Evidence (270 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
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 claims
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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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Unprecedented AI capital expenditure coexists with persistent operating losses, speculative valuations, and fragile revenue models.
Empirical characterization asserted in the paper (references implied); the provided excerpt does not state specific datasets, firms counted, dates, or sample size.
The modality gap (weaker penalty for visual vs. textual AI-use disclosure) widens when AI is used in final products but narrows when AI is used in marketing materials.
Interaction analyses across application stages (final product vs. marketing material) within the 41,073 Kickstarter projects, using LLM-assisted classification to label both modality and application stage and entropy balancing for covariate control.
A standard learning agent can obtain near-reference revenue per available room (RevPAR) while failing to learn market-like yield management: it sells too aggressively, undercuts, or collapses to modal price buckets.
Experiments in a two-hotel revenue-management simulator where Hotel A is trained against a fixed rule-based competitor (Hotel B); comparison of learned agent behavior to market-like yield management patterns observed in traces.
AI adoption moves value creation away from physical resources and human collaboration toward continuous token flows produced through data refinement loops.
Theoretical/analytical claim within the Structural Dissolution Framework and illustrative discussion; no empirical quantification provided in the text excerpt.
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.
Reimbursement models (fee-for-service vs. capitation) will influence whether cost savings from GenAI are realized or offset by increased service volume.
Economic incentive framework and prior health-economics literature cited; the paper does not provide direct empirical tests but references plausible incentive channels.
Digital and even non-digital sectors generate no profit without data, technology, and infrastructure.
Author's theoretical argument and interpretation of contemporary observations (paper's conceptual analysis); not reported as a quantified empirical estimate.
Free overrides also cut sales by 1.19%.
Randomized field experiment comparing free-overrides arm to control; effect reported as 1.19% reduction in sales.
Disclosing AI involvement in visual content creation is associated with a weaker funding penalty than disclosing AI involvement in textual content creation.
Subgroup/moderation analysis within the same dataset (41,073 Kickstarter projects) comparing projects that disclosed AI-use in visual modalities versus textual modalities, using LLM-assisted classification to determine modality and entropy balancing for covariate adjustment.
AI-use disclosure is associated with a significant decline in funding performance for Kickstarter projects.
Observational analysis of 41,073 Kickstarter projects using LLM-assisted text classification to identify AI-use disclosure and entropy balancing to adjust for covariate differences; statistical tests reported as significant in the paper.
Capital expenditure has accelerated faster than observed monetization in some layers of the AI stack.
Comparative analysis of capex trends vs monetization metrics presented in the paper (layered AI stack comparison); specific sample counts not provided in the abstract.
AI platforms transform this uncertainty into extractable value through subscription models, token-based pricing, and prompt marketplaces.
Political-economic / theoretical tracing in the paper citing platform business models (subscription, token pricing, prompt marketplaces) as mechanisms that monetize user uncertainty; no quantitative revenue or case-study sample sizes given in the abstract.
AI adoption intensifies existing sustainability challenges for the newsroom, as journalistic content and labour increasingly support AI systems without corresponding financial return.
Qualitative interview data and organisational analysis from Al-Masry Al-Youm indicating increased use of journalistic outputs for AI purposes and lack of matched revenue; sample size not reported in the excerpt.
Existing generative AI models do not directly optimize marketplace performance.
Stated as an observed limitation / motivation for the proposed method in the paper (conceptual claim; not an empirical test reported in the excerpt).
Firms working under such conditions often experience limited access to finance and markets.
Claim derived from literature on firm constraints in weak institutional/sanctioned contexts as reviewed in the paper; no primary empirical data reported.
Rising data velocity renders legacy systems obsolete—threatening approximately $3.4 trillion in global marketing spending.
Paper reports an estimate/claim about threatened global marketing spending tied to legacy systems becoming obsolete (derivation likely from the study's quantitative analysis or economic estimate described in the paper).
Autonomous software populations can amass computing budgets without ever achieving general intelligence.
