Evidence (14156 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Applying the ATE framework across five major US technology regions (Seattle-Tacoma, San Francisco Bay Area, Austin, New York, and Boston) over a 2025-2030 horizon, 93.2% of the 236 analyzed occupations across six information-intensive SOC groups cross the moderate-risk threshold (ATE >= 0.35) in Tier 1 regions by 2030.
Modeling/application of the ATE score to O*NET-derived tasks for 236 occupations in six SOC groups across five named US regions with forecasts for 2025-2030; explicit numeric result reported (93.2%).
Agentic AI systems execute end-to-end workflows (multi-step reasoning, tool invocation, autonomous decision-making) and substantially expand occupational displacement risk beyond what existing task-level analyses capture.
Theoretical extension of the Acemoglu-Restrepo task exposure framework described in the paper; conceptual argument contrasting prior automation (subtask substitution) with agentic AI (end-to-end workflow automation). No empirical sample size reported for this conceptual claim.
Agent contributions are associated with more churn over time compared to human-authored code.
Longitudinal comparison between agent-generated and human-authored contributions reported in the paper (churn/survival estimates described; association between agent contributions and higher churn asserted).
Practitioners identified specific functional deficiencies in AI: inability to maintain sustained partnerships.
Theme from semi-structured interviews with 10 practitioners; cited as an example of the functional gap.
Practitioners identified specific functional deficiencies in AI: inability to adapt contextually.
Theme from semi-structured interviews with 10 practitioners; cited as an example of the functional gap.
Practitioners identified specific functional deficiencies in AI: inability to negotiate responsibilities.
Theme from semi-structured interviews with 10 practitioners; cited as an example of the functional gap.
Practitioners currently view AI models as intellectual teammates rather than social partners and expect fewer SEI attributes from them than from human teammates.
Qualitative findings from semi-structured interviews with 10 software practitioners reported in the study.
Current AI systems lack SEI capabilities that humans bring to teamwork, creating a potential gap in collaborative dynamics.
Framed as background/context in the paper; asserted rather than empirically tested in this study.
Informal workers cannot capture augmentation rents: the estimated coefficient for H^A in informal sector is negative (beta_2 = -0.044).
Subsample or interaction estimate from the augmented Mincer regression using the same merged dataset (N = 105,517); reported coefficient beta_2 = -0.044 for informal workers.
New mechanisms of surplus value distribution operate in platform-based business models and through AI-mediated processes.
Analytical/theoretical argumentation and literature synthesis in the conceptual paper (no empirical validation reported).
Unbalanced or poorly governed adoption of Big Data and AI contributes to increased systemic risk, cybersecurity vulnerability, regulatory fragmentation and third-party dependence on BigTech platforms.
Argument based on qualitative literature review and synthesis of international empirical studies and comparative sector analysis; no single-sample empirical study in this paper.
Extreme automation (high AI intensity) causes employment decline.
Part of the U-shaped relationship reported by the paper's empirical results; described qualitatively in the abstract/summary.
Task orchestration is the most under-researched dimension among the five workplace-design components.
Finding from the PRISMA-guided systematic review of 120 papers, which mapped coverage across the five dimensions and identified task orchestration as having the least research attention.
Decision authority allocation emerges as the binding constraint for Society 5.0 transitions.
Result synthesized from the systematic review and theoretical analysis mapping the five workplace-design dimensions; stated as the binding constraint in the paper's findings.
The environmental impact of AI is weaker in energy-efficient countries.
Heterogeneity analysis in the paper dividing sample by energy-efficiency status (energy-efficient vs. energy-inefficient countries) shows a smaller AI→CO2 association in energy-efficient countries (104-country panel, 2000–2023).
Advanced digital infrastructure (DII) significantly mitigates the positive effect of AI on CO2 emissions.
Moderation analysis in the panel regressions (104 countries, 2000–2023) including interaction terms between AI adoption and digital infrastructure; results reported that stronger DII reduces the environmental impact of AI.
High institutional quality (GQI) significantly mitigates the positive effect of AI on CO2 emissions.
Moderation analysis in the panel regressions (same 104-country sample, 2000–2023) including interaction terms between AI adoption and governance quality; reported results indicate the AI→CO2 effect is weaker when GQI is stronger.
The literature shows persistent gaps in empirical validation, standardized evaluation methods, and sector-specific comparative analyses of agentic AI in financial services.
