Evidence (8066 claims)
Adoption
5586 claims
Productivity
4857 claims
Governance
4381 claims
Human-AI Collaboration
3417 claims
Labor Markets
2685 claims
Innovation
2581 claims
Org Design
2499 claims
Skills & Training
2031 claims
Inequality
1382 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 417 | 113 | 67 | 480 | 1091 |
| Governance & Regulation | 419 | 202 | 124 | 64 | 823 |
| Research Productivity | 261 | 100 | 34 | 303 | 703 |
| Organizational Efficiency | 406 | 96 | 71 | 40 | 616 |
| Technology Adoption Rate | 323 | 128 | 74 | 38 | 568 |
| Firm Productivity | 307 | 38 | 70 | 12 | 432 |
| Output Quality | 260 | 71 | 27 | 29 | 387 |
| AI Safety & Ethics | 118 | 179 | 45 | 24 | 368 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 75 | 37 | 19 | 312 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 74 | 34 | 78 | 9 | 197 |
| Skill Acquisition | 98 | 36 | 40 | 9 | 183 |
| Innovation Output | 121 | 12 | 24 | 13 | 171 |
| Firm Revenue | 98 | 35 | 24 | — | 157 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 87 | 16 | 34 | 7 | 144 |
| Inequality Measures | 25 | 76 | 32 | 5 | 138 |
| Regulatory Compliance | 54 | 61 | 13 | 3 | 131 |
| Task Completion Time | 89 | 7 | 4 | 3 | 103 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 33 | 11 | 7 | 98 |
| Wages & Compensation | 54 | 15 | 20 | 5 | 94 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 27 | 26 | 10 | 6 | 72 |
| Job Displacement | 6 | 39 | 13 | — | 58 |
| Hiring & Recruitment | 40 | 4 | 6 | 3 | 53 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 11 | 6 | 2 | 41 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 6 | 9 | — | 27 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Higher levels of selective exposure are positively associated with increased ideological polarization.
Correlational analyses (reported associations / regression-style tests) using survey measures of selective exposure and measures of opinion/political polarization in the same cross-sectional sample (N = 450).
A large majority of respondents reported frequent exposure to content aligned with their preexisting views (widespread echo chambers / filter bubbles).
Quantitative cross-sectional survey of N = 450 active social media users; self-reported measures of content consumption and indicators of selective exposure; descriptive statistics showing most respondents frequently encounter ideologically consonant content.
An AI agent given revealed-preference data predicts subjects' choices more accurately than an AI agent given stated-preference prompts.
Online experiment in which subjects provided written instructions (prompts) and revealed preferences via choices in a series of binary lottery questions; AI agents were given either the revealed-preference data or the stated-preference prompts and their prediction accuracy on subjects' choices was compared.
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.
The findings demonstrate that technological innovation strategies, when effectively implemented, provide measurable competitive advantages for banks and offer evidence-based insights for policymakers and practitioners.
Authors' interpretation/conclusion drawing on the reported statistically significant relationships between innovation (product and technological) and competitiveness.
Technological innovation is positively and statistically significantly related to bank competitiveness (simple linear regression result reported).
Simple linear regression reported in the paper testing the hypothesis that technological innovation influences competitiveness; data collected from innovation-focused executives across licensed banks (paper states data from 39 licensed banks).
Product innovation strategy has a positive and statistically significant effect on competitiveness (F(1,134) = 74.983, p < .001).
Bivariate regression analysis reported in the paper with F(1,134)=74.983, p < .001; based on survey data from innovation-focused executives (regression degrees of freedom indicate n≈136 observations).
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.
In the user study, AI-expanded 5W3H prompts increase user satisfaction from 3.16 to 4.04.
Reported pre/post or baseline vs AI-expanded satisfaction scores in the N=50 user study with numeric scores 3.16 and 4.04.
In the user study, AI-expanded 5W3H prompts reduce interaction rounds by 60 percent.
Reported comparison in the N=50 user study between baseline interaction rounds and rounds after AI-assisted 5W3H expansion; percentage reduction reported as 60%.
A weak-model compensation pattern was observed: the lowest-baseline model (Gemini) shows a much larger D-A gain (+1.006) than the strongest model (Claude, +0.217).
