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 |
The transition is in trivia count, not rate; the gap 1-α is the unrecorded mass.
Analytic argument/proof in the model showing that whether trivia allowance is finite or infinite (count) determines the phase transition in achievable coverage, and identifying 1-α as the portion of valuable mass not recorded by the literature core.
Sharp dichotomy on the tight family: generators emitting finitely many trivia achieve optimal coverage α/2, while any infinite trivia allowance, even at vanishing rate, jumps the optimum to 1-α/2 (both tight, for cores presented as the candidate intersection), and one generator attains both ends.
Mathematical theorem(s) in the paper establishing tight upper/lower bounds on coverage for the 'tight family' under two regimes (finite trivia vs infinite trivia), expressed as functions of the core density parameter α.
Board composition, particularly the presence of female and minority directors, impacts AI adoption.
Statement in abstract reporting an analysis linking board composition variables (female and minority directors) to AI adoption outcomes in the dataset.
With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.
Extended model incorporating endogenous capital accumulation: analytical solution/characterization showing unbounded (explosive) growth in aggregate variables while real wages remain stagnant in the long run (model derivation).
Along the transition path of automation, data simultaneously augments the productivity of already-automated tasks and expands the automation frontier (dual role).
Analytical results from the dynamic model showing two mechanisms: (i) data increases productivity of tasks already automated; and (ii) data enables automation of additional tasks (model derivations).
Defining query difficulty is one of the hardest problems in deployment engineering.
Statement/assertion in the paper (introductory claim); no specific empirical measurement in the abstract.
There were no significant differences in AI use based on most accountant characteristics, except in auditing where business owners reported a higher frequency of AI use.
Inferential statistical analysis of questionnaire data (comparative design); specific statistical tests and sample size not reported in the summary.
This study identifies critical gaps in current Nvidia-centric roadmaps and proposes a competing reference architecture.
Paper's comparative analysis of existing (described as Nvidia-centric) roadmaps and presentation of an alternative reference architecture; no empirical validation or case-study evaluation reported.
Current models achieve penetration success rates ranging from 10.7% to 69.3%.
Empirical results reported from evaluation of the 19 LLMs across the designed target servers (success-rate measurements).
Frontier proprietary models achieve near-zero success under GUI-based interaction, whereas COM-based execution yields substantial immediate gains.
Experimental comparison reported in the paper on ComCADBench between GUI-based interaction by proprietary models and COM-based execution (authors report success rates and comparative performance).
Environment engineering can amplify productive behaviors (e.g., open-ended exploration, systematic artifact management, inter-agent collaboration) while suppressing harmful behaviors (e.g., reward hacking and high-friction human oversight).
Framing and argument in the paper describing expected effects of environment design (conceptual; no quantification provided in the excerpt).
Trust is conceptualized as network-mediated expectation stabilization in the embodied finance framework.
Theoretical claim in the framework articulating trust as stabilized through network interactions among humans, machines, and platforms; no empirical data.
The proposed framework—the machine–platform–crowd triangle—reframes agency, trust, and value as emergent properties rather than institutional attributes.
Conceptual framing and argumentation within the paper; synthesis of theory to reconceptualize agency, trust, and value; no empirical testing reported.
Architectural smell density (ASD) declines by 6.7% (p = 0.004), but this decline is a denominator effect resulting from lines-of-code growth rather than an actual architectural improvement.
Observed ASD change computed from estimated smell counts and LOC changes in the 151-repository panel and interpreted by decomposing density into numerator (smells) and denominator (LOC).
The same observation is seen with the amount of changes (e.g., code churn, number of modified files) and with the efforts to merge an agentic PR (e.g., merge time and number of comments).
Reported that pre/post comparison across projects shows mixed/no consistent improvement patterns for code churn, modified files, merge time, and comment counts after instruction-file creation (analysis over 15,549 PRs in 148 projects).
Specifying instructions for AI-agents does not necessarily lead to better results.
