Evidence (8807 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 |
Productivity
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We offer a three-stage lens: Augmentation, Automation, and Reconstruction.
Conceptual framework proposed by the authors; presented as a taxonomy in the paper (no empirical validation reported in the excerpt).
There is a suggestive non-linear relationship between embodiment and team performance.
Analysis reported in the paper indicating a non-linear (not strictly monotonic) association between degree of agent embodiment (Box, Avatar, humanoid) and measured team performance; described as 'suggestive' in the abstract, without quantified functional form or statistics included there.
Artificial agents have an uneven impact on team outcomes, with some mixed human–AI teams performing exceptionally well and others markedly worse.
Observed performance outcomes across mixed human–AI teams in the escape room experiment, showing high between-team variability; exact sample size and statistical details not provided in the abstract.
Adverse employment and compensation effects are concentrated among workers in non-AI tasks and non senior-level positions, indicating an asymmetric distribution of gains from AI adoption.
Heterogeneity analysis / subgroup results showing larger negative employment/compensation responses for workers in non-AI tasks and for non senior-level positions across the sample.
Human capital structure moderates the relationship between AI application and enterprise innovation efficiency.
Moderation analysis on A-share listed firms (2012–2023) indicating significant interaction effects between AI application and measures of human capital structure.
Fiscal support intensity moderates the impact of AI application on enterprise innovation efficiency.
Empirical moderation tests using firm-level panel data (2012–2023) showing interaction between AI application measures and fiscal support intensity.
Market segmentation exerts a moderating effect on the relationship between AI application and enterprise innovation efficiency.
Moderation analysis in the empirical framework applied to the 2012–2023 panel of Shanghai and Shenzhen A-share firms showing interaction effects between AI application and market segmentation measures.
These findings demonstrate the feasibility and current limits of automated expertise mapping.
Synthesis/conclusion based on model performance (e.g., MAE results) and observed limitations reported across evaluations.
The UPCT framework offers a unified explanation for varied phenomena: pandemic resilience patterns, divergent digital transformation outcomes, and emerging risks of AI-driven organizational rigidity.
Synthesis claim by the author asserting explanatory scope of the theoretical framework; no empirical cross-case synthesis or formal validation included.
The paper's Universal Phase Crystallization Theory (UPCT) reconceptualizes organizations as recursive generative cycles (Φ→R→S→Φ′) and asserts organizational existence is better described as E = ΦR rather than E = S.
Theoretical/model claim introduced and developed in the paper; purely conceptual without empirical testing.
Resilience should be redefined not as reserve magnitude (accumulated buffers) but as recoverability of generative relational capacity.
Normative/theoretical redefinition proposed by the paper; no empirical validation provided.
AI is changing skill requirements—some skills become obsolete and new skills are required.
Paper identifies changing skill requirements as a key area of examination (abstract). This is stated as an asserted trend based on the paper's review rather than a quantified empirical finding in the provided text.
AI has changed how work is executed (work processes and execution).
Explicit statement in the paper's abstract; presented as a qualitative/general finding from the paper's evaluation and literature synthesis (no numerical sample provided).
AI has changed who works in jobs (i.e., workforce composition).
Stated in the paper's abstract as an asserted effect of AI on employment composition; presented as part of the paper's review rather than a specific empirical estimate.
The penetrating utilization of AI-based methods to perform tasks has drastically changed how jobs are performed.
Claim asserted in the paper (abstract) as a descriptive conclusion from the paper's review/analysis; no empirical sample or quantified effect reported in the provided text.
AI is altering nearly every aspect of human interaction—such as work and society.
Statement in the paper's abstract/intro; presented as a general observation in the paper (literature review/qualitative synthesis implied). No primary sample size or empirical estimate reported in the provided text.
Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping both answer quality and serving cost.
Paper's conceptual description of retrieval pipelines and configuration dimensions (LLM, retriever, number of documents, number of hops, synthesis strategy). No empirical sample size reported for this descriptive claim.
cBCI synergy is heavily contingent on the temporal dynamics of trust, providing a critical framework for designing dynamically gated Human-AI systems.
Interpretive/concluding claim based on experimental results (timing-dependent failure modes, Oracle gating, Hybrid Fusion effects) reported in the study.
AI timing dictates the mechanism of team failure: high-speed AI interventions risk inducing reflexive blind compliance while delayed interventions can induce ambiguous cognitive conflict.
