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).
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 |
Adoption
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Du et al. (2026) find that information-based team faultlines can enhance proactive behavior via deep information processing, while AI adoption moderates and mitigates the negative effects of social-based faultlines on team cooperation.
Information-processing theoretical framing and empirical analysis reported in the paper (study type and sample size not specified in the excerpt).
Liao et al. (2026) identify multiple equifinal pathways to high performance in digit-oriented spin-offs (parent-oriented, independent-oriented, ambidextrous-oriented configurations) using fuzzy-set qualitative comparative analysis (fsQCA).
fsQCA analysis reported in the paper (methodological approach described; sample not specified in excerpt).
Implementation success depends heavily on data quality, workflow redesign, interpretability, governance, and procurement alignment.
Synthesis of factors identified across included studies and supporting regulatory/industry documents as important determinants of successful deployment.
However, evidence is uneven: many studies are simulation-based.
Review observation from the synthesis of the 35 included studies noting study designs (simulation prevalence noted but not numerically specified).
Perkembangan AI mengotomatisasi tugas rutin sekaligus menciptakan peluang pekerjaan baru berbasis digital.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
The comparative evaluation shows differences in economic inclusiveness between ML, DL, and Generative AI.
Abstract states differences in economic inclusiveness found in the review; no quantitative inclusiveness metrics or sample sizes provided in abstract.
The comparative evaluation shows differences in explainability among ML, DL, and Generative AI.
Abstract notes comparative differences in explainability as part of review findings; no empirical measures of explainability included in abstract.
The comparative evaluation shows differences in patterns of substituting labor across ML, DL, and Generative AI.
Abstract states comparative differences in labor-substitution patterns based on the systematic review of literature; no empirical counts or sizes in abstract.
The comparative evaluation shows differences in scale of impact across ML, DL, and Generative AI.
Abstract reports a comparative evaluation highlighting scale differences across AI phases; no quantitative scale measures given in abstract.
Generative AI brings innovative disruption with profound effects on the structure of employment, knowledge-based ecosystems, and high-skill industries.
Synthesis claim in abstract based on reviewed peer‑reviewed literature; no specific studies, sample sizes, or quantitative effects reported in abstract.
Although the geometry (bipolar structure) is stable, its content is not: across a decade the polarity has inverted relative to Frey and Osborne (2013).
Comparison of macro-level placements between the paper's LLM-era OAI and the Frey-Osborne (2013) rankings; authors report inversion and supporting correlation statistics.
Tool-Mediated Physical (M2) and Planning & Design (M7) are separated by Cohen's d = 2.41 (H = 172.88, p = 6.21e-34).
Statistical comparison reported in the paper (Cohen's d, H-statistic, p-value) between the two macro clusters' OAI distributions.
Projecting the DWA-level Occupational Automation Index (OAI) onto a 7-macro semantic typology produces a bipolar structure (two poles separated by a low-contrast middle band).
Authors' projection of previously computed DWA-level OAI onto a 7-cluster semantic typology and subsequent analysis of cluster structure.
Coding agents already know how to navigate files, edit code, run commands, and repair outputs, but lack the simulator's executable contract (vocabulary, structural constraints, validation rules, termination conditions).
Framing/assumption presented in the paper motivating the approach (not an empirical claim).
While net-zero targets for 2050 may be achieved, critical emission risks may appear in intermediate years and the EU may compromise its carbon‑neutral goals unless policies adapt to the accelerating digital transformation.
Scenario trajectories from the optimisation model indicating that 2050 net-zero remains attainable in some scenarios but with interim emissions overshoots; policy conclusions drawn by the authors.
After 2030, the geography of AI infrastructure will be shaped more by firm power and system flexibility than by the mere abundance of clean energy.
Modelled spatial deployment outcomes across the 21 AI growth scenarios indicating determinant factors for infrastructure siting after 2030.
There is a significant U-shaped relationship between AI application and employees' job insecurity: moderate AI application reduces insecurity, whereas excessive application heightens it.
Empirical analysis of cross-sectional self-reported questionnaire data collected from employees (411 valid responses) using regression-type analyses reported as showing a significant U-shaped relationship between AI application intensity and job insecurity.
The economic consequences of generative AI in financial markets depend critically on institutional context (regulatory and governance capacity).
Synthesis of heterogeneous treatment effects and interaction results across markets with varying governance/regulatory quality in the cross-market panel analysis.
Both risk perception and guilt play a role in GenAI adoption (they are relevant predictors of employees' intention to continue using the technology).
