Evidence (1325 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
Filter claims →
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 |
Although SMEs anchor employment and output across Sub‑Saharan Africa, their uptake of AI lags global benchmarks, and prevailing explanations emphasize capital, infrastructure, and institutional voids while overlooking leadership competencies.
Background/introductory claim made by the authors to motivate the study (presented as context rather than an empirical finding from this study).
Adoption is slowly accelerating among non-technology firms but very aggressive adoption in the technology sector which accounts for two-thirds of deeply integrated enterprise adoption.
Reported sectoral breakdown and temporal trend in adoption (authors' sector analysis of SEC 10-K–based adoption measure; statement that tech sector comprises two-thirds of deep adopters).
The DIAC model identifies three regimes of AI adoption and absorption: adoption without absorption, constrained complementarity, and adaptive complementarity.
Taxonomy and regime definitions derived in the paper's theoretical model (analytical/theory-building).
The paper analyzes the technical basis of the Context Access Divide in Model Context Protocol (MCP) and retrieval-augmented generation (RAG) architectures.
Technical analysis and architectural examination reported in the paper (discussion of MCP and RAG as implementation-relevant architectures).
Macroeconomic evidence remains cautious because AI diffusion is still uneven across industries and many firms are in early adoption stages.
Paper's synthesis of macroeconomic and industry-level sources (OECD, IMF, BLS, McKinsey, etc.) reporting uneven diffusion and early-stage adoption.
The long-term success of AI-enabled talent acquisition depends not only on technological performance but also on the ability to ensure fairness, accountability, transparency, and ethical decision-making throughout the recruitment lifecycle.
Concluding synthesis drawn from the systematic review of 34 studies combining evidence on technical performance, bias risks, governance, and regulatory considerations.
The analysis reveals the emergence of five levels of talent acquisition maturity, ranging from traditional applicant tracking systems and data-driven workforce acquisition to predictive talent acquisition and fully autonomous recruiting models.
Qualitative synthesis and classification produced from the systematic review of 34 studies.
There is a similar shift to agentic tooling outside OpenAI, particularly within organizations, although external adoption remains lower and more uneven.
Comparative usage analysis across three populations (external personal-account users, external organizational-account users, and OpenAI workers) from Codex logs.
Cluster analysis reveals diverse yet cohesive national profiles across the EU that reflect differences in digital readiness, human capital, and institutional factors.
Cluster analysis performed on country-level indicators (AI adoption, digital readiness, human capital measures, institutional factors) to group EU countries into profiles; summary reports heterogeneous but cohesive clusters; exact cluster counts and sample size not reported.
The Simpson's paradox in the pooled result is driven entirely by agent composition: Codex dominates 64.9% of the dataset.
Descriptive composition statistics from the AIDev dataset showing agent shares; explicit statement that Codex comprises 64.9% of dataset.
The relevance of Chinese experience for Russia can be assessed in contexts such as eGrocery, O2O services, ecosystem delivery and remote/northern regions, and Russian material serves as an applied block for that assessment.
Methodological claim based on the study's comparative framework combining Chinese case analysis with applied Russian regional material (Sakha Republic).
AI brand visibility can be measured, differs by platform, and varies strongly by brand maturity.
Synthesis claim supported by cross-platform/brand analyses reported in the paper (Ranqo dataset across multiple AI engines and >100 brands, March–May 2026); empirical results (tiered visibility, citation patterns) underpin the assertion.
The guarded engagement loop framework conceptualizes generative AI adoption as a feedback process in which risk perceptions may shape interaction conditions that, in turn, can influence observed performance and subsequent trust calibration.
Central conceptual claim of the paper; framework articulated by the authors and presented as a set of testable propositions (theoretical contribution rather than empirical finding in the abstract).
Tranquil periods lower subjective risk assessments, raise AI substitution intensity, and compound leverage, generating a cognitive Minsky moment in which subjective risk falls while true systemic fragility rises.
Derived dynamics and comparative statics in the formal model; stated as one of the paper's propositions. No empirical data.
Board composition, particularly the presence of female and minority directors, impacts AI adoption.
Statement in abstract reporting an analysis linking board composition variables (female and minority directors) to AI adoption outcomes in the dataset.
There were no significant differences in AI use based on most accountant characteristics, except in auditing where business owners reported a higher frequency of AI use.
Inferential statistical analysis of questionnaire data (comparative design); specific statistical tests and sample size not reported in the summary.
This study identifies critical gaps in current Nvidia-centric roadmaps and proposes a competing reference architecture.
