Evidence (3231 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5921 claims
Human-AI Collaboration
5192 claims
Org Design
3497 claims
Innovation
3492 claims
Labor Markets
3231 claims
Skills & Training
2608 claims
Inequality
1842 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 738 | 1617 |
| Governance & Regulation | 671 | 334 | 160 | 99 | 1285 |
| Organizational Efficiency | 626 | 147 | 105 | 70 | 955 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 349 | 109 | 48 | 322 | 838 |
| Output Quality | 391 | 121 | 45 | 40 | 597 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 277 | 145 | 63 | 34 | 526 |
| AI Safety & Ethics | 189 | 244 | 59 | 30 | 526 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 106 | 40 | 6 | 188 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 79 | 8 | 1 | 152 |
| Regulatory Compliance | 69 | 66 | 14 | 3 | 152 |
| Training Effectiveness | 82 | 16 | 13 | 18 | 131 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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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.
Under economy-wide deployment, the share of computer-vision-exposed labor compensation that is cost-effectively automatable rises sharply (relative to the firm-level 11% estimate).
Model counterfactuals or calibration scenarios comparing firm-level deployment vs economy-wide deployment; qualitative statement that share increases substantially.
At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation.
Calibration and implementation in computer vision; reported firm-level estimate from the framework.
Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks.
Modeling and calibration arguments showing fixed-cost spreading effects increase set of tasks for which automation is cost-effective; qualitative and quantitative comparisons in implementation.
Because higher accuracy is disproportionately costly (convex cost), full automation is often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium.
Theoretical model combined with calibration (scaling laws + task mappings); equilibrium outcomes reported from the framework implementation.
We model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation.
The paper develops a theoretical framework / model that treats automation intensity as a continuous decision variable; described as the central modeling approach.
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.
Social security solutions must be adapted to evolving human-technology interactions to secure social justice and cohesion.
Normative conclusion/recommendation from the paper's discussion; advanced as a necessary policy direction without reported empirical validation in the provided text.
Establishing contributory frameworks based on technology-generated income will ensure the sustainability of social protection in the era of labor displacement.
Presented as a novel policy proposal in the paper; stated as a solution with the asserted effect of ensuring sustainability rather than demonstrated via empirical testing or simulation within the text provided.
The Internet of Things (IoT) represents a transformative force, integrating digital intelligence with the physical world and catalyzing new relationships across economic sectors.
Stated as a conceptual assertion in the paper's introduction/overview; presented as a high-level literature-informed claim (no empirical sample or quantitative analysis reported).
Proposition 2: An increase in the pace of technology creation (m(b) rising from m to m') generates a transitory increase in the skill premium (even if the increase is permanent, because new technologies eventually age).
Analytical result (proposition) proved in the paper's model appendix; intuition and special-case (γ=σ) illustrated in text.
The college premium rose first among young workers and later among older workers; a model extension that assumes younger workers have a comparative advantage in new technologies generates age-specific increases that account for half of the observed age gaps.
Extension of the model with worker demographics; calibration using CPS data on computer use by worker age (showing young workers used computers more intensively initially) and simulation comparing model to observed age-specific wage premium changes.
Slow diffusion, combined with the rapid pace of technology creation, accounts for 6.2 of the 8.7 log-point differential increase in the skill premium between high- and low-density regions over 1980–2005.
Model calibrated with estimated diffusion rates across regions from the text-based dataset; quantitative decomposition attributing portions of the regional differential to the mechanism.
The mechanism explains why the college premium is higher in dense cities and why its increase was mainly urban.
Model extension incorporating regional diffusion of technologies combined with estimates of diffusion rates across locations (using the Kalyani et al. dataset); comparison of model predictions to documented urban–rural wage premium patterns.
Total demand for college-educated workers increased by 100 log points since 1980; changes in the pace of technology creation account for one-third of that increase, with the remainder attributed to residual structural changes in production.
Model-based decomposition calibrated to data (demand and supply of college-educated workers since 1980); quantitative accounting exercise reported in the paper.
When calibrated to the observed pace of technology creation, the model generates a 28 log-point (32 percent) increase in the college premium between 1980 and 2010, which then flattens and begins to revert.
Quantitative calibration of the model to novel text-based technology data (arrival and diffusion) and wage series (CPS); simulation results.
The data show a temporary increase in the pace of new technology creation beginning in the 1970s, accelerating in the 1980s, and tapering off in the 2000s.
Time series of identified new technologies from text-based measures (patent text/job posting linkage) covering 1976–2007 (as in Kalyani et al., 2025) used to measure arrival rates by cohort.
The pace of technology creation is a key driver of the skill premium: a rapid pace of technology creation leads to a sustained increase in the skill premium (because skilled workers learn to use new technologies faster).
Theoretical model developed in the paper in which new technologies arrive exogenously and skilled workers have a comparative advantage in learning new technologies; supported by calibration using novel text-based data (patent text and job postings) and CPS wage data.
Autor et al. (2024) show that the majority of current employment is in job specialties that did not exist in 1940, with new task creation driven by augmentation-type innovations.
Citation reported in the paper summarizing Autor et al. (2024); no sample size provided in excerpt.
Firms may not sufficiently account for non-monetary aspects of technological progress (well-being, safety, quality of work); a planner would include such considerations in steering technological progress.
Normative conclusion based on theoretical analysis comparing firm objective functions (profits) vs social planner objectives (including non-monetary utility).
