Evidence (4857 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 |
Productivity
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Many perceived alignment failures of large language models (LLMs) are not inevitable consequences of model scale or capability; they largely result from operational choices made in training and deployment.
Conceptual analysis and literature synthesis presented in the paper; references to prior case studies and examples of deployment failures are used to support the argument. No new empirical dataset or controlled experiment is reported.
Hybrid norms combined with AI platforms lower coordination costs and may encourage more decentralized or platform‑based organizational structures, changing the premium on co‑location.
Theoretical integration of organizational economics and digital platform literature; supported by conceptual examples but no firm‑level causal analysis in the paper.
Differential access to informal learning and sponsorship in hybrid settings can produce long‑term human‑capital inequalities; AI-based mentoring and visibility tools may partially mitigate these gaps but risk biased recommendations if trained on skewed data.
Synthesis of literature on mentorship, social capital, and algorithmic bias; illustrative case examples rather than empirical evaluation of AI mentoring systems.
Geographic dispersion plus AI-enabled remote hiring can widen the labor supply for firms, potentially compressing wages for some roles while raising returns to digital-collaboration skills.
Economic reasoning and literature review on remote hiring and labor supply effects; the paper offers conceptual arguments rather than presenting empirical wage-impact estimates.
Automation of routine tasks may shift task content toward relational and creative work, areas where hybrid arrangements influence social capital accumulation.
Theoretical argument combining automation literature with sociological perspectives on social capital; no direct empirical measurement or longitudinal data in the paper.
Hybrid work complicates traditional productivity metrics, making AI-driven analytics and monitoring tools more attractive but creating trade-offs between measurement accuracy, privacy, and employee trust.
Conceptual argument synthesizing literature on measurement, monitoring, and AI tools; no empirical evaluation of specific tools or datasets in the paper.
Sustaining productivity and organizational culture under hybrid arrangements depends crucially on leadership practices—trust, communication, and fairness—and on inclusive policies that explicitly manage equity, well‑being, and flexibility.
Comparative case illustrations and management literature integration; recommendations derived from secondary sources and theoretical argumentation rather than controlled empirical testing.
Dispersed work alters identity construction, belonging, and social cohesion; digital interactions reshape workplace rituals and norms.
Sociological literature synthesis and qualitative case illustrations emphasizing identity and ritual processes; no longitudinal or quantitative measures provided in the paper.
Demand for defensive AI engineers and incident responders will rise, while demand for traditional offensive hacking labor may decline as automation substitutes some roles.
Labor-market reasoning based on substitution/complementarity between automation and human tasks (qualitative; no labor-market data).
The paper proposes an 'algorithmic workplace' framework emphasising hybrid agency (agents composed of humans plus GenAI), decentralised decision processes, and erosion of rigid managerial boundaries.
Conceptual synthesis derived from thematic mapping, co‑word analysis and interpretive discussion of the mapped literature; framework presented as the article's conceptual contribution.
Passive AI use produced an initial increase in enjoyment/satisfaction that reversed once participants returned to manual work.
Pre-registered experiment (N = 269) measured enjoyment/satisfaction before and after return to manual work; passive-copy condition showed short-term increases in enjoyment/satisfaction that declined after returning to manual tasks.
The benefits of AI come with governance, ethical, and sustainability challenges (standards, control, accountability) that require balancing against innovation incentives.
Synthesis of policy, ethics, and governance literature documenting concerns about standards, accountability, and incentive trade-offs; argument is qualitative and prescriptive rather than empirically tested within this paper.
AI has enhanced delivery in education, health, transportation, and government, improving some service outcomes while persistent issues like bias, privacy, transparency, and accountability remain.
Synthesis of applied-AI case studies and sectoral evaluations drawn from interdisciplinary literature; evidence described qualitatively without new empirical aggregation or meta-analysis in this paper.
AI reshapes demand for skills, redefines occupations, and accelerates the need for reskilling, with distributional effects that can increase inequality.
Narrative review of labor-economics and workforce studies documenting task reallocation and shifting skill requirements; based on observational studies and sectoral analyses summarized in the review (no unified sample size or new empirical test in this paper).
Technical milestones (scalable, error-corrected qubits; hybrid algorithms) create fat-tailed outcome distributions where a small probability of breakthrough could yield outsized long-run effects.
Monte Carlo experiments and scenario ensembles that include low-probability, high-impact technical breakthrough parameters; expert elicitation of milestone probabilities.
R&D funding, standards, regulatory clarity, export controls, and public–private partnerships shape quantum adoption trajectories; policy missteps can slow adoption and concentrate benefits.
Policy counterfactual scenarios and qualitative analysis of ecosystem roles; calibration informed by historical effects of policy on diffusion of strategic technologies.