Paper's comparative analysis of existing (described as Nvidia-centric) roadmaps and presentation of an alternative reference architecture; no empirical validation or case-study evaluation reported.
Both risk perception and guilt play a role in GenAI adoption (they are relevant predictors of employees' intention to continue using the technology).
Empirical finding reported from the vignette experiment linking risk perception and guilt to GenAI adoption intention (paper states 'highlight the role of both risk perception and guilt in GenAI adoption').
Advanced economies have integrated AI technologies at scale, while emerging economies such as Algeria face structural and institutional challenges that limit the potential impact of AI on productivity growth.
Asserted in the paper with supporting literature citations (Agrawal et al., 2019; Acemoglu & Restrepo, 2020) and comparative use of World Bank and Oxford Insights indices; no specific sample-size based causal estimate provided.
Visibility in LLM-integrated search is shifting from click-through optimization to 'Answer Inclusion Optimization' (AIO), where visibility depends on whether content is selected, synthesized, and cited within AI-generated responses rather than on SERP ranking alone.
Conceptual proposition and terminology introduced by the authors (AIO); presented as a reframing of visibility metrics rather than backed by quantified experiments in the excerpt.
The newsroom adopts, adapts, and governs AI across data journalism, fact-checking, and generative applications.
Empirical observations and interview data from Al-Masry Al-Youm detailing specific domains of AI integration (data journalism, fact-checking, generative tools). Sample size not reported in the excerpt.
AI-flagged complaints are geographically unevenly distributed.
Geographic analysis of AI-flagged complaint shares across jurisdictions using case metadata; authors report uneven distribution.
Any measurement of AI brand perception must condition on the buyer persona supplying the query: the same prompt produces materially different recommendation sets depending on who the model thinks is asking, and a measurement protocol that aggregates across personas systematically obscures that variation.
Argument based on observed persona-driven variation in recommendation sets across the audit; policy/methodological recommendation derived from empirical results.
The Anthropic model shows a larger point-estimate effect than the OpenAI configurations, though clustered CIs overlap for the closer contrast (sonnet vs. OpenAI/high).
Comparison of point estimates and clustered confidence intervals across model configuration cells in the audit.
We offer a three-stage lens: Augmentation, Automation, and Reconstruction.
Conceptual framework proposed by the authors; presented as a taxonomy in the paper (no empirical validation reported in the excerpt).
The utility-aware framework preserves inverse U-shaped demand patterns for attributes such as aesthetics and uniqueness, improving demand-based performance while preserving fidelity and semantic consistency.
Empirical claim from the paper that their method maintains observed inverse U-shaped demand relationships for certain attributes in their experiments while improving demand-related metrics.
Public data from Anthropic's Mythos Preview and Mozilla Firefox collaborations, along with public exploit-market price anchors and vulnerability reward programs, support the argument that the near-term shift is toward increased defender remediation throughput rather than simply more zero-days.
Explicit statement that the paper's argument is based on public datasets: Anthropic Mythos Preview, Mozilla Firefox collaboration records, exploit-market price anchors, and vulnerability reward program information (no sample sizes provided in the abstract).
Following the advent of high-performance generative models, AI use has been rapidly encouraged in some sectors while being restricted in others.
Descriptive claim in the paper's introduction/abstract; based on observation and literature context rather than new empirical data.
The framework does not force domains into the same shape; it surfaces each domain's actuarial geometry.
Empirical observation of differing frontier shapes and capital demands across the instantiated domains and traces.
Required reserve capital varies by 22x (Capital@50 from 289 to 6457).
Quantitative results reported in experiments across domains (Capital@50 values reported for domains; ratio computed).
The frontier exhibits a common low-reserve refusal and intermediate-release pattern across domains, with saturation only where the budget grid reaches full reserve demand.
Observed pattern reported across the four instantiated environments and the retail/airline tau-bench traces in experimental results.
Managerial traits, such as risk tolerance and patience, play a role in shaping firms' AI adoption decisions.
Inclusion of manager-level trait measures (risk tolerance, patience) in the ifo Business Survey and analysis showing associations between these traits and reported AI adoption.
Drivers and barriers to AI adoption include firm-specific characteristics and industry dynamics.
Survey-based analysis linking firm characteristics and industry-level factors to reported AI adoption decisions in the ifo Business Survey (likely correlational/regression analysis).
AI adoption/diffusion varies across firm sizes.
Analysis of adoption patterns by firm size using ifo Business Survey firm-level responses (comparison across size categories).
AI adoption correlates with more-recent digital infrastructure—cloud computing and predictive analytics—rather than legacy on-premises IT or descriptive analytics.
