Evidence (3492 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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Operationalizing hardware-based governance must address transition realities including legacy hardware, attestation at scale, and protection of civil liberties.
Policy implementation analysis in the paper identifying practical challenges to deploying hardware-layer controls (conceptual/operational analysis; no empirical trial data provided).
Multi-agent workflows and benchmark evaluation reveal current capabilities, limitations, and research frontiers in agentic AI for physical design.
The paper states it analyzes recent experience with multi-agent workflows and benchmark evaluation; the abstract does not provide specific benchmark names, metrics, or sample sizes.
Effective AI policy mixes are contingent on regional resource endowments and development conditions (i.e., variation across configurations indicates contingency on regional context).
Observed variation across the fsQCA-derived configurations; authors interpret differences as reflecting dependence on regional resources and development conditions.
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.
AI is becoming a geopolitical tool that defines trade, finance, supply chains, surveillance abilities, and diplomatic bargaining power.
Conceptual/qualitative synthesis in the paper's argument; no empirical methods or sample size reported in the abstract.
Targeted prompt interventions significantly alter the magnitude of market bubbles (they can amplify or suppress bubble size).
Randomized (or otherwise experimentally manipulated) prompt interventions applied to LLM agents in the simulated open-call auction, with resulting differences in measured bubble magnitude reported.
By analyzing agents' reasoning text through a twenty-mechanism scoring framework, targeted prompt interventions causally amplify or suppress specific behavioral mechanisms.
Qualitative and quantitative analysis of agents' chain-of-thought / reasoning text using a 20-mechanism scoring framework; experimental manipulations of prompts reported to change mechanism scores (interpreted causally as interventions on prompts).
Both US and Chinese strategies depend on cross-country relationships in AI innovation.
Conceptual assertion motivating the network analysis of international collaborations and citations.
Overall, the proposed HRL framework improves learning efficiency and scalability, outperforming heuristic baselines while remaining below the perfect-information oracle bound.
Results reported in the paper from simulation experiments comparing the HRL framework to heuristic baselines and the oracle; pairwise differences analyzed (Wilcoxon tests referenced). The paper asserts better performance than heuristics but still worse than the oracle.
The proposed safety-filter outperforms a standalone deep reinforcement learning-based controller in energy and cost metrics, with only a slight increase in comfort temperature violations.
Reported experimental comparison between the safety-filter-enhanced controller and a standalone DRL controller in the paper; specific metrics and sample size not provided in the excerpt.
Digitization is reshaping the structures of Resource Dependence Theory (RDT) instead of eliminating it completely (Yordanova & Hristozov, 2025).
Conceptual/theoretical claim supported by citation to Yordanova & Hristozov (2025); presented as an interpretive conclusion about how digitization interacts with organizational dependence structures. No empirical details 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).
The organization of AI innovation differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises.
Analysis of assignee types, geographic dispersion, and institutional composition of AI patents in the two countries (concentration metrics and assignee categorizations described in paper).
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).
Big data analytics (BDA) adoption is a risky strategy with potentially high rewards for start-ups.
Stated as a summary conclusion based on empirical analysis of a large sample of start-ups in Germany comparing adopters and non-adopters across multiple performance measures (survival, costs, sales, employee growth, access to financing).
The spatial spillover effects are geographically constrained and vary significantly across regions.
Reported heterogeneity in spatial Durbin model results and discussion of geographic constraint and inter-regional variation (regional heterogeneity analysis).
The positive effect of big data applications on firms' markups exhibits heterogeneity across organizational, technological, and environmental dimensions.
Paper reports heterogeneity analysis showing variation in the magnitude of the positive markup effect across organizational, technological and environmental factors; based on model implications and empirical subgroup/interaction tests using micro-level firm data (sample size not reported).
The rapid deployment of multi-agentic AI systems is reshaping the foundations of copyright law and creative markets.
Theoretical and conceptual argumentation presented in the paper; no empirical sample or quantitative analysis reported.
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.
Only a small subset of LLM retailers can consistently achieve capital appreciation, while many hover around the break-even point.
Empirical results from the 20-agent benchmark experiments reported in the paper, contrasting capital appreciation for winners vs break-even for many agents.
Benchmarking on 20 open- and closed-source LLM agents reveals significant performance disparities and a winner-take-most phenomenon.
Empirical evaluation described in the paper using 20 LLM agents (open- and closed-source); results reported show uneven performance distribution.
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).
There is a robust inverted U-shaped relationship between robotics manufacturing development and urban carbon emissions.
Panel data analysis using 277 Chinese prefecture-level cities from 2008 to 2019; econometric analysis reported in the paper finds an inverted U-shaped association and robustness checks are claimed.
As technological progress devalues labor, the welfare benefits of steering initially increase but, beyond a critical threshold, decline and optimal policy shifts toward greater redistribution.
Analytical result from the paper's theoretical model that compares planner's optimal technology choice under varying degrees of labor devaluation and redistribution costs.
Automation leads economic growth to accelerate, but the acceleration is remarkably slow because of the prominence of 'weak links' (an elasticity of substitution among tasks substantially less than one); even when most tasks are automated by rapidly-improving capital, output is constrained by the tasks performed by slowly-improving labor.
