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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The negative quadratic term confirms a concave (inverted-U) relationship between AI and economic growth (diminishing marginal returns of AI).
Panel data for 19 G20 countries (2005–2023) estimated with a quadratic specification in GMM; reported negative and statistically significant coefficient on the AI-squared term.
Specialized detectors generally perform better but remain inconsistent across generators and can produce false positives on real-damaged samples.
Experimental comparison showing specialized AI-generated image detectors outperform MLLMs on some generator subsets, yet show variability across generators and some false positives on genuine damaged images.
Generative AI lowers barriers to solo entrepreneurship while reinforcing team-based advantages.
Synthesis of the observed patterns in the Product Hunt data: sharp increase in solo launches after ChatGPT-3.5 (barrier lowering) combined with persistent team dominance among top-quality outcomes (reinforcing team advantages).
AI exhibits a significant U-shaped spatial effect on Lae.
Spatial econometric analysis (spatial Durbin model) on panel data for 30 Chinese provincial regions (2012–2022); kernel density estimation used for distributional analysis.
AI has a significant inverted U-shaped impact on the low-altitude economy (Lae), with diminishing marginal returns after a certain turning point.
Panel data from 2012–2022 for 30 Chinese provincial regions; composite AI and Lae indices constructed via the entropy method; estimated using spatial Durbin models and non-linear specification to detect inverted U-shape.
The study reframes VTech adoption as legitimacy-seeking rather than efficiency-driven.
Thematic analysis using Rogers' diffusion of innovations and institutional theory, resulting in the institutionally mediated diffusion of innovations (IDOI) framework which emphasizes legitimacy concerns.
Practitioners stress that human judgement remains indispensable, positioning technology as an aid rather than a replacement.
Interview responses from valuers and firm leaders emphasizing the continued role of human judgement; thematic analysis framed by the IDOI model.
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.
The study provides new empirical evidence that technological innovation (specifically generative AI) reshapes financial spillover networks and highlights the importance of considering both the level and structure of connectedness in assessing systemic risk.
Overall empirical results from the TVP-VAR analysis of connectedness across AI equities, cryptocurrencies, and traditional assets, and discussion of implications for systemic risk assessment.
The impact of AI on financial markets is better understood as a structural transformation of interconnectedness rather than a simple intensification of linkages.
Synthesis of empirical findings from the TVP-VAR showing changes in network structure and heterogeneous directional roles across asset groups, rather than a monotonic increase in aggregate connectedness.
The structure of spillovers undergoes significant changes over the sample period.
TVP-VAR estimated time-varying spillover/connectedness network showing changes in directional spillovers and network topology (paper states 'significant changes').
Introducing taxes on AI returns (τ_ai) and financial gains (τ_f) yields three distinct long-run regimes: low-tax (extreme inequality), moderate-tax (stable mixed economy), and high-tax (post-scarcity with universal basic income).
Model extension with tax parameters τ_ai and τ_f and analysis of steady states/long-run regimes; bifurcation analysis identifying regime types associated with ranges of (τ_ai, τ_f).
Aesthetic and functional attributes load onto a single latent factor, suggesting users perceive quality as a unified construct rather than separable aesthetic and functional dimensions.
Factor analysis (or similar latent-variable analysis) on participant ratings of multiple attributes showing a single dominant factor combining aesthetic and functional attributes.
The strategic interplay between antitrust regulation and vertical integration materially influences the evolutionary transitions of the computing power ecosystem.
Core focus of the paper's tripartite evolutionary game model which explicitly models government regulators, incumbents, and downstream innovators and analyzes resulting equilibria and transitions (method: theoretical evolutionary game + analytical derivation).
The evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics, potentially transitioning from an initial 'natural monopoly and passive dependence' state through intermediary states (e.g., 'comfort zone trap' or 'regulatory stalemate') toward a mature configuration of 'co-opetition and endogenous growth.'
Derived from the paper's tripartite evolutionary game model and analytical derivation of evolutionarily stable strategies, with supporting numerical simulations exploring parametric sensitivities (method: theoretical evolutionary game + numerical simulation).
The computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems driven by the rapid proliferation of generative artificial intelligence.
