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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AI is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms.
Qualitative study reported in the paper combining 20 expert interviews and a participatory workshop with 24 participants; findings derive from thematic analysis of participant accounts and workshop outputs.
The study distinguishes foundational theoretical perspectives from the contemporary 2015–2025 evidence base and clarifies the relationship between task transformation and structural transformation, emphasizing institutional complementarity as the key mechanism shaping AI-driven growth outcomes.
Analytic separation of theoretical literature and empirical studies in the structured review (2015–2025); thematic mapping linking task-level changes to broader structural transformation contingent on institutional complementarities.
Rather than proposing a deterministic growth model, the study advances a conditional and ecosystem-centered interpretation of AI-led development.
Authors' interpretive conclusion based on their structured review and the integrative innovation-ecosystem framework synthesizing mechanisms and contextual dependencies in the 2015–2025 literature.
Interpreting task-based automation models alongside endogenous-growth and open-innovation frameworks clarifies why similar AI investments may lead to divergent structural outcomes.
Theoretical synthesis combining task-based automation literature with endogenous-growth and open-innovation models, illustrated by examples from the reviewed empirical literature (2015–2025).
The paper develops an integrative innovation-ecosystem framework linking three core transmission channels: (i) total factor productivity (TFP), (ii) task reallocation and labor-market restructuring, and (iii) innovation and knowledge-generation dynamics.
Conceptual framework constructed by the authors via integrative review of theoretical and empirical literature from 2015–2025; framework synthesizes mechanisms reported across studies.
Empirical evidence remains heterogeneous, and estimates of AI’s macroeconomic contribution vary across institutional and structural contexts.
Synthesis of heterogeneous empirical studies from the 2015–2025 literature identified in the structured review; comparative thematic classification highlighting variation by institutional/structural context.
AI adoption does not generate uniform or automatic growth effects.
Structured literature review / mechanism-oriented synthesis covering studies from 2015–2025; transparent search, screening and thematic classification (no formal meta-analysis).
AI has a significant positive impact on value chain upgrading in the eastern and western regions of China, while its effect in the central region is insignificant.
Region-specific panel regressions / heterogeneity analysis using the 30-province 2010–2022 panel split by region; reported significance levels for eastern, western, and central subsamples.
The effects of talent introduction on AI development are heterogeneous: they vary by firm characteristics such as pollution status, regional location, and industry affiliation, and are particularly pronounced in the manufacturing sector.
Subgroup / heterogeneity analyses using the panel data showing differential effects across pollution status, regions, and industries (notably manufacturing).
There is a similar shift to agentic tooling outside OpenAI, particularly within organizations, although external adoption remains lower and more uneven.
Comparative usage analysis across three populations (external personal-account users, external organizational-account users, and OpenAI workers) from Codex logs.
Cluster analysis reveals diverse yet cohesive national profiles across the EU that reflect differences in digital readiness, human capital, and institutional factors.
Cluster analysis performed on country-level indicators (AI adoption, digital readiness, human capital measures, institutional factors) to group EU countries into profiles; summary reports heterogeneous but cohesive clusters; exact cluster counts and sample size not reported.
Displacement (asymmetric substitution between brand pairs) was industry-dependent, ranging from co-recommendation in consulting (0.4:1) to one-directional substitution up to 4.3:1, with an unweighted mean of 2.4:1 across the five industries.
Computation of the Displacement Score across brand pairs within each of the five sampled industries; manuscript reports per-industry ratios and the unweighted mean.
Cross-model agreement on the top-recommended brand was 41.6%; a top position on one model did not reliably hold on another.
Empirical comparison of top-recommended brands across the three models for the sampled queries, yielding a 41.6% cross-model agreement rate.
Better measurement matters, but improved measurement alone will not close the coordination gap between researchers and policymakers.
Authors' analytical conclusion arguing that measurement improvements are necessary but insufficient.
Other strategic factors (differentiation of work, digital reputation, adaptability) continue to influence illustrators' financial sustainability despite AI's effect.
Author conclusion/interpretation in the discussion, inferred from the relatively low R² and domain knowledge; these moderators/alternative determinants are asserted rather than estimated in the reported regression.
AI explains a relatively small share of income variation among illustrators (model R² = 7.4%), so its contribution to income variation is limited.