Theoretical mechanism from the task-based model (σ < 1 weak-links structure) combined with calibrated simulations that incorporate historical accounting results.
The effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations.
Analytical comparative-statics in the theoretical model: varying the fraction of R&D tasks performable by AI yields a non-monotonic relationship between AI task-share and optimal recombination distance, with a threshold determined by human-AI complementarity.
Higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals.
Comparative-static result from the analytical model: the paper derives a condition comparing the direct facilitation effect of AI on accessing distant knowledge and the indirect effect from increased competition; when the former dominates, equilibrium recombination distance increases with AI productivity.
The influence of human capital (number of specialists in scientific and technological fields) on value added varies across sectors.
Number of specialists in scientific and technological fields included as a covariate in MMQR; reported heterogeneous effects across sectors/quantiles in the results section.
The influence of R&D expenditure on value added varies across sectors.
R&D expenditure included as a core explanatory variable in panel MMQR estimations; authors report differing coefficient sizes/signs across sectors/quantiles.
Policy enforcement maintains a 52.8% success rate for legitimate requests.
Quantitative result reported from the paper's experiments (52.8% success rate for legitimate requests under policy enforcement).
The inequality-reducing impact of AI is weaker when carbon inequality is measured by the Theil index, implying persistent structural divides between advanced and less developed regions.
Same provincial panel dataset (2003–2021) with the Theil index as the dependent variable; results show a weaker (and impliedly less robust) association between AI development and Theil-measured carbon inequality.
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.
Pigouvian safety taxes partially correct the safety externality but cannot eliminate the preemption distortion on their own.
Model policy counterfactuals: introducing a tax on unsafe releases reduces the externality-driven distortion but leaves residual preemption incentives so the first-best is not fully attained by tax alone.
AI adoption is positively associated with exports to all destination regions examined except China (multivariate probit model that accounts for correlated errors across destination-specific export decisions).
Multivariate probit model of destination-specific export decisions (model accounts for correlation among error terms); result indicates significant associations for AI with exports to all regions except China (sample size not reported in prompt).
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.
Big Data-based FinTech can contribute to financial stability only when its implementation is strategically justified, ethically grounded and supported by effective regulation, robust data governance and investment in human capital.
Normative conclusion drawn from systemic and structural analysis of literature and synthesis of empirical studies; no empirical test provided within the paper.
The effectiveness of Big Data solutions varies across the financial sphere and depends critically on data quality, regulatory alignment and organisational readiness.
Derived from comparative analysis of sector-specific applications and synthesis of findings in the reviewed literature; no quantified cross-sector sample reported.
Network externalities create an opportunity for win-win industrial policies, but the realisation of such mutually beneficial outcomes depends on market structure (product differentiation/substitutability) and the nature of innovation (product vs process).
Synthesis of model results across parameter regimes in the two-country strategic trade and R&D model showing conditional win-win equilibria; theoretical arguments (no empirical sample).
The welfare consequences of an industrial policy targeting a sector with network externalities are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods.
Analytical results from a two-country theoretical model with strategic trade and R&D investment; comparative-static analysis of equilibrium outcomes (no empirical sample).
As technological progress devalues labor, the welfare benefits of steering are at first increased but, beyond a critical threshold, decline and optimal policy shifts toward greater redistribution.
Theoretical model extension analyzing planner's optimal choice as labor's economic value changes; the paper states a non-monotonic relationship with a critical threshold.
Once efficiency is made explicit, the main practical question becomes how many efficiency doublings are required to keep scaling productive despite diminishing returns.
Framing/forecasting claim in the paper presenting an operational research question (conceptual; no empirical sample in excerpt).
The practical burden of scaling depends on how efficiently real resources are converted into that (logical) compute.
Argument in the paper linking conceptual 'logical compute' to real-world conversion efficiency (qualitative claim; no empirical sample in excerpt).
The compute variable is best understood as logical compute, an implementation-agnostic notion of model-side work.
Conceptual argument presented in the paper reframing 'compute' as an abstract, implementation-agnostic quantity (no empirical sample provided).
These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies.
Interpretation linking observed changes in budget allocation, team size, and task breadth (from the proposal dataset and task-level analyses) to theoretical predictions about general-purpose technologies (GPTs); empirical findings show organizational change rather than large average short-run productivity gains.
This paper offers a forward-looking framework that emphasizes the decentralizing potential of AI on labor markets, moving beyond the traditional displacement-versus-creation dichotomy.
Paper's stated contribution; based on conceptual framework and synthesis of historical and contemporary analyses (no empirical validation presented in the abstract).
The emergence of artificial intelligence and robotics is catalyzing a profound transformation in the nature of human labor.
Stated as a central premise in the paper's abstract; supported by the paper's synthesis of economic history, contemporary labor market data, and analysis of digital platform growth (no specific datasets or sample sizes reported in the abstract).
AI agents are approaching an inflection point where the binding constraint shifts from raw capability to how work is delegated, verified, and rewarded at scale.
Conceptual argument presented in the paper's introduction/positioning; no empirical data, experiments, or sample reported.