Conceptual claim presented in the paper's introduction/motivation; supported by the paper's theoretical framing and literature-based motivation rather than empirical data (method: narrative/theoretical framing).
These findings challenge the notion of a universal technological dividend from AI (i.e., AI does not automatically deliver uniform productivity gains across firms).
Overall interpretation/synthesis of heterogeneous empirical results from the panel and cluster analyses showing variation in productivity effects across firm types.
AI adoption yields asymmetric productivity gains depending on firms' resource constraints and competitive environments (i.e., heterogeneity rather than a homogeneous effect).
Heterogeneity analysis using multidimensional clustering (firm size, age, market competitiveness, digital infrastructure) applied to the panel dataset; reported differential effects across clusters.
AI adoption affects Total Factor Productivity (TFP) of firms.
Panel regression analysis using the stated panel dataset examining relationship between AI adoption and firm-level TFP.
Scientific institutions, distinctively, manufacture legitimate judgment, so they do not merely adapt to AI; they compete with it for the same functional role.
Conceptual/theoretical assertion in the paper describing institutional roles; no empirical data or sample size provided in the excerpt.
While Agentic AI enhances economic performance, its benefits are mediated by structural conditions and are unevenly distributed across countries (i.e., reinforcing core–periphery inequalities).
Combined findings from fixed-effects regressions, mediation analysis, and observed heterogeneity between developed and emerging economies in the 2015–2024 panel.
AI learns indiscriminately from implicit knowledge, acquiring both beneficial patterns and harmful biases.
Asserted in the paper as a conceptual point about training data and learned patterns; no empirical evaluation or quantified bias measures provided.
The rise of digital agents will transform the foundations of production, labour markets, institutional arrangements and the international distribution of economic power.
Synthesis and theoretical projection across sections of the paper; presented as a broad conclusion without reported empirical quantification in the provided text.
There is a fundamental asymmetry between economic and social reproduction: digital agents can compensate for productive functions of the population but are unable to substitute the population's functions of social reproduction.
Theoretical argument and conceptual distinction in the paper; no empirical study measuring substitution in social reproduction provided.
LLMs are able to extract signals from unstructured text (financial news headlines) but have limitations without explicit quantitative optimization.
Interpretation in discussion/conclusion: empirical finding that LLM-based portfolios beat naive diversification but underperform AI-optimized strategies, implying LLMs extract signals from text yet lack full optimization capability.
Statistical tests confirmed significant performance differences (p ≤ 0.01).
Reported inferential statistics in results: statistical tests comparing strategy performances produced p-values at or below 0.01.
Firms may continue to exist as legal and physical entities, but their coordinating function will be displaced as they become data nodes within regionally governed AI infrastructure.
Predictive/conceptual claim within the framework; no empirical sample reported in the excerpt and presented as a theoretical outcome of Interface Internalization.
The Structural Dissolution Framework challenges the Coasian view that organizational boundaries are determined by transaction cost minimization, arguing that AI makes such boundaries economically obsolete.
Theoretical critique of transaction-cost-based explanations for firm boundaries presented in the paper; argumentative and conceptual rather than supported by empirical tests in the provided summary.
Regional data sovereignty entities will emerge as organizational forms that replace the coordinating role of firms and markets.
Normative/predictive claim within the paper's framework arguing for new organizational forms (regional data sovereignty entities); illustrated conceptually (e.g., through resource-dependent regional economies) rather than empirically tested in the provided text.
Domain-specific data refinement infrastructure will become the new basis of positional control in industries.
Theoretical claim in the framework asserting a shift in positional control to data refinement infrastructure; presented as a predicted structural outcome rather than supported by empirical data in the provided text.
AI adoption moves value creation away from physical resources and human collaboration toward continuous token flows produced through data refinement loops.
Theoretical/analytical claim within the Structural Dissolution Framework and illustrative discussion; no empirical quantification provided in the text excerpt.
The mechanism driving this restructuring is 'Interface Internalization', through which inter-agent coordination is absorbed into intra-system computation.
Conceptual mechanism defined and argued in the paper; presented as the central theoretical mechanism rather than as an empirically validated finding.
