Evidence (4781 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
9875 claims
Filter claims →
Productivity
8807 claims
Filter claims →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filtered →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filter claims →
Inequality
2332 claims
Filter claims →
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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Innovation
Remove filter
The spatial spillover effects are geographically constrained and vary significantly across regions.
Reported heterogeneity in spatial Durbin model results and discussion of geographic constraint and inter-regional variation (regional heterogeneity analysis).
The positive effect of big data applications on firms' markups exhibits heterogeneity across organizational, technological, and environmental dimensions.
Paper reports heterogeneity analysis showing variation in the magnitude of the positive markup effect across organizational, technological and environmental factors; based on model implications and empirical subgroup/interaction tests using micro-level firm data (sample size not reported).
The rapid deployment of multi-agentic AI systems is reshaping the foundations of copyright law and creative markets.
Theoretical and conceptual argumentation presented in the paper; no empirical sample or quantitative analysis reported.
Each country's legal framework could influence the ultimate trajectory of the AI race.
Framed in the chapter as a concluding implication of the comparative analysis; presented as a reasoned projection rather than an empirically validated prediction in the provided text.
Data privacy, intellectual property (IP rights), and export restrictions are three critical aspects of the American and Chinese legal infrastructure that significantly impact AI innovation.
Author(s) state this as the organizing premise of the chapter; comparative legal analysis and normative argumentation rather than empirical measurement.
Only a small subset of LLM retailers can consistently achieve capital appreciation, while many hover around the break-even point.
Empirical results from the 20-agent benchmark experiments reported in the paper, contrasting capital appreciation for winners vs break-even for many agents.
Benchmarking on 20 open- and closed-source LLM agents reveals significant performance disparities and a winner-take-most phenomenon.
Empirical evaluation described in the paper using 20 LLM agents (open- and closed-source); results reported show uneven performance distribution.
Evolutionary dynamics in the model reflect not just current fitness but factors related to the long-run growth potential of descendant lineages.
Mathematical analysis of the proposed model showing lineage growth potential influences dynamics (theoretical derivations/proofs within the paper).
There is a robust inverted U-shaped relationship between robotics manufacturing development and urban carbon emissions.
Panel data analysis using 277 Chinese prefecture-level cities from 2008 to 2019; econometric analysis reported in the paper finds an inverted U-shaped association and robustness checks are claimed.
As technological progress devalues labor, the welfare benefits of steering initially increase but, beyond a critical threshold, decline and optimal policy shifts toward greater redistribution.
Analytical result from the paper's theoretical model that compares planner's optimal technology choice under varying degrees of labor devaluation and redistribution costs.
Automation leads economic growth to accelerate, but the acceleration is remarkably slow because of the prominence of 'weak links' (an elasticity of substitution among tasks substantially less than one); even when most tasks are automated by rapidly-improving capital, output is constrained by the tasks performed by slowly-improving labor.
Theoretical mechanism from the task-based model (σ < 1 weak-links structure) combined with calibrated simulations that incorporate historical accounting results.
The effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations.
Analytical comparative-statics in the theoretical model: varying the fraction of R&D tasks performable by AI yields a non-monotonic relationship between AI task-share and optimal recombination distance, with a threshold determined by human-AI complementarity.
Higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals.
Comparative-static result from the analytical model: the paper derives a condition comparing the direct facilitation effect of AI on accessing distant knowledge and the indirect effect from increased competition; when the former dominates, equilibrium recombination distance increases with AI productivity.
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.
The results generalize to other technologies that feature safety externalities and first-mover advantages.
Authors' argument and model generalization: the mechanisms identified (preemption, externality, policy responses) are argued to apply beyond frontier AI to other technologies with similar strategic features.
Pigouvian safety taxes partially correct the safety externality but cannot eliminate the preemption distortion on their own.
Model policy counterfactuals: introducing a tax on unsafe releases reduces the externality-driven distortion but leaves residual preemption incentives so the first-best is not fully attained by tax alone.
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).
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.
Network externalities create an opportunity for win-win industrial policies, but the realisation of such mutually beneficial outcomes depends on market structure (product differentiation/substitutability) and the nature of innovation (product vs process).
Synthesis of model results across parameter regimes in the two-country strategic trade and R&D model showing conditional win-win equilibria; theoretical arguments (no empirical sample).
The welfare consequences of an industrial policy targeting a sector with network externalities are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods.
Analytical results from a two-country theoretical model with strategic trade and R&D investment; comparative-static analysis of equilibrium outcomes (no empirical sample).
As technological progress devalues labor, the welfare benefits of steering are at first increased but, beyond a critical threshold, decline and optimal policy shifts toward greater redistribution.
Theoretical model extension analyzing planner's optimal choice as labor's economic value changes; the paper states a non-monotonic relationship with a critical threshold.
Once efficiency is made explicit, the main practical question becomes how many efficiency doublings are required to keep scaling productive despite diminishing returns.
Framing/forecasting claim in the paper presenting an operational research question (conceptual; no empirical sample in excerpt).
The practical burden of scaling depends on how efficiently real resources are converted into that (logical) compute.
Argument in the paper linking conceptual 'logical compute' to real-world conversion efficiency (qualitative claim; no empirical sample in excerpt).
The compute variable is best understood as logical compute, an implementation-agnostic notion of model-side work.
Conceptual argument presented in the paper reframing 'compute' as an abstract, implementation-agnostic quantity (no empirical sample provided).
These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies.
Interpretation linking observed changes in budget allocation, team size, and task breadth (from the proposal dataset and task-level analyses) to theoretical predictions about general-purpose technologies (GPTs); empirical findings show organizational change rather than large average short-run productivity gains.
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.
The resulting AI safety profile is asymmetric: AI is bottlenecked on frontier research (novel tasks) but unbottlenecked on exploiting existing knowledge.
Theoretical implication of the novelty-bottleneck model distinguishing novel (human-judgment) vs. routine (covered by agent prior) components of tasks.
Wall-clock time can be reduced to O(√E) through team parallelism, but total human effort remains O(E).
Model-derived result showing parallelism across humans can speed wall-clock completion time while aggregate human effort does not drop asymptotically.
Better agents improve the coefficient on human effort but not the exponent (i.e., they reduce the constant factor but do not change the asymptotic scaling class).
Analytic result from the stylized model under the paper's assumptions about task decomposition and novelty fraction ν.
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.
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.
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.
The growth effects of AI are conditional on institutional quality and organizational adaptability.
Theoretical/analytical claim in the paper's framework and supported by the stylized-facts analysis indicating heterogeneity in productivity and growth outcomes by institutional and digital capacity indicators.
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.
The paper's primary contribution is to combine established ingredients—attention scarcity, free-entry dilution, superstar effects, and preferential attachment—into a unified framework directed at claims about AI-enabled entrepreneurship.
Stated contribution and methodological description in the paper (synthesis and applied formalisation); this is a descriptive/methodological claim rather than an empirical result.
Modern pretrained time-series foundation models can forecast without task-specific training, but they do not fully incorporate economic behavior.
Statement in paper's introduction/abstract summarizing prior capabilities and limitations of pretrained time-series foundation models (no experimental sample or numeric evidence provided in the excerpt).
The governance risk-mitigation effects of AI operate through increasing financial risk exposure.
Authors' mechanism tests indicate a relationship between AI adoption and changes in financial risk exposure measures, which they interpret as a channel affecting executive behavior.