Evidence (4560 claims)
Adoption
5267 claims
Productivity
4560 claims
Governance
4137 claims
Human-AI Collaboration
3103 claims
Labor Markets
2506 claims
Innovation
2354 claims
Org Design
2340 claims
Skills & Training
1945 claims
Inequality
1322 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 378 | 106 | 59 | 455 | 1007 |
| Governance & Regulation | 379 | 176 | 116 | 58 | 739 |
| Research Productivity | 240 | 96 | 34 | 294 | 668 |
| Organizational Efficiency | 370 | 82 | 63 | 35 | 553 |
| Technology Adoption Rate | 296 | 118 | 66 | 29 | 513 |
| Firm Productivity | 277 | 34 | 68 | 10 | 394 |
| AI Safety & Ethics | 117 | 177 | 44 | 24 | 364 |
| Output Quality | 244 | 61 | 23 | 26 | 354 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 168 | 74 | 37 | 19 | 301 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 89 | 32 | 39 | 9 | 169 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 106 | 12 | 21 | 11 | 151 |
| Consumer Welfare | 70 | 30 | 37 | 7 | 144 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 75 | 11 | 29 | 6 | 121 |
| Training Effectiveness | 55 | 12 | 12 | 16 | 96 |
| Error Rate | 42 | 48 | 6 | — | 96 |
| Worker Satisfaction | 45 | 32 | 11 | 6 | 94 |
| Task Completion Time | 78 | 5 | 4 | 2 | 89 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 17 | 9 | 5 | 50 |
| Job Displacement | 5 | 31 | 12 | — | 48 |
| Social Protection | 21 | 10 | 6 | 2 | 39 |
| Developer Productivity | 29 | 3 | 3 | 1 | 36 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Skill Obsolescence | 3 | 19 | 2 | — | 24 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Labor Share of Income | 10 | 4 | 9 | — | 23 |
Productivity
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Results are sensitive to model and prompt choice; researchers should perform robustness checks across LLMs, soft prompts, and embedding models.
Caveat explicitly stated in the paper summary noting model and prompt sensitivity; recommended validation steps include robustness checks across models and prompts.
Measurement issues (task-based output measurement, attributing output changes to AI) and selection into early adoption bias estimated productivity gains upward.
Methodological robustness checks reported in the paper: task-based measures, bounding exercises, placebo tests, and analysis of pre-trends; discussions of selection on unobservables and potential upward bias.
Implementing the governed hyperautomation pattern raises upfront costs (governance tooling, monitoring, validation, compliance processes).
Economic and cost-structure discussion in the paper, based on qualitative reasoning and industry experience; no quantified cost estimates or sample-based cost analysis provided.
VIS inherits the limitations of input–output assumptions (fixed coefficients, no price feedbacks); AI-driven structural change may violate those assumptions, so dynamic extensions or calibration are needed.
Paper explicitly cautions about input–output model limitations and the need for dynamic extensions/calibration under structural/technological change.
Increases in K_T reduce employment levels in affected firms and industries even when aggregate productivity rises.
Panel econometric estimates at firm and industry levels relating K_T intensity to employment outcomes, controlling for demand, input prices, and firm characteristics; difference-in-differences specifications and instrumental-variable robustness checks; corroborated by sectoral case studies.
Rising technological capital (K_T) — proxied by robot/automation density, software and intangible capital accumulation, AI adoption surveys, and AI-related patenting — leads to a decline in labor’s share of output.
Firm- and industry-level panel regressions linking constructed K_T intensity measures to labor shares, supported by macro growth-accounting decompositions; robustness checks include difference-in-differences and instrumenting adoption with plausibly exogenous shocks (e.g., cross-border technology diffusion, trade shocks); validated with cross-country comparisons and case studies.
Fuel subsidy reform imposed an enormous fiscal burden that peaked at 2.8% of GDP in 2022, limiting the macroeconomic leverage of AI-driven efficiency gains.
Reported fiscal statistic in the paper (2.8% of GDP in 2022) and its role in analysis of why AI savings do not translate into large macro gains.
The oil and gas trade balance remained in deficit at -1.55 billion USD in May 2025 and -1.58 billion USD in July 2025 despite an overall national trade surplus.
Reported trade-balance figures in the paper (monthly trade statistics for May and July 2025).
The framework is calibrated with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, and implemented in computer vision.
Calibration and empirical implementation using O*NET, a domain expert survey (n=3,778), and GPT-4o task decompositions; applied to computer vision tasks.
We introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level.
New metric proposed in the paper (entropy-based task complexity) and mapping procedure from accuracy to substitution ratio; implemented in the framework.
Costinot and Werning (2023) develop a sufficient-statistic approach and find optimal technology taxes of 1–3.7% on robots.
