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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Our findings show qualitative and enduring differences between hyperscaler-based platforms and non-hyperscaler providers.
Stated as a conclusion based on the paper's taxonomy and comparative analysis; phrasing indicates interpretive/qualitative evidence rather than longitudinal empirical demonstration (no temporal sample or size reported in abstract).
Non-hyperscaler providers embody distinct value-creation logics beyond hyperscaler efficiency.
Claim arises from the taxonomy and comparative analysis contrasting hyperscaler-based platforms with non-hyperscaler alternatives; evidence appears qualitative and conceptual as presented in the paper summary (no empirical sample size reported in abstract).
Human capital structure moderates the relationship between AI application and enterprise innovation efficiency.
Moderation analysis on A-share listed firms (2012–2023) indicating significant interaction effects between AI application and measures of human capital structure.
Fiscal support intensity moderates the impact of AI application on enterprise innovation efficiency.
Empirical moderation tests using firm-level panel data (2012–2023) showing interaction between AI application measures and fiscal support intensity.
Market segmentation exerts a moderating effect on the relationship between AI application and enterprise innovation efficiency.
Moderation analysis in the empirical framework applied to the 2012–2023 panel of Shanghai and Shenzhen A-share firms showing interaction effects between AI application and market segmentation measures.
The utility-aware framework preserves inverse U-shaped demand patterns for attributes such as aesthetics and uniqueness, improving demand-based performance while preserving fidelity and semantic consistency.
Empirical claim from the paper that their method maintains observed inverse U-shaped demand relationships for certain attributes in their experiments while improving demand-related metrics.
Comparative analysis of Japanese, European, and United States legal frameworks shows differing treatments of translation data and points toward the need for redistributive design to remedy unequal attribution and capture.
Comparative legal analysis across jurisdictions (Japan, EU, US) and normative argument proposing redistributive design directions; no experimental or quantitative evaluation provided.
AutoResearch autonomy is domain-conditioned: more credible in structured, executable, and rapidly verifiable settings but limited in embodied, delayed, heterogeneous, ethical, or institutionally accountable contexts.
Authors' synthesis of system capabilities and application domains from the surveyed literature; qualitative assessment of where autonomy is plausible vs limited.
Emerging AI-led systems coordinate larger portions of the discovery loop without achieving robust autonomy.
Survey of recently proposed AI scientist and AI-led systems showing increased coordination across workflow steps but lacking evidence of fully autonomous, robust operation; qualitative synthesis.
We propose the Shannon Scaling Law, a unified theoretical framework that models LLM training as information transmission over a noisy channel, grounded in the Shannon-Hartley theorem, mapping model parameters to channel bandwidth and training tokens to signal power.
Theoretical formulation presented in the paper, grounded on Shannon-Hartley theorem and a mapping between model/data quantities and communication-theoretic quantities (bandwidth, signal power).
Managerial traits, such as risk tolerance and patience, play a role in shaping firms' AI adoption decisions.
Inclusion of manager-level trait measures (risk tolerance, patience) in the ifo Business Survey and analysis showing associations between these traits and reported AI adoption.
Drivers and barriers to AI adoption include firm-specific characteristics and industry dynamics.
Survey-based analysis linking firm characteristics and industry-level factors to reported AI adoption decisions in the ifo Business Survey (likely correlational/regression analysis).
AI adoption/diffusion varies across firm sizes.
Analysis of adoption patterns by firm size using ifo Business Survey firm-level responses (comparison across size categories).
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.
Environmental dynamism and complexity differently moderate the relationship between decision-making agility and firm performance.
Reported moderation analyses in the PLS-SEM results indicating interaction effects of environmental dynamism and environmental complexity on the decision-making agility → performance path; based on survey of 251 firms.
Consumer decision-making is shifting from linear to nonlinear patterns under intelligent technologies.
Synthesis from the paper's systematic review and content analysis of literature (2010–2025); no sample size or primary empirical study reported in the summary.
GenAI enables small teams to expand capacity while creating new dependencies and coordination logics.
