Evidence (7870 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).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 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 | 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 |
Governance
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Empirical evidence remains heterogeneous, and estimates of AI’s macroeconomic contribution vary across institutional and structural contexts.
Synthesis of heterogeneous empirical studies from the 2015–2025 literature identified in the structured review; comparative thematic classification highlighting variation by institutional/structural context.
AI adoption does not generate uniform or automatic growth effects.
Structured literature review / mechanism-oriented synthesis covering studies from 2015–2025; transparent search, screening and thematic classification (no formal meta-analysis).
The field is shifting from building models from existing data to actively creating data for building models (characterised as 'hyper-datafication').
Conceptual argument supported by observed trends in dataset creation and growth in the analysed dataset collection and the paper's theoretical framing.
The intended contribution is an Information Systems framework explaining when AI supports human augmentation and when it produces functional substitution.
Stated intended theoretical contribution in the abstract (proposed framework). This is an intended outcome rather than an empirically demonstrated result in the provided text.
The study investigates both perceived and enacted managerial agency.
Stated measurement targets in the abstract (descriptive of dependent variables). No measurement instruments or sample reported in the provided text.
The research uses a sequential multi-phase design combining experiments and qualitative fieldwork.
Stated methodology in the abstract (methodological claim about study design). No sample sizes or procedural details provided in the excerpt.
The study focuses on how technological design features, including transparency and override flexibility, interact with governance structures such as accountability and incentive systems.
Stated focus of the study in the abstract (descriptive of independent variables and governance moderators). No empirical details or sample reported in the provided text.
This doctoral research examines how AI-enabled decision systems affect human agency in data-driven organizations.
Stated research scope and aim in the paper (descriptive claim about the study's focus). No sample or results provided in the abstract.
Artificial intelligence is increasingly embedded in organizational decision-making, reshaping how managers exercise discretion and responsibility.
Stated as a background/motivation statement in the paper (literature-driven claim in the abstract). No empirical evidence or sample reported in the provided text.
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 proposed model demonstrates how natural resource dynamics, financial systems, and AI technologies form an interdependent triadic structure in which disturbances in one domain propagate across the entire system.
Presentation of a conceptual/formal model (systems analysis) in the paper showing interdependencies; no empirical dataset or sample size provided.
The research conceptualizes sustainability as a nonlinear adaptive process characterized by dynamic feedback loops and emergent systemic behavior.
Theoretical/systems analysis and conceptual argumentation in the paper; no empirical validation or sample size reported.
There exists a six-bit prior for which R_max(μ)/R_0(μ) = 39/31 > 5/4, so no universal 5/4 bound holds.
Constructive counterexample provided in the paper: an explicit six-bit prior is presented and analyzed to compute the ratio. This is a theoretical construction, not empirical data.
If the prior μ is close to the independent product prior with the same marginals in the sense that μ(x) ≥ (1−η) π_μ(x) for every state x, then R_max(μ) ≤ R_0(μ) + η n.
Mathematical derivation/proof in the paper under the stated closeness assumption (formal theorem conditional on parameter η and number of bits n). No empirical/sample data.
For any prior μ, R_max(μ)/R_0(μ) ≤ 3/2.
Mathematical proof (theorem) within the paper's Bayesian persuasion model where the sender is strategic and the receiver guesses bits. The result is presented as a proven upper bound under the model's assumptions (no empirical/sample data).
Better measurement matters, but improved measurement alone will not close the coordination gap between researchers and policymakers.
Authors' analytical conclusion arguing that measurement improvements are necessary but insufficient.
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).
Policy-related AI development, rather than national AI development alone, may be more relevant for observed adult participation in education and training.
Comparative interpretation of the (null) contemporaneous association for total AI Vibrancy Score and the positive lagged association for AI-related Policy and Government activity in the panel regressions (2017–2024, 18 European countries).
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).
Risk salience may shape interaction dynamics with LLMs via a multilevel feedback mechanism called the 'guarded engagement loop', in which risk perceptions shape interaction strategies that influence observed performance and, in turn, recalibrate trust in generative AI systems.
