Evidence (9875 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
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 |
Adoption
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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.
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.
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.
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.
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.
AIO’s decarbonization effects vary systematically across climate risk, industry competition, and AI exposure (heterogeneity analyses).
Authors state they performed heterogeneity/subgroup analyses showing systematic variation in the AIO–decarbonization relationship by climate risk, the degree of industry competition, and firms' AI exposure.
"General knowledge application" is the second most popular category among highlighted benchmarks, yet it is vaguely defined.
Categorization results from applying the paper's taxonomy to the Benchmarking-Cultures-25 dataset (counts/rankings reported by category). The paper comments on the vagueness of the label.
Benchmarks are attributed different competencies by different builders, depending on their narrative.
Qualitative and comparative analysis mapping benchmark labels and builders' claims in the Benchmarking-Cultures-25 dataset (139 model releases); the paper documents instances where the same benchmark is presented as evidence of different capabilities by different builders.
The primary way to establish and compare competencies in foundation and generative AI models has shifted from peer-reviewed literature to press releases and company blog posts, where model builders highlight results on selected benchmarks.
Descriptive/argumentative claim in the paper's introduction framing the research question; based on the authors' survey of contemporary practices and motivation for the dataset and analysis.
Classical categories (labour, capital, firm, market, productivity, trust) remain necessary but are incomplete for describing economic action when technologies prepare decisions, coordinate workflows, support tasks, verify transactions, and reshape responsibility.
Conceptual analysis supported by diagnostic indicators showing distributed decision/action capacity across humans, AI agents, robots, protocols, compute and energy systems; argumentative/theoretical evidence rather than causal inference.
Labour projections are more consistent with task reallocation than labour disappearance.
Analysis of labour-market reallocation data and labour projections (public sources) interpreted under a task-reallocation framework rather than full employment loss, using relative growth and reallocation indicators.
Readiness and performance-related variables are associated with higher predicted success, whereas higher barrier levels are associated with lower predicted success.
Model coefficients/feature effect analyses and nonlinear diagnostics from the fitted models.
The central challenge is whether commercial influence in generative systems can be made trustworthy, i.e., attributable, measurable, contestable, and aligned with user welfare.
Normative claim and formulation of research and policy challenge presented by the authors as the central problem motivating the paper; based on their analysis of gaps in detection, measurement, and governance.
This reframes generative AI advertising as a problem of trustworthy intervention rather than content placement.
Authors' normative and conceptual reframing based on their analysis and taxonomy; presented as an argument about how to think about regulatory and design priorities.
Joint estimation confirms simultaneous adjustments across financing and innovation margins.
Joint estimation (likely a system or simultaneous-equations approach) showing concurrent changes in financing costs and innovation-related variables following the shock (method stated; no sample size or exact estimates in abstract).
The same observable behavioral signal can carry opposite meaning for different agent configurations.
Synthesis of the cross-configuration empirical findings (directional disagreements such as the error-rate example and other features).
Five other continuous features and three of seven binary patterns from prior SE literature show similar directional disagreement across configurations.
Aggregate empirical finding across the set of features and binary patterns analyzed in the 126-configuration dataset.
Error rate is the cleanest case: 47 configurations resolve more issues when their error rate is lower, while 48 resolve more when it is higher.
Empirical counts from the paper's analysis of configurations (reported 47 vs 48 configurations showing opposite sign relations between error rate and issue resolution).
On most signals, configurations disagree not merely in magnitude but in direction (i.e., the same signal correlates positively with resolution in some configurations and negatively in others).
Across-configuration comparison of behavior–outcome correlations for many signals in the dataset of 126 configurations / 64,380 runs.
Swapping the framework while the LLM is held fixed produces large behavioral differences in every action feature.
Comparative analysis across configurations holding LLM fixed; reported observation across action features.
The system is generically bistable, with a stable partial adoption equilibrium coexisting alongside full genuine adoption.
Analytical results from the evolutionary game-theoretic model demonstrating multiple stable equilibria (bistability). No empirical sample (theoretical proof / model analysis).
Doctors choose among three strategies: genuine adoption, partial adoption, and rejection, where genuine adoption is required for systemic benefits to materialise above a population threshold.
Model specification in an evolutionary game-theoretic framework; analytical description of strategy set and threshold condition. No empirical sample (theoretical model).
Outcome-only evaluation can certify economically unsafe agents: a policy can hit a business KPI while violating deployable behavioral discipline.
Illustrated by a hotel-pricing experiment (hidden competitor state) in which a learner achieves plausible revenue per available room while failing to preserve the rate discipline of a rule-based revenue-management competitor; based on experimental results in the paper's two-hotel benchmark.
Rising density from rack- and pod-scale AI systems shapes these outcomes (deployable capacity, capex, performance) — we quantify how density changes these outcomes.
Modeling/simulation results reported in the paper quantifying the impact of rising rack/pod-scale density on deployable capacity, capex, and performance; specific numeric quantification not included in the abstract.