Claim supported by the scenario narratives (Lamarck/Remora/Mycelium) and conceptual reasoning in the paper; no empirical quantification reported.
Strong heuristic, single-agent RL, and multi-agent RL baselines (including Greedy, SAC, MAPPO, and MADDPG) achieved net profit in the range $0.58M--$0.70M in the same experiments.
Empirical comparison in the paper's experiments on the NYC-taxi-based EV fleet simulator listing baseline methods and their reported net profits ($0.58M--$0.70M).
SMEs are suffering from various financial constraints, mostly relying heavily on traditional financial institutions for their survival (Kadzima et al., 2025).
Statement supported by citation to Kadzima et al. (2025); presented as a literature-supported empirical generalization in the paper's background/introduction. No sample size or empirical details given in the excerpt.
Lower survival rates among BDA adopters are driven by greater uncertainty in sales.
Paper states greater uncertainty in sales is an interrelated factor explaining lower survival for BDA adopters, based on empirical analysis of German start-ups.
A single ungoverned automated agent produced $5.4-10 billion in losses in the 2024 CrowdStrike outage.
Statement in paper attributing a $5.4-10B loss to an ungoverned automated agent during the 2024 CrowdStrike outage; no citation or method shown in excerpt.
Many agents hover around the break-even point despite similar semantic matching scores.
Observed empirical pattern reported in benchmark results: agents with similar semantic matching scores nevertheless show different financial outcomes (many near break-even).
AI can worsen financial and market performance if it crowds out normal R&D.
Paper's empirical analysis and interpretation linking AI dependence to poorer financial/market performance through displacement of standard R&D activities; presented as a study finding.
High AI dependency disclosed in financial reports does not improve firms' financial health and may even endanger it.
Empirical results drawn from the study's analysis of listed new energy vehicle and automobile manufacturers (2013–2023); statement appears in the paper's findings/conclusions.
AI dependency reduces financial safety for listed new energy vehicle and automobile manufacturers.
Empirical analysis of a sample of listed new energy vehicle and automobile manufacturers covering 2013–2023; the paper reports data analysis showing AI dependency reduces financial safety.
These harms increasingly translate into financial loss through litigation, enforcement penalties, brand erosion, and failed deployments.
Paper argues this linkage using conceptual reasoning and illustrative examples/case vignettes; cites regulatory and market incidents but does not provide systematic empirical estimates or a sample size.
When the framework is extended to include quality heterogeneity and reinforcement dynamics, equilibrium outcomes exhibit declining average payoffs.
Analytical extension of the baseline formal model to incorporate heterogeneous quality and reinforcement (preferential attachment) dynamics; theoretical derivation in the paper; no empirical sample.
In markets with near-zero marginal costs and free entry, increases in the number of producers dilute average attention and returns per producer.
Formal theoretical model introduced in the paper (Builder Saturation Effect) that assumes near-zero marginal costs, free entry, and finite human attention; no empirical sample or experimental data reported.
We quantify an annual opportunity gap of 865,917 unrealized visits, equivalent to approximately 11.96 billion yen (USD 76.2 million) in lost revenue.
Model-based estimate produced by the DSS using the analyzed datasets and the DHDE-informed optimization; figure reported directly in the paper.
Traditional methods, such as rule-based algorithms and statistical scale forecasting, struggle to adapt to rapidly changing market conditions, competitive maneuvers, and evolving consumer strategies, leading to sub-optimal pricing and decreased profitability.
Paper asserts this as background/motivation; no detailed empirical study or sample size provided in the excerpt.
Africa is abundant in natural resources but exhibits relatively low development/outcomes from those resources, creating resource allocation and value-capture problems relevant to OSCM.
Development economics and regional studies literature cited in the paper's synthesis; conceptual claim without new empirical testing.
Higher complaint volume is significantly associated with near-term stock price declines.
Fixed-effects panel path models estimated on monthly data for 261 financial firms (2018–2023) report statistically significant negative associations between firm–month complaint volume and subsequent abnormal returns.
