Evidence (4114 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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Interaction2Eval, an LLM-based framework, addresses domain-specific challenges (child speech recognition, Mandarin homophone disambiguation, rubric-based reasoning).
Methodological description in the paper: a specialized LLM-based pipeline designed to handle listed domain challenges; presented as the approach used to extract structured quality indicators.
TEPE-TCI-370h is the first large-scale dataset of naturalistic teacher-child interactions in Chinese preschools (370 hours, 105 classrooms) with standardized ECQRS-EC and SSTEW annotations.
Authors' dataset construction and description: 370 hours of recorded interactions from 105 classrooms, annotated with ECQRS-EC and SSTEW rubrics as reported in the paper.
The dataset provides a reproducible and scalable foundation for research on technological diffusion, regional digitalisation, and industry-level transformation, and can be readily extended to future years or adapted to other countries.
Text asserts reproducibility, scalability, and extendability of the dataset and methods for future years and other countries.
By providing indicators for two benchmark years, the dataset supports the study of how AI adoption evolves across the Spanish business landscape.
Text highlights the availability of indicators for 2023 and 2025 and claims this supports temporal study of adoption evolution.
This multi-dimensional structure enables users to explore territorial patterns, sectoral differences, and size-related disparities in the uptake of AI.
Text claims that the dataset's dimensions make it possible to explore spatial (territorial), sectoral, and size-related patterns in AI uptake.
For each province–sector–size combination, the dataset reports whether firms adopt AI, whether they apply it internally, whether it is embedded in their offerings, and how many firms have valid website content.
Text explicitly lists the reported indicators at the province–sector–size aggregation level (adoption, internal use, embedded in offerings, count of valid website content).
The dataset offers a detailed portrait of AI adoption across regions (NUTS 3), industries, and firm size categories.
Text claims multi-dimensional reporting by region (NUTS 3), industry, and firm size categories in the dataset.
The pipeline identifies explicit evidence of AI use both in firms' internal processes and embedded in their products or services.
Text states the structured rubric is used to identify explicit evidence of AI use in internal processes and in products/services.
The paper uses a systemic pipeline based on large language models (LLMs) to segment website text, semantically filter it, and evaluate it with a structured rubric.
Text describes methodological pipeline components (LLM-based segmentation, semantic filtering, structured rubric evaluation).
The dataset results in 225,628 firm-year observations.
Text explicitly reports 225,628 firm-year observations derived from the dataset across the two benchmark years.
The paper introduces a nationwide dataset that maps how 112,814 Spanish firms communicate and implement artificial intelligence (AI) on their corporate websites in 2023 and 2025.
Text states dataset coverage and firm count (112,814 firms) and benchmark years (2023 and 2025).
Those extended-model equilibria also show increasing concentration consistent with power-law-like distributions (i.e., winner-take-most / superstar effects).
Theoretical model combining quality heterogeneity and reinforcement dynamics that yields equilibrium distributions with heavy tails; argument and formalization presented in the paper; no empirical testing reported.
Even as the number of producers increases and average attention per producer falls, total output expands (production scales elastically).
Same formal theoretical model (analytical result): production scales elastically in the model despite finite attention; no empirical validation provided.
Mechanisms identified — network structure evolution and increased relational embeddedness — contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy.
Synthesis of empirical network evolution results and mediation/structural analyses from the 2011–2021 dataset of digital transformation indicators and patent collaboration networks among cities and firms.
These results provide empirical evidence from a major emerging economy (China) that can offer insights to inform policies and strategies in other regions undergoing digital transition.
Generalization claim based on empirical findings from the 2011–2021 analysis of A-share listed companies' digital transformation and patent collaboration patterns in China.
When the volume of digital patent applications surpasses a certain threshold, the positive effect of digital transformation on the quality of cross-regional collaborative innovation accelerates (nonlinear threshold effect).
Threshold regression / nonlinear analysis relating counts of digital patent applications to the marginal effect of digital transformation on collaborative innovation quality, using 2011–2021 patent and digitalization data from A-share listed firms.
