Evidence (4131 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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The paper constructs a multidimensional digitalization index composed of digital infrastructure, digital service capacity, and the digital development environment.
Index construction described in data/methods: composite indicator combining measures of connectivity/broadband (infrastructure), e-commerce/digital finance (service capacity), and policy/institutional/human capital indicators (development environment).
Attributing productivity changes specifically to AI requires causal identification beyond VIS accounting (e.g., experiments, instrumental variables, difference-in-differences).
Paper notes that VIS is an accounting framework and that causal attribution to AI requires econometric/experimental methods beyond input–output accounting.
The method uses BEA for industry output and industry-by-industry transactions, BLS for employment and hours worked, and IMPLAN for detailed input–output structure and sector mapping; coverage period is 2014–2023.
Explicit data sources and time coverage stated: public BEA, BLS, and IMPLAN annual data 2014–2023 used to construct input–output matrices and labor measures.
The study employs a multidimensional clustering approach based on firm size, age, market competitiveness, and digital infrastructure to examine heterogeneous AI effects.
Methodological description in the paper stating multidimensional clustering variables (size, age, competitiveness, digital infrastructure) used to form firm clusters for heterogeneity analysis.
The study uses panel data of 3,366 Chinese A-share listed firms from 2015 to 2023.
Direct statement of dataset and time period in the paper (panel of 3366 Chinese A-share listed firms, 2015–2023).
Policy design to align high-tech industrial development with carbon-reduction goals should account for industrial life-cycle stages and value-chain positions.
Policy implication drawn from the empirical findings (inverted U-shape, stage-dependent mechanism, regional heterogeneity, and subsector differences) in the paper.
A market design with a data intermediary that internalizes cross-creator externalities and subsidizes innovative contributions can restore efficiency in the content-for-training market.
Proposed market-design mechanism and theoretical analysis in the paper showing how an intermediary can internalize externalities and subsidize originality to achieve efficient outcomes; no empirical validation reported.
The paper proposes an evolutionary framework of AI-Economy transformation and calls for further research on governance, sustainability, and inclusive growth.
Abstract states the paper suggests an evolutionary framework and points to future research directions (governance, sustainability, inclusive growth); this is a conceptual recommendation rather than an empirical result.
Generative AI can transform value generation by enriching cognitive work instead of automating habitual processes.
Abstract claim synthesizing reviewed literature that generative models augment cognitive work; no empirical effect sizes or study counts given in abstract.
Deep Learning (DL) hastens automation and capital deepening in high-skill industries.
Synthesis claim in abstract from reviewed literature; no specific empirical estimates or sample sizes provided in abstract.
Machine Learning (ML) mainly boosts productivity by increasing predictive efficiency.
Synthesis claim in abstract based on the systematic review of peer-reviewed literature (Scopus and SCI); no specific empirical studies or sample sizes cited in abstract.
The review estimates sectoral, macroeconomic, and labor market effects of ML, DL, and Generative AI.
Stated scope in abstract: review used to estimate sectoral, macroeconomic, and labor market effects; no quantitative details provided in abstract.
This paper systematically reviewed peer-reviewed journal articles indexed in the Scopus and SCI databases.
Stated method in abstract: systematic review of peer-reviewed journal articles indexed in Scopus and SCI; no sample size or study count reported in abstract.
A Qwen3-14B reward model we post-trained on human ratings captures field taste nuances, beats SOTA models by up to 27%, and closes the gap to the inter-rater consistency of independent peer reviewers.
Evaluation of a Qwen3-14B reward model post-trained on the collected human ratings; reported performance improvement 'up to 27%' over SOTA and improved alignment with inter-rater consistency benchmarks.
Social scientists tolerate risk more readily than life scientists.
Field-stratified analysis of ratings showing social scientists more likely to favor higher-risk (e.g., novel/less-probable) ideas compared to life scientists.
Scientists reward ideas that resemble their own and prize probability over novelty.
Analysis of scientist ratings showing higher scores for ideas similar to authors' own work and a preference for perceived probability (probability of being true) relative to novelty in ratings.
