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 |
Naive full-history persistence actively degrades task completion (by biasing the agent with stale traces) compared to no memory and selective memory.
Empirical comparison reported in the paper showing full-history persistence produced 71% completion vs. 79% for no memory and 96% for selective memory; rationale given that stale reasoning traces bias agents.
There are critical gaps in governance mechanisms that are tuned to the scale of SME deployment of BI and AI.
Conclusion drawn in the narrative review of literature (2020–2025); no specific policy evaluations or sample sizes cited in the excerpt.
SMEs face unequal/fairness issues in access to AI and there are biases in algorithms affecting SME deployment.
Identified as a key gap across the peer‑reviewed literature (2020–2025) in the review; the excerpt provides no quantitative measures or specific studies.
There are critical gaps in data literacy among SME personnel.
Reported as a recurring theme in the reviewed literature (2020–2025) in the narrative review; no numeric prevalence or sample sizes provided in the excerpt.
This structural under‑serving of SMEs by advanced BI and analytics is threatening inclusive economic growth and resiliency.
Argument presented in the review synthesizing literature (2020–2025); no quantified causal estimates or sample sizes provided in the excerpt.
SMEs are systematically under-served by advanced business intelligence (BI) and predictive analytics infrastructure.
Narrative synthesis of peer‑reviewed literature (2020–2025) reported in the review; no specific studies or sample sizes given in the excerpt.
Self-reported cognitive outsourcing predicts lower originality specifically in human-human dyads.
Correlation / regression result from the in-person pilot (N = 62) reporting that self-reported cognitive outsourcing is associated with lower originality in human-human dyads but not in other conditions.
The results caution against using one LLM-generated skill per data-science workflow as a default single-shot prompting strategy.
Authors' interpretation and recommendation based on the null-findings from the ablation and control experiments.
Brown AI’s infrastructure investment crowds out household expenditure, causing the reported consumption cost.
Mechanism described in the paper: modelled exogenous IT investment surge (S3) reallocates resources toward investment and away from household consumption in the CGE results.
These factors (surveillance anxiety, loss of autonomy, deskilling) negatively affect worker well-being and contribute to turnover.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). The paper synthesizes prior empirical and theoretical studies but does not report an original sample size.
Automation and algorithmic systems introduce risks of deskilling that affect workers' capabilities.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No primary sample size stated.
Algorithmic management reduces worker autonomy (loss of autonomy) in warehouse settings.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). Sample sizes not reported in this paper.
Algorithmic management in automated logistics generates surveillance anxiety among workers.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No sample size given.
Across models spanning 1 billion to 8 billion parameters, controlling output length saves up to 97% of total energy, with the energy dominance of decoding growing stronger at larger model scale.
Empirical measurements across models of sizes 1B–8B parameters within the profiling suite showing energy savings up to 97% when output length is controlled; observed trend of increasing decoding energy dominance with model scale.
Even removing all visual tokens saves at most 10% of total energy for fixed-token models, exposing a fundamental limitation of visual token pruning.
Counterfactual/ablation-style analysis in the profiling study estimating maximum energy savings from eliminating visual tokens in fixed-token model configurations; reported upper bound of ≈10% energy savings.
The paper formalises an AI productivity transmission gap between technical adoption and inclusive productivity realisation.
Formal definition and derivation within the DIAC theoretical framework (analytical/modeling content).
AI does not translate directly from firm-level task efficiency into national productivity; its effect is filtered through complementary intangible investment, skills formation, data governance, competition policy, labor-market mobility, and social insurance.
Analytical DIAC model and accompanying theoretical argumentation in the paper; no empirical sample reported.
AI use can reduce visibility of real skill differences among employees.
Reported findings from performance management and knowledge-work studies indicating that AI-mediated outputs can obscure underlying employee skill variation.
Use of AI can produce over-reliance on AI recommendations, reducing active human judgment and accountability.
Cited empirical observations and prior literature on automation bias and AI-supported decision processes in organizational settings.
AI systems miss contextual information that humans use to make better decisions.
Examples and studies cited from hiring, performance management, healthcare, and knowledge work demonstrating omissions of context by AI tools.
Empirical studies of AI use show recurring problems including mistakes in unusual cases.
Cited recent studies across domains (hiring, performance management, healthcare, knowledge work) reporting AI errors on atypical or edge-case instances.
Human judgment rooted in experience cannot be fully replaced by current AI systems.
Argument based on literature synthesis drawing on cognitive science, neuroscience, and organizational studies; supported by cited recent empirical studies of AI use in hiring, performance management, healthcare, and knowledge work (no single new experiment reported).
The study highlights the limited integration of GenAI in the choice phase of organizational decision-making.
Analysis of task-to-component mappings from the 68 reviewed studies showing relatively fewer GenAI applications mapped to the 'choice' component compared to other components.
Our findings reveal a fragmented application landscape for GenAI in organizational decision-making.
Synthesis of the 68 reviewed publications showing diverse, heterogeneous uses of GenAI across tasks and categories; authors describe the landscape as fragmented.
Existing studies are largely fragmented across industries, organizational contexts, and individual AI applications, with limited systematic evidence synthesizing how AI-aided SIS tools collectively influence organizational performance and sustainable competitive advantage.
Findings from the PRISMA-guided literature search and eligibility assessment that resulted in 22 included studies; thematic analysis highlighted heterogeneity and gaps in the literature.
