Evidence (4781 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 |
Innovation
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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.
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).