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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Non-routine cognitive roles highly dependent on symbolic manipulation (e.g., Data Scientists) face unprecedented exposure, with OAI ≈ 0.70.
Reported OAI value for example occupation(s) (Data Scientists) derived from the algorithmic aggregation across DWAs; claim presented as a key empirical finding.
We utilize a multi-agent LLM ensemble to score both technical feasibility and business risk for DWAs.
Method description: deployment of a multi-agent LLM ensemble to produce scores on technical feasibility and business risk per DWA. Specific ensemble composition and hyperparameters not provided in the excerpt.
We introduce a Tech-Risk Dual-Factor Model that jointly scores technical feasibility and business risk to re-evaluate occupational exposure to LLMs.
Methodological contribution described in the paper (model specification). Implementation details described elsewhere in paper (see multi-agent scoring and aggregation), but claim itself is the introduction of the model.
The study introduces 'career reconfiguration' as a framework explaining intra-role task transformation, extending existing career mobility and job transition theories.
Theoretical/conceptual contribution presented in the paper (framework proposition; not an empirical effect).
Mediation analysis confirms that training and organizational support significantly mediate the relationship between AI adoption and career shifts.
Mediation analysis reported in the study (method stated; no mediation coefficients or sample size provided in abstract).
Together, these variables explain 61% of the variance in adaptive outcomes (R² = 0.61).
Multiple regression model summary reported in the paper (R-squared value provided; sample size not stated).
Readiness to change is a significant predictor of career adaptation (beta = 0.298, p = 0.011).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Openness to technology is a significant predictor of career adaptation (beta = 0.367, p = 0.003).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Organizational support is a significant predictor of career adaptation (beta = 0.389, p = 0.005).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Skills training is the strongest predictor of career adaptation (beta = 0.412, p = 0.002).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Overcoming the structural skill deficit through deliberate investment in tertiary education reform and strong private-public partnerships for continuous vocational learning is mandatory for Nigeria to successfully leverage the AI revolution for inclusive economic growth and ensure long-term workforce resilience.
Study conclusion synthesizing survey results (150 firms) and qualitative policy/workforce analysis to make policy recommendations.
The rate of new job creation hinges critically on the immediate implementation of targeted, scalable reskilling programs.
Paper's projections and analysis drawing on the survey of 150 firms and qualitative interviews; presented as a conditional/projection based on current skills gap and training initiatives.
Applying the Method of Moments Quantile Regression (MMQR) allows the study to capture heterogeneous impacts of robotics across performance levels.
Authors describe use of MMQR in methodology and justify it as appropriate for detecting heterogeneity across quantiles of the dependent variable (value added).
The study uses panel data from Eurostat, the International Federation of Robotics (2024), and World Robotics covering three key sectors in selected EU countries.
Data sources explicitly listed in the paper (Eurostat, IFR 2024, World Robotics); the scope is described as three key sectors in selected EU countries.
Policymakers should support automation through fiscal incentives, invest in reskilling programs, and develop innovation strategies tailored to specific sectors to foster inclusive and sustainable growth.
Policy recommendations derived from empirical findings showing heterogeneous effects of robot density, R&D and human capital across sectors; authors explicitly recommend fiscal incentives, reskilling, and sector-targeted innovation strategies.
The paper’s novelty lies in its differentiated, cross-sectoral approach integrating technological adoption (robotics) with sectoral gross value added using advanced econometric techniques (MMQR).
Authors state the study's contribution is differentiated cross-sectoral analysis and use of MMQR to capture heterogeneous impacts; methodological description provided in paper.
The positive effect of robot density on value added is particularly strong in higher-performing sectors (i.e., at higher quantiles of the value-added distribution).
Results from MMQR showing heterogeneous impacts across performance levels/quantiles; authors state larger positive coefficients of robot density at upper quantiles.
Increased robot density significantly enhances value added.
Empirical analysis using panel data (Eurostat, International Federation of Robotics 2024, World Robotics) estimated with Method of Moments Quantile Regression (MMQR); gross value added used as dependent variable and robot density as a core explanatory variable; authors report statistically significant positive coefficients.
Design implication: adaptive AI coaching systems should align support intensity with individual readiness, rather than assuming universal effectiveness.
