Evidence (4560 claims)
Adoption
5267 claims
Productivity
4560 claims
Governance
4137 claims
Human-AI Collaboration
3103 claims
Labor Markets
2506 claims
Innovation
2354 claims
Org Design
2340 claims
Skills & Training
1945 claims
Inequality
1322 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 378 | 106 | 59 | 455 | 1007 |
| Governance & Regulation | 379 | 176 | 116 | 58 | 739 |
| Research Productivity | 240 | 96 | 34 | 294 | 668 |
| Organizational Efficiency | 370 | 82 | 63 | 35 | 553 |
| Technology Adoption Rate | 296 | 118 | 66 | 29 | 513 |
| Firm Productivity | 277 | 34 | 68 | 10 | 394 |
| AI Safety & Ethics | 117 | 177 | 44 | 24 | 364 |
| Output Quality | 244 | 61 | 23 | 26 | 354 |
| Market Structure | 107 | 123 | 85 | 14 | 334 |
| Decision Quality | 168 | 74 | 37 | 19 | 301 |
| Fiscal & Macroeconomic | 75 | 52 | 32 | 21 | 187 |
| Employment Level | 70 | 32 | 74 | 8 | 186 |
| Skill Acquisition | 89 | 32 | 39 | 9 | 169 |
| Firm Revenue | 96 | 34 | 22 | — | 152 |
| Innovation Output | 106 | 12 | 21 | 11 | 151 |
| Consumer Welfare | 70 | 30 | 37 | 7 | 144 |
| Regulatory Compliance | 52 | 61 | 13 | 3 | 129 |
| Inequality Measures | 24 | 68 | 31 | 4 | 127 |
| Task Allocation | 75 | 11 | 29 | 6 | 121 |
| Training Effectiveness | 55 | 12 | 12 | 16 | 96 |
| Error Rate | 42 | 48 | 6 | — | 96 |
| Worker Satisfaction | 45 | 32 | 11 | 6 | 94 |
| Task Completion Time | 78 | 5 | 4 | 2 | 89 |
| Wages & Compensation | 46 | 13 | 19 | 5 | 83 |
| Team Performance | 44 | 9 | 15 | 7 | 76 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 18 | 17 | 9 | 5 | 50 |
| Job Displacement | 5 | 31 | 12 | — | 48 |
| Social Protection | 21 | 10 | 6 | 2 | 39 |
| Developer Productivity | 29 | 3 | 3 | 1 | 36 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Skill Obsolescence | 3 | 19 | 2 | — | 24 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Labor Share of Income | 10 | 4 | 9 | — | 23 |
Productivity
Remove filter
Firms that adopt passive, copy-based AI workflows risk psychological costs that could offset short-run productivity gains from AI.
Inference drawn from experimental findings of reduced efficacy/ownership/meaningfulness under passive use and short-term enjoyment gains; not directly tested for firm-level productivity or turnover—extrapolation from individual-level psychological measures.
Teams often produce evaluation outputs (tests, metrics, user feedback) but lack mechanisms, processes, or technical levers to convert those outputs into actionable engineering or product changes—a novel “results-actionability gap.”
Recurring theme from the 19 practitioner interviews and coding; authors explicitly articulate and label this gap based on participants' reports.
The study confirms several previously documented evaluation challenges with LLMs: model unpredictability, metric mismatch, high human-evaluation costs, and difficulty reproducing failures.
Interview data from 19 practitioners; thematic analysis flagged these recurring problems as reported by participants and aligned with prior literature.
Rapid deployment of autonomous learners could accelerate displacement in affected sectors and widen inequality if gains concentrate among capital owners or platform providers.
Socioeconomic risk assessment and projection; conceptual and not empirically quantified in the paper.
Faster, more generalist embodied AI could substitute for routine physical and social tasks, shifting human labor toward oversight, high-level planning, creativity, and flexible social cognition roles.
Labor-market impact hypothesis derived from automation literature; conceptual projection only.
