Evidence (4793 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Productivity
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Conventional policy instruments have failed to resolve pressures that include severe long-term care workforce shortfalls across leading ageing economies.
Synthesis of findings from the structured narrative review of 81 sources (2020–2025) indicating persistent workforce shortfalls. The abstract does not provide quantitative workforce shortfall magnitudes or country-specific data.
Demographic ageing is projected to reduce annual GDP growth by 0.3–1.2 percentage points by 2035.
Projection estimates referenced in the review literature (2020–2025). The abstract reports the 0.3–1.2 p.p. range but does not specify which models or studies generated these projections.
Ageing-related expenditure already absorbs up to 18% of GDP in the most affected economies.
Spending estimates drawn from the reviewed literature (2020–2025). The paper states 'up to 18% of GDP' for the most affected economies but does not list which economies or the original data sources in the abstract.
Advanced economies face a compounding demographic crisis: populations aged 65 and over will reach 30–40% in several nations by 2050.
Demographic projection claims cited in the paper's background literature (sources from the structured narrative review). No specific datasets or country-by-country breakdown provided in the abstract.
Current literature has primarily focused on automation-based views of decision support and lacks insight into systematic human–AI coordination aided by analytics.
Literature review and conceptual critique within the paper. No systematic mapping study or bibliometric counts reported.
Most organizations have difficulties converting algorithmic results into sustainable managerial decisions due to low levels of trust, lack of explanation, and poor integration between AI systems and human judgment.
Synthesis of existing literature presented in the conceptual paper (literature review). No empirical study or sample provided to quantify 'most organizations.'
AI adoption has augmented complexity, uncertainty in decision-making, and accountability stresses for managers.
Claim supported by conceptual argument and literature integration (qualitative synthesis). No empirical sample size or quantitative testing reported.
Traditional methods for assessing and developing employees' skills often fail to provide real-time feedback.
Statement supported by literature review cited by the authors; the abstract does not provide empirical comparisons, metrics, or sample sizes.
The findings constitute a cautionary case for the effectiveness of LLM use in strategic decision-making.
Authors' interpretation based on the experimental results: representational changes occurred with LLM use but did not translate into improved strategic foresight, combined with observed increases in overload and decreases in ownership.
LLM use reduces psychological ownership (additional analyses).
Reported follow-up/additional analyses from the experiment showing a statistically significant decrease in psychological ownership measures for participants using LLMs.
Existing research on AI-driven decision-making remains fragmented and often framed through substitution-oriented narratives that position AI as a replacement for human judgment.
Assessment based on the author's interdisciplinary literature synthesis (conceptual meta-analysis); descriptive evaluation of research framing rather than new empirical testing.
Skills mismatch and SME adoption constraints constitute a binding bottleneck for inclusive digital–green upgrading.
Synthesis of studies on skills, firm capabilities, and SME adoption of digital and green technologies (review-level evidence; no single dataset or sample size provided).
Absent complementary institutions and infrastructure, digitalization may increase electricity demand, widen inequality, and incentivize strategic disclosure (greenwashing).
Literature review drawing on empirical studies of energy consumption from digital systems, labor-market studies, and analyses of ESG disclosure practices (review-level synthesis; no single sample size reported).
The IT sector is currently witnessing significant workforce restructuring, including employee layoffs, necessitating a critical reassessment of existing competency mapping frameworks.
Asserted in the paper as a motivating observation; no specific layoffs data or statistics provided in the excerpt.
More experienced translators appear more likely to exit the market after ChatGPT’s launch than less experienced translators.
Heterogeneous (subgroup) analysis by experience level within the translation market reported in the paper; evidence presumably from DiD estimates of exit/participation rates across experience levels. (Exact sample sizes and exit definitions not provided in the abstract.)
Following ChatGPT’s launch, some online labor markets experienced displacement effects characterized by reduced work volume and earnings, exemplified by the translation & localization OLM.