Reported model fit statistic from the above simple linear regression (R² = 7.4%) on the sample of 385 illustrators.
The relevance of Chinese experience for Russia can be assessed in contexts such as eGrocery, O2O services, ecosystem delivery and remote/northern regions, and Russian material serves as an applied block for that assessment.
Methodological claim based on the study's comparative framework combining Chinese case analysis with applied Russian regional material (Sakha Republic).
This article adopts a contextual approach to technology, considering it in conjunction with the social context in which it is situated.
Methodological statement made by the author about the approach taken in the paper (contextual rather than purely technical); not an empirical claim.
While localized speculation and valuation excesses may exist in AI markets, the underlying economic foundations of the AI cycle differ substantially from those that characterized the collapse of the internet bubble.
Comparative evaluation using financial market data, historical analyses of the dot-com collapse, and contemporary literature cited in the paper (qualitative comparative review).
AI brand visibility can be measured, differs by platform, and varies strongly by brand maturity.
Synthesis claim supported by cross-platform/brand analyses reported in the paper (Ranqo dataset across multiple AI engines and >100 brands, March–May 2026); empirical results (tiered visibility, citation patterns) underpin the assertion.
The guarded engagement loop framework conceptualizes generative AI adoption as a feedback process in which risk perceptions may shape interaction conditions that, in turn, can influence observed performance and subsequent trust calibration.
Central conceptual claim of the paper; framework articulated by the authors and presented as a set of testable propositions (theoretical contribution rather than empirical finding in the abstract).
Risk salience may shape interaction dynamics with LLMs via a multilevel feedback mechanism called the 'guarded engagement loop', in which risk perceptions shape interaction strategies that influence observed performance and, in turn, recalibrate trust in generative AI systems.
Conceptual framework proposed by the authors, integrating theories from trust in automation, privacy calculus, algorithm aversion, and social amplification of risk; presented as a theoretical model rather than an empirical test.
The ICH framework predicts three distinct augmentation regimes (determined by combinations of A and C) with distinct policy implications.
Theoretical classification derived from the model; conceptual prediction presented in the paper.
AI-induced changes are displacing existing labor jobs while also creating new jobs that require high technological skills.
Summary claim from the SLR reporting that reviewed empirical studies report both displacement of existing jobs and creation of new, high-skill jobs; no quantified displacement/creation rates provided in the excerpt.
Between 2017 and 2025, studies identified current trends of AI-induced changes affecting both blue-collar and white-collar occupations.
Synthesis statement in the paper reporting that reviewed empirical studies identified trends across blue- and white-collar jobs (timeframe 2017–2025). Specific studies or counts not provided in the excerpt.
AI's rapid evolution has profound effects on the labor market, influencing the levels, skills needed for jobs, and overall jobs content.
Statement from the paper's synthesis/introduction summarizing reviewed empirical studies (systematic literature review covering studies from 2017–2025). Number of underlying studies not reported in the excerpt.
While AI has the potential to improve operational efficiency and strengthen adaptive capacity, inadequate readiness can increase systemic risks arising from algorithmic opacity, cybersecurity challenges, data dependence, coordination failures, and disruptions that may spread across interconnected administrative systems.
Conclusion drawn from the integrative conceptual framework and the systematic review of 68 empirical studies documenting both benefits and risks in different contexts.
Evidence on the productivity, risk, and resilience implications of AI adoption remains fragmented and dispersed across different fields of research.
Author's assessment of the literature based on the systematic review (PRISMA) of 68 empirical studies published 2015–2025.
Organisational performance becomes more dependent on the reliability of algorithms, the quality of data, effective governance, and coordination among public institutions.
Conceptual argument supported by synthesis of empirical studies in the systematic review (68 peer-reviewed empirical studies).
Artificial intelligence (AI) is becoming increasingly embedded in the digital infrastructure of local government, creating new opportunities to improve public sector productivity while also influencing systemic risk and organisational resilience across interconnected public systems.
Statement based on literature synthesis in the paper; theoretical framing and review of empirical studies (systematic review).
The paper develops the concept of 'bidirectional dynamics' in digital sovereignties, applying a paradoxical view to interpret institutional control objectives and individual autonomy aspirations as persistent organizational tensions in AI adoption.
