Evidence (16496 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
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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 |
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.
The study distinguishes foundational theoretical perspectives from the contemporary 2015–2025 evidence base and clarifies the relationship between task transformation and structural transformation, emphasizing institutional complementarity as the key mechanism shaping AI-driven growth outcomes.
Analytic separation of theoretical literature and empirical studies in the structured review (2015–2025); thematic mapping linking task-level changes to broader structural transformation contingent on institutional complementarities.
Rather than proposing a deterministic growth model, the study advances a conditional and ecosystem-centered interpretation of AI-led development.
Authors' interpretive conclusion based on their structured review and the integrative innovation-ecosystem framework synthesizing mechanisms and contextual dependencies in the 2015–2025 literature.
Interpreting task-based automation models alongside endogenous-growth and open-innovation frameworks clarifies why similar AI investments may lead to divergent structural outcomes.
Theoretical synthesis combining task-based automation literature with endogenous-growth and open-innovation models, illustrated by examples from the reviewed empirical literature (2015–2025).
The paper develops an integrative innovation-ecosystem framework linking three core transmission channels: (i) total factor productivity (TFP), (ii) task reallocation and labor-market restructuring, and (iii) innovation and knowledge-generation dynamics.
Conceptual framework constructed by the authors via integrative review of theoretical and empirical literature from 2015–2025; framework synthesizes mechanisms reported across studies.
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.
Projected yield distributions vary substantially across locations, with some lower productivity sites exhibiting yield increases under future climate scenarios.
Results from simulated climate-projection experiments across multiple locations showing heterogenous yield distribution changes, including increases in some lower-productivity sites.
AI has a significant positive impact on value chain upgrading in the eastern and western regions of China, while its effect in the central region is insignificant.
Region-specific panel regressions / heterogeneity analysis using the 30-province 2010–2022 panel split by region; reported significance levels for eastern, western, and central subsamples.
The effects of talent introduction on AI development are heterogeneous: they vary by firm characteristics such as pollution status, regional location, and industry affiliation, and are particularly pronounced in the manufacturing sector.
Subgroup / heterogeneity analyses using the panel data showing differential effects across pollution status, regions, and industries (notably manufacturing).
The simulation offers a template of how firms ought to reorganize internal promotion ladders when junior positions are significantly automated.
Model-based policy/reorganization recommendation derived from the simulation results; presented as guidance for firm-level reorganization rather than an empirically tested organizational intervention in the abstract.
We simulate the elasticity of substitution between human intuition and the output of an algorithm.
Paper reports a simulation exercise modeling the elasticity of substitution between human inputs (intuition) and algorithmic outputs; no simulation parameters or sample size provided in the abstract.
We measure the change in the skill premium using a difference-in-differences design on freelance websites worldwide.
Statement of empirical method: difference-in-differences design applied to data from freelance platforms with global coverage; no sample size provided in the abstract.
The present wave of automation targets non-routine cognitive activity such as coding, technical writing, and graphic design, unlike past automation which mainly involved routine manual activity.
Framing/background statement in the paper contrasting historical automation (routine manual tasks) with current AI-driven automation of non-routine cognitive tasks; no sample size or quantitative test reported in the abstract.
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 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).
Displacement (asymmetric substitution between brand pairs) was industry-dependent, ranging from co-recommendation in consulting (0.4:1) to one-directional substitution up to 4.3:1, with an unweighted mean of 2.4:1 across the five industries.
Computation of the Displacement Score across brand pairs within each of the five sampled industries; manuscript reports per-industry ratios and the unweighted mean.
Cross-model agreement on the top-recommended brand was 41.6%; a top position on one model did not reliably hold on another.
Empirical comparison of top-recommended brands across the three models for the sampled queries, yielding a 41.6% cross-model agreement rate.
Some skills generalize broadly across tasks and models, whereas others become specialized to role-specific workflows and lose effectiveness under transfer.
Analyses reported in the paper showing heterogeneous transfer behavior across the 22 procedural skills in the AFTER benchmark, with some skills showing broad cross-task and cross-model generalization and others showing role-specific specialization and reduced transfer performance.