Aggregate gains hinge on how quickly and broadly quantum technologies diffuse; early gains concentrated in frontier firms/sectors can take decades to propagate economy-wide.
Diffusion modeling using logistic/S-curve and Bass models calibrated to historical analog technologies; scenarios show long lag between frontier adoption and economy-wide diffusion.
As successive pilot batches of urban green data center policies are rolled out, the aggregate policy impact follows a nonlinear rise-then-fall (increase followed by decline) diffusion trajectory.
Analysis across pilot-batch rollout timing showing a nonlinear (rise-then-fall) pattern in aggregate estimated effects as the number of pilot batches expands; modeled/visualized within the staggered-adoption DID framework.
Realizing NLP value in banks requires organizational investments (data pipelines, model deployment, CRM integration) and complementarity between AI tools and managerial/IT capabilities; returns will depend on these complementarities.
Conceptual implication derived from review of applied/engineering papers and literature on technology complementarities; not directly estimated empirically in the review.
Automated tax-preparation and filing could increase compliance rates but also make tax bases more sensitive to automated tax-optimization strategies, requiring updated regulatory oversight and audit tools.
Paper's policy and economic implications section combining case-based observations and literature; presented as plausible outcomes rather than measured effects.
AI-driven productivity and data externalities can reconfigure which countries/regions specialize in which activities, with implications for labor demand, offshoring, and services trade patterns.
Mechanism and theory-based analysis drawing on literature about comparative advantage, automation, and data externalities; empirical testing recommended but not performed in the paper.
Standard international trade models should be updated to incorporate data as an input, platform-mediated matching, algorithmic complementarities, and costs of regulatory fragmentation.
Theoretical critique and modeling recommendations based on mechanism analysis; no new formal model calibration or empirical testing presented in the paper.
AI-enabled markets tend toward winner-take-most platforms amplified by network effects.
Theoretical reasoning supported by platform literature and case illustrations of platform concentration dynamics; empirical magnitudes not estimated in the paper.
Competitive advantage is shifting away from asset- and labor-intensive models toward data-, model-, and platform-driven advantages, altering comparative advantage and market structure.
Mechanism/theoretical analysis drawing on platform and AI economics literature and qualitative examples; no empirical estimation provided in the paper.
Regulatory design acts as an economic instrument that can balance social value from AI with protection of rights, affecting social welfare, public trust, and long-term adoption rates.
Normative synthesis combining legal and economic reasoning; suggested as a theoretical mechanism rather than empirically validated within the paper.
Automation of routine administrative tasks may reduce demand for certain clerical roles while increasing demand for oversight, auditing, and legal-technical expertise, altering public-sector labor composition and retraining needs.
Qualitative labor-market reasoning based on task-based automation literature and the administrative context; no field labor-data or sample provided.
Current LLMs produce deep, reliable reasoning mainly in domains with rigorous, pre-existing abstractions (mathematics, programming) and underperform in domains that lack such formal abstractions.
Performance comparisons and observed patterns referenced qualitatively (e.g., better behavior on math and code tasks) drawn from existing literature and practitioner reports; the paper does not present new controlled benchmark experiments.
AI feedback may either augment teacher productivity (complementarity) or substitute for routine teacher feedback tasks (substitution), with unclear net labor impacts.
Workshop deliberations among 50 scholars highlighting competing theoretical scenarios; no causal labor-market evidence provided.
The approach shifts some resource demand from GPU clusters to CPU, memory, and storage I/O, meaning local SSD and CPU provisioning can become the new bottleneck.
Authors note the system relies on multi-tier I/O and CPU-side updates to enable single-GPU fine-tuning; the summary highlights this resource-shift as a risk/consideration. No quantitative cost or workload-specific tradeoff analysis is provided in the summary.
Human experts will likely shift roles from sole decision-makers to adjudicators, challengers, and validators of AI-generated arguments, changing required skills toward critical evaluation and dialectical oversight.
Conceptual labor-market projection; no empirical labor studies or surveys presented.
Productivity gains from partial automation may be offset by negative externalities (incorrect legal outcomes, appeals, reputational damage) that impose social and private costs not captured by narrow productivity measures.
Theoretical economic analysis and illustrative case vignettes describing error propagation; no empirical quantification of externalities.
Market demand will likely split between providers offering generative convenience with liability exposure and providers offering certified/verified, explainable tools at a premium, creating a two-tier market.
Market-structure analysis and illustrative projections; no empirical market data or sample size.
Reported monetary supervision cost was low (~$200) for this project, but the paper cautions that general equilibrium effects and scaling may change costs as demand for supervisors rises.
Paper provides reported supervision cost (≈$200) for the single project and includes a caveat about external validity and scaling; cost is self-reported and contextualized by authors.