Advancement of digital transformation positively contributes to both the quality and the quantity of cross-regional cooperative innovation.
Empirical econometric analysis (panel regressions) linking measures of corporate/urban digital transformation to indicators of cross-regional cooperative innovation quality and counts, using A-share listed companies' digital transformation indicators and patent collaboration data, 2011–2021.
China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern over time.
Spatio-temporal network analysis based on the same 2011–2021 dataset of digital transformation indicators and patent/co-patent links among cities inferred from A-share listed companies' patent data.
The cooperative innovation network exhibits pronounced small-world characteristics.
Network analysis of cross-regional collaborative innovation using digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges (2011–2021).
If you can prove the value and the effort behind API token spending (agent memory), you can resell it.
Normative/operational claim within the paper's proposal; presented as an implication of verifiable provenance and market layering, with no empirical proof or transactional data.
Enabling timely memory transfer reduces repeated exploration.
Argument in the paper asserting that shared/tradable memory decreases redundant exploration; no experimental or observational data provided.
Together, clawgang and meowtrade transform one-shot API token spending into reusable and tradable assets.
High-level systems argument in the paper; no empirical measurements of reuse or tradability presented.
Meowtrade is a market layer for listing, transferring, and governing certified memory artifacts.
Design proposal described in the paper; no pilot deployment, user adoption metrics, or experimental data provided.
Clawgang binds memory to verifiable computational provenance.
System/design claim describing the proposed mechanism (clawgang) in the paper; no implementation results or empirical validation reported.
Agent memory can serve as an economic commodity in the agent economy, if buyers can verify that it is authentic, effort-backed, and produced in a compatible execution context.
Conceptual argument in the paper's proposal; no empirical evaluation, sample size, or experiments reported.
Economic theory can be used to generate structured synthetic data that improves foundation-model predictions when the theory implies observable patterns in the data.
General conclusion drawn from the paper's experimental findings: improvement in model predictions after fine-tuning on theory-derived synthetic data.
Fine-tuning on GARP-consistent synthetic data substantially improves prediction relative to zero-shot Chronos-2 at all forecast horizons we study.
Empirical results comparing fine-tuned Chronos-2 to zero-shot Chronos-2 across multiple forecast horizons on the authors' experimental panel (no numeric metrics or sample sizes given in the excerpt).
The fine-tuned model serves as a rationality-constrained forecasting prior: it learns price-quantity relations from GARP-consistent synthetic histories and then uses those relations to predict the choices of real consumers.
Empirical approach described in paper: model fine-tuned on synthetic GARP-consistent histories and then evaluated on real consumer choice data (supports claim that model transfers learned relations to predicting real choices).
GARP is a simple condition to check that allows us to generate time series from a large class of utilities efficiently.
Methodological argument in the paper: authors use GARP as a constructive condition to generate synthetic time series from many utility functions (no numeric efficiency metrics provided in the excerpt).
Teaching them basic economic logic improves how they predict demand using an experimental panel.
Reported experimental results in the paper: fine-tuning models on synthetic, economics-consistent data and evaluating on an experimental panel of consumer demand (no numeric sample size or metrics provided in the excerpt).
AI adoption and the associated improved governance lead to higher total factor productivity (TFP).
Empirical analysis showing a positive association between firm-level AI application index and measures of total factor productivity in the 2010–2023 Chinese A-share panel.
AI adoption and the associated improved governance lead to a lower cost of debt financing for firms.
Empirical tests linking firm-level AI application and governance improvements to measures of debt financing costs (e.g., interest rates on debt, financing spreads) in the Chinese A-share firm sample.
The governance risk-mitigation effects of AI operate through enhancing external monitoring.
Mechanism analyses showing that AI adoption is associated with measures of stronger external monitoring (e.g., analyst coverage, media scrutiny, regulator activity) in the firm-year panel, linking that channel to reduced misconduct.
The governance risk-mitigation effects of AI operate through strengthening internal control capacity.
Mechanism analyses showing that higher AI application is associated with improved internal control measures (as reported by firms or regulatory/financial-control indicators) in the dataset of Chinese A-share firms.