Project-level before/after comparison of performance metrics for projects that created instruction files (pre/post comparison across 148 projects using the 15,549 PRs).
The image of a single transformative step change caused by the introduction of human-level AGI may be inaccurate; a more apt prospect is a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology.
Interpretive claim in the report arguing for a multi-step, multifaceted impact scenario rather than a single-step discontinuity; based on conceptual synthesis of possible pathways and impacts.
There exist frictions and bottlenecks along these AGI→ASI pathways, and whether their impacts are negligible or substantial is an open set of concrete research questions.
Report analysis identifying potential frictions and bottlenecks and posing open research questions; conceptual analysis without quantified empirical measures.
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).
A store-level policy learned from logged marketplace data selects a discrete multiplier that shifts the dispatch optimizer's tradeoff between delivery quality and batching efficiency.
Methodological description: store-level policy trained from logged data that outputs a discrete multiplier to alter optimizer objective weights; stated design and training approach in paper (no numerical evaluation details provided in the excerpt).
The article develops a conceptual framework linking GenAI use in higher education to knowledge transformation, critical thinking, ethical judgment, digital capability, managerial decision-making, business ethics, workforce readiness, and organizational readiness.
Presentation of a conceptual framework by the authors as part of the review (theoretical/conceptual work; no empirical validation reported).
GenAI should be understood as more than an educational technology: it affects the development of managerial decision-making, business ethics, and workforce readiness for future managers, entrepreneurs, administrators, policymakers, and business professionals.
Conceptual argument and literature synthesis presented in the review article (no primary empirical sample).
Generative AI (GenAI) is reshaping higher education by changing learning practices, academic writing, knowledge access, assessment preparation, research support, and student engagement.
Narrative literature review / synthesis (review article). No primary empirical sample reported — claim drawn from cited literature and conceptual synthesis.
AI agents can rival or exceed human methodological diversity at the design layer while remaining vulnerable at the verdict layer.
Synthesis of above experimental findings: Claude Code and Codex matched/exceeded human methodological diversity measures (20 runs) but exhibited vulnerability to prompt-induced changes in verdict behavior (especially Claude Code).
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.
For all the hype, today's scientific AI still represents a collaborator whose imagination, outputs and judgment benefit from human grounding.
Synthesis of study findings: limited diversity in non-reasoning models, field-specific failures, weak agreement of automated evaluators with experts, and modest gains from augmentations, all supporting the conclusion that human grounding improves AI outputs and judgment.
Reasoning models roam a wider hypothesis space, yet no model class spontaneously proposes null hypotheses — a move humans make more freely.
Model-output analysis comparing 'reasoning' vs 'non-reasoning' classes on hypothesis-space breadth and presence/absence of null hypotheses; human responses used as comparison.
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.
The paper characterises the Glassbox architecture and grounds it in a benefit eligibility scenario, identifying foundational challenges — semantic alignment, dynamic model construction, probabilistic grounding, and human governance — that must be solved to realise it at scale.
Descriptive summary of the paper's contributions and identified research/engineering challenges; based on the authors' conceptual analysis and scenario exposition.
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 research contrasts tool-shaping (AI behavior/prototype) and mind-shaping (user strategy training) pathways and reports differing effects between them.
Paper presents both a tool-shaping experiment (Study 1) and a mind-shaping experiment (Study 2) and discusses comparative findings across these pathways.
Cognitive flexibility is examined as a moderator (boundary condition) of the interventions' effects.
Paper reports including cognitive flexibility as an individual-differences moderator in analyses across the two studies (moderation analysis planned/reported).
Reasoning scaffolds (public tools, playbook, verifier, objectivity policy, red-team) improve calibration and audit discipline, but proprietary evidence sets the upper bound of what the AI Scientist can know and therefore decide.
Synthesis of experimental results showing B improved calibration/audit metrics while C (with proprietary data) markedly increased coverage and informed decision-quality.