Authors' design recommendation derived from experimental results showing heterogeneous effects by personality profile.
Effective collaboration with AI for software engineering (SE) tasks may benefit from functional design rather than replicating human SEI traits, thereby redefining collaboration as functional alignment.
Authors' conclusion and recommendation derived from qualitative interview evidence (10 practitioners) and the proposed concept of functional equivalents.
The authors introduce the concept of 'functional equivalents': technical capabilities (internal cognition, contextual intelligence, adaptive learning, and collaborative intelligence) that achieve collaborative outcomes comparable to human SEI attributes.
Conceptual contribution proposed by the authors based on interview findings and theoretical argumentation (no quantitative validation reported).
Socio-emotional intelligence (SEI) enhances collaboration among human teammates.
Stated as background in the paper (no primary data from this study provided to support the claim).
Results may be applied in the development of financial institution strategies, regulatory frameworks, risk management systems and professional training programmes.
Applied implications drawn from the literature synthesis and comparative analysis; presented as potential uses rather than empirically validated interventions.
Significant changes in human resource needs are occurring, with growing demand for analysts and specialists combining financial and technological competencies.
Conclusion from literature review and synthesis of international studies on labour demand in finance under Big Data/AI adoption; no original labour-market survey included.
Big Data and AI technologies significantly improve efficiency, risk assessment accuracy, fraud detection and financial inclusion.
The paper reports results from a qualitative analysis of recent academic literature, comparative analysis of sector-specific applications, and synthesis of empirical findings from international studies; no primary sample size reported.
Overall, findings highlight that AI serves as a revolutionary (transformative) tool rather than merely a replacement tool for employment—changing the nature of human work rather than simply disengaging it.
Synthesis conclusion in the paper drawing on the literature review and the authors' empirical results indicating task reallocation and changing job content.
The paper argues for equal technology governance as a necessary policy response to AI's labor market effects.
Policy recommendations discussed in the paper that call for equitable governance of AI; based on literature synthesis and empirical findings.
The analysis raises policy implications emphasizing reskilling and education to address AI-driven changes in the labor market.
Policy discussion section summarized in the paper; draws on empirical findings and literature to recommend reskilling/education.
Moderate AI usage is associated with employment growth.
Part of the U-shaped relationship reported in the paper's empirical results; described qualitatively in the abstract/summary.
Secondary empirical evidence from Colombia's EDIT manufacturing survey (N=6,799 firms) shows that management practice quality amplifies the return to technology investment (interaction coefficient 0.304, p<0.01).
Secondary empirical analysis of EDIT manufacturing survey data; sample size reported as N = 6,799 firms; regression interaction term reported as coefficient 0.304 with p < 0.01.
We endogenize the augmentation function as phi(D, W), where W is a five-dimensional workplace design vector (AI interface design, decision authority allocation, task orchestration, learning loop architecture, psychosocial work environment), and prove that human-centric design is profit-maximizing when the workforce's augmentable cognitive capital exceeds a critical threshold.
Theoretical model and formal proof presented in the paper (analytical derivation of phi(D,W) and threshold condition).
The results (conceptual/model results) support corporate GenAI policies, leadership development programs, and HR assessment of leader readiness for GenAI-enabled delegation and communication.
Practical implications and recommendations section arguing policy and HR applications based on the conceptual model.
The article introduces an EI-driven trust-calibration framework as an explanatory mechanism showing when generative AI improves leadership effectiveness and when it amplifies managerial errors.
Novel theoretical framework developed in the paper synthesizing EI, trust calibration, and psychological safety to explain boundary conditions of AI in leadership.
The paper provides an operationalization toolkit including measures: GenAI use intensity; delegation quality indices (clarity, boundaries, success criteria); communication quality indices (empathy, tone, transparency); psychological safety markers; and behavioral trust-calibration measures.
Operationalization section in the paper listing suggested indices and markers for empirical measurement.
As a follow-up validation path, the paper proposes a two-wave time-lag design and 180° assessment (leader + subordinates) to reduce common-method bias.
Methodological proposal in the paper describing longitudinal and multi-rater validation approaches.
The paper proposes a 'Package B' rapid empirical design: a randomized online experiment manipulating access to generative AI in core managerial tasks (decision, delegation, team communication), combined with EI measurement and trust-calibration indicators.