Review-level assessment noting limited empirical studies, heterogeneous evaluation metrics, and few direct cross-sector comparisons up to mid-2024.
Significant implementation barriers persist, notably workforce transformation challenges, legacy system integration difficulties, and trust deficits.
Thematic synthesis across empirical and conceptual papers in the review reporting implementation barriers and change management issues.
Ethical concerns—including bias, lack of transparency, and regulatory compliance risks—remain critical for agentic AI in financial services and necessitate layered governance and human-AI collaboration.
Collation of ethical, legal, and governance issues reported across the reviewed multidisciplinary studies and normative discussions.
Insurance is comparatively underrepresented in the literature and in reported agentic AI deployments compared with banking and investment.
Review finding (counts/themes across included studies indicating fewer studies/applications in insurance relative to banking and investment).
A weak manager directing a weak worker achieves a 42% success rate, performing worse than the weak agent alone which achieves 44%.
Empirical comparison across the same 200 SWE-bench Lite instances and pipeline configurations, comparing weak-manager+weak-worker pipeline to weak single-agent baseline.
When predictions from the two sources conflict, the AI agent aligns more frequently with the prompt, despite its lower accuracy.
Analysis of cases where prompt-based and revealed-data-based AI predictions differed; reported frequency with which the AI's action matched the prompt versus the revealed-preference prediction.
Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation.
Calibration of the model to O*NET tasks + expert survey + GPT-4o decompositions; implementation results reported for computer vision showing substitution varies with task complexity.
AI systems exhibit predictable but diminishing returns to data, compute, and model size (scaling-law experiments), implying the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly.
Scaling-law experiments estimating performance as a function of data, compute, and model size; described experimental estimation of production function.
Under low emotional intelligence, the model predicts higher risks of over-reliance on AI, emotionally detached communication, and weaker delegation quality.
Theoretical predictions derived from the EI-moderated human–AI model presented in the paper.
Kerangka hukum ketenagakerjaan Indonesia saat ini bersifat reaktif, dengan fokus pada kompensasi pasca-PHK yang belum mampu menjawab dampak jangka panjang disrupsi AI.
Analisis normatif terhadap peraturan perundang-undangan dan temuan dari literatur yang ditinjau; kesimpulan yang dilaporkan oleh penulis penelitian.
Belum terdapat pengaturan eksplisit mengenai kewajiban pelatihan ulang (retraining) maupun mekanisme distribusi manfaat teknologi secara adil dalam kerangka hukum ketenagakerjaan Indonesia saat ini.
Temuan dari analisis peraturan perundang-undangan nasional (UU Cipta Kerja dan peraturan turunannya) dan literatur yang dikaji dalam penelitian normatif.
Fenomena adopsi AI menimbulkan tantangan hukum terkait perlindungan hak pekerja, keadilan sosial, dan keberlanjutan sistem ketenagakerjaan.
Analisis normatif terhadap konsekuensi sosial-ekonomi AI yang disintesis dari literatur nasional (SINTA) dan internasional; pendekatan konseptual dan komparatif dijelaskan dalam metode.
Perkembangan pesat Artificial Intelligence (AI) telah membawa perubahan mendasar dalam struktur pasar tenaga kerja di Indonesia dengan meningkatnya risiko penggantian pekerjaan manusia oleh teknologi otomatisasi.
Pernyataan latar belakang yang didukung oleh tinjauan literatur pada jurnal nasional terindeks SINTA dan jurnal internasional bereputasi (metode: penelitian hukum normatif dengan pendekatan perundang-undangan, konseptual, dan komparatif).
When externalities are weak or the goods are close substitutes, the business-stealing effect produces a race to the bottom that dissipates more surplus than the industrial policy creates.
Comparative-static equilibrium results from the two-country strategic trade/R&D model showing welfare losses under weak externalities or high product substitutability (theoretical derivation; no empirical sample).
The intersection of IoT, artificial intelligence, cloud computing, and robotics collectively impacts social security systems.
The paper presents this as the focal analytic topic—an argument based on theoretical discussion and synthesis rather than reported empirical measurement (no sample size given).
The common claim that generative AI simply amplifies the Dunning–Kruger effect is too coarse to capture the available evidence.
Paper's synthesis of heterogenous empirical findings from human–AI interaction, learning research, and model evaluation used to critique the uniform-amplification interpretation; no single empirical countertest reported.
