Evidence (7395 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Adoption
Remove filter
Developers actively manage the collaboration, externalizing plans into persistent artifacts, and negotiating AI autonomy through context injection and behavioral constraints.
Observed behaviors in chat transcripts and committed artifacts showing developers creating persistent plans, injecting context, and specifying constraints to shape AI behavior.
Developers redistribute cognitive work to AI, delegating diagnosis, comprehension, and validation rather than engaging with code and outputs directly.
Content and interaction analyses of chat sessions showing developer prompts delegating diagnosis, comprehension, and validation tasks to the AI assistants (Cursor and GitHub Copilot) across the dataset.
Conversational programming operates as progressive specification, with developers iteratively refining outputs rather than specifying complete tasks upfront.
Qualitative/content analysis of the 74,998 messages across 11,579 sessions indicating patterns of iterative prompts and refinements rather than one-shot complete specifications.
The influence of human capital (number of specialists in scientific and technological fields) on value added varies across sectors.
Number of specialists in scientific and technological fields included as a covariate in MMQR; reported heterogeneous effects across sectors/quantiles in the results section.
The influence of R&D expenditure on value added varies across sectors.
R&D expenditure included as a core explanatory variable in panel MMQR estimations; authors report differing coefficient sizes/signs across sectors/quantiles.
Policy enforcement maintains a 52.8% success rate for legitimate requests.
Quantitative result reported from the paper's experiments (52.8% success rate for legitimate requests under policy enforcement).
The inequality-reducing impact of AI is weaker when carbon inequality is measured by the Theil index, implying persistent structural divides between advanced and less developed regions.
Same provincial panel dataset (2003–2021) with the Theil index as the dependent variable; results show a weaker (and impliedly less robust) association between AI development and Theil-measured carbon inequality.
AI adoption is positively associated with exports to all destination regions examined except China (multivariate probit model that accounts for correlated errors across destination-specific export decisions).
Multivariate probit model of destination-specific export decisions (model accounts for correlation among error terms); result indicates significant associations for AI with exports to all regions except China (sample size not reported in prompt).
These findings carry implications for workforce transition policy, regional economic planning, and the temporal dynamics of labor market adjustment.
Paper's discussion/interpretation of modeled ATE results and their policy/economic implications; no empirical test provided for policy outcomes.
AI is reshaping entrepreneurship by enhancing innovation, streamlining operations, and creating new business opportunities, but its impact varies across levels of financial development and economic contexts.
Introductory/motivating statement in the abstract; supported by the cross-country panel analysis (23 countries, 2002–2023) reported in the paper.
Big Data-based FinTech can contribute to financial stability only when its implementation is strategically justified, ethically grounded and supported by effective regulation, robust data governance and investment in human capital.
Normative conclusion drawn from systemic and structural analysis of literature and synthesis of empirical studies; no empirical test provided within the paper.
The effectiveness of Big Data solutions varies across the financial sphere and depends critically on data quality, regulatory alignment and organisational readiness.
Derived from comparative analysis of sector-specific applications and synthesis of findings in the reviewed literature; no quantified cross-sector sample reported.
AI intensity and employment elasticity are linked by a U-shaped relationship.
Result reported by the paper based on the authors' empirical/econometric analysis of international datasets (OECD/ILO/World Bank).
The paper analyzes AI as a continuous process using data from the OECD, ILO, and the World Bank to study job displacement, creation, and reallocation.
Empirical analysis described in the paper using datasets from OECD, ILO, and World Bank; econometric approach implied.
AI is recognized as a primary change agent that influences various aspects of economies the world over, and thus it profoundly changes not only the number of jobs but also their quality.
Stated as a high-level conclusion in the paper's introduction/abstract; based on literature synthesis of studies from 2013-2025 and references to international sources (OECD, ILO, World Bank).
AI plays a dual role by enhancing productivity while intensifying energy use in the short run.
Synthesis of empirical findings in the paper: documented short-run increase in electricity growth (energy use) following AI adoption alongside statements/evidence that AI enhances productivity (exact productivity measures and estimates not provided in the summary).
Perceived algorithmic influence varies across users and moderates how personalization translates into opinion outcomes.
Survey measures of perceived algorithmic influence combined with moderation tests (interaction terms) in regression-style analyses on the N = 450 sample; authors report heterogeneity in perceived algorithmic impact and moderation of the selective exposure–polarization association.
The four-variable account (produced output, underlying understanding, calibration accuracy, self-assessed ability) better explains phenomena like overconfidence, over- and under-reliance on AI, 'crutch' effects, and weak transfer than the simpler claim that generative AI merely amplifies the Dunning–Kruger effect.
