Evidence (3308 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 |
Skills Training
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Essay quality changes little while students have AI access but improves in style and relevance one week later when students write unaided.
Open-ended essay assessments (higher-order skills) collected immediately (with AI access for treatment group) and one week later (unaided) in the randomized experiment; quality measured on dimensions including style and relevance.
The research is limited by the current state of AI technology and the available proxies; therefore the validity of the present optimistic findings must be continually re-evaluated.
Authors' stated limitations in the abstract noting rapid AI advancement and proxy measurement constraints.
These positive results are not supported in all contexts (i.e., the positive effects are not universally found across all specifications/contexts).
Abstract statement noting heterogeneity/robustness: preferred results hold but are 'not supported in all contexts.' Implies some specifications or subsets do not show the effects.
Empirical claims across the reviewed literature vary in methodological rigor and should be viewed with caution before standardized replication.
Meta-level assessment presented in the review of peer‑reviewed literature (2020–2025); no formal quality-assessment statistics provided in the excerpt.
Traditional jobs based on manual work are transforming into collaborative management and exception-handling roles that demand new cognitive and ethical skills from employees.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No specific sample size reported.
The model yields propositions on threshold effects, productivity J-curve dynamics, distributional stress, and policy sequencing.
Model-derived propositions and theoretical implications presented in the paper (analytical derivations and theory-building).
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 same AI shock can produce divergent outcomes in small open economies.
Core theoretical claim derived from the Dynamic Institutional Absorptive Capacity (DIAC) model developed in the paper (analytical/theory-building).
Artificial intelligence is widely expected to raise productivity, yet its macroeconomic gains remain uncertain, uneven, and institutionally mediated.
Statement and literature-motivated framing in the paper's introduction; supported by analytical theory-building (DIAC model) rather than empirical data.
AI performs best in routine, data-rich situations but falls short when decisions require lived experience and contextual understanding.
Synthesis of cross-domain empirical studies and theoretical arguments showing differential AI performance by task type (routine/data-rich vs. experience-dependent/contextual).
The dominant paradigm has shifted from 'substitution' (machines replacing workers) to 'augmentation' (AI augmenting human work).
Interpretive conclusion in the paper drawn from secondary literature (WEF, ILO, McKinsey, PwC) and observed policy/industry trends.
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 productivity effect of AI is not automatic; it depends on firm-level adoption, worker skills, complementary investment in software and data systems, managerial readiness, task suitability, and the ability of organisations to redesign workflows around AI.
Paper's conceptual argument and synthesis of secondary literature highlighting conditional factors for realizing productivity gains.
The net effect of AI on work is better described as displacement than wholesale elimination.
Author's conceptual argument and synthesis of literature/reports (qualitative argumentation in the paper).
Hybrid (human-AI) performance, analyzed at the individual forecaster level, is trimodal: most people either deferred to the model (matching it) or rubber-stamped a prior guess (performing worse than the model alone), while a minority engaged in genuine complementary reasoning and reached accuracy matching or even exceeding the market.
Pilot empirical analysis comparing individual forecasters' hybrid forecasts to both the model and the Polymarket benchmark; claims reported at individual level in the paper.
The study reveals an 'AI Competency Paradox'—AI raises technical skills while increasing demand for meta-competencies that established frameworks fail to assess.
Synthesis of empirical findings reported in the paper linking measured increases in technical skills with unmet assessment needs for meta-competencies.
There are two distinct regional catch-up trajectories: Digital Leapfrogging in the Baltic States and Industrial Deepening in the Visegrad Group.
Systematic empirical documentation across the Visegrad Group and Baltic States (2022–2025) using the paper's assessment approach; patterns labeled and interpreted by the author.
Key human factors—trust calibration, output-quality sensemaking, expertise depth, feedback latency, cognitive load, and metacognitive skill development—serve as performance-shaping mechanisms within AI-enabled systems.
Presentation of a socio-technical evaluation model synthesizing prior research across several disciplines (conceptual synthesis; no empirical sample reported).
A 2025 forecasting study of experts reveals an apparent disconnect between expectations of significant AI capability improvements and modest near-term economic projections.
2025 forecasting study / expert elicitation involving 69 leading economists and 52 AI experts, plus additional expert panels; comparison of experts' expectations about AI capability progress versus their near-term economic projections.
The effect of AI adoption on inequality is heavily moderated by a country's educational infrastructure and baseline economic development.
Reported moderation analysis / subgroup comparisons using OLS regression and Random Forest on the World Bank/OECD cross-country dataset indicating that the AI–inequality relationship varies with measures of education and development.
From a sociomaterial perspective, auditor reconfiguration depends both on the evolution of technological capabilities (material agency) and on professionals' engagement and adaptation (social agency).
Theoretical framing and interpretive synthesis in the SLR of 43 studies; application of sociomateriality theory to the empirical patterns identified in the literature.
The introduction of AI reconfigures the auditor’s role through an ongoing, dynamic process: as technology evolves, organizational practices and arrangements transform, rebalancing functions and responsibilities between auditors and tools.