Methodology section proposing the rapid randomized online experiment design as the primary empirical test.
Emotional intelligence strengthens the positive impact of generative AI on managerial outcomes when trust is properly calibrated and psychological safety is maintained.
Conceptual model and integrative argument combining EI, trust-calibration, and psychological safety; supported by proposed empirical test design.
The paper conceptualizes human–AI leadership as an integrated managerial competence.
Conceptual modeling presented in the paper integrating EI theory, psychological safety, and trust calibration (theoretical synthesis).
Hukum diharapkan tidak hanya berfungsi sebagai alat perlindungan, tetapi juga sebagai instrumen strategis dalam mengelola transisi menuju masa depan kerja yang lebih inklusif, adil, dan berkelanjutan di era kecerdasan buatan.
Kesimpulan dan rekomendasi normatif penulis berdasarkan analisis perundang-undangan dan literatur yang dikaji.
Pengakuan 'hak atas pengembangan keterampilan berkelanjutan' (right to lifelong learning) penting dan perlu dimasukkan sebagai bagian integral dari perlindungan pekerja di era digital.
Klaim normatif dan rekomendasi kebijakan yang muncul dari studi konseptual dan tinjauan literatur komparatif.
Diperlukan reformasi hukum yang lebih progresif dan adaptif, termasuk penguatan sistem jaminan sosial dan pembaruan kebijakan fiskal untuk menangani dampak AI.
Rekomendasi kebijakan yang disimpulkan dari analisis normatif dan komparatif serta tinjauan literatur dalam penelitian.
Diperlukan dasar hukum bagi penerapan model kompensasi inovatif seperti Universal Basic Income (UBI), pajak otomasi, dan skema distribusi manfaat produktivitas AI.
Rekomendasi kebijakan hasil analisis normatif dan komparatif yang dikemukakan penulis berdasarkan tinjauan literatur.
Large language model (LLM) use can improve observable output and short-term task performance.
Paper synthesizes empirical findings from human–AI interaction studies, learning-research experiments, and model-evaluation work indicating improved produced outputs and short-term task performance when humans use LLMs; no single pooled sample size or unified effect estimate is reported in the paper.
Education and workforce development should shift focus from rote knowledge accumulation to cultivating skills in human-AI collaboration, creative problem-solving, and the design of novel economic domains.
Normative policy recommendation derived from the paper's framework and analysis of anticipated labor market changes (no empirical evaluation or trial data reported in the abstract).
Human-AI co-evolution will significantly increase individual productivity and open new frontiers of economic activity.
Projected outcome based on combined analysis of AI capabilities, historical patterns, and platform growth; the abstract does not report empirical measurement or sample sizes for this projection.
AI-driven productivity augmentation dramatically lowers the barriers to creating economic value, enabling the decentralized generation of employment.
Argument supported by paper's analysis of contemporary labor market dynamics and the growth of digital platforms; no quantified empirical estimates or sample sizes provided in the abstract.
The transition to an AI-civilization will fundamentally restructure the mechanisms of employment creation from a centralized model (few organizations creating jobs for the many) to a decentralized ecosystem where individuals are empowered to generate their own employment opportunities.
Central thesis of the paper, motivated by theoretical argumentation and synthesis of contemporary data on labor markets and digital platforms (no empirical test or sample sizes specified in the abstract).
Historical precedents from past technological revolutions suggest that innovation tends to expand, rather than shrink, the scope of economic activity and employment in the long run.
Paper draws on analysis of economic history (qualitative historical analysis implied; no specific historical datasets or sample sizes provided in the abstract).
By formalizing the end-to-end transaction model together with its asset and incentive layers, EpochX reframes agentic AI as an organizational design problem focused on infrastructures where verifiable work leaves persistent, reusable artifacts and value flows support durable human-agent collaboration.
Theoretical framing and normative claim in the paper; no empirical evaluation demonstrating that this reframing yields measurable benefits.
Credits lock task bounties, allow budget delegation, settle rewards upon acceptance, and compensate creators when verified assets are reused.
Functional description of the credit mechanics and settlement rules within the proposed EpochX marketplace; presented as part of system design without empirical settlement or user-behavior data.