Empirical finding reported from the vignette experiment linking risk perception and guilt to GenAI adoption intention (paper states 'highlight the role of both risk perception and guilt in GenAI adoption').
The effect of embeddedness (GenAI being integrated into internal software environments) on employees depends on the presence of organizational authorization.
Reported empirical result from the vignette experiment indicating an interaction effect between embeddedness and organizational authorization (text states 'the effect of embeddedness depends on the presence of organizational authorization').
This research employed a vignette experiment to investigate how the embeddedness of GenAI and organizational authorization impact employees' negative emotion (specifically guilt) and risk perception.
Stated method in paper: a vignette experiment was used to test effects on guilt and risk perception. (No sample size reported in the provided text.)
The prominence of machine learning, Internet of Things (IoT), and cybersecurity varies depending on organisational context and role requirements within the wind sector.
Paper reports variation across data sources and organisational contexts based on interviews, surveys, and job-posting patterns; no subgroup sample sizes or statistical tests reported in summary.
AI is best understood as a real technological revolution with localized bubble dynamics rather than as either a pure speculative mania or a bubble-free productivity miracle (central conclusion).
Synthesis of the paper's review and diagnostic findings combining asset-pricing theory, empirical evidence on fundamentals, and bubble-detection diagnostics.
Current evidence shows both genuine fundamentals and bubble-like fragilities in AI valuations.
Synthesis of reviewed empirical findings in the paper: realized revenue growth, enterprise adoption, productivity evidence (supporting fundamentals) and faster capex vs monetization, concentrated private-market valuations, and narrative-driven investor behavior (supporting fragilities).
The same practice input carries opposite signs depending on whether the environment screens for it.
Synthesis of empirical patterns: in unscreened CF environment AI-style practice predicts smaller rating gains (for non-affiliated users) while in screened ICPC environment it predicts higher non-AI-aided scores.
In open Codeforces contests a stronger AI-style signature predicts smaller rating gains for users with no ICPC/IOI affiliation, but not for those who qualified for the AI-prohibited contests.
Comparative empirical analysis of CF contest rating gains by users' affiliation (ICPC/IOI qualification status) and individual AI-style signature strength; methods likely regression/heterogeneity analysis—sample sizes not reported in abstract.
National AI development can be interpreted as a controlled balance between information injection and entropy dissipation.
Theoretical mapping using HCLM; paper presents this dynamical framing and definitions of the two processes; no empirical sample.
Advanced economies have integrated AI technologies at scale, while emerging economies such as Algeria face structural and institutional challenges that limit the potential impact of AI on productivity growth.
Asserted in the paper with supporting literature citations (Agrawal et al., 2019; Acemoglu & Restrepo, 2020) and comparative use of World Bank and Oxford Insights indices; no specific sample-size based causal estimate provided.
These examples show an important shift in the governance of wealth chains – the creation of new forms of infrastructural power through which algorithmic models may become central nodes in tax governance.
Synthesis/interpretive conclusion in the abstract that the illustrative examples imply a governance shift and new infrastructural power; presented as interpretive argument rather than empirically demonstrated in the abstract.
This signals a transformation of the assumed information asymmetries between suppliers, clients, and regulators that sits at the heart of the Global Wealth Chains framework.
Conceptual claim in the abstract linking technological change to shifts in information asymmetries within the Global Wealth Chains framework; presented as interpretive argument rather than supported by reported empirical data in the abstract.
A key development is a move away from deliberate opacity for secrecy purposes into systems that search for the optimal exploitation of legal affordances.
Analytic/interpretive claim made in the abstract about a shift in practices; presented as an argument based on the authors' reflection and examples rather than empirical measurement in the abstract.
There is significant cross-national, cross-industry, and cross-regional heterogeneity in AI's impact.
Conclusion from the systematic literature review indicating variation across countries, industries and regions in the effects reported by prior studies.
Research has shown that artificial intelligence is primarily driven by substitution effects in the short term, but will generate complementary and creative effects in the long term.
Synthesis claim from the literature review; the paper reports this as an aggregate finding from prior studies (no single-study sample size provided).
The paper analyzes the direct impact of artificial intelligence on employment structure, occupational tasks, and skill demand, as well as its indirect effects on job mobility, cross-border and industry differences, and policy interventions.
Descriptive claim of scope drawn from the systematic literature review conducted by the authors; no single empirical sample reported.
The rapid development of artificial intelligence is profoundly reshaping the global labor market landscape.
Statement in paper based on a systematic literature review synthesizing prior studies; no single empirical sample reported.