Consumer complaints—measured by monthly volume, topic composition, and VADER sentiment of complaint narratives—contain behavioral signals that predict short-term abnormal stock returns in U.S. financial firms.
CFPB complaint records matched to 261 publicly traded U.S. financial firms (monthly observations, 2018–2023); analyses use fixed-effects panel path models to link firm–month complaint features (volume, LDA topic prevalences, aggregated VADER sentiment) to firm-level abnormal returns; complementary machine-learning models evaluate out-of-sample predictive performance.
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.
In a portfolio-management case study, an agent infers latent market regimes from market and macroeconomic information, generates belief-conditioned forecasts, and constructs portfolios using a Black–Litterman framework.
Empirical/methodological case study described in the paper implementing POMDP-based agent with Black–Litterman portfolio construction.
Digital transformation, AI adoption, and foreign direct investment (FDI) do not display statistically significant direct effects on export performance in the baseline specification.
Null statistical significance reported for these predictors in the study's baseline pooled OLS / fixed-effects regressions (abstract statement); no specific coefficients reported in abstract.
Each firm in CoffeeBench seeks to maximize cumulative net income through communication and transactions while managing cash, inventory, and pricing.
Specification of agent objectives and state variables in the benchmark design (cumulative net income objective; resources: cash, inventory; decision variables: pricing and transactions).
More than 5,000 ETH was deployed by agents during the experiment.
Accounting of ETH held/deployed by agent-controlled vaults during deployment.
Agents executed about $20M in trading volume over the deployment.
Aggregate trading-volume accounting from the bounded onchain market during deployment.
The average effect of AI adoption on market value (Tobin's Q) was not statistically significant across all firms.
TWFE and PSM estimates on KOSDAQ-listed firms (2018–2025) reporting firm-level Tobin's Q before and after identified AI-adoption timing.
The emerging AI agent economy is projected to handle trillions of dollars in transactions by 2030.
Projection/assertion in report (no supporting empirical method or citation provided in the excerpt).
AI adoption moderated the relationship, strengthening the direct impact of green investment on stock returns.
Moderation analysis reported in the paper using the firm-level sample of heavily polluting Chinese firms (2012–2023), testing interaction between green investment and AI adoption on stock returns.
Green technological innovation (G_patent) mediated the relationship between green investment and stock returns.
Mediation analysis using the same firm-level dataset (heavily polluting Chinese firms, 2012–2023) with G_patent included as the mediator variable.
Green investment had a direct effect on the firm’s stock return.
Empirical analysis performed on firm-level data from heavily polluting firms in China (2012–2023); reported regression results showing a direct relationship between green investment and stock returns.
AI-powered CRM and predictive analytics systems improve marketing effectiveness and customer engagement.
Synthesis of findings from the 22 studies included in the systematic review (PRISMA-guided search across Scopus, ScienceDirect, Google Scholar, 2017–2026) using thematic analysis.
In large-scale online A/B tests on Tao Factory, AIGP achieved +13.21% in Gross Merchandise Value (GMV) over 14 days compared to the production baseline.
Reported result from 'large-scale online A/B tests on Tao Factory' comparing AIGP to production baseline over a 14-day period; exact experiment sample size not provided in the excerpt.
Digitalization enables service-sector expansion through fintech and e-commerce.
Empirical sectoral data and comparative case studies highlighting fintech and e-commerce impacts in services; policy analysis situates enabling conditions. No numeric sample size or quantified effect in summary.
Organizations that deliberately architect human-AI relationships are 2.5 times more likely to report superior financial performance.
Reported association from Deloitte's 2026 Global Human Capital Trends survey analysis (paper states '2.5 times more likely').
Organizations that deliberately architect human-AI relationships are twice as likely to exceed AI investment returns.
Association reported in the paper based on analysis of Deloitte's 2026 Global Human Capital Trends survey (over 3,000 business leaders); specific comparative statistic 'twice as likely' reported.