Participants' IAT scores were predictive of the time they spent in human-AI collaboration.
Reported predictive relationship between individual IAT scores and measured time spent interacting with/considering resumes during human-AI collaborative screening tasks (likely from regression or correlation analyses); exact statistics and sample size not provided in the excerpt.
Using survey data from AI startups in Qatar, the study will employ PLS-SEM to examine the relationships between these factors, AI capability, and venture performance.
Methods statement in the paper/abstract indicating planned empirical approach (survey of AI startups; use of Partial Least Squares Structural Equation Modeling). No sample size or empirical estimates provided in the abstract.
Instrumental-variable estimates using lagged AI diffusion produce similar patterns (attenuation of overeducation penalty and slight lowering of undereducation premium), although results should be interpreted with caution.
IV estimation using lagged AI diffusion as an instrument in models applied to CLDS data; IV results reported to be qualitatively similar to OLS/fixed-effects estimates but noted as requiring cautious interpretation.
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.
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.
Other strategic factors (differentiation of work, digital reputation, adaptability) continue to influence illustrators' financial sustainability despite AI's effect.
Author conclusion/interpretation in the discussion, inferred from the relatively low R² and domain knowledge; these moderators/alternative determinants are asserted rather than estimated in the reported regression.
AI explains a relatively small share of income variation among illustrators (model R² = 7.4%), so its contribution to income variation is limited.
Reported model fit statistic from the above simple linear regression (R² = 7.4%) on the sample of 385 illustrators.
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).
This article adopts a contextual approach to technology, considering it in conjunction with the social context in which it is situated.
Methodological statement made by the author about the approach taken in the paper (contextual rather than purely technical); not an empirical claim.
Longevity produces a short-run welfare loss that recedes as capital deepening raises wages, since households initially compress consumption and fertility to finance a longer retirement.
Model-derived welfare time path following a longevity shock showing initial welfare decline and subsequent recovery as aggregate capital deepens and wages rise; mechanism traced to household saving and fertility responses in simulations.
Robustness checks across the capital share, shock persistence, and the utility specification show that only an empirically implausible labor–AI elasticity reverses the wage and fertility signs.
Sensitivity/robustness analysis of model results by varying parameters (capital share, shock persistence, utility functional form) and the labor–AI elasticity, reporting conditions under which sign flips occur.
A forecast-error variance decomposition attributes most aggregate volatility to the longevity shock, while the AI shock dominates the variance of the return to AI capital.
Model-based forecast-error variance decomposition implemented on the simulated stochastic model to apportion variance of aggregate variables and the return to AI capital across shocks.
The two shocks move fertility in opposite directions: the AI shock raises fertility modestly through an income effect, while the longevity shock lowers fertility by strengthening life-cycle saving motives and increasing the cost of childrearing.
Endogenous-fertility overlapping-generations model with counterfactual simulations for AI and longevity shocks; comparative statics and simulation results regarding fertility responses and their mechanisms.
The AI shock reallocates investment from physical to AI capital.
Model simulation showing changes in investment allocation across capital types following the AI technology shock.
These patterns suggest a commoditization effect of AI on labor, with implications for online labor market design, workers' incentives to invest in human capital, and labor welfare.
Interpretation synthesized from the three empirical findings above (decline in human-capital importance, rise in price importance, decline in demand premium for high-human-capital workers, and reallocation toward lower-priced workers). This is presented as the paper's conceptual/mechanistic conclusion and policy implication rather than a separately tested causal estimate. (Empirical basis: Upwork analysis and difference-in-differences; sample size not reported in abstract.)
While localized speculation and valuation excesses may exist in AI markets, the underlying economic foundations of the AI cycle differ substantially from those that characterized the collapse of the internet bubble.
Comparative evaluation using financial market data, historical analyses of the dot-com collapse, and contemporary literature cited in the paper (qualitative comparative review).
Stronger synchronization can increase collective output but may also increase systemic fragility and reduce mobility.
Analytical results and trade-off analysis in the model showing the effects of synchronization on collective output, fragility, and mobility; theoretical deduction without empirical sample.