Conceptual framework proposed by the authors, integrating theories from trust in automation, privacy calculus, algorithm aversion, and social amplification of risk; presented as a theoretical model rather than an empirical test.
The empirical tests reported in the study use a sample of agricultural enterprises.
Paper text explicitly frames findings and implications for agricultural enterprises and states empirical tests were conducted on agri-business firms.
While AI has the potential to improve operational efficiency and strengthen adaptive capacity, inadequate readiness can increase systemic risks arising from algorithmic opacity, cybersecurity challenges, data dependence, coordination failures, and disruptions that may spread across interconnected administrative systems.
Conclusion drawn from the integrative conceptual framework and the systematic review of 68 empirical studies documenting both benefits and risks in different contexts.
Evidence on the productivity, risk, and resilience implications of AI adoption remains fragmented and dispersed across different fields of research.
Author's assessment of the literature based on the systematic review (PRISMA) of 68 empirical studies published 2015–2025.
Organisational performance becomes more dependent on the reliability of algorithms, the quality of data, effective governance, and coordination among public institutions.
Conceptual argument supported by synthesis of empirical studies in the systematic review (68 peer-reviewed empirical studies).
Artificial intelligence (AI) is becoming increasingly embedded in the digital infrastructure of local government, creating new opportunities to improve public sector productivity while also influencing systemic risk and organisational resilience across interconnected public systems.
Statement based on literature synthesis in the paper; theoretical framing and review of empirical studies (systematic review).
The paper develops the concept of 'bidirectional dynamics' in digital sovereignties, applying a paradoxical view to interpret institutional control objectives and individual autonomy aspirations as persistent organizational tensions in AI adoption.
Theoretical/conceptual development grounded in the empirical single-case study; concept introduced and motivated by observed tensions in the organization (empirical method details and sample size not provided).
Early digital transformation presents tensions but also synergies between digital sovereignty levels in AI adoption.
Empirical observations from the single-case study of a Nordic public transportation organization during early AI adoption; qualitative examples and analysis (specific methods/sample size not stated).
Embodied intelligence is driving the human-machine relationship from a "human-dominated" model toward "collaborative co-creation," which, while boosting productivity, also triggers deep-seated contradictions in production relations.
Conceptual/theoretical argumentation in the paper, drawing on Marx's theory of reproduction; no empirical sample or quantitative data reported.
The near-term value of Agentic AI does not lie in full autonomy or workforce reduction, but in controlled partial autonomy for simple and medium complexity business processes.
Central argumentative claim/recommendation in the paper (theoretical justification; no empirical study or sample size reported).
This shift raises fundamental questions for consumer theory, which has traditionally modeled humans as the primary decision-makers.
Conceptual argument presented in the paper framing the research problem and motivating the new theoretical framework; literature critique rather than empirical test.
Panel autoregressive distributed lag estimates reveal strong support for the load capacity curve (LCC) hypothesis, indicating a nonlinear income–environment relationship.
Panel ARDL econometric analysis on G-7 countries over 1990–2019 (authors report use of LCC framework and panel ARDL estimation).
The effectiveness of AI in strategic core functions is contingent upon the human–AI interface.
Stated as a conditional claim in the paper—AI effectiveness depends on the quality of the human–AI interface; no empirical quantification provided in the summary.
A third possibility — the collective and self-organized stewardship of AI-relevant resources by communities (commons-governed approaches) — remains comparatively under-theorized in scholarship even as it proliferates in practice (e.g., data trusts, cooperatives, federated learning consortia, public compute initiatives, open-weight collaborations, community data sovereignty regimes).
Comparative literature review noting fewer theoretical treatments of commons approaches alongside cited examples of practical manifestations (lists of existing initiatives and models).
The U-shaped pattern is concentrated in software-based AI applications rather than supporting hardware.
Heterogeneity/subgroup analyses in paper that separate software-based AI applications from supporting hardware and find the non-linear pattern concentrated in software applications.