Despite benefits, challenges persist including data privacy concerns, algorithmic bias, ethical risks, workforce skill gaps, organizational resistance, and high implementation costs.
Recurring themes identified across the 22 studies included in the PRISMA-guided systematic review (Scopus, ScienceDirect, Google Scholar searches, 2017–2026) and summarized via thematic analysis.
The gross tax gap in the U.S is over 600 billion a year.
Statement in paper citing standard U.S. tax-gap estimates (presumably IRS estimates); presented as a factual background statistic in the literature review.
Repository-mining studies measure surface trends but seldom explain the mechanisms beneath them, and the trends themselves prove unstable.
Critical observation by the authors supported by their own GitHub observational analysis showing sensitivity of trends to analysis choices; presented as an interpretive claim in the paper.
Agent-authored pull requests are discussed less than human-authored ones.
Observational analysis of public GitHub activity reported in the paper (no sample size reported in abstract); comparison of discussion volume/length for agent- vs human-authored PRs.
Agent-authored pull requests are reviewed less often than human-authored ones.
Observational analysis of public GitHub activity reported in the paper (no sample size reported in abstract); comparison between agent-authored and human-authored pull requests.
The paper identifies an emergent phenomenon called 'Precariousness 2.0' — a state of manufactured uncertainty characterized by loss of professional autonomy and chronic anxiety among workers.
Conceptual/qualitative construct developed in the paper from synthesis of secondary reports and national observations; no primary survey data cited supporting prevalence or magnitude.
Women in high-income countries face a risk of automation nearly three times higher than men due to their concentration in administrative roles.
Paper's secondary quantitative synthesis attributing a ~3x relative risk to occupational gender segregation (administrative roles); based on international report data referenced in the study.
39% of current skills become obsolete.
Reported statistic in the paper synthesizing projections from the cited reports (WEF, ILO, McKinsey, PwC); no primary sample size stated.
22% of employment undergoes structural change (masking the net job gain).
Reported summary statistic from the paper's secondary quantitative analysis of international reports; no primary sample size stated.
Estimated coefficients for young workers are negative, in line with the existing literature, but they are small and statistically insignificant.
Reported coefficient signs and statistical significance levels in the paper's main estimations (negative point estimates for young workers; described as small and insignificant).
ABS adoption negatively affects high-status batters' BB/K (walks-to-strikeouts ratio) relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148); BB/K listed among impacted outcomes.
ABS adoption negatively affects high-status batters' strikeout rate (SO%) relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148); SO% reported among affected metrics.
ABS adoption negatively affects high-status batters' walk rate (BB%) relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148); BB% listed among impacted outcomes.
ABS adoption negatively affects high-status batters' IsoD relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148); IsoD reported among affected metrics.
ABS adoption negatively affects high-status batters' on-base percentage (OBP) relative to low-status batters.
Difference-in-differences linear regressions using KBO 2023 and 2024 season data for batters (n = 148).
Economic analysis of the information society, digital platforms, and artificial intelligence requires rebuilding the 'hard core' of economic science and abandoning textbook-based learning.
Author's normative/methodological recommendation based on the paper's theoretical critique of existing frameworks and empirical observations about digital sector dynamics.
Market power is shifting to the ownership of the digital assets that underpin markets.
Theoretical and interpretive claim supported by the paper's analysis of digital platforms and asset ownership (no single quantified causal estimate provided).
Digital and even non-digital sectors generate no profit without data, technology, and infrastructure.
Author's theoretical argument and interpretation of contemporary observations (paper's conceptual analysis); not reported as a quantified empirical estimate.
A conceptual model of the AI productivity paradox is proposed to explain underlying causes of efficiency loss and formalize the role of micro-mechanisms in slowing macroeconomic growth.
Theoretical model development drawing on empirical BLS trend analysis and micro-level case evidence; presented as an explanatory framework in the paper.
Key micro-mechanisms underlying the labor productivity paradox under AI are: task expansion, blurring of boundaries between work and non-work time, intensification of multitasking, and accumulation of 'AI debt' by organizations.
Identification and systematization based on theoretical development and analysis of corporate cases and empirical reports.
The productivity gap is attributable to organizational and behavioral factors.
Theoretical analysis, generalization, and synthesis of corporate cases and empirical reports linking observed micro-behaviors to macro-level productivity outcomes.
There is a gap between anticipated macroeconomic efficiency gains (aggregate labor productivity) and observed micro-level outcomes following AI adoption.
Comparison of aggregate productivity trends (BLS series/AAPC calculations) with micro-level evidence drawn from corporate case studies and empirical reports documenting localized impacts of AI.
The CAD has implications for knowledge-work stratification and AI platform governance.
Argumentative/policy discussion in the paper linking the CAD to potential stratification among knowledge workers and governance considerations for AI platforms.
The probabilistic model demonstrates that manual context attachment leads to a combinatorial collapse in task-success probability as corpus size and task conjunctivity grow.
Results from the paper's probabilistic model (analytic/theoretical demonstration based on fan effect reasoning); no empirical sample reported.
For knowledge-intensive workers whose intellectual capital spans tens of thousands of files, the CAD constitutes a qualitative threshold in AI usefulness: below it, the cognitive burden of context curation falls on the human, reproducing the inefficiencies AI is meant to eliminate.
Theoretical argument grounded in the paper's conceptual discussion about large personal/organizational corpora (stated scale: tens of thousands of files) and the user burden of manual context attachment.