Ultimately, this framework provides technology managers with a verifiable, evidence-based pathway toward resilient, net-zero Industry 5.0 ecosystems.
Conclusion/assertion in paper positioning the framework as a practical pathway; described qualitatively without empirical outcome measures or quantified evidence.
The architecture demonstrates how fabs can export cryptographically signed compliance tokens via International Data Spaces (IDS) connectors without exposing proprietary process recipes.
Claim of demonstration in paper; implies a prototype or illustrative workflow using cryptographic signing and IDS connectors, but no empirical deployment, sample, or measured disclosure-risk reduction reported.
By executing Virtual Metrology (VM) predictions and Federated Machine Learning (FML) inside hardware-rooted Trusted Execution Environments (TEEs), this architecture resolves the Data Sovereignty Paradox.
Technical claim based on proposed use of TEEs with VM and FML in the paper; presented as conceptual/architectural resolution rather than empirically validated result.
Structured as an interoperable network protocol stack, the framework coordinates an automated, five-step "relay race" between Facility, Process Engineering, and Finance proxy teams to align factory-floor yield models with macro-level sustainability mandates.
Architectural and protocol-level description in the paper (system design); no quantitative alignment metrics or empirical validation reported.
We propose a shift from reactive automation to autonomous governance through "Professional Proxies"—role-based agentic workflows executing within hardware-isolated trust zones.
Design proposal and conceptual workflow model presented in the paper; no field trial or user study reported.
We introduce a zero-trust socio-technical orchestration framework that operationalizes a six-layer SSbD reference architecture within trustworthy industrial data spaces.
Proposed system architecture described in the paper (design/proposal); no reported empirical deployment or quantitative evaluation.
Scaling participation provides a technical foundation for transitioning from the monolithic status quo toward an open, bottom-up, and collaborative AI future.
Normative/architectural claim advanced in the paper arguing that the proposed paradigm enables a transition away from centralized monolithic models (framing/conclusion rather than an empirical result).
Participants contribute small models trained on their own interests and priorities; these models then collaborate in modular frameworks as compositional AI systems.
Description of the proposed 'scaling participation' paradigm and system architecture presented in the paper (design/method claim).
Participatory AI systems exhibit emergent capabilities that allow them to solve over 15% of problems where all individual models fail.
Reported experimental finding that the compositional system solved more than 15% of instances that none of the individual contributor models solved (exact problem count not provided in excerpt).
Participatory AI systems benefit from contributor diversity.
Further experiments reported in the paper that examine system performance as a function of contributor diversity (details not provided in the excerpt).
Participatory AI systems outperform monolithic LLMs by up to 15.4% across 15 tasks, such as reasoning and factuality, surpassing models larger than all contributed components combined.
Experimental evaluation reported in the paper comparing participatory (modular/compositional) systems to monolithic LLMs on a benchmark of 15 tasks (including reasoning and factuality); claims include quantitative comparison ("up to 15.4%") and a statement that the participatory systems outperform even models larger than the sum of contributed components.
Gains in institutional efficiency from GenAI adoption transmit to trading dynamics by stabilizing market volatility.
Empirical analysis in the panel linking institutional-efficiency improvements to lower/stabilized volatility metrics; methods include fixed effects, IV, and difference-in-differences.
Gains in institutional efficiency from GenAI adoption transmit to trading dynamics by increasing market liquidity.
Panel regressions linking estimated institutional-efficiency improvements to trading outcomes (liquidity measures) in the cross-market dataset; robustness checks with IV and difference-in-differences.
Institutional quality acts as a key moderating factor, amplifying the institutional-efficiency gains from GenAI adoption in markets with strong governance and regulatory capacity.
Interaction models and heterogeneity analysis in the cross-market panel; results reported for subsamples or interacted terms by governance/regulatory capacity measures.
GenAI adoption significantly improves institutional efficiency.
Cross-market panel dataset using a novel proxy for GenAI adoption; empirical approach includes fixed effects regressions, interaction models, instrumental variable estimation, and difference-in-differences designs to address endogeneity.
Generative AI enables autonomous software agents that can search, compare, and transact across digital marketplaces, promising large reductions in consumer search costs and improved matching between buyers and sellers.
