Evidence (4114 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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The R&D deduction policy has stronger effects on firms with high capital intensity.
Heterogeneity analysis in the paper showing larger estimated policy effects for high capital intensity firms.
The R&D deduction policy has stronger effects on firms characterized by rapid technological obsolescence.
Heterogeneity analysis reported in the paper comparing treatment effects across firms with different rates of technological obsolescence.
The policy effect operates by improving total factor productivity (TFP).
Mechanism analysis showing a positive association between the R&D deduction policy and firms' estimated TFP.
The policy effect operates by boosting firms' innovation capabilities.
Mechanism analysis in the paper linking the R&D deduction policy to measures of innovation capability (e.g., innovation output/indicators).
The policy effect operates by alleviating financing constraints for firms.
Mechanism analysis reported in the paper (mediation/heterogeneity analyses linking policy to reduced financing constraints).
The additional deduction policy for R&D expenses (the R&D policy) significantly enhances the sustainable development outcomes of intelligent manufacturing enterprises.
Panel data from listed manufacturing firms in China analyzed using a quasi-natural experiment design; main empirical specification shows a statistically significant treatment effect (abstract reports significance). Robustness checks reported.
Smart devices adoption is particularly influential (positively associated) for exports to China and to other countries (multivariate probit result).
Multivariate probit model of destination-specific export decisions showing significant positive associations for smart devices with exports to China and 'other countries' (sample size not reported in prompt).
Robotics adoption is a key factor (positively associated) for exports to all destination regions examined (multivariate probit result).
Multivariate probit analysis of destination-specific export decisions indicating significant positive associations between robotics adoption and exports across all destinations (sample size not reported in prompt).
Cloud computing adoption is significantly associated with exports to countries outside the European Union and China (multivariate probit model result).
Multivariate probit analysis of destination-specific export decisions indicating significant effects of cloud computing for exports to non-EU, non-China countries (sample size not reported in prompt).
Adopting smart devices significantly increases the likelihood that a firm exports (probit model result).
Probit regression analysis of firms' export probability using smart devices adoption as an explanatory variable (sample size not reported in prompt).
Adopting robotics significantly increases the likelihood that a firm exports (probit model result).
Probit regression analysis of firms' export probability using robotics adoption as an explanatory variable (sample size not reported in prompt).
Adopting cloud computing significantly increases the likelihood that a firm exports (probit model result).
Probit regression analysis of firms' export probability using cloud computing adoption as an explanatory variable (sample size not reported in prompt).
Adopting artificial intelligence (AI) significantly increases the likelihood that a firm exports (probit model result).
Probit regression analysis of firms' export probability using AI adoption as an explanatory variable (sample size not reported in prompt).
TAI introduces recursive feedback loops between technology, knowledge, and output that redefine long-term growth trajectories and the equilibrium conditions of economies.
Derived from the paper's dynamic model: analytical results showing feedback mechanisms between technology, knowledge stock, and output; presented as theoretical model implications rather than validated empirical findings.
The model integrates AI as both a productivity amplifier and an autonomous driver of capital accumulation.
Stated methodological contribution: the authors extend Solow (1956) and Romer (1990) frameworks to build a dynamic model in which AI enters production as an amplifier of productivity and as an autonomous engine for capital accumulation; evidence is theoretical/model construction rather than empirical.
Transformative artificial intelligence (TAI) is capable of driving structural economic change comparable to the industrial revolution.
The paper asserts this claim by analogy and conceptual argument in the introduction; it frames TAI as 'capable of driving structural economic change comparable to the industrial revolution' without reporting empirical data — supported by theoretical reasoning and historical analogy.
Governments should create an enabling environment that aligns AI innovation with inclusive financial systems to stimulate entrepreneurship, including strengthening entrepreneurship support, enhancing R&D incentives and STEM capacity, sustaining targeted innovation funding, and reforming financial regulations to improve start-up financing and reduce early-stage capital constraints.
Policy recommendations given in the abstract, presented as implications of the empirical findings from the analysis of 23 countries (2002–2023).
AI significantly stimulates entrepreneurship only in financially advanced environments (i.e., above a threshold of financial development), where robust financial institutions and capital investment unlock its transformative potential.
Threshold results from dynamic panel threshold regression reported in the abstract for a sample of 23 countries (2002–2023) showing the AI effect on entrepreneurship is significant only in higher financial development regimes.
Financial development has a positive moderating effect on the AI–entrepreneurship nexus, suggesting complementarities between technological innovation and financial systems.
Abstract states moderation/interaction evidence from dynamic panel threshold regression applied to the panel of 23 countries (2002–2023) showing financial development strengthens the AI–entrepreneurship relationship.
Capital formation, human development, and financial development also play essential roles in driving entrepreneurial growth.
Reported as significant predictors in the dynamic fixed-effects panel analysis on 23 countries (2002–2023) described in the abstract.
AI promotes entrepreneurship by fostering innovation and efficiency.
Estimated with dynamic fixed-effects and dynamic panel threshold regressions on a panel of 23 developed and developing countries covering 2002–2023; abstract reports a positive association between AI technology innovation and entrepreneurship.
Results may be applied in the development of financial institution strategies, regulatory frameworks, risk management systems and professional training programmes.
Applied implications drawn from the literature synthesis and comparative analysis; presented as potential uses rather than empirically validated interventions.
Significant changes in human resource needs are occurring, with growing demand for analysts and specialists combining financial and technological competencies.
Conclusion from literature review and synthesis of international studies on labour demand in finance under Big Data/AI adoption; no original labour-market survey included.
Big Data and AI technologies significantly improve efficiency, risk assessment accuracy, fraud detection and financial inclusion.