AI dissolves the boundaries that once separated firms, markets, experts, and consumers by internalizing human multimodal interfaces (language, vision, and behavioral data) into computational systems.
Theoretical argument and conceptual framework introduced in the paper (Structural Dissolution Framework); no empirical sample or quantitative analysis reported for this claim in the text provided.
Differences between models are large enough to shape outcomes in practice, so reliability should be incorporated alongside average performance when assessing and deploying LLMs in high-stakes decision contexts.
Authors' interpretation of empirical differences in funding decisions, scores, confidence, and reliability across models in the controlled experiment; presented as an implication/recommendation.
This hybrid Make governance form has qualitatively different economics, capability requirements, and governance structures than pre-AI in-house development.
Paper's conceptual comparison between pre-AI hierarchy and post-AI hybrid Make governance (theoretical reasoning and examples; no empirical quantification).
AI reshapes seven canonical decision determinants for make-or-buy choices: cost, strategic differentiation, asset specificity, vendor lock-in, time-to-market, quality and compliance, and organizational capability.
Paper's factor-level conceptual analysis enumerating and discussing seven determinants (theoretical synthesis rather than empirical measurement).
Objectives, constraints, and prompt guidance affect reliability and generalization.
Authors' analysis and discussion based on experiments and ablations described in the paper (qualitative/empirical observations about sensitivity to objectives, constraints, and prompts).
The architect's role is shifting, but the human remains central.
Authors' discussion and interpretive analysis about the role of humans in agentic AI-driven design processes.
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).
The paper extends paradox theory to conceptualise the Creativity Paradox in the context of GenAI.
Theoretical extension and conceptual development within the paper (no empirical tests reported).
Within that n=11 subset, 9 of 11 agents shift by at least 2 ranks between composite and benchmark-only rankings.
Comparison of rank positions between composite and benchmark-only rankings on the 11-agent subset; reported count of agents that moved at least 2 ranks.
The four factors capture largely complementary information (n=50; ρ_max = 0.61 for Adoption-Ecosystem, all others |ρ| ≤ 0.37).
Correlation analysis among the four factor scores computed on the 50-agent sample; reported maximum inter-factor Pearson/Spearman correlation coefficients.
Firms with a high market position tend to imitate the peer leader, whereas firms in middle and low market positions are more likely to follow the peer group.
Heterogeneity analysis / subgroup regressions in fixed-effects models on panel data of publicly listed Chinese firms (2012–2023), stratifying firms by market position (high, middle, low).
Semiconductors are a representative case study for analyzing weaponized interdependence in advanced technology sectors.
Methodological claim in the paper: selection and focus on the semiconductor sector as illustrative of broader advanced-technology sector dynamics under export restraints and chokepoint activation.
Previous literature is based primarily on the short-term effectiveness of coercion; this paper shifts attention to the longer-term structural consequences of technological restraints.
Literature review and positioning in the paper contrasting prior studies' short-term focus with the paper's longer-term structural emphasis (methodological/literature-critique claim).
Over time, U.S.–China reaction–counterreaction interactions generate three structural transformations: supply-chain reconfiguration, substitution, and regulations reinforcing segmentation.
Synthesis from the paper's longitudinal/case-analysis of semiconductor-related export restraints and subsequent industry and regulatory responses (qualitative identification of three emergent structural outcomes).
Current instability in U.S.–China relations arises less from complete ideological divergence or failure of outright containment policy than from a structured reaction–counterreaction dynamic triggered by chokepoint activation.
Argument based on qualitative analysis of U.S. export restraints after the first Trump administration and application of the 'weaponized interdependence' framework to advanced-technology sectors (paper's theoretical argument and case discussion).
The study explores implications of algorithmic enterprises for competitive advantage, labour markets, and regulatory policy.
Declared scope of the paper in the abstract; exploration is conceptual and analytical rather than reporting empirical findings or quantified effects.
Agentic AI differs from traditional algorithmic trading and generative AI through its capacity for goal-oriented autonomy, continuous learning, and multi-agent coordination.
Analytic comparison and synthesis across prior research and technical architectures in the survey; descriptive/definitional rather than empirical testing.