Synthesis claim derived from experimental contrasts between Fast/Less-Accurate and Slow/Accurate AI conditions and observed human/team behaviors (blind compliance vs. delayed conflict).
The workflow was cache-dominant, suggesting that persistent agentic environments may shift the economic unit from cost per token to cost per completed artifact.
Observed high cache-read fraction (82.9% in May subset) and interpretation by authors that caching dominates token usage, leading to the suggestion about economic-unit shifts.
Depending on operational parameters, the most time-efficient way to complete a workflow may undergo a transition between two task-processing regimes: a fully AI-assisted regime and a fully manual regime.
Analytical results derived from the paper's formal queueing model (theoretical/model-based derivation; no empirical sample reported).
AI assistance can generate a deceptive productivity signature: average completion times fall because AI tools typically supply a fast first draft, yet workflow-level performance can deteriorate when a subset of AI errors escapes review and returns as costly downstream rework.
Analytical derivation and discussion based on the paper's queueing model (theoretical/model-based evidence; no empirical sample provided).
Drawing on the partial equilibrium model of Gries and Naudé (2022), existing economic frameworks may inadvertently overlook these factors.
The paper's theoretical critique referencing Gries & Naudé (2022); argument is based on model comparison and conceptual analysis rather than new empirical tests.
We identify five key moderating factors: human resource composition, baseline capability of individuals, learning curve of practitioners, incentives for fair use, and flexibility of objectives.
Explicit enumeration of proposed moderating factors in the paper (conceptual identification rather than empirical measurement).
Following the advent of high-performance generative models, AI use has been rapidly encouraged in some sectors while being restricted in others.
Descriptive claim in the paper's introduction/abstract; based on observation and literature context rather than new empirical data.
These findings have broader implications for productivity, equity, and capacity across the global research system.
Discussion/interpretation in paper based on causal results from randomized experiment; inference from observed behavioral changes and heterogeneous effects.
AI redefines job roles.
Authors' thematic analysis of secondary sources and peer-reviewed literature (qualitative synthesis). No sample size reported.
Artificial Intelligence (AI) has changed how people work across various fields and businesses, especially in the Indian Information Technology (IT) industry.
Authors' qualitative synthesis of peer-reviewed literature and thematic evaluation of secondary data (literature review). No sample size reported.
Completion time itself is not sufficient to characterize efficiency gains.
Authors' inferential conclusion in the abstract based on observed dissociation between completion time (no difference) and subjective effort (lower with AI) in their preregistered study (N = 1237).
Including narrative explanations with AI predictions may involve tradeoffs for decision-making performance.
Synthesis and conclusion based on the experiment's findings (null effect on accuracy, increased reliance, and exploratory detrimental effects on response time and discrimination).
AutoResearch autonomy is domain-conditioned: more credible in structured, executable, and rapidly verifiable settings but limited in embodied, delayed, heterogeneous, ethical, or institutionally accountable contexts.
Authors' synthesis of system capabilities and application domains from the surveyed literature; qualitative assessment of where autonomy is plausible vs limited.
Emerging AI-led systems coordinate larger portions of the discovery loop without achieving robust autonomy.
Survey of recently proposed AI scientist and AI-led systems showing increased coordination across workflow steps but lacking evidence of fully autonomous, robust operation; qualitative synthesis.
The effects of digital transformation on labor demand vary substantially across types of digital technologies.
Analysis across different digital technology categories reported in the paper showing heterogeneous effects on labor demand (data: Chinese A-share manufacturing firms, 2011–2024). (Sample size not stated in provided text.)
The impact of digital transformation on labor demand differs across firms with different ownership structures, factor intensity, and asset sizes.
Heterogeneity analysis reported in the paper using subsample or interaction regressions by firm ownership, factor intensity, and asset size (Chinese A-share manufacturing firms, 2011–2024). (Sample size not stated in provided text.)
Managerial traits, such as risk tolerance and patience, play a role in shaping firms' AI adoption decisions.
Inclusion of manager-level trait measures (risk tolerance, patience) in the ifo Business Survey and analysis showing associations between these traits and reported AI adoption.
Drivers and barriers to AI adoption include firm-specific characteristics and industry dynamics.
Survey-based analysis linking firm characteristics and industry-level factors to reported AI adoption decisions in the ifo Business Survey (likely correlational/regression analysis).
AI adoption/diffusion varies across firm sizes.
Analysis of adoption patterns by firm size using ifo Business Survey firm-level responses (comparison across size categories).
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 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.
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.
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.
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).