Theoretical/conceptual development grounded in the empirical single-case study; concept introduced and motivated by observed tensions in the organization (empirical method details and sample size not provided).
Early digital transformation presents tensions but also synergies between digital sovereignty levels in AI adoption.
Empirical observations from the single-case study of a Nordic public transportation organization during early AI adoption; qualitative examples and analysis (specific methods/sample size not stated).
The relationship between AI use levels and corporate carbon emission intensity exhibits a significant inverted U-shaped curve: at early stages AI adoption may increase emissions, but beyond a critical point further AI use significantly reduces emissions.
Empirical two-way fixed effects (TWFE) analysis on provincial panel data from China, with robustness checks; the paper reports a statistically significant inverted U-shaped relationship.
The endurance budget is dormant on premium 3,000-P/E TLC at datasheet prices and binding on the commodity QLC/eMMC (~1,000 P/E) that cheaper edge robots run.
Comparative statement based on device endurance specifications cited in the paper (3,000 P/E for TLC vs ~1,000 P/E for QLC/eMMC) and cost/pricing considerations; presented as boundary conditions for when the endurance budget matters. No empirical sample size reported.
Measured on real robot logs, the sign of the value-write association χ is a property of the deployment regime: positive on recurrent long-horizon manipulation (ĥχ ≈ +1.0 × 10^{-3}, replicated at full power), null on a shorter-horizon suite, and negative on non-recurrent teleoperation.
Empirical measurement on real robot logs at a pre-specified gate; reports an estimated value ĥχ ≈ +1.0 × 10^{-3} for recurrent long-horizon manipulation and qualitatively reports null and negative signs for other regimes. The paper states the +1.0e-3 estimate was replicated at full power. Exact sample size not reported in the excerpt.
The index is cost-optimal whatever the sign of the value-write association χ; only when χ > 0 does the optimum turn non-monotone, sending a robot's most valuable memories off its flash.
Theoretical result from the paper's model/analysis. The claim states a general optimality property (index cost-optimal for all χ) and a conditional structural result (non-monotone placement when χ>0). No empirical sample size reported.
Generative engine optimization (GEO) should be studied not only as a security risk, but also as an emerging marketing practice that shapes market competition.
Paper's concluding/interpretive statement based on the experimental findings about LLM recommendation dynamics and GEO effects on brand recommendations.
The near-term value of Agentic AI does not lie in full autonomy or workforce reduction, but in controlled partial autonomy for simple and medium complexity business processes.
Central argumentative claim/recommendation in the paper (theoretical justification; no empirical study or sample size reported).
This shift raises fundamental questions for consumer theory, which has traditionally modeled humans as the primary decision-makers.
Conceptual argument presented in the paper framing the research problem and motivating the new theoretical framework; literature critique rather than empirical test.
The modality gap (weaker penalty for visual vs. textual AI-use disclosure) widens when AI is used in final products but narrows when AI is used in marketing materials.
Interaction analyses across application stages (final product vs. marketing material) within the 41,073 Kickstarter projects, using LLM-assisted classification to label both modality and application stage and entropy balancing for covariate control.
Tranquil periods lower subjective risk assessments, raise AI substitution intensity, and compound leverage, generating a cognitive Minsky moment in which subjective risk falls while true systemic fragility rises.
Derived dynamics and comparative statics in the formal model; stated as one of the paper's propositions. No empirical data.
Dominant comments shifted in tone from mockery toward gatekeeping and structural protest.
Speech-act coding of 300 confirmed accusations and sentiment/trajectory analysis showing relative decline in mockery-coded acts and increase in gatekeeping/structural-protest acts over time.
Technological containment policies may unintentionally accelerate open innovation ecosystems as a competitive response, with implications for global leadership in both academic and commercial artificial intelligence.
Synthesis and inferential claim in the paper drawing on the temporal association of containment measures, policy shifts, developer behavior, diffusion patterns, and patent/research evidence described earlier in the paper.
Forms of resistance exist, including localisation efforts and Indigenous ethical frameworks, but they remain structurally limited.
Synthesis of examples and themes across the 50 reviewed articles noting reported resistance strategies and their limits.
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).
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.
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).
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).