The governance risk-mitigation effects of AI operate through lowering agency costs.
Mechanism analyses reported by authors linking AI adoption to reductions in measures interpreted as agency costs (e.g., agency-cost proxies, corporate governance metrics) in the same firm-year panel.
AI application significantly reduces the monetary amount of penalties associated with executive misconduct.
Regression analyses on monetary penalty data for Chinese A-share firms (2010–2023) showing a statistically significant negative relationship between firm AI application index and penalty amounts.
AI application significantly reduces the frequency (number) of violations by executives.
Empirical frequency/regression analyses on the firm-year panel of Chinese A-share firms using the AI application index; authors report robust reductions in the number/frequency of violations conditional on AI adoption.
AI application significantly reduces the incidence of executive misconduct.
Empirical analysis on Chinese A-share listed firms (2010–2023) using the constructed firm-level AI application index; reported significant negative association between AI application and whether a firm experiences executive misconduct (incidence).
Using Chinese A-share firms listed in Shanghai and Shenzhen from 2010 to 2023, we construct a firm-level AI application index and examine whether and how AI adoption mitigates executive misconduct.
Authors report building a firm-level AI application index and applying it to Chinese A-share listed firms (Shanghai and Shenzhen) over 2010–2023 to study links between AI adoption and executive misconduct (method: panel analysis using firm-year observations).
The paper provides recommendations for designing strategic indicators to drive adoption, foster innovation, and objectively assess whether digital tools are delivering top-line impact.
Descriptive claim about the content of the perspective article (the authors state they provide these recommendations); the excerpt itself summarizes this contribution.
The shift from expert-driven computer-aided drug design (CADD) to semiautonomous AI necessitates a new framework of impact-oriented KPIs.
Stated by the EFMC2 community authors as a normative conclusion in the perspective piece; based on the characterisation of a technological shift rather than on presented empirical tests in the excerpt.
Harnessing AI's potential requires moving beyond measuring technical model performance (e.g., predictive accuracy) to measuring strategic impact.
Authors argue this as a conceptual requirement for realizing AI's benefits in R&D; presented as a recommendation rather than supported by quantified empirical evidence in the excerpt.
Preliminary analyses suggest that 'AI-native' companies may be outpacing traditional peers.
Explicitly stated in the paper as based on preliminary analyses; the excerpt provides no details on the analyses, metrics, or sample sizes.
The broad introduction of AI into the R&D landscape over the last years holds the promise to lift pharmaceutical R&D out of its productivity problem.
Framed as an expectation/promise in the paper; based on recent broad adoption trends of AI in R&D (no specific empirical evaluation or sample size reported in the excerpt).
In this verifiable domain, simple arbitrage strategies generate net profit margins of up to 40%.
Empirical result from the SWE-bench case study comparing arbitrage strategy returns using GPT-5 mini and DeepSeek v3.2 (reported maximum net profit margin = 40%).
The paper discusses a regulatory framework for token futures markets, providing a theoretical foundation and practical roadmap for the financialization of compute resources.
Policy/regulatory discussion and recommendations included in the paper; draws on comparisons to existing commodity regulation and futures markets.
The paper explores the feasibility of GPU compute futures as an alternative or complement to token futures.
Discussion/feasibility analysis in the paper (conceptual and comparative discussion; not presented as empirical field evidence).
Simulation results show that, under an application-layer demand explosion scenario, token futures can reduce enterprise compute cost volatility by 62%–78%.
Monte Carlo simulation results based on the constructed mean-reverting jump-diffusion stochastic process model; scenario described as 'application-layer demand explosion'. (No numerical sample size reported in the abstract.)
The authors propose a complete design for standardized token futures contracts, including the definition of a Standard Inference Token (SIT), contract specifications, settlement mechanisms, margin systems, and market-maker regimes.
Normative/proposal section of the paper specifying contract design components and market microstructure recommendations.
Tokens consumed by AI inference are evolving into a new type of commodity.
Conceptual/systematic analysis and argumentation presented in the paper (comparisons to established commodities and discussion of commodity attributes).