Evidence (7278 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).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 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 | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Governance
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The positive effect of intelligent manufacturing on green innovation is stronger in technology-intensive firms.
Heterogeneity analysis reported by authors showing larger DID treatment effects for technology-intensive firms within the 2011–2023 panel.
The positive effect of intelligent manufacturing on green innovation is stronger in non–heavily polluting industries.
Heterogeneity analysis in the DID framework comparing effects between heavily polluting and non–heavily polluting industry groups in the Chinese manufacturing panel.
The positive effect of intelligent manufacturing on green innovation is stronger in non-high-tech firms.
Heterogeneity analysis reported in the paper comparing treatment effects across firm categories (non-high-tech vs high-tech) within the 2011–2023 Chinese manufacturing panel under the DID design.
Intellectual property protection strengthens the mechanism by increasing innovation returns and enhancing the capability-to-innovation conversion efficiency.
Heterogeneity/moderation analysis in the DID framework showing stronger mechanism effects under conditions of higher intellectual property protection (moderation tests reported in the paper).
The effect of intelligent manufacturing on green innovation operates through an integrated dynamic capability channel: firms strengthen adaptive capability, absorptive capability for green knowledge and digital technologies, and innovation capability via technological integration, thereby improving green innovation.
Mechanism analysis reported in the paper (channel/mediation tests) using the same panel and DID framework to examine intermediate capability measures (adaptive, absorptive, innovation capabilities).
The reported effect of intelligent manufacturing on green innovation is robust across multiple checks.
Authors state results remain after several unspecified robustness checks following the DID estimation on the 2011–2023 panel of Chinese A-share manufacturing firms.
Intelligent manufacturing significantly enhances firms’ green innovation.
Empirical analysis using panel data of Chinese A-share manufacturing firms (2011–2023). The study exploits a pilot policy of intelligent manufacturing as a quasi-natural experiment and estimates effects with a difference-in-differences (DID) approach; authors report significance and multiple robustness checks.
To address these dilemmas, coordinated reconstruction of production relations is needed across three levels: macro-level institutional constraints, meso-level organizational transformation, and micro-level rights protection (e.g., recognition of data labor rights, anti-monopoly regulation, and algorithmic transparency).
Prescriptive policy recommendations based on the paper's theoretical analysis; no empirical evaluation of these measures is provided.
The results have significant policy implications for using AI to achieve Sustainable Development Goal 1 (no poverty) in developing economies.
Policy implications section of the paper stating that empirical findings inform strategies to achieve SDG 1.
AI can support poverty alleviation as a general-purpose technology provided robust governance systems and supplementary funding for human development are in place.
Interpretation and policy discussion drawing on the empirical results and theoretical lenses (endogenous growth theory and Sen's capability approach) presented in the paper.
Robustness checks using an alternative measure of poverty confirm the consistency of the findings.
Robustness analyses reported in the paper using a different poverty metric showed consistent results with the main CS-ARDL estimates.
The error-correction term is negative and highly significant, indicating the stability of the long-run equilibrium relationship.
Estimated error-correction (ECM) coefficient from the CS-ARDL model reported as negative and statistically significant.
Effective governance reinforces poverty reduction.
Statistically significant positive coefficient for a governance indicator in the CS-ARDL model applied to BRICS (2008–2023).
Economic growth reinforces poverty reduction.
Positive and statistically significant association between GDP growth (or economic growth measure) and poverty reduction in the CS-ARDL estimates for BRICS (2008–2023).
Access to clean cooking fuels reinforces poverty reduction.
Positive and statistically significant coefficient for access to clean cooking fuels in the CS-ARDL model (BRICS, 2008–2023).
Human development reinforces poverty reduction.
Positive and statistically significant association between a human development indicator and poverty reduction in the CS-ARDL model (BRICS, 2008–2023).
The poverty-reducing benefits of AI increase over time (long-run effect larger than short-run effect).
Comparison of estimated short-run (0.14%) and long-run (0.39%) coefficients from CS-ARDL results.
A 1% increase in AI adoption results in a 0.39% reduction in poverty in the long run.
Estimated long-run coefficient from the CS-ARDL model on BRICS panel data (2008–2023); reported as a long-run elasticity.
A 1% increase in AI adoption results in a 0.14% reduction in poverty in the short run.
Estimated coefficient from a Cross-Sectionally Augmented ARDL (CS-ARDL) model applied to BRICS panel data (2008–2023); reported as a short-run elasticity.
Agentic AI can become a productivity lever when implemented as a human-centered capability with responsibility and accountability retained by people.
Paper's concluding recommendation (argumentative; no empirical evaluation or sample reported).
For small and medium sized companies, agentic systems can improve the use of organizational knowledge.
Paper's conceptual claim about better leveraging organizational knowledge (argumentative; no empirical sample).
For small and medium sized companies, agentic systems can accelerate routine processes.
Paper's argument about process speedups in SMEs (conceptual reasoning; no experimental data reported).
For small and medium sized companies, agentic systems create potential to reduce administrative burden.
Paper's argument about expected benefits for SMEs (conceptual reasoning; no reported empirical sample or trial).
We introduce mojo-deterministic, an open-source library of reproducible reduction kernels.
Paper announces the release/introduction of an open-source library (mojo-deterministic) providing reproducible reduction kernels; likely accompanied by repository link or code artifacts in the full text.
On Apple Silicon, Mojo demonstrates 20x to 180x speedups over pure Python on directly measured kernels.
Reported benchmark results on Apple Silicon comparing Mojo to pure Python on directly measured kernels; exact kernel count and experimental details not provided in the abstract.
Its MLIR compilation infrastructure further allows a single codebase to target scalar, SIMD, multicore, and GPU execution, reducing the translation bottleneck between research and production.