Citation reported in the paper summarizing Costinot and Werning (2023)'s quantitative sufficient-statistic estimate.
Guerreiro et al. (2022) characterize optimal Mirrleesian tax system with automation and find that robot taxes should be transitional—high when incumbent workers cannot retrain, converging to zero as new cohorts adjust skill investments.
Citation reported in the paper summarizing Guerreiro et al. (2022)'s theoretical result on transitional robot taxes.
If labor becomes economically redundant, the policy focus shifts from steering innovation to redesigning public finance and redistribution (e.g., new tax instruments, redistribution mechanisms).
Theoretical scenario analysis in the paper with references to related works (Korinek and Juelfs 2024; Korinek and Lockwood 2026).
Evaluation is carried out under three frozen context configurations (diff only: config_A; diff with file content: config_B; full context: config_C) enabling systematic ablation of context provision strategies.
Methodological description: three fixed context configurations defined and used for ablation experiments.
Traffic performance is evaluated using the Fundamental Diagram (FD) under varying driver heterogeneity, heterogeneous time-gap penetration levels, and different shares of RL-controlled vehicles.
Description of experimental/evaluation setup in the paper: macroscopic evaluation via Fundamental Diagram across varied scenario parameters. No numeric sample size provided in the claim text.
CriQ is a sister app to Dream11, India's largest fantasy sports platform with over 250 million users.
Descriptive statement in the paper providing context about the application domain and user base.
We performed an extensive evaluation of 37 state-of-the-art Vision-Language Models on MultihopSpatial.
Empirical evaluation described in the paper listing the number of models evaluated (37).
The paper treats data as a new type of production factor and endogenizes it within the production function.
Theoretical/methodological: the paper constructs a macro-level theoretical model that explicitly includes data as an endogenous input in the production function (no empirical/sample data).
Economic evaluations of GLAI should account for end-to-end risk externalities (error propagation, institutional trust, rights impacts), not only short-term productivity gains.
Methodological recommendation grounded in conceptual synthesis of technical, behavioral, and legal risks; normative argument rather than empirical result.
Generative Legal AI (GLAI) systems are built on token-prediction (LLM) architectures rather than formal legal-reasoning architectures.
Conceptual and technical analysis in the paper distinguishing GLAI from other legal-tech; literature synthesis on common LLM architectures. No original empirical dataset or sample size—qualitative/technical review.
The paper's formalism shows that prompt/system messages shape distributions over possible execution paths (indirect control) but do not evaluate actual partial paths at runtime.
Formal mapping in the paper that treats prompts as shaping prior over paths; conceptual argument and illustrative examples.
Through a thematic review of existing research, the authors identified recurring themes about incentive schemes: their components, how researchers manipulate them, and their impact on research outcomes.
Authors' stated method and findings: thematic review (the scope/number of reviewed papers not specified in excerpt).
A critical aspect of conducting human–AI decision-making studies is the role of participants, often recruited through crowdsourcing platforms.
Claim based on the authors' thematic literature review noting participant sourcing practices (specific studies and counts not given in excerpt).
Researchers conduct empirical studies investigating how humans use AI assistance for decision-making and how this collaboration impacts results.
Statement summarizing the research landscape; supported implicitly by the authors' thematic review of existing empirical studies (number of studies not specified in excerpt).
The study provides empirical evidence specific to a small open EU economy (Slovakia) on the relationship between AI adoption and labour productivity.
Use of harmonised Eurostat enterprise and productivity data for Slovakia and EU27 over 2021–2024, analysed with descriptive statistics, gap analysis, dynamics of change, correlation, and an illustrative regression model.
Returns to AI are heterogeneous across firms; estimating treatment effects requires attention to selection, complementarities, and dynamic adoption pipelines.
Methodological argument referencing treatment-effect literature and observed firm heterogeneity; supported by conceptual examples rather than a single empirical treatment-effect estimate.
The study adopted a positivist philosophy and a descriptive-correlational design.
Methods section statement in the paper describing the research philosophy and study design.
Data were collected from innovation-focused executives across 39 licensed Kenyan commercial banks.
Paper statement specifying sample source: 'Using data from innovation-focused executives across 39 licensed banks.'
Technological innovation was assessed via adoption of new systems, integration of digital channels, and use of Artificial Intelligence and data analytics.
Measurement description provided in the paper listing the components used to operationalize technological innovation.
Competitiveness in the study was measured through market share, return on equity and customer satisfaction.
Measurement description provided in the paper describing dependent variable operationalization (explicit list of three indicators).
The user study had N=50 participants.
Reported user study sample size (N=50) used to evaluate AI-assisted intent expansion in ecologically valid settings.
Under the current evaluation resolution, 5W3H, CO-STAR, and RISEN achieve similarly high goal-alignment scores, suggesting that dimensional decomposition itself is an important active ingredient.