The planner can raise social welfare by focusing technological progress on making goods cheaper that are disproportionately consumed by relatively poorer agents, thereby raising their real income.
Extension of the baseline model to multiple goods showing distributional gains via composition of price changes (real income channel).
When capital and labor are gross complements, a planner concerned with workers' welfare would favor capital-augmenting innovations to raise wages.
Analytical result from the model analyzing factor-augmenting technological progress and complementarity between capital and labor.
A planner with sufficient welfare weight on workers will impose positive robot taxes, with the tax rate increasing in the planner's concern for workers' welfare.
Application of the baseline model to robot taxation; analytical derivation of optimal robot tax under planner preferences.
As labor's economic value diminishes, steering progress focuses increasingly on enhancing human well-being (non-monetary aspects) rather than labor productivity.
Theoretical discussion and model results in the paper showing planner's shifting objective when labor is devalued.
The welfare benefits of steering technology are greater the less efficient social safety nets are.
Analytical result from the paper's theoretical model comparing a planner who can/cannot perform transfers and evaluating steering as second-best when redistribution is costly.
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).
The productivity channel raises corporate cash flows and is equity-bullish.
Model mechanism described in the paper: productivity effects of AI increase corporate cash flows which, within the model, produce an equity-bullish effect on the ERP/valuations.
Endogenous structural break analysis identifies 2007 as the break year for AI introduction in India.
Empirical analysis reported in the paper using an endogenous structural break test applied to relevant time-series data (paper states 2007 was identified as the break year).
A shift in preference towards non-traded AI services exacerbates income inequality among previously homogeneous workers in the non-traded sector (model finding).
Results from the paper's Finite Change General Equilibrium (theoretical) model which introduces AI as a shock in the non-traded sector and analyzes effects via price adjustments.
Artificial intelligence (AI) induced services are a reality in India and other developing countries.
Statement in paper citing existence/emergence of AI-powered services (examples given: Windows Live, AI ride-hailing apps such as Ola and Uber); descriptive assertion rather than quantified empirical analysis in the paper.
The framework provides a roadmap for coordinated response across educational institutions, government agencies, and industry to ensure workforce resilience and domestic leadership in the emerging agentic finance era.
Authors' proposed integrated roadmap (prescriptive recommendation; no empirical testing or outcome measurement reported in the provided text).
We develop a comprehensive government policy framework including: 1) Federal AI literacy mandates for post-secondary business education; 2) Department of Labor workforce retraining programs with income support for displaced financial professionals; 3) SEC and Treasury regulatory innovations creating market incentives for workforce development; 4) State-level workforce partnerships implementing regional transition support; and 5) Enhanced social safety nets for workers navigating career transitions during the estimated 5-15 year transformation period.
Author-presented policy framework and recommendations (policy design proposals and an asserted 5–15 year transformation timeframe; no empirical evaluation reported).
We propose a multi-layered integration strategy for higher education encompassing: 1) Foundational AI literacy modules for all business students; 2) A specialized "Agentic Financial Planning" course with hands-on labs; 3) AI-augmented redesign of core courses (Investments, Portfolio Management, Ethics); 4) Interdisciplinary project-based learning with Computer Science; and 5) A governance and policy module addressing regulatory compliance (NIST AI RMF, SEC regulations).
Proposed curricular framework presented by the authors (recommendation/proposal, not empirically tested within the paper).
Recommended regulatory responses include algorithmic transparency mandates, mandatory mental health risk audits, participatory co-design, human review of deactivations, and minimum wage protections aligned with ILO principles.
Authors' policy recommendations derived from the review's synthesis and identified psychological risks.
Investments in education and training are crucial for mitigating AI-induced employment disruptions and enhancing workforce adaptability.
Policy recommendation drawn from the paper's empirical findings (PLS-SEM, n = 351) and discussion.
Job displacement intensifies the demand for new skills, highlighting the need for reskilling and upskilling initiatives.
Finding reported from the study's PLS-SEM analysis of survey responses (n = 351).
AI has also fostered employment growth in emerging industries.
Empirical finding reported from the study's analysis of survey data (PLS-SEM, n = 351).
Policy should address not only the aftermath of AI labor displacement but also the competitive incentives that drive it.
Normative implication drawn from the model's findings; recommendation in the paper's conclusion based on theoretical results.
Only a Pigouvian automation tax can eliminate the excess automation in the model.
Theoretical welfare analysis demonstrating that a properly set Pigouvian tax that internalizes the demand externality restores the socially optimal level of automation in the model; analytical result, no empirical sample.
Human-replacing technologies have a strategic role in enhancing industrial productivity and ensuring the long-term resilience of Ukraine’s mining and metallurgical sector amid workforce shortages and structural labour-market changes due to war and demographic decline.
Integrated sectoral assessment in the paper combining current context (workforce shortages, structural changes), literature on technology-driven productivity/resilience, and industry-specific considerations; presented as a high-level conclusion.
Integrating ergonomic assessments and human–systems–interaction approaches into automation projects is important to prevent cognitive overload, occupational stress and operational risks for control‑room operators.
Recommendation and emphasis in the paper, supported by references to ergonomics and human-factors literature; presented as a preventive/mitigative approach rather than a quantified empirical result for the sector.
Successful technological modernization requires continuous investment in human capital, reskilling and the development of digital and engineering competencies.
Policy/recommendation based on the paper's synthesis of the sector analysis and literature on skill requirements and technology adoption; not presented as an original empirical estimate in the summary.