Evidence (401 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 research contributes by connecting AI adoption to inclusive economic modernization and proposing a governance-based framework for managing its risks in low- and middle-income contexts.
Originality / Value section claims conceptual contribution and a proposed governance framework; based on the paper's synthesis of comparative data and theoretical discussion (not an empirically validated framework in this study).
There are two distinct regional catch-up trajectories: Digital Leapfrogging in the Baltic States and Industrial Deepening in the Visegrad Group.
Systematic empirical documentation across the Visegrad Group and Baltic States (2022–2025) using the paper's assessment approach; patterns labeled and interpreted by the author.
The LCCP effect on AI industry development varies across local contexts, with stronger effects observed in established innovation hubs and in some follower regions undergoing industrial transition.
Heterogeneity analyses in the staggered DID framework on the 285-city panel (2007–2022) that split the sample by city type/region (innovation hubs vs. followers/industrial-transition regions) and report differential policy coefficients.
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.
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).
The pattern of timing and magnitudes for publication volume and VC investment is theoretically consistent with a multi-stage technology diffusion process, implying two complementary pathways: a research output channel and a commercial adoption channel.
Interpretation based on differential lags and elasticities (2‑year lag for publications vs 1‑year for VC) and theoretical framing in discussion.
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.
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.
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 results of fsQCA demonstrate how the combination and roles of strategic resources (e.g. AI capabilities and decision-making agility) shift in response to varying organizational and environmental conditions.
fsQCA configurational analysis reported in paper showing multiple causal pathways and differing configurations of AI capabilities, decision-making agility, and contextual conditions associated with performance; based on the same survey of 251 firms.
Through case studies and architectural illustrations, the paper highlights both the innovation potential and governance challenges posed by agentic systems.
Case studies and architectural illustrations cited in the abstract as the basis for highlighting benefits and challenges. No numeric evaluation provided in the abstract.
Joint estimation confirms simultaneous adjustments across financing and innovation margins.
Joint estimation (likely a system or simultaneous-equations approach) showing concurrent changes in financing costs and innovation-related variables following the shock (method stated; no sample size or exact estimates in abstract).
Digital transformation reconfigures development patterns across regions and countries, altering established trajectories of regional development.
Theoretical integration of a technology–labor–space framework together with comparative regional field evidence illustrating changing development patterns (no quantified effect sizes or sample sizes reported).
The turning point of the inverted-U relationship occurs at 2.948 (AI measure).
Estimated quadratic model that yields a calculated turning point value of 2.948.
There is an inverted-U-shaped relationship between firm-level AI adoption and firm innovation.
Estimated fixed-effects models and U-tests on the 25,204 firm-year sample showing a non-linear (quadratic) AI–innovation coefficient pattern.
Across evolved designs, components often correspond to known techniques; the novelty lies in how they are coordinated.
Authors' qualitative analysis of evolved architectures and components reported in the paper (design inspection and interpretation of evolved solutions).
AI influences innovation performance in organizations.
Discussion and synthesis of studies and reports on AI adoption and innovation performance presented in the review.
Artificial intelligence raises the threshold at which refinement adds value.
Theoretical/analytical statement in the paper describing AI's effect on the marginal value of refinement; no empirical quantification provided in the excerpt.
Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa.
Citation analysis of cross-border patent citations between Chinese and U.S. AI patents (paper reports asymmetry in reliance based on citation patterns).
Practitioners construct alternative data using technical and legal workarounds.
Field observations and interviews showing practitioners employing technical methods and legal strategies to create or repurpose alternative data sources for credit scoring.
The inverted U-shaped pattern between AI knowledge stickiness and technological concentration is more clearly detected in eastern cities and in small and medium-sized cities; in large cities the quadratic term is not statistically significant.
Heterogeneity/subsample regressions by region (east vs. other) and city size categories within the city-year panel (2014–2023); statistical significance of quadratic term differs across subsamples.
Technological complexity moderates the nonlinear (inverted U) association between AI knowledge stickiness and technological concentration by altering its strength and curvature rather than producing a simple, uniform shift in the turning point.
Interaction/heterogeneity analyses in the two-way fixed-effects city-year panel (2014–2023), examining moderating role of a technological complexity measure on the quadratic association.
There is an inverted U-shaped association between AI knowledge stickiness and technological concentration: higher stickiness up to a limit leads to more concentration and thereafter the opposite.
City-year panel combining AI patent applications with urban statistics for 2014–2023; two-way fixed-effects regression showing a significant positive linear and negative quadratic term (nonlinear association).
Each country's legal framework could influence the ultimate trajectory of the AI race.
Framed in the chapter as a concluding implication of the comparative analysis; presented as a reasoned projection rather than an empirically validated prediction in the provided text.
Data privacy, intellectual property (IP rights), and export restrictions are three critical aspects of the American and Chinese legal infrastructure that significantly impact AI innovation.
Author(s) state this as the organizing premise of the chapter; comparative legal analysis and normative argumentation rather than empirical measurement.
Evolutionary dynamics in the model reflect not just current fitness but factors related to the long-run growth potential of descendant lineages.
Mathematical analysis of the proposed model showing lineage growth potential influences dynamics (theoretical derivations/proofs within the paper).
The results generalize to other technologies that feature safety externalities and first-mover advantages.
Authors' argument and model generalization: the mechanisms identified (preemption, externality, policy responses) are argued to apply beyond frontier AI to other technologies with similar strategic features.