Analysis of recent benchmark evidence including SWE-bench Verified, EvoClaw, and LangChain's multi-agent coordination studies demonstrates both the transformative potential of the agentic paradigm and its current limitations.
Empirical/benchmark analysis referencing SWE-bench Verified, EvoClaw, and LangChain multi-agent studies as sources of evidence; the paper analyzes these benchmarks qualitatively or comparatively (specific sample sizes and quantitative effect sizes not stated in the abstract).
By redefining discoverability metrics and authority signals, LLM-integrated search ecosystems are reshaping digital marketing economics.
Argumentative claim in the paper linking shifts in discoverability and authority to broader digital marketing economic effects; presented as conceptual synthesis without quantitative evidence in the excerpt.
Visibility in LLM-integrated search is shifting from click-through optimization to 'Answer Inclusion Optimization' (AIO), where visibility depends on whether content is selected, synthesized, and cited within AI-generated responses rather than on SERP ranking alone.
Conceptual proposition and terminology introduced by the authors (AIO); presented as a reframing of visibility metrics rather than backed by quantified experiments in the excerpt.
The rapid integration of large language models (LLMs) into search engines and conversational AI platforms is fundamentally transforming the landscape of search engine optimization (SEO).
Statement in paper's introduction asserting observed integration of LLMs into search engines and conversational platforms; based on conceptual analysis and literature synthesis (no empirical sample or quantified measurement provided).
Aggregate AI metrics (the composite AI Vibrancy Score) obscure heterogeneous pillar-level effects on tourism’s economic contribution.
Comparison of null result for the aggregate AI Vibrancy Score with significant positive effects for specific pillars (R&D, Policy and Governance, lagged Talent) in the same fixed-effects analyses on 33 countries (2017–2023).
Grounding the concept of defensive AI governance in organisation-level evidence from the Global South contributes to debates on platform power, journalistic agency, and AI governance in journalism.
Theoretical/interpretive claim based on the study's case of Al-Masry Al-Youm and its empirical insights; presented as a contribution to scholarly debates. Sample size not reported in the excerpt.
The authors introduce the concept of 'defensive AI governance' to describe how AI adoption is managed through organisational practices of limitation, supervision, and infrastructural self-protection.
Conceptual contribution grounded in organisation-level qualitative evidence from interviews and analysis of Al-Masry Al-Youm's practices; the concept is derived from the study's empirical findings. Sample size not reported in the excerpt.
The newsroom adopts, adapts, and governs AI across data journalism, fact-checking, and generative applications.
Empirical observations and interview data from Al-Masry Al-Youm detailing specific domains of AI integration (data journalism, fact-checking, generative tools). Sample size not reported in the excerpt.
AI functions as a conditional capability amplifier, expanding agency while producing uneven inclusion shaped by disparities in connectivity, skills, and infrastructure.
Analytical synthesis and illustrative empirical evidence from interviews showing differential effects tied to connectivity, skills, and infrastructure.
An explicit thinking mode raises rank-order correlation without moving accuracy.
Empirical comparison of reasoning modes showing increased rank-order correlation (e.g., Spearman/Fisher-z) when explicit 'thinking' mode is used, with no significant change in accuracy.
Most published twins are either coarse persona bots conditioned on a few demographic questions or detailed individual-level twins built on purpose-collected surveys and interview transcripts.
Author's literature summary / positioning statement in paper (qualitative assessment of existing published twins).
AI-mediated financial decisions are reflexive: they reshape organizational workflows, prices, liquidity, credit allocation, and the future data on which subsequent decisions rely.
Conceptual argument supported by literature across finance and related fields (review-level synthesis; no single empirical sample size reported).
Human–AI complementarity in finance is conditional rather than automatic, depending on task structure, private information, feedback quality, incentives, explanation design, and governance.
Synthesis of literature from finance, management, HCI, and AI showing moderating factors for complementarity (conceptual integration; no unified empirical sample size reported).
Overall, the digital economy brings both opportunities (raising incomes overall) and challenges (contributing to greater inequality).
Synthesis of empirical findings from the two-way fixed effects panel (31 provinces, 2011–2021) and robustness checks indicating positive average income effects alongside heterogeneous effects that widen disparities.
Non-state-owned enterprises (non-SOEs) benefit more from the digital economy than state-owned enterprises (SOEs), attributed to their greater flexibility and adaptability.
Ownership-type heterogeneity tests in the paper's two-way fixed effects panel (31 provinces, 2011–2021) showing larger estimated income/benefit effects for non-SOEs than for SOEs; interpretation links this to adaptability.