Empirical finding from 17 interviews indicating both expanded capacity and emergent dependencies/coordination needs.
GenAI drives structural recomposition across four domains: shifting roles, AI-embedded workflows, evolving capability expectations, and leaner work architectures.
Empirical finding from thematic analysis of 17 expert interviews reported in the results.
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.
The integration of artificial intelligence (AI) agents into payment systems signals a profound shift in the architecture of financial transactions.
Conceptual and technical analysis presented in the paper (argumentative claim in abstract). No empirical sample or quantitative data reported in the abstract.
The benchmark therefore assigns value to coordination only when the corresponding performance, provenance, or representation claim is supported by explicit comparators.
Concluding statement in the paper tying value of coordinated AI agents to evidence from explicit baseline/comparator evaluations across performance, provenance, and representation dimensions.
For molecular sonification, the gain is representational rather than predictive.
Reported outcome for molecular structure to music task indicating improvements in representation/sonification quality but not in predictive performance.
AI changes the traditional relationship between learning and performance: in AI-intensive environments, learning must be supported by systems that coordinate knowledge and build intelligence rather than relying on learning alone.
Authors' synthesis and interpretation of their cross-sectional mediation results (AIDLC → KO → OI → IP) and comparison with prior management models.
AI alters strategizing practices (Strategy-as-Practice) by making strategy processes continuous and AI-augmented rather than episodic and purely human-driven.
Conceptual synthesis of Strategy-as-Practice literature; theoretical claim about process change to continuous, AI-augmented strategizing; no empirical sample.
AI redistributes resource control to stakeholders, challenging the Stakeholder Resource-Based View by changing who holds and controls strategically valuable resources.
Theoretical argument within the Stakeholder Resource-Based View stream; conceptual synthesis asserting redistribution of resource control to external stakeholders and algorithmic actors; no empirical evidence reported.
AI reconfigures ecosystems and platforms around foundation models, shifting how complementary actors interact and altering platform/ecosystem structure.
Analytical review of Ecosystems and Platforms literature; conceptual claim that foundation models act as central coordinating technologies; no empirical data or sample.
AI embeds algorithmic actors into the microfoundations of strategy, altering the role and behavior of individual-level actors that underlie firm-level phenomena.
Conceptual analysis of Microfoundations literature; theoretical proposition that algorithms act as actors at micro levels; no empirical sample provided.
AI creates hybrid cognitive architectures by integrating algorithmic cognition with human cognition, thereby changing how strategic decisions are made.
Theoretical argument drawing on literature in Behavioral Strategy and cognitive theory; conceptual synthesis without reported empirical tests or sample.
AI introduces a theoretical discontinuity that challenges core assumptions of strategic management (specifically those rooted in industry-structure and resource-based perspectives).
Conceptual/theoretical analysis across literatures in strategic management; the paper synthesizes prior debates and argues AI undermines prior assumptions. No empirical sample or quantitative data reported.
AIO’s decarbonization effects vary systematically across climate risk, industry competition, and AI exposure (heterogeneity analyses).
Authors state they performed heterogeneity/subgroup analyses showing systematic variation in the AIO–decarbonization relationship by climate risk, the degree of industry competition, and firms' AI exposure.
AI functions both as a general-purpose technology and as an innovation in the method of innovation.
Conceptual/theoretical framing presented in the paper (the authors characterize AI as both a GPT and an innovation in methods of innovation).
Classical categories (labour, capital, firm, market, productivity, trust) remain necessary but are incomplete for describing economic action when technologies prepare decisions, coordinate workflows, support tasks, verify transactions, and reshape responsibility.
Conceptual analysis supported by diagnostic indicators showing distributed decision/action capacity across humans, AI agents, robots, protocols, compute and energy systems; argumentative/theoretical evidence rather than causal inference.
Labour projections are more consistent with task reallocation than labour disappearance.
Analysis of labour-market reallocation data and labour projections (public sources) interpreted under a task-reallocation framework rather than full employment loss, using relative growth and reallocation indicators.
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).