Interpretive synthesis from the SLR of 43 studies using a sociomateriality theoretical lens; cross-study observations about changing tasks, responsibilities and human–machine interactions.
The paper develops a task-to-firm conversion framework explaining why task-level GenAI productivity gains do not automatically translate into firm-level improvements.
Theoretical and conceptual contribution presented in the review, integrating multiple literatures (GPT theory, digital economics, task experiments, China studies).
Despite task-level gains, GenAI produces uneven or limited firm-level productivity effects in many settings.
Review synthesizing discrepancies between task-level experiments and firm-level outcome studies, and discussion of conversion frictions in the paper.
Generative AI (GenAI) should not be treated as a standalone productivity shock; its economic value depends on the interaction between model capability, task fit, human-AI calibration, organizational complementary assets, and regional digital infrastructure.
Conceptual framework developed in this review synthesizing literature from AI research, task-level productivity experiments, general-purpose technology theory, digital economics, and China-focused digital transformation studies; no new firm-level empirical analysis in this paper.
Existing user-role frameworks (e.g., the BTP User Type Matrix) require adaptation because the workforce is undergoing significant role-specific changes.
Authors' analysis based on 20 expert interviews and a 24-person workshop that uncovered mismatches between current role taxonomies and emergent AI-influenced responsibilities.
There is a growing reliance on agentic AI systems within the platform context.
Qualitative evidence from the 20 interviews and the 24-participant workshop reporting increased dependence on AI agents for tasks and decision support.
There is increasing automation of operational tasks in the development domain.
Participant reports and workshop discussions from 20 interviews and a 24-person workshop indicating automation of operational activities; qualitative thematic evidence.
The results reveal substantial shifts in day-to-day tasks and roles in the development domain.
Reported findings from 20 expert interviews and a 24-participant participatory workshop; claim based on participants' reported changes to responsibilities and observed themes in the data.
AI is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms.
Qualitative study reported in the paper combining 20 expert interviews and a participatory workshop with 24 participants; findings derive from thematic analysis of participant accounts and workshop outputs.
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).
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.
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).
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.)
Returnees face a short-run employment penalty after returning from cross-border work, but this penalty fades with cross-border tenure and with time since return.
Chapter 4: causal analysis using linked Belgian administrative registers comparing returnees to stayers; reported short-run employment penalty and dynamic fade-out with tenure and time since return.
Random-forest models (Belgian administrative registers) reveal sharply nonlinear transition patterns predicting entry and exit into cross-border work, with commuting time, prior employment instability, earnings, and household cross-border exposure as strong predictors.
Chapter 4: linked Belgian administrative registers identifying cross-border spells in Luxembourg; predictive analysis using random-forest models; individual-level predictors and nonlinear patterns reported.
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.
LLM guidance was associated with increased pupil size variability.
Physiological eye-tracking measure (pupil size variability) reported and compared across conditions in the simulated SAR experiment.
Eye-tracking data revealed an attention-guidance trade-off: visual resources shifted to the chat interface when LLM guidance was present.
Eye-tracking measures collected during the experiment showing changes in gaze allocation (increased fixations/dwell time on the chat interface) across LLM-guided vs baseline conditions.
The ICH framework predicts three distinct augmentation regimes (determined by combinations of A and C) with distinct policy implications.
Theoretical classification derived from the model; conceptual prediction presented in the paper.
AI-induced changes are displacing existing labor jobs while also creating new jobs that require high technological skills.
Summary claim from the SLR reporting that reviewed empirical studies report both displacement of existing jobs and creation of new, high-skill jobs; no quantified displacement/creation rates provided in the excerpt.
Between 2017 and 2025, studies identified current trends of AI-induced changes affecting both blue-collar and white-collar occupations.
Synthesis statement in the paper reporting that reviewed empirical studies identified trends across blue- and white-collar jobs (timeframe 2017–2025). Specific studies or counts not provided in the excerpt.
AI's rapid evolution has profound effects on the labor market, influencing the levels, skills needed for jobs, and overall jobs content.
Statement from the paper's synthesis/introduction summarizing reviewed empirical studies (systematic literature review covering studies from 2017–2025). Number of underlying studies not reported in the excerpt.
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.
The article develops a conceptual framework linking GenAI use in higher education to knowledge transformation, critical thinking, ethical judgment, digital capability, managerial decision-making, business ethics, workforce readiness, and organizational readiness.
Presentation of a conceptual framework by the authors as part of the review (theoretical/conceptual work; no empirical validation reported).
GenAI should be understood as more than an educational technology: it affects the development of managerial decision-making, business ethics, and workforce readiness for future managers, entrepreneurs, administrators, policymakers, and business professionals.
Conceptual argument and literature synthesis presented in the review article (no primary empirical sample).
Generative AI (GenAI) is reshaping higher education by changing learning practices, academic writing, knowledge access, assessment preparation, research support, and student engagement.
Narrative literature review / synthesis (review article). No primary empirical sample reported — claim drawn from cited literature and conceptual synthesis.
Perkembangan AI mengotomatisasi tugas rutin sekaligus menciptakan peluang pekerjaan baru berbasis digital.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).