Spline regressions, the Lind–Mehlum U-test, an instrumental-variable analysis using leave-one-out peer AI investment, and entropy balancing all support the non-linear (U-shaped) pattern.
Robustness and identification methods reported in paper: spline regressions, Lind–Mehlum U-test for U-shape, IV using leave-one-out peer AI investment, and entropy balancing.
There is a U-shaped association between AI investment and internal control deficiency (ICD) risk.
Main empirical finding reported in paper based on analyses of 41,725 firm-year observations; supported by spline regressions and Lind–Mehlum U-test.
Technological containment policies may unintentionally accelerate open innovation ecosystems as a competitive response, with implications for global leadership in both academic and commercial artificial intelligence.
Synthesis and inferential claim in the paper drawing on the temporal association of containment measures, policy shifts, developer behavior, diffusion patterns, and patent/research evidence described earlier in the paper.
The paper engages six credible objections: commercial pressure and practical feasibility; democratic legitimacy; regulatory compliance; over-reliance on institutionalist explanations; the charge that the floor itself is culturally laden; and the limits of Coherent Extrapolated Volition.
Descriptive claim listing the objections the authors address in the paper; asserted in the abstract as part of paper structure.
The pluralistic-alignment program correctly diagnoses that there is no single 'humanity' to align with, but is dangerous if taken as the main directive.
Analytic claim about the merits and risks of the pluralistic-alignment approach; presented as argumentation rather than empirical result in the abstract.
Human values produce societies that thrive or fail on the merits of those values — from failed states and extreme inequality to declining happiness, political polarization, and government dysfunction in the world's wealthiest democracies.
Descriptive/causal claim asserted by authors linking values to a range of societal outcomes; no specific empirical studies or samples cited in the abstract.
Forms of resistance exist, including localisation efforts and Indigenous ethical frameworks, but they remain structurally limited.
Synthesis of examples and themes across the 50 reviewed articles noting reported resistance strategies and their limits.
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.
Current models achieve penetration success rates ranging from 10.7% to 69.3%.
Empirical results reported from evaluation of the 19 LLMs across the designed target servers (success-rate measurements).
Trust is conceptualized as network-mediated expectation stabilization in the embodied finance framework.
Theoretical claim in the framework articulating trust as stabilized through network interactions among humans, machines, and platforms; no empirical data.
The proposed framework—the machine–platform–crowd triangle—reframes agency, trust, and value as emergent properties rather than institutional attributes.
Conceptual framing and argumentation within the paper; synthesis of theory to reconceptualize agency, trust, and value; no empirical testing reported.
The image of a single transformative step change caused by the introduction of human-level AGI may be inaccurate; a more apt prospect is a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology.
Interpretive claim in the report arguing for a multi-step, multifaceted impact scenario rather than a single-step discontinuity; based on conceptual synthesis of possible pathways and impacts.
There exist frictions and bottlenecks along these AGI→ASI pathways, and whether their impacts are negligible or substantial is an open set of concrete research questions.
Report analysis identifying potential frictions and bottlenecks and posing open research questions; conceptual analysis without quantified empirical measures.
Du et al. (2026) find that information-based team faultlines can enhance proactive behavior via deep information processing, while AI adoption moderates and mitigates the negative effects of social-based faultlines on team cooperation.
Information-processing theoretical framing and empirical analysis reported in the paper (study type and sample size not specified in the excerpt).
Liao et al. (2026) identify multiple equifinal pathways to high performance in digit-oriented spin-offs (parent-oriented, independent-oriented, ambidextrous-oriented configurations) using fuzzy-set qualitative comparative analysis (fsQCA).
fsQCA analysis reported in the paper (methodological approach described; sample not specified in excerpt).
AI agents can rival or exceed human methodological diversity at the design layer while remaining vulnerable at the verdict layer.
Synthesis of above experimental findings: Claude Code and Codex matched/exceeded human methodological diversity measures (20 runs) but exhibited vulnerability to prompt-induced changes in verdict behavior (especially Claude Code).