Argumentative synthesis in the paper comparing explanatory power of the proposed four-variable framework against the more general Dunning–Kruger metaphor; draws on examples and empirical patterns from the reviewed literature rather than a single empirical test.
A useful working model is 'AI-mediated metacognitive decoupling': LLM use widens the gap among produced output, underlying understanding, calibration accuracy, and self-assessed ability.
Conceptual synthesis and theoretical proposal grounded in reviewed empirical findings from multiple literatures (human–AI interaction, learning research, model evaluation); presented as the paper's working model rather than as a single empirical estimate.
Using pre-existing exposure as an instrument for ChatGPT adoption in a long-difference IV design, ChatGPT adoption causes households to spend more time on digital leisure activities while leaving total time spent on productive online activities unchanged.
IV long-difference empirical design: instrumenting household adoption with pre-ChatGPT exposure (2021 browsing); outcome measured as changes in categorized browsing durations (LLM-based classification into 'leisure' vs 'productive' sites); controls include demographic-by-region fixed effects and browsing composition controls.
This paper offers a forward-looking framework that emphasizes the decentralizing potential of AI on labor markets, moving beyond the traditional displacement-versus-creation dichotomy.
Paper's stated contribution; based on conceptual framework and synthesis of historical and contemporary analyses (no empirical validation presented in the abstract).
The emergence of artificial intelligence and robotics is catalyzing a profound transformation in the nature of human labor.
Stated as a central premise in the paper's abstract; supported by the paper's synthesis of economic history, contemporary labor market data, and analysis of digital platform growth (no specific datasets or sample sizes reported in the abstract).
AI agents are approaching an inflection point where the binding constraint shifts from raw capability to how work is delegated, verified, and rewarded at scale.
Conceptual argument presented in the paper's introduction/positioning; no empirical data, experiments, or sample reported.
Country-specific (fixed) effects show substantial heterogeneity: some countries (e.g., Denmark, Estonia, Korea) exhibit strongly positive deviations, while others (e.g., India, South Africa) show persistently negative deviations from average trajectories.
Reported country-specific fixed effects/deviations in abstract with illustrative examples of countries with positive and negative deviations. No numeric country-level effect sizes provided in abstract.
At equilibrium prices in symmetric markets, consumer surplus is improved by cheaper search but may be decreased by more informative search, due to weakened inter-business competition.
Equilibrium price analysis within the theoretical model for symmetric firms; comparative statics showing how search cost and signal informativeness affect pricing, competition intensity, and consumer surplus. No empirical validation reported.
The market (in the model) tracks indications of fit for searched products and indications of quality for chosen products, thereby guiding subsequent searches.
Model structure and assumptions specified in the paper: an endogenous information-tracking mechanism that records signals from searches and purchases and which then influences future search behavior; presented as part of the theoretical framework rather than empirical evidence.
Behavioral factors — specifically trust calibration, cognitive load, and affective reactions — shape the transition of corporate AI initiatives from pilot deployments to scalable, sustained use.
Synthesis of human-AI interaction literature integrated with adoption frameworks (TAM and TOE); conceptual linkage rather than new empirical testing in this paper.
The proportion of consumers who adopt AI-induced services influences the pricing of those services and through price adjustments will further impact wages across traditional and non-traditional services.
Theoretical development and analysis in the paper via a demand-switching model and a Finite Change General Equilibrium framework introducing AI as a technological shock modeled through price adjustments.
AI accelerates value-chain maturation while creating distinct risks — including professional responsibility tensions and potential system-level externalities.
Conceptual argument and risk analysis in the Article (theoretical reasoning and synthesis of management/ethics literature). No empirical causal estimate reported in the excerpt.
The legal profession is at a crossroads, caught between intensifying fears of AI-driven displacement and a generational opportunity for transformation.
Author's synthesis and framing in the Article (conceptual assessment; literature/contextual synthesis). No empirical sample or experiment reported in the excerpt.
This advantage is contingent upon robust AI governance, ethical frameworks, and the transition from 'pilot-lite' projects to integrated, data-driven 'AI-first' business models.
Conditional claim in the paper linking success to governance, ethics, and organizational integration; appears to be normative/analytical rather than empirical in the abstract.
Actual sharing often contradicted willingness to share (the privacy paradox), with consistently high sharing rates across all conditions.
Observed discrepancy reported in the experimental results (N=240): despite variation in willingness-to-share, behavioral sharing rates remained high and similar across human, white-box AI, and black-box AI conditions.
Energy policy uncertainty has a nonlinear effect on AI investment: moderate uncertainty fosters innovation, whereas high volatility hinders long-term investment.