Because these agents will be embedded in safety‑critical infrastructure, economic and technical outcomes will depend heavily on system architecture choices.
Systems‑engineering and policy reasoning drawing on analogies to Internet/IoT evolution and domain examples (disaster response, healthcare, industrial automation, mobility); conceptual argumentation rather than empirical measurement.
The study documents a 'silent empathy' effect: people often feel empathic concern but fail to express it in ways that align with normative empathic communication; targeted feedback helps close that expression gap.
Analysis showing mismatch between internal empathic concern (implied by context/self-report/ratings) and the presence of idiomatic empathic moves in participants' messages; targeted personalized feedback increased use of normative empathic expressions.
Liability regimes and penalties should account for limits of enforced compliance and false positives/negatives from probabilistic policy evaluations.
Normative/economic discussion in the paper highlighting probabilistic outputs of the Policy function and calibration challenges; no empirical validation.
Firms will trade off compliance strictness against service quality (task completion rates), creating an economic tradeoff that shapes market offerings (e.g., safer-but-slower vs. faster-but-riskier agents).
Economic reasoning and conceptual models in the paper; suggested objective balancing task completion and legal/reputational costs; no empirical market data.
For models/dynamics with negative LLE (contracting behavior), investment in parallel Newton tooling is likely to pay off; for expanding/chaotic dynamics (positive LLE), alternative architectural or modeling changes may be more cost-effective.
Application of the LLE convergence criterion derived in the thesis combined with empirical demonstrations on representative tasks indicating correlation between LLE sign and parallel solver performance; economic recommendation is interpretive.
The economic value of deploying DeePC-based controllers depends critically on representativeness of training data and the costs of online adaptation and safety verification.
Authors' deployment-risk analysis and discussion of trade-offs (qualitative), grounded in methodological requirements of DeePC (need for representative, persistently exciting data and safeguards).
System-level improvements from the controller do not imply uniform spatial/temporal benefits—distributional effects may favor certain routes or neighborhoods.
Authors' discussion and caution about distributional effects and equity; possibly supported by spatial analyses in simulation (qualitative discussion in paper).
Fine-tuning TSFMs on the high-frequency 5G data provides limited recovery; many configurations still perform poorly after fine-tuning.
Paper reports experiments including fine-tuning regimes where TSFMs were fine-tuned on the new dataset; results indicate limited improvement in many configurations. Specific fine-tuning procedures, datasets sizes, and quantitative results are not provided in the summary.
Reducing payrolls raises short-term firm profitability but reduces aggregate household income and consumption.
Macroeconomic accounting and labor-demand theory combined with historical examples of payroll reductions; argument is theoretical/conceptual rather than estimated with new aggregate time-series regression evidence.
Reviving model-based central planning tools (ISB+NDMS) risks political-economy problems and requires evaluation of efficiency and flexibility compared to market coordination.
Analytic discussion and normative argument in the paper; no empirical comparative study provided.
Russia's digitalization and adoption of AI/Big Data are reshaping the country's socio-economic infrastructure in multifaceted and systemic ways.
Qualitative analysis of national strategies and policy documents plus the author's expert assessments; no sample size or statistical testing reported.
Improved matches and clearer skill signals can raise short-term wages for matched youth, while longer-term wage dynamics will depend on supply responses and bargaining power shifts.
Pilot reports higher reported short-term wages; longer-term effects are discussed as conditional and not measured in the pilot.
Overall, economic benefits from AI in radiology are plausible but conditional on human-AI interaction design, governance, workforce effects, and payment structures; net value is not determined by algorithmic accuracy alone.
Synthesis of the heterogeneous literature (laboratory, reader, observational, qualitative) and conceptual economic analysis highlighting dependencies beyond algorithmic performance.
The net effect of AI on clinician burnout is ambiguous: tools can remove tedious tasks but may introduce new cognitive, administrative, and liability stresses.
Mixed qualitative and small-scale observational studies with variable findings on burnout-related measures after AI introduction.
Changes in workload composition can reduce routine burdens but may shift cognitive load to follow-up decisions and managing AI outputs.
Observational and qualitative studies of deployed systems reporting redistribution of tasks and clinician-reported changes in cognitive demands.
Economic outcomes depend on complementarity versus substitution: AI that augments radiologists can raise output per worker; AI that substitutes tasks may reduce demand for certain diagnostic activities.
Theoretical economic frameworks and case studies of task reallocation in early deployments; empirical workforce-impact studies limited.
Automation bias can increase undue reliance on AI, while algorithmic aversion can drive underuse of helpful tools.
Cognitive and behavioral studies and reader simulations demonstrating both increased acceptance/overtrust in automated outputs in some settings and rejection/discounting of AI advice in others.