LLM use degrades metacognitive accuracy and flattens the classic competence–confidence gradient across skill groups (i.e., reduces calibration and narrows differences in self-assessed confidence by skill level).
Synthesis of studies from human–AI interaction and learning research reported in the paper that document worsened calibration and a reduction in the competence–confidence gradient when users rely on LLM outputs; the paper does not report a single combined sample size.
The authors introduce the concept of 'cascading bounded rationality' to describe how failures compound across bounded principals, agents, and evaluators.
Paper explicitly coins and defines the concept 'cascading bounded rationality' as part of its theoretical contribution.
Open-weight models cluster a full tier below the frontier models (Cohen's d larger than 1.4).
Between-group comparison reported in the paper showing a large standardized effect (Cohen's d > 1.4) separating frontier models from open-weight Meta models across the semantic closeness metric.
The agent team topology exhibits higher operational fragility due to multi-author code generation.
Reported empirical observation from experiments comparing architectures, attributing increased fragility/errors to multi-author code generation in the agent team setup (stated qualitatively; no quantitative failure rates provided in the abstract).
New technologies are initially skill intensive (demand more college-educated workers) but become less so as they age (they get standardized and accessible to less-skilled workers).
Empirical descriptive evidence from novel text-based data combining patent text and job postings (building on Kalyani et al., 2025) tracking technologies and their changing demand for skills as they age.
Azar et al. (2023) show that monopsonistic employers have stronger incentives to automate and document that US commuting zones with higher labor market concentration experienced more robot adoption.
Citation reported in the paper summarizing Azar et al. (2023); empirical analysis across US commuting zones (no sample size provided here).
Acemoglu and Restrepo (2022) attribute 50–70% of the increase in US wage inequality between 1980 and 2016 to displacement of workers from tasks by automation.
Citation reported in the paper summarizing Acemoglu and Restrepo (2022)'s attribution of the rise in wage inequality to automation-driven task displacement.
Dechezleprêtre et al. (2025) exploit Germany's Hartz reforms to estimate an elasticity of automation innovation to low-skill wages of 2–5 at the firm level.
Citation reported in the paper summarizing Dechezleprêtre et al. (2025)'s empirical estimate (elasticity 2–5); the paper states this was estimated at the firm level.
Eloundou et al. (2024) predict that half of US jobs are significantly exposed to recent advances in generative AI.
Citation reported in the paper summarizing Eloundou et al. (2024)'s prediction; no sample size provided in the excerpt.
When employers have monopsony power, they choose technologies that expand this power beyond what a social planner would consider optimal.
Model results on monopsonistic employer incentives and their technological choices; discussion supported by citations.
Profit-maximizing firms pursue innovations that erode workers' market power by making them more easily replaceable, even at the expense of production efficiency; a social planner who values worker welfare would employ technologies that preserve workers' market power.
Theoretical analysis of interactions between technological choice and market power; supported by cited empirical evidence (e.g., Azar et al. 2023) in the paper.
A welfare-maximizing planner would choose to automate fewer tasks than production efficiency would dictate when workers' welfare is heavily weighted.
Model analysis of welfare-maximizing automation level compared to production-efficient automation; analytical result in the automation application.
Observed declines in browsing time due to ChatGPT adoption are concentrated in website categories such as search and news, which are highly exposed to substitution by generative AI.
Category-level browsing time changes across website classification; concentration of declines in categories identified as highly overlap-exposed to chatbot capabilities using web-scraping and LLM site-level overlap classification.
High-income and younger households adopt generative AI substantially faster than low-income and older counterparts, and this gap is widening over time ('generative AI divide').
Descriptive heterogeneity analysis using Comscore household demographics (income and age bins) and observed adoption trajectories across 2021–2024; authors report widening gap rather than convergence.
Diminishing returns are not only a geometric flattening of the loss curve, but also rising pressure for cost reduction, system-level innovation, and the breakthroughs needed to sustain Moore-like efficiency doublings.
Analytical claim in the paper about the implications of diminishing returns for cost pressure and innovation requirements (qualitative; no sample size in excerpt).
Most of today's agents remain isolated tools or closed-ecosystem orchestrators rather than socially integrated participants in open networks.
Author claim/assessment presented as current-state analysis; no empirical breakdown or study sample provided in the text.
Prominent studies predict substantial job displacement due to automation.
Paper asserts this as background, referencing the existence of prominent studies in the literature (no specific citations or sample sizes provided in the abstract).