Technical claim in paper about Mojo's MLIR-based compilation pipeline enabling multiple backend targets from one codebase; described as reducing translation work.
While closing the Python-to-C++ performance gap, Mojo uniquely combines native interoperability with the low-level systems control required to construct bit-exact deterministic kernels.
Paper claim, supported by the authors' benchmarks and description of language features (native interop and low-level control); specific benchmark details partly provided elsewhere in the paper.
This article surveys Mojo, Modular's 2026 Python-like systems language, as a structural response for capital markets engineering.
Paper declares itself a survey of the Mojo language applied to capital markets engineering; descriptive statement rather than empirical evidence.
The paper serves as a resource for policymakers and researchers addressing the economic and social impacts of robotics, artificial intelligence, and automation.
Stated in the paper's implications; reflects intended audience and utility rather than an empirical finding.
The study contributes to a limited body of research on robot taxation and offers guidance on adapting tax systems to technological change.
Claim about the paper's original contribution and scope, stated in the implications/originality/value section; based on the authors' review and synthesis of existing literature.
Implementing a robot tax approach supports responsible automation, reduces inequality, and fosters sustainable economic growth.
Conclusion/implication in paper based on synthesis of reviewed literature and normative argument; not presented as an empirically tested result within the study.
A robot tax would address tax policy biases that favour capital over labour.
Paper argues this normative point based on literature synthesis; presented as a rationale for the tax rather than proven empirically within the paper.
A robot tax could fund workforce retraining.
Policy recommendation in the paper deriving from the scoping review; framed as intended use of tax proceeds (no empirical trial or evaluation reported).
A robot tax is proposed to offset lost income tax revenue.
Paper proposes robot taxation as a policy response based on review of literature; presented as a policy recommendation rather than reporting new empirical estimation.
We unify previously fragmented literature on LLM decision-making, human behavior simulation, and preference elicitation under a common economic lens.
Survey and synthesis of prior literature across multiple subfields, presented in the paper as an integrative literature review and conceptual unification.
We formalize how human preferences are reflected and acted upon by LLM-based agents, and how agent-level decisions aggregate into market demand.
Paper claims to provide formal models and theoretical formalization combining economic theory and NLP advances; theoretical modeling and formal definitions rather than empirical estimation.
We introduce LLM Consumer Behavior Theory, a new field of study concerned with analyzing consumer behavior in agentic markets.
Author(s)' stated contribution in the paper: formulation of a new theoretical field and framework (conceptual/theoretical contribution).
Large language models (LLMs) are increasingly deployed as autonomous agents that make consumption decisions on behalf of users.
Explicit statement in the paper's abstract/introduction asserting a trend; no empirical data, based on literature observations and motivating narrative.
The proposed decision-centric portfolio framework provides a pathway to resolving the AI-investment paradox by linking AI investments to identifiable, governable, and accumulative sources of business value.
Synthesis/concluding claim based on the theoretical framework developed in the paper (AIPNs + Expected Net Benefit + staging and portfolio assembly); no empirical test of whether using the framework actually resolves the paradox is provided in this paper.
AIPNs can be staged using real options logic and assembled into a broader portfolio using risk–return principles to guide investment sequencing and allocation.
Conceptual/methodological claim in which the authors show how real-options reasoning and portfolio theory apply to staged investment in AIPNs; presented as framework guidance without empirical implementation in this paper.
Node-level value of an AIPN can be formalized through Expected Net Benefit.
Theoretical formalization presented in the paper (mathematical/analytic definition of Expected Net Benefit at the node level); no empirical estimation reported.
Introducing AI-Investable Process Nodes (AIPNs) — bounded decision points in workflows where AI can alter expected outcomes — enables ex ante assessment of benefits, risks, and costs.
Conceptual framework and definition introduced by the authors; formal description of AIPNs and argumentation showing how they permit ex ante assessment (no empirical validation reported).
Financial accessibility significantly improves the Load Capacity Factor (LCF), indicating inclusive finance can promote sustainability.
Panel ARDL estimates for G-7 countries (1990–2019) include financial accessibility (Global Findex) and find a statistically significant positive relationship with LCF.
AI innovation significantly improves the Load Capacity Factor (LCF), suggesting technological progress promotes ecological sustainability through efficiency gains and cleaner production.
Statistical significance of AI innovation (measured via AI patent data) in panel ARDL estimates for the G-7 panel (1990–2019); AI patent data sourced from Our World in Data.
The study provides critical theoretical and practical insights for firms integrating AI into high-level governance frameworks.
Claim about the contribution of the paper (theoretical and practical insights); this is a statement of scope/contribution rather than an empirical result—no evidence metrics supplied in the summary.
By fostering collaborative intelligence, organizations can leverage GenAI’s computational reach to improve decision outcomes.
Paper argues as a practical implication that collaborative intelligence enables firms to use GenAI's computational capacity to enhance decision outcomes; no measured effect sizes or sample reported in the summary.
AI's role has shifted from a peripheral tool to a central architect in strategy development.
Framed as an interpretation of the study's findings about role-change in governance; no longitudinal adoption data or counts reported in the summary.
AI can surpass human proficiency in complex domains.
Presented in the paper's findings as an asserted empirical/general conclusion; the summary does not include experimental design, comparative metrics, or sample size.
GenAI agency functions as a mediator between human skill development and algorithmic trust.
Paper explicitly states this mediation relationship as part of its theoretical model; the summary provides no empirical mediation analysis details (no N, no coefficients).
Human-machine shared intentionality enables navigation of organizational complexity.
Framed in the paper as a conceptual mechanism (shared intentionality) that helps organizations manage complexity; summary does not report empirical tests or sample details.