Başta ABD, Avrupa Birliği ve Çin olmak üzere büyük ekonomilerin yapay zekâ alanında benimsediği sanayi ve ticaret politikaları karşılaştırmalı olarak incelenmektedir; bu ekonomilerin teknolojik hegemonya arayışının ekonomik olduğu kadar jeopolitik bir boyut kazandığı değerlendirilmektedir.
Karşılaştırmalı politika incelemesi (kavramsal ve betimleyici); çalışmada belirli politika örnekleri tartışılıyor ancak sistematik nicel karşılaştırma ya da örneklem büyüklüğü belirtilmiyor.
Yapay zekâ teknolojilerindeki hızlı ilerleme, küresel üretim ve ticaret organizasyonunu köklü biçimde dönüştürme potansiyeline sahiptir.
Kavramsal değerlendirme ve literatüre dayalı tartışma; çalışmada ampirik örnek veya nicel örneklem sunulmamaktadır.
Anhand von Fallstudien aus den G7-Ländern werden verschiedene Einsatzmöglichkeiten veranschaulicht und die wichtigsten Erfolgsfaktoren benannt – Netzanbindung, KI-Inputs, Kompetenzen und Finanzierung.
Evidence comes from G7 country case studies reported in the paper; method = qualitative case studies identifying key success factors (no number of case studies or sample size provided in excerpt).
This lack of focus creates uncertainty about whether regulatory technology helps legitimate economic recovery or instead strengthens exclusion and informality.
Interpretive observation from gaps identified in the reviewed literature; no empirical resolution provided.
The results vary across the 10 selected countries: the magnitude and significance of AI’s effects differ due to varying technological readiness and differing industrial structures.
Paper statement that results vary across the 10 selected countries and that nuances differ across countries due to varying industrial structures and technological readiness. Implied heterogeneity analysis across countries using the firm-level dataset and regression approaches; no country-level sample counts provided in the excerpt.
Digital transformation reconfigures development patterns across regions and countries, altering established trajectories of regional development.
Theoretical integration of a technology–labor–space framework together with comparative regional field evidence illustrating changing development patterns (no quantified effect sizes or sample sizes reported).
There is a fundamental reward-coverage tradeoff: concentrating probability mass on high-reward actions reduces variance but risks missing signal on actions the target policy may take.
Explicit characterization in abstract; claimed theoretical analysis/derivation of the tradeoff between variance reduction and coverage when designing logging policies.
Perceived procedural improvement (participants preferring facilitation and higher reported trust) can coexist with measurable steering of outcomes and unchanged participation inequality, motivating evaluation practices treating outcomes, interaction dynamics, and perceptions as distinct governance targets.
Synthesis of the experimental findings: null effect on consensus and participation equity, positive effects on participant preference/trust, and measurable allocation shifts (up to 5.5 percentage points) across facilitation conditions in the two experiments (total N=879).
Facilitators shifted select charity-level allocations by up to 5.5 percentage points, directly affecting the final charitable payout.
Analysis of final group allocation outcomes across experimental conditions showing shifts in allocation to specific charities; reported maximum observed shift of 5.5 percentage points attributable to facilitator condition(s). (Study-level sample covering the two experiments; participants organized in groups of three.)
Beyond length biases, fine-tuning amplifies sycophancy and relationship-seeking behaviours in models.
Behavioral analysis of model outputs in the within-subject experiment (530 participants) showing increased incidence/intensity of sycophantic and relationship-seeking responses after preference fine-tuning compared to baseline models.
Adapting to individual preference data yields only marginal gains over training on pooled preferences from a diverse population.
Comparison within the same within-subject experiment (530 participants) between models fine-tuned on individual preferences versus models trained on pooled preferences across participants; reported as 'marginal gains'.
The research challenges for this vision stem from a broader flexibility–robustness tension that requires moving beyond the on-the-fly paradigm to navigate effectively.
Analytical claim in paper identifying a design trade-off (flexibility vs. robustness) as the core challenge motivating the proposed shift; no empirical demonstration provided.
Aggregate effects are geographically uneven (geographic unevenness in AI-driven labor market impacts).
Synthesis across studies observing variation by geography and noting non-Anglophone markets and developing economies as under-studied and differentially affected.
Wage polarization characterizes the aggregate pattern of labor market change associated with recent AI advances.
Aggregate characterization from synthesized studies reporting divergent wage outcomes (higher wages for AI-augmented workers, pressures on junior/routine roles) consistent with polarization.
Sectoral effects are heterogeneous: infrastructure, security, and quality-assurance roles have expanded while developer roles have contracted.
Qualitative and quantitative results aggregated across the included studies noting role-level expansions and contractions; no single pooled effect size provided.
Under open-ended prompts, trust drops to 3-55%, confirming prompt framing as a confound; we report both conditions.
Experimental comparison reported by authors between directed queries and open-ended prompts; observed trust rates under open-ended prompts ranged from 3% to 55% (no explicit per-model sample sizes reported in the summary).