Correlational analysis using variables from the Census Bureau survey that measure presence of cloud computing, predictive analytics, on-premises IT, and descriptive analytics; sample derived from ~28,500 establishments.
AI is less prevalent in simpler channels of automation overall, but AI is more prevalent on labour-substituting margins in lower-income settings and tends to augment labour in higher-income settings.
Task-level coding for technological channel and whether AI is involved, aggregated across 124 countries (2.33M task-country labels) and compared across income groups and labour margins (substitute vs augment).
For the country as a whole and for the eastern, central, and western regions, there is a deviation from the conjugate (coordinated) state between digital talent agglomeration and industrial digitalization.
Subsample/regional analysis across China’s regions (national and by eastern/central/western regions) reported in the paper indicating lack of positive coordination between talent agglomeration and industrial digitalization in these areas. Exact methodology and sample sizes by region not provided in the excerpt.
"General knowledge application" is the second most popular category among highlighted benchmarks, yet it is vaguely defined.
Categorization results from applying the paper's taxonomy to the Benchmarking-Cultures-25 dataset (counts/rankings reported by category). The paper comments on the vagueness of the label.
The primary way to establish and compare competencies in foundation and generative AI models has shifted from peer-reviewed literature to press releases and company blog posts, where model builders highlight results on selected benchmarks.
Descriptive/argumentative claim in the paper's introduction framing the research question; based on the authors' survey of contemporary practices and motivation for the dataset and analysis.
The system is generically bistable, with a stable partial adoption equilibrium coexisting alongside full genuine adoption.
Analytical results from the evolutionary game-theoretic model demonstrating multiple stable equilibria (bistability). No empirical sample (theoretical proof / model analysis).
Doctors choose among three strategies: genuine adoption, partial adoption, and rejection, where genuine adoption is required for systemic benefits to materialise above a population threshold.
Model specification in an evolutionary game-theoretic framework; analytical description of strategy set and threshold condition. No empirical sample (theoretical model).
Rising density from rack- and pod-scale AI systems shapes these outcomes (deployable capacity, capex, performance) — we quantify how density changes these outcomes.
Modeling/simulation results reported in the paper quantifying the impact of rising rack/pod-scale density on deployable capacity, capex, and performance; specific numeric quantification not included in the abstract.
Anhand von Fallstudien aus den G7-Ländern werden verschiedene Einsatzmöglichkeiten veranschaulicht und die wichtigsten Erfolgsfaktoren benannt – Netzanbindung, KI-Inputs, Kompetenzen und Finanzierung.
Evidence comes from G7 country case studies reported in the paper; method = qualitative case studies identifying key success factors (no number of case studies or sample size provided in excerpt).
Generative AI lowers barriers to solo entrepreneurship while reinforcing team-based advantages.
Synthesis of the observed patterns in the Product Hunt data: sharp increase in solo launches after ChatGPT-3.5 (barrier lowering) combined with persistent team dominance among top-quality outcomes (reinforcing team advantages).
Evidence suggests both top-down and bottom-up diffusion: worker use can occur without firm adoption, and vice versa.
Cross-tabulation of firm-level adoption indicators and reports of worker-level use in the BTOS AI supplement (Nov 2025–Jan 2026) indicating non-perfect overlap between firm-declared adoption and reported worker use; analytic approach descriptive (no sample size in excerpt).
The study reframes VTech adoption as legitimacy-seeking rather than efficiency-driven.
Thematic analysis using Rogers' diffusion of innovations and institutional theory, resulting in the institutionally mediated diffusion of innovations (IDOI) framework which emphasizes legitimacy concerns.
Successful AI implementation in logistics requires not only technological capability but also organizational readiness and effective data governance.
Conclusion drawn from the structured qualitative review of 31 scholarly sources synthesizing reported success factors and preconditions for AI adoption.
Empirical analysis of cases demonstrates that diverse, and often non-ethics-related, levers motivate organizations to abandon AI development.
Analysis of cases drawn from the AI incident database and practitioner survey contrasted with the taxonomy from the scoping review; specific counts/effect measures not provided in the summary.
AI capabilities can be copied, invoked, embedded in workflows, and scaled across institutions at low marginal cost.
Descriptive claim about AI technology characteristics made in the paper; supported by conceptual argument and examples rather than quantified empirical data.
Earlier high-risk technologies were slowed by capital intensity, physical bottlenecks, organizational inertia, and specialized supply chains.
Historical/analytic claim presented as background context in the paper; supported by conceptual comparison rather than a specific empirical study.