The paper reports results from a qualitative analysis of recent academic literature, comparative analysis of sector-specific applications, and synthesis of empirical findings from international studies; no primary sample size reported.
Under economy-wide deployment, the share of computer-vision-exposed labor compensation that is cost-effectively automatable rises sharply (relative to the firm-level 11% estimate).
Model counterfactuals or calibration scenarios comparing firm-level deployment vs economy-wide deployment; qualitative statement that share increases substantially.
At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation.
Calibration and implementation in computer vision; reported firm-level estimate from the framework.
Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks.
Modeling and calibration arguments showing fixed-cost spreading effects increase set of tasks for which automation is cost-effective; qualitative and quantitative comparisons in implementation.
Because higher accuracy is disproportionately costly (convex cost), full automation is often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium.
Theoretical model combined with calibration (scaling laws + task mappings); equilibrium outcomes reported from the framework implementation.
We model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation.
The paper develops a theoretical framework / model that treats automation intensity as a continuous decision variable; described as the central modeling approach.
The findings demonstrate that technological innovation strategies, when effectively implemented, provide measurable competitive advantages for banks and offer evidence-based insights for policymakers and practitioners.
Authors' interpretation/conclusion drawing on the reported statistically significant relationships between innovation (product and technological) and competitiveness.
Technological innovation is positively and statistically significantly related to bank competitiveness (simple linear regression result reported).
Simple linear regression reported in the paper testing the hypothesis that technological innovation influences competitiveness; data collected from innovation-focused executives across licensed banks (paper states data from 39 licensed banks).
Product innovation strategy has a positive and statistically significant effect on competitiveness (F(1,134) = 74.983, p < .001).
Bivariate regression analysis reported in the paper with F(1,134)=74.983, p < .001; based on survey data from innovation-focused executives (regression degrees of freedom indicate n≈136 observations).
The case for mutually beneficial industrial policy is stronger for product innovation than for process innovation, because product innovation directly affects demand and triggers stronger network effects while process innovation operates indirectly through supply.
Model variants distinguishing product vs. process R&D within the two-country framework; comparative analysis showing larger demand-driven network effects for product innovation (theoretical model results; no empirical sample).
Under sufficiently strong network externalities and weak substitutability (or weak complementarity) of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of a mutual business-enhancement effect.
Theoretical demonstration within the two-country model: parameter regions (strength of externality, degree of product differentiation) where simultaneous welfare improvements occur relative to laissez-faire (analytical/model results; no empirical sample).
Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.
Aggregate of reported experimental results across architecture design, pretraining data curation, reinforcement learning algorithm design, and preliminary transfer experiments.
In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +5.04 points on OlympiadBench.
Reinforcement learning algorithm design experiments reported in the paper comparing discovered algorithms to GRPO on OlympiadBench.
In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +11.67 points on AIME24.
Reinforcement learning algorithm design experiments reported in the paper comparing discovered algorithms to GRPO on AIME24.
In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32.
Reinforcement learning algorithm design experiments reported in the paper comparing discovered algorithms to GRPO on AMC32.
In pretraining data curation, gains exceed 18 points on MMLU.
Reported experimental result on MMLU benchmark within pretraining data curation experiments.
In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points.
Pretraining data curation experiments reported in the paper showing an average benchmark performance improvement of +3.96 points.
The best discovered model surpasses DeltaNet by +0.97 points, nearly 3x the gain of recent human-designed improvements.
Reported performance comparison between the best discovered model and DeltaNet in neural architecture experiments; statement comparing relative gain to recent human-designed improvements.
In neural architecture design, it discovered 105 SOTA linear attention architectures.
Neural architecture design experiments reported in the paper, with 105 discovered architectures labeled as SOTA.
ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations.
Method description of ASI-Evolve's architecture/components in the paper (cognition base and analyzer added to evolutionary agents).
We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle.
Methodological contribution described in the paper: presentation and implementation of the ASI-Evolve framework and its learn-design-experiment-analyze loop.
Proposition 2: An increase in the pace of technology creation (m(b) rising from m to m') generates a transitory increase in the skill premium (even if the increase is permanent, because new technologies eventually age).
Analytical result (proposition) proved in the paper's model appendix; intuition and special-case (γ=σ) illustrated in text.
The college premium rose first among young workers and later among older workers; a model extension that assumes younger workers have a comparative advantage in new technologies generates age-specific increases that account for half of the observed age gaps.
Extension of the model with worker demographics; calibration using CPS data on computer use by worker age (showing young workers used computers more intensively initially) and simulation comparing model to observed age-specific wage premium changes.
Slow diffusion, combined with the rapid pace of technology creation, accounts for 6.2 of the 8.7 log-point differential increase in the skill premium between high- and low-density regions over 1980–2005.
Model calibrated with estimated diffusion rates across regions from the text-based dataset; quantitative decomposition attributing portions of the regional differential to the mechanism.
The mechanism explains why the college premium is higher in dense cities and why its increase was mainly urban.
Model extension incorporating regional diffusion of technologies combined with estimates of diffusion rates across locations (using the Kalyani et al. dataset); comparison of model predictions to documented urban–rural wage premium patterns.
Total demand for college-educated workers increased by 100 log points since 1980; changes in the pace of technology creation account for one-third of that increase, with the remainder attributed to residual structural changes in production.
Model-based decomposition calibrated to data (demand and supply of college-educated workers since 1980); quantitative accounting exercise reported in the paper.
When calibrated to the observed pace of technology creation, the model generates a 28 log-point (32 percent) increase in the college premium between 1980 and 2010, which then flattens and begins to revert.
Quantitative calibration of the model to novel text-based technology data (arrival and diffusion) and wage series (CPS); simulation results.