Controlled comparison between three structured frameworks (5W3H, CO-STAR, RISEN) across the evaluated outputs, with no meaningful differences reported between them.
The study evaluated 3,240 model outputs (3 languages x 6 conditions x 3 models x 3 domains x 20 tasks) using an independent judge (DeepSeek-V3).
Reported experimental design and evaluation: 3 languages, 6 conditions, 3 models, 3 domains, 20 tasks; judged by DeepSeek-V3.
We implement a rigorously controlled execution-based testbed featuring Git worktree isolation and explicit global memory to evaluate agent coordination frameworks.
Methodological description in the paper indicating the testbed design choices (Git worktree isolation, explicit global memory) used to ensure controlled, reproducible execution of agent-generated code.
We benchmark a single-agent baseline against two multi-agent paradigms: a subagent architecture (parallel exploration with post-hoc consolidation) and an agent team architecture (experts with pre-execution handoffs) using a rigorously controlled, execution-based testbed.
Description of experimental setup in the paper: an execution-based testbed with Git worktree isolation and explicit global memory; experiments explicitly compare single-agent, subagent, and agent-team architectures under fixed computational time budgets.
Limitations: the Comscore data observe household internet activity on home (non-mobile) devices and do not capture offline or mobile device activities, so extrapolation to total at-home activities should be done with caution.
Authors' explicit limitation discussion in paper stating data do not include mobile devices or offline activities.
ChatGPT adoption leaves the total time spent on productive online activities (including any time spent using ChatGPT) unchanged.
Same IV long-difference estimates as above; authors state 'leaving time spent on productive digital tasks unchanged' and that total productive activity time does not decline significantly.
The analysis uses detailed Internet browsing microdata from over 200,000 U.S. households' home devices from 2021 to 2024.
Comscore web browsing panel described in paper; authors state dataset covers 'over 200,000 U.S. households' across 2021-2024; data provides timestamps, visit durations, URLs, demographic bins, etc.
We release the anonymized dataset and analysis with a new query intent taxonomy to inform future designs of real-world AI research assistants and to support realistic evaluation.
Paper states that the anonymized Asta Interaction Dataset, accompanying analysis, and a new query intent taxonomy are being released publicly.
The Asta Interaction Dataset comprises over 200,000 user queries and interaction logs from two deployed tools (a literature discovery interface and a scientific question-answering interface) within an LLM-powered retrieval-augmented generation platform.
Statement in paper describing dataset composition: >200,000 user queries and interaction logs collected from two deployed tools (literature discovery and scientific Q&A) within an RAG platform. Dataset release described in methods/dataset section.
Methods combine targeted literature synthesis, comparative conceptual analysis, and framework building (with recent scholarly and institutional sources reviewed).
Explicit methodological statement in the paper describing the review and analytic approach; no primary-data methods used.
AI coding assistants are a high-visibility class of corporate AI and are given special attention as an illustrative case in the paper.
Paper specifically calls out AI coding assistants as a focal example in the conceptual analysis and discussion; based on literature review rather than original measurement.
The Article translates these insights into risk-sensitive guideposts for modernizing governance of AI-enabled tools and emerging modalities, from agentic systems to blockchain-deployed smart contracts.
Prescriptive/conceptual policy guidance presented in the Article (normative recommendations; governance framework).
The Innovation Frontier traces LegalTech’s evolution from 2000s-vintage e-discovery to generative AI.
Historical/chronological analysis in the Article (literature review/history of LegalTech provided by authors).
The Legal Services Value Chain disaggregates the lifecycle of a legal matter into five distinct nodes of activity.
Model description in the Article (conceptual architecture; decomposition of legal work).
The Article develops two core organizing models: the Legal Services Value Chain and the Innovation Frontier.
Explicit claim in the Article describing conceptual/model contributions (theoretical/model-building).
This Article provides a practical framework for navigating the shifting terrain of legal innovation and AI.
Statement of purpose in the Article (conceptual contribution; framework development). No empirical validation reported in the excerpt.
The SRL did not generate designs with significantly better performance than RWL, even though it explored a different region of the design space.
Empirical comparison on the battery pack design task showing no significant performance improvement of SRL over RWL despite differing exploration; exact statistical tests, p-values, and sample sizes are not provided in the excerpt.
The empirical analysis is based on A-share listed companies from 2015 to 2023.
Data description in the paper stating the study sample and time period (A-share listed firms, 2015–2023).
Three interlocking threads characterize AI for science: (1) AI as research instrument, (2) AI for research infrastructure, and (3) the reshaping of scholarly profiles and incentives by machine-readable metrics.
Conceptual framework presented in the paper; organization of topics rather than empirical measurement. The paper indicates these threads are followed through historical and contemporary examples.