Organizations without access to high-frequency operational data may face increased barriers to entry in latency-sensitive markets, concentrating rents with incumbents who can collect such data.
Paper presents this as an implication of the dataset/value results: proprietary high-frequency data can create competitive advantages. This is a policy/economic implication derived from model performance observations rather than a tested market analysis.
Widespread adoption of predictive HR tools raises distributional and fairness concerns (algorithmic bias, disparate impacts) and privacy risks that may prompt regulatory responses affecting adoption costs and equilibrium outcomes.
Discussion/implications section raises these risks conceptually; the paper does not empirically measure downstream policy or distributional effects.
Unclear liability frameworks increase perceived and real costs and can slow adoption by hospitals and insurers.
Policy analyses and procurement narratives noting liability uncertainty cited as a barrier to procurement and deployment.
Up-front implementation costs commonly include procurement, integration with PACS/EMR, UI/UX development, regulatory compliance, and staff training; recurring costs include monitoring, data labeling, software updates, and cybersecurity.
Implementation reports, vendor and hospital accounts, and qualitative studies documenting cost categories (specific dollar amounts vary across settings and are rarely published in detail).
Uneven organizational supports can concentrate returns to AI in firms and workers that successfully actualize affordances, potentially widening wage and employment disparities; targeted policy and training investments can mitigate these effects.
Theoretical implication from the framework with policy recommendations; no empirical testing or sample reported in the paper.
Without continuous support for upskilling/reskilling and inclusive policies, AI risks becoming a source of exclusion rather than an enabler of human advancement.
Normative conclusion derived from reviewed literature and thematic interpretation in the qualitative study (literature-based; evidence is secondary and not quantified).
At the national level, AI-related innovations are yet to be transformed into measurable economic gains.
Interpretation based on the observed negative association between AI patent counts and GDP growth from the panel regressions (OLS, FE, Difference and System GMM) and theoretical reasoning about adoption/diffusion lags and complementary requirements; empirical support derives from the same models (sample details not provided).
Research literature synthesis demonstrates 70-75% automation potential.
Quantitative estimate offered by the authors (70-75%) as part of function-by-function analysis; no described empirical evaluation or sample supporting the figure.
Knowledge transmission (teaching/lecturing) shows 75-80% AI substitutability.
Authors' quantitative estimate presented in the analysis (75-80%); the paper does not detail empirical methods or validation samples for this percentage.
Administrative tasks face 75-80% disruption risk from AI.
Paper provides a quantitative estimate (75-80%) as part of its functional disruption assessment; no empirical methodology, dataset, or sample size is described to support the numeric range.
Over 400,000 [individuals] are projected to die before obtaining permanent residency.
Mortality projection applied to the estimated backlog and projected wait times (authors' projection); exact demographic assumptions (age distribution, mortality rates) and method are not provided in the excerpt.
There is a risk of a two‑tier market where high‑quality temporal‑preserving enhancements are costly, increasing inequality in experiential welfare and cognitive capital.
Speculative socioeconomic implication based on cost/access arguments and distributional concerns; no inequality modeling or empirical pricing data provided.
Technical expansion without an accompanying theory of lived temporality risks increasing capabilities while degrading the qualitative depth of human experience (presence, attentional flow, felt meaning).
Argumentative claim supported by philosophical analysis and literature synthesis (neurophenomenology, attention economics); no empirical test reported (N/A).
Differential access to higher-quality (paid) versus free GenAI tools and differing ability to engage with the tool could widen inequality among students and institutions.
Authors' implication based on student-reported concerns about limitations of free ChatGPT versions and on heterogeneous gains across disciplines; this is a policy/implication claim not directly measured in the experiment.
High-quality, equitable climate information displays public-good characteristics (nonrival, nonexcludable at scale), so private incentives alone will underprovide geographically representative data and shared infrastructure.
Economic reasoning supported by observed concentration of compute and model development (mapping) and standard public-goods theory; no formal empirical market model estimated in the paper.
Heterogeneous trust levels across firms and schools may produce uneven productivity gains and widen performance gaps.