Model-level comparison of D-A gain (difference between structured and unstructured conditions) across three models (Claude, GPT-4o, Gemini) on the evaluated outputs; reported gains for Gemini and Claude.
The strongest structured conditions reduce cross-language sigma from 0.470 to about 0.020.
Reported numeric comparison of sigma (variance) between unstructured baseline and strongest structured prompting conditions across evaluated outputs.
Structured prompting substantially reduces cross-language score variance relative to unstructured baselines.
Empirical comparison across 3,240 outputs evaluated by DeepSeek-V3, comparing structured vs. unstructured prompting across three languages.
Prior work showed that PPS (Prompt Protocol Specification), a 5W3H-based structured intent framework, improves goal alignment in Chinese and generalizes to English and Japanese.
Statement referring to prior work (not new experiments in this paper); no sample size or methods provided in this text excerpt.
The case for mutually beneficial industrial policy is stronger for product innovation than for process innovation, because product innovation directly affects demand and triggers stronger network effects while process innovation operates indirectly through supply.
Model variants distinguishing product vs. process R&D within the two-country framework; comparative analysis showing larger demand-driven network effects for product innovation (theoretical model results; no empirical sample).
Under sufficiently strong network externalities and weak substitutability (or weak complementarity) of the goods, industrial policy competition can make both countries simultaneously better off compared to the laissez-faire outcome because of a mutual business-enhancement effect.
Theoretical demonstration within the two-country model: parameter regions (strength of externality, degree of product differentiation) where simultaneous welfare improvements occur relative to laissez-faire (analytical/model results; no empirical sample).
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).
Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.
Aggregate of reported experimental results across architecture design, pretraining data curation, reinforcement learning algorithm design, and preliminary transfer experiments.
In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +5.04 points on OlympiadBench.
Reinforcement learning algorithm design experiments reported in the paper comparing discovered algorithms to GRPO on OlympiadBench.
In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +11.67 points on AIME24.
Reinforcement learning algorithm design experiments reported in the paper comparing discovered algorithms to GRPO on AIME24.
In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32.
Reinforcement learning algorithm design experiments reported in the paper comparing discovered algorithms to GRPO on AMC32.
In pretraining data curation, gains exceed 18 points on MMLU.
Reported experimental result on MMLU benchmark within pretraining data curation experiments.
In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points.
Pretraining data curation experiments reported in the paper showing an average benchmark performance improvement of +3.96 points.
The best discovered model surpasses DeltaNet by +0.97 points, nearly 3x the gain of recent human-designed improvements.
Reported performance comparison between the best discovered model and DeltaNet in neural architecture experiments; statement comparing relative gain to recent human-designed improvements.
In neural architecture design, it discovered 105 SOTA linear attention architectures.
Neural architecture design experiments reported in the paper, with 105 discovered architectures labeled as SOTA.
ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations.
Method description of ASI-Evolve's architecture/components in the paper (cognition base and analyzer added to evolutionary agents).
We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle.
Methodological contribution described in the paper: presentation and implementation of the ASI-Evolve framework and its learn-design-experiment-analyze loop.
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.
Frontier models (Claude Haiku 4.5, GPT-5-chat, GPT-5-mini) achieve statistically indistinguishable semantic closeness scores above 4.6 out of 5.0.
Reported semantic closeness scores from the LLM-as-Judge evaluation on the 15-proposal dataset; the paper states frontier models scored above 4.6/5.0 and were statistically indistinguishable from each other.
These empirical insights provide actionable guidelines advocating dynamically routed architectures that adapt their collaborative structures to real-time task complexity.
Authors' recommendation derived from reported empirical findings comparing architectures under varying time budgets and task complexities (prescriptive claim based on study results).
Given extended compute budgets, the agent team topology achieves the deep theoretical alignment necessary for complex architectural refactoring.
Empirical benchmarks run with longer/extended computational budgets showing agent teams perform better on complex architectural refactoring tasks (qualitative claim; no numeric effect sizes or sample counts provided in the abstract).
The subagent mode functions as a highly resilient, high-throughput search engine optimal for broad, shallow optimizations under strict time constraints.
Benchmark comparisons in the execution-based testbed under strictly fixed computational time budgets showing subagent architecture excels in throughput/resilience for broad, shallow optimization tasks (qualitative claim in paper; no numeric effect sizes provided).
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.