Statement in abstract arguing potential enabled capabilities of generative AI agents; based on conceptual/technological description rather than reported empirical trial in the paper. No sample size or empirical protocol given in abstract.
Deeper AGI adoption raises the organic composition of capital.
Analytical derivation within the political-economy model showing that substituting living labor with machine-based productive systems increases the capital-to-labor composition (theoretical model; no empirical sample).
Realized revenue growth, enterprise adoption, and productivity evidence support a nontrivial share of AI valuations.
Reported empirical evidence in the paper citing realized revenue growth, measures of enterprise adoption, and productivity studies linking AI use to value creation (methods/metrics discussed in the paper but no single sample size reported in the abstract).
The proposed viewpoint reframes AI policy as the governance of an open, strategic, non-equilibrium learning system.
Conceptual reframing central to the paper; supported by the theoretical model and simulations presented.
The French AI debate should move beyond the binary opposition between techno-optimism and regulation-first caution.
Normative recommendation argued in the paper based on the HCLM framework and its implications for policy trade-offs.
The paper provides measurable policy indicators, game-theoretic propositions, illustrative simulations of national AI regimes, and concrete policy implications for France.
Claim about the paper's contents; the manuscript states that it includes indicators, propositions, simulations, and policy implications.
The paper connects HCLM with neural scaling laws, endogenous growth theory, creative destruction, and game theory.
Stated theoretical linkage and discussion sections; no empirical integration reported.
A competitive and human-centered AI strategy requires a controlled regime in which information injection grows faster than institutional dissipation while avoiding unstable, unequal, or energy-intensive expansion.
Prescriptive conclusion supported by the paper's mathematical model and illustrative simulations (model-based evidence rather than empirical causal estimation).
Information injection corresponds to compute, data, talent, research, capital, industrial deployment, and institutional experimentation.
Definition/mapping provided in the paper as part of the HCLM framework; conceptual rather than empirical.
AI sovereignty does not emerge from scale alone but from a country's capacity to regulate its own information dynamics.
Central theoretical claim supported by HCLM-based argumentation and linked conceptual arguments (neural scaling laws, endogenous growth theory, game theory); no empirical dataset reported.
France should be understood as a national AI learning system.
Conceptual/theoretical framing presented in the paper using Human-Centered Learning Mechanics (HCLM); no empirical sample or statistical test reported.
Generative AI is being used for automation of tax compliance.
Listed in the abstract as an illustrative example of algorithmic application to international tax (generative AI for automating tax compliance); no empirical measurement reported in the abstract.
Blockchains are being used for instant trade verification in international tax contexts.
Presented in the abstract as one of three illustrative examples of how algorithmic technologies are being used for international tax purposes; no empirical details provided in the abstract.
It empowers owners of data and code.
Explicit claim in the abstract asserting a power shift toward those who own data and code; presented as a conceptual conclusion from the authors' reflection and examples.
Global professional service firms are actively developing TaxTech to capture this market.
Direct statement in the abstract indicating market activity by global professional service firms; presented as an observed trend rather than supported by reported empirical data in the abstract.
Technological leaps in the algorithmic processing of information are providing financial actors with new opportunities for transnational financial and legal management that optimize asset allocation.
Stated as a conceptual observation in the paper's abstract; no empirical sample, presented as a general claim about technological change and its opportunities for financial actors.
A four-stage roadmap toward self-evolving agent ecosystems and concrete recommendations for practitioners can guide navigation of the transition to agentic systems.
Prescriptive contribution of the paper: a proposed four-stage roadmap and practitioner recommendations derived from the preceding analysis (theoretical/prescriptive; no empirical validation or sample size reported).
Agentic Engineering is an emergent discipline that is distinct from software engineering in its core object of study, control model, and human role.
Conceptual proposal and definitional work in the paper outlining new discipline characteristics (theoretical, no empirical testing or sample size reported).
The historical arc from licensed software to SaaS to what we term Agent-as-a-Service (AaaS) shows that each shift transferred additional complexity away from end-users.
Historical/architectural trend analysis presented in the paper (qualitative; references to industry evolution, no quantitative sample size reported).