Logical implication and policy discussion in the paper; the cross-sectional study documents relationships between trust and outcomes but does not provide aggregate diffusion or cross-firm longitudinal evidence to confirm unequal sectoral diffusion.
Overreliance on unvetted AI can propagate biases; economic gains from AI therefore require governance, auditing, and accountability mechanisms.
Framed as a risk and policy recommendation in the discussion; not an empirical finding from the cross-sectional survey reported in the summary.
Full replacement of physicians would require breakthroughs in robust generalization, embodied capabilities, and legal/regulatory change—currently lacking.
Conceptual inference based on documented limitations (OOD generalization, lack of embodied/sensorimotor capability, unsettled legal/regulatory environment) summarized in the review.
Emerging agentic/AGI capabilities introduce new failure modes and governance challenges that standard ML oversight may not cover.
Emerging literature, theoretical analyses, and expert opinion summarized in the synthesis; authors note limited empirical long-term data and characterize this as an emergent risk.
Centralized provision of high-quality coding models by a few vendors could produce vendor lock-in and increase platform power in software development inputs.
Market-structure analysis and industry observations synthesized in the paper; the claim is forward-looking and not established by longitudinal market data within the review.
If many firms adopt AI generation without matching verification, aggregate fragility in software-dependent infrastructure could rise, increasing downtime costs and systemic economic risk.
Macro-level risk projection and system fragility argument in the paper; no macroeconomic modeling or empirical scenario analysis provided.
This reversal of the burden of proof creates moral-hazard-like behavior: incentives for speed reduce verification effort.
Theoretical argument built on the micro-coercion mechanism and economic reasoning; no empirical validation provided.
Under time pressure, developers adopt an implicit default of accepting plausible machine outputs unless they can disprove them (the 'micro-coercion of speed'), effectively reversing the burden of proof.
Behavioral mechanism posited from descriptive reasoning and thought experiments; no behavioral experiments, surveys, or observational data reported.
DAR dynamics (authority states, hysteresis, safe-exit times) introduce path-dependence and switching costs that should be treated as state variables in production and decision models of human–AI joint work.
Theoretical implications section arguing these elements add path-dependence and switching costs to economic/production models; analytic reasoning, not empirical measurement.
Concentration risks exist because high fixed costs for safe integration and model adaptation may favor larger incumbents or platform providers.
Conceptual economic reasoning and practitioner commentary synthesized in the review; no empirical market-structure analysis or sample-based evidence included here.
Rich contextual memories and continuous home interaction create valuable data streams that could enable firms to capture substantial value, raising concerns about data governance, consent, and monetization.
Authors' policy and economic implications discussion noting that MMCM-like memories generate valuable data; this is a conceptual/policy claim rather than empirically tested within the study.
Imported AI systems may impose foreign values and norms, risking erosion of indigenous knowledge and social cohesion.
Normative and conceptual argument supported by cited case studies and policy analyses; no original anthropological or sociological fieldwork in the paper.
Deployed AI systems can produce algorithmic bias that harms marginalized groups when models are trained on skewed or non‑representative data.
Synthesis of prior empirical findings and case studies on algorithmic bias and fairness in ML systems; paper does not present new empirical tests.
Human reviewers may over-trust machine-generated language and explanations (automation bias), reducing the likelihood of detecting fraudulent outputs.
Reference to automation-bias literature and conceptual examples; threat modeling and illustrative vignettes in the article.
Existing internal audit and compliance frameworks focus on access, transaction, and system controls, not on content-generation integrity.
Literature and standards review combined with threat-control mapping demonstrating gaps in content/provenance coverage.
Using calibrated, employee-level predictions enables marginal-cost analyses and prioritization (micro-targeting) to improve retention-efficiency versus uniform, across-the-board policies.
Methodological argument: calibrated individual probabilities plus counterfactual impact estimates enable ranking employees by expected gain from interventions and thus marginal-cost prioritization (no empirical cost–benefit calculations provided).