AI is reshaping entrepreneurship by enhancing innovation, streamlining operations, and creating new business opportunities, but its impact varies across levels of financial development and economic contexts.
Introductory/motivating statement in the abstract; supported by the cross-country panel analysis (23 countries, 2002–2023) reported in the paper.
The effects of financial digital intelligence on the innovative development of strategic emerging industries vary across regions and sectors: there are differences across central, eastern, and western regions and across capital‑intensive and technology‑intensive sectors, while no significant impact is noted in other regions and industries.
Heterogeneity analysis reported on the panel dataset (5,731 observations, 2015–2022) examining regional and industry subsamples (details of subgroup sizes and statistical tests not provided in excerpt).
The study identifies the main AI-enabled mechanisms advancing CE principles in smart manufacturing, waste valorisation, supply-chain transparency, and sustainable design.
Bibliometric network analysis of 196 peer-reviewed articles (2023–2024) and systematic review of 104 studies, per the abstract; identification is presented as a product of these analyses.
Long-run integration (degree of long-run association) between core AI and AI-enhanced robotics differs systematically across national innovation systems.
Country-level decomposition of patent filing series and time-series econometric tests for long-run relationships / cointegration between core AI and AI-enhanced robotics patent series for each country/region (China, U.S., Europe, Japan, South Korea).
Core AI, traditional robotics, and AI-enhanced robotics follow distinct historical trajectories over 1980–2019 and do not move together uniformly.
Time-series analysis using annual patent filing counts (1980–2019) for each domain; tests for common long-run relationships / co-movement across the three patent series (as reported in the paper). Country-aggregated and domain-specific patent time series were analyzed; exact sample size (total patents) not specified in the summary.
Reframing AI as a manifestation of accumulation crisis and hegemonic instability challenges accounts that treat it as an autonomous driver of capitalist renewal.
Theoretical critique and reframing based on Marxian crisis theory and related literatures; no empirical sampling or quantified tests described in the excerpt.
The recent surge in artificial intelligence (AI) investment functions less as the basis of a new productive regime than as a crisis response within financialised capitalism.
Analytical argumentation and interpretation of contemporary investment patterns; no empirical sample size or formal causal identification reported in the provided text.
AI has a significant negative influence on value chain upgrading in labor-intensive equipment manufacturing industries.
Industry-type heterogeneity analysis within the same 30-province panel (2010–2022) showing a statistically significant negative coefficient for labor-intensive subsectors.
Early detection of disruptive technologies is difficult because disruptive impact is uncertain and often becomes visible only years after invention.
Conceptual background statement in the paper; literature-motivated assertion (no empirical sample or experiment reported for this claim).
The African continent does not build frontier models.
Paper asserts lack of frontier-model development capacity in Africa and compares current figures (descriptive / capacity assessment).
The design process exhibits high variance.
Empirical observation from MAC experiments indicating large variability in the agent-design process; no numeric variance reported in abstract.
Overusing export controls can complicate dispute resolution and hinder AI progress.
Normative and legal-political argument in the paper: overuse raises legal disputes (e.g., WTO litigation) and may slow cross-border AI development and diffusion (qualitative reasoning).
Ekonomik büyüme ile yapay zekâ patent sayıları arasında negatif bir ilişki bulunmaktadır.
Panel regresyon (random effects) sonuçları (G8 + Türkiye, 2010-2020) raporlanmıştır; ekonomik büyüme (muhtemelen GSMH büyüme oranı) değişkeninin AI patent sayıları ile negatif ilişki gösterdiği bildirilmiştir.
Kamunun Ar-Ge harcamaları ile yapay zekâ patent sayıları arasında negatif bir ilişki bulunmaktadır.
Rassal etkiler panel regresyonu üzerine raporlanan sonuçlar (G8 + Türkiye, 2010-2020); kamu Ar-Ge harcamaları değişkeninin AI patent sayısı ile negatif ilişki gösterdiği bildirilmiştir.
Industry concentration negatively moderates the AI–innovation relationship.
Moderation analysis/interacted fixed-effects models indicating that higher industry concentration weakens the AI→innovation effect.
AI adoption deepens the negative indirect effect of CEO–TMT faultlines on green innovation via reduced eco-attention (moderated mediation).
Reported moderated mediation analysis on the panel dataset (35,347 firm-year observations) showing that AI moderates the indirect path from CEO–TMT faultlines to green innovation through eco-attention, making the indirect effect more negative when AI is greater.
CEO–TMT faultlines negatively affect green innovation through reduced eco-attention.
Empirical mediation analysis on the panel dataset (35,347 firm-year observations, 2010–2023) testing CEO–TMT faultlines -> eco-attention -> green innovation.
Mechanism tests indicate innovation stagnation in mature firms with redundant AI is a pathway that limits productivity gains (i.e., AI can be associated with stagnant innovation in mature firms).
Mechanism analysis reported in the paper showing signs of reduced innovation-related gains or stagnation in mature, advanced firms using AI (interpreted as redundant AI leading to limited incremental innovation).
Targeted disruption simulations based on intrinsic technological capability cause a more pronounced decline in the knowledge network than targeted attacks based on topological (structural) baselines.
Simulation experiments on collaboration/knowledge networks constructed from the 282,778-patent dataset comparing network decline under removal strategies: (a) based on intrinsic technological capability vs (b) based on topological centrality baselines.