Empirical analysis using nonlinear methods (WQR and WQC) on US quarterly data 2013Q1–2024Q4 (48 quarters), assessing distributional asymmetries across quantiles and time–frequency bands.
Machine-readable metrics and open scholarly infrastructure are reshaping scholarly profiles and incentives.
Conceptual and historical discussion referring to platforms and metrics (e.g., arXiv, Google Scholar, ORCID) as mechanisms changing incentives; no new empirical estimates provided.
That interconnected ecosystem is fundamentally restructuring who can do science (access), how fast discoveries propagate, and what counts as a valid scientific contribution.
Argumentative claim linking infrastructural and tool changes to changes in access, dissemination speed, and norms of contribution. The paper presents examples and narrative but no systematic empirical evaluation or sample.
The most consequential development is not any single tool but the emergence of an interconnected ecosystem—AI agents, preprint platforms, open source codebases, and citation infrastructure—that forms a feedback loop.
Synthesis/argument based on multiple examples (LLM agents, preprint servers like arXiv, open-source code repositories, citation indices). No quantitative measurement or causal identification reported.
The central tension in AI for science is between automation (building systems that replace human researchers) and augmentation (tools that amplify human creativity and judgement).
Analytical claim based on the paper's review of historical examples and conceptual discussion; no primary data or experimental design reported.
Science has repeatedly delegated its bottlenecks to machines—first inference, then search, then measurement, then the full workflow—and each delegation solves one problem while exposing a harder one underneath.
Interpretive historical argument drawing on examples across AI-for-science milestones (e.g., DENDRAL, search and inference systems, measurement automation, and contemporary end-to-end workflows). No quantitative sample or experimental method reported.
AI agents implicate many areas of law, ranging from agency law and contracts to tort liability and labor law.
Legal/policy analysis in the paper enumerating legal domains implicated by AI agents (qualitative analysis; no sample size).
Firms of different ownership structures and industries exhibit different responses to the income distribution changes brought by AI (heterogeneous effects).
Paper reports performing grouped regressions by ownership type and industry to identify heterogeneous responses.
Financing constraints are a key factor that hinder firms' choice of technology level, which alters the corresponding income distribution effect of AI.
Paper posits financing constraint as a moderator and states it is considered in empirical analysis (interaction/moderation tests).
The development of AI may trigger new changes in the interest pattern between corporate profits and labor compensation.
Framed as the central research question/hypothesis; paper conducts empirical tests on firm panel data to evaluate this.
Artificial intelligence is profoundly reshaping the organizational form, operating model and operating mechanism of enterprises, and bringing unprecedented impact to the income distribution structure within enterprises.
Statement asserted in the paper's introduction/abstract; motivates empirical analysis using panel data of Shanghai and Shenzhen A-share non-financial listed firms (2010–2022).
Traffic performance is sensitive to the distribution of safe time gaps and the proportion of RL vehicles.
Simulation results comparing Fundamental Diagrams across scenarios with different distributions of safe time gaps and shares of RL-controlled vehicles. Number of simulation runs or replicates not stated in the claim text.
Chat intent varies systematically with both the timing of chat relative to search and the category of products later purchased within the same journey.
Cross-tabulation/regression-style descriptive analysis relating classified chat intents to timing (relative to search) and subsequent purchased product categories in journey-level logs.
AI assistance in safety engineering is fundamentally a collaboration design problem rather than merely a software procurement decision: the same tool can either degrade or improve analysis quality depending entirely on how it is used.
Synthesis of the formal framework and analytic results in the paper (theoretical argument; no empirical sample reported).
AUROC_2 and M-ratio produce fully inverted model rankings, demonstrating these metrics answer fundamentally different evaluation questions.
Metric comparison across models showing that AUROC_2-based ranking and M-ratio-based ranking are fully inverted in the reported results on the evaluated dataset.
Temperature manipulation shifts Type-2 criterion while meta-d' remains stable for two of four models, dissociating confidence policy from metacognitive capacity.
Experimental manipulation (temperature changes) applied to models; reported result that Type-2 criterion shifted with temperature while meta-d' was stable for two models (out of four) in the 224,000-trial dataset.
Metacognitive efficiency is domain-specific, with different models showing different weakest domains, invisible to aggregate metrics.
Domain-level analyses reported in the paper showing per-domain M-ratio results and identification of different weakest domains per model, contrasted with aggregate metric behavior.
Metacognitive efficiency varies substantially across models even when Type-1 sensitivity is similar — Mistral achieves the highest d' but the lowest M-ratio.
Empirical comparison of Type-1 sensitivity (d') and metacognitive efficiency (M-ratio) across the four evaluated LLMs on the 224,000 QA trials; explicit statement that Mistral had highest d' but lowest M-ratio.