relying on learning alone.
Authors' synthesis and interpretation of their cross-sectional mediation results (AIDLC → KO → OI → IP) and comparison with prior management models.
The economics literature uses specific quantitative arguments and methods to estimate the changes produced by automation, and there is an ongoing debate in the field about these quantification methods.
Paper presents and synthesizes economic studies and methodological approaches (task-based methods, decomposition analyses, etc.) as part of a literature review and critical discussion.
The study evaluates contemporary mitigation frameworks for algorithmic bias in HR settings.
Statement of the paper's evaluative aim; implies review/assessment of mitigation strategies but no specific methods or metrics provided in excerpt.
The paper analyses three primary vectors of AI bias in hiring: data bias, interaction bias, and evaluation bias.
Stated analytic framework in the paper (categorization of bias vectors); descriptive content rather than quantified empirical result.
This study examines the dual role of AI in the workplace: as a tool for bias reduction and as a potential vehicle for systemic discrimination.
Statement of the paper's research aim / framing; descriptive claim about the paper's scope rather than empirical finding.
Techno-sovereignty is a mode of authority grounded in control over data, computation, and AI infrastructures, exercised through state, corporate, and community or Indigenous configurations.
Conceptualization and normative-theoretical analysis drawing on political theory and community/Indigenous approaches (qualitative, no quantitative data).
AI alters strategizing practices (Strategy-as-Practice) by making strategy processes continuous and AI-augmented rather than episodic and purely human-driven.
Conceptual synthesis of Strategy-as-Practice literature; theoretical claim about process change to continuous, AI-augmented strategizing; no empirical sample.
AI redistributes resource control to stakeholders, challenging the Stakeholder Resource-Based View by changing who holds and controls strategically valuable resources.
Theoretical argument within the Stakeholder Resource-Based View stream; conceptual synthesis asserting redistribution of resource control to external stakeholders and algorithmic actors; no empirical evidence reported.
AI reconfigures ecosystems and platforms around foundation models, shifting how complementary actors interact and altering platform/ecosystem structure.
Analytical review of Ecosystems and Platforms literature; conceptual claim that foundation models act as central coordinating technologies; no empirical data or sample.