Recommended research priorities include hierarchical/temporal-decomposition methods, continual learning, robust adaptation to non-stationarity, and causal/structured reasoning to handle multi-factor interactions.
Paper discussion linking observed failure modes to methodological gaps and proposing research directions to address limitations; these are recommendations rather than experimentally validated claims.
Regulators and payers will require clinical validation, safety guarantees, and clear liability frameworks for human–AI shared decision-making before widescale deployment.
Policy implication stated in the paper's discussion section based on general regulatory considerations; not an empirical result from the study.
Empirical economics research should use firm-level and pipeline microdata and quasi-experimental designs to estimate causal effects of AI adoption on outcomes like time-to-hit, preclinical attrition, IND filings, and NME approvals per R&D dollar.
Research recommendation offered in the paper based on identified gaps; not an evidence claim but an explicit methodological suggestion.
Policy does not predict individuals' intent to increase usage but functions as a marker of maturity—formalizing successful diffusion by Enthusiasts while acting as a gateway the Cautious have yet to reach.
Analysis of a policy variable within the survey dataset (N=147) showing no predictive relationship with individual intent to increase AI use, but an association between presence of policy and indicators of organizational adoption/maturity and differential reach into archetype groups.
Prospective studies are needed to evaluate AI's real-world clinical impact in acute GIB.
Authors' recommendation in the discussion and conclusion based on the predominance of retrospective evidence and few prospective/RCTs.
The study recommends iterative prompt refinement, integration with adaptive learning models, and further exploration of autonomous self-prompting mechanisms.
Concluding recommendations derived from the study's results and interpretation; presented as future directions rather than empirically tested interventions within this study.
Recommended future research includes scalable interoperability solutions, longitudinal lifecycle value validation, human‑centred adoption strategies, and sustainability assessment methods.
Authors' explicit recommendations at the end of the review based on identified gaps in the literature.
Future research priorities include obtaining causal estimates (e.g., field experiments) of productivity gains from trust-mediated AI adoption and conducting cost–benefit analyses of trust-building interventions.
Study’s stated research agenda/recommendations; not an empirical claim but a recommended direction for follow-up research.
Key research priorities include improving measurement of AI usage across countries, causal identification of long-run effects, and sectoral reskilling strategy evaluation.
Identified gaps and methodological limitations in the reviewed empirical literature (measurement heterogeneity, limited long-run panels, sectoral variation) motivating suggested future research agenda.
To measure and monitor these effects, researchers should track firm-level adoption of AI features, fulfillment automation intensity, platform-mediated market entry, and task-level labor shifts.
Author recommendations based on gaps identified in the case-based and multi-modal empirical work and the sensitivity of results to adoption measures; not an empirical finding but a methodological claim.
The threshold for taxing AI may be crossed once AI becomes sufficiently capable in substituting humans across cognitive tasks.
Model-based comparative-static/threshold analysis showing that higher AI substitutability for cognitive tasks increases the likelihood that cognitive workers will consider switching to manual jobs, thereby meeting the model's tax-initiation condition.
Economic and organizational benefits (e.g., cost-effective retention, preserved human capital for environmental innovation) are plausible outcomes of applying the approach, but require further causal and cost analyses.
Paper discusses implications and hypothesizes ROI from reduced turnover (less recruiting/onboarding/productivity loss) and preservation of green capabilities; no empirical cost or productivity data provided in the presented summary.
Firms investing in human–AI co‑creation infrastructure may gain a resilience premium; policymakers and standards bodies should consider governance frameworks for adaptive algorithmic systems balancing responsiveness with oversight.
Policy and investment implication inferred from empirical results on resilience and detection performance; direct evidence of market valuation or policy outcomes is not reported.
Greater reliance on algorithmic co‑creation shifts labor demand toward roles skilled in model oversight, interpretive judgment, and human‑machine interaction rather than purely manual segmentation tasks.
Inference from the operationalization of human–AI co‑creation via the Canvas and observed changes in practitioner workflows during 6‑month ethnography (n = 23); workforce composition effects are not empirically measured at scale in the study.