Empirical analysis using a Difference-in-Differences (DiD) design on online labor market (OLM) data; the abstract identifies translation & localization OLM as an example. (Sample size and exact data window not specified in the abstract.)
The AI productivity paradox reflects organizational constraints rather than technological failure.
Synthesis of the theoretical productivity funnel and empirical findings from firm-level data across Serbia, Croatia, Czechia, and Romania indicating conditional (not universal) productivity effects of AI.
Measurable productivity gains remain modest for firms lacking standardized processes and management systems.
Empirical comparisons within the firm-level dataset showing smaller productivity gains among firms characterized as lacking standardized processes/management systems (organizational readiness measures).
Within this framework, we identify a complementarity trap: firms lacking organizational readiness become stuck in the funnel, unable to convert AI diffusion into productivity gains.
Theoretical argument supplemented by empirical analysis using firm-level data from a subset of Central and Eastern European economies and AI diffusion indicators (countries named: Serbia, Croatia, Czechia, Romania).
The system forces many children to age out at 21, creating deportation risks for those who are American in every meaningful sense except paperwork.
Policy consequence of long backlogs: derivative status rules cause dependents to 'age out' at 21; deportation risk implication is a legal/administrative outcome. The excerpt does not quantify the number affected or present a dataset.
The backlog traps H-4 dependent spouses, over 90% of whom hold bachelor's degrees, in years-long employment prohibition, removing skilled labor from the workforce.
Claim combines (a) an asserted >90% college-degree rate for H-4 spouses—presumably from ACS/DHS or authors' survey analysis—and (b) immigration policy facts that many H-4 spouses lack work authorization for extended periods; the excerpt does not provide the underlying dataset, sample size, or citations.
Constrained mobility suppresses H-1B wages by 12.2%.
Empirical estimate asserted in the paper (likely from econometric analysis comparing wages under constrained vs. unconstrained mobility); the excerpt does not cite the specific study, dataset, sample size, or methods that produced the 12.2% figure.
Employer-specific sponsorship combined with high switching costs—$5,000+ in fees and multi-year delays—concentrates labor-market power among employers.
Policy/mechanism claim supported by typical filing fee estimates and observed multi-year adjudication/porting constraints; the excerpt does not report a formal empirical test or sample size demonstrating employer market power concentration.
These provisions have generated wait times as extreme as 195 years for Indian nationals in the EB-2 category.
Projection based on visa bulletin/backlog dynamics and issuance rates for EB-2 India; the paper does not show the step-by-step projection or assumptions in the excerpt.
The U.S. employment-based immigration system traps over 1.8 million skilled workers and their families in legal limbo.
Paper's aggregate/backlog calculation presumably using Department of State visa bulletin backlogs, USCIS pending adjustment of status (I-485) inventories, and derivative family counts; the paper does not provide the detailed method or sample breakdown in the excerpt.
When policy uncertainty is high, the market's pricing of AI-intensive firms becomes less anchored to real economic performance.
Interpretation of model results that show a reduced linkage between labor productivity growth and equity valuations during high EPU periods, as estimated by the smooth-transition local projection model on U.S. data.
Economic policy uncertainty disrupts how stock markets value fundamental productivity in the AI-intensive (AI and robotics) sector.
Inference from the same smooth-transition local projection estimates showing a change in the productivity→valuation relationship across EPU regimes, based on U.S. productivity and EPU series used in the paper.
Economic policy uncertainty (EPU) weakens the positive effect of labor productivity growth on equity valuations in the AI and robotics sector.
Estimated from a smooth-transition local projection model using U.S. labor productivity and EPU data; the paper reports that the positive productivity→valuation effect 'weakens significantly' as EPU rises (statistical significance claimed). Python code and data for replication are provided in the appendix.
AI causes job loss due to the automation of repetitive tasks.
Narrative literature review and synthesis of recent economic studies presented in the paper; no original empirical sample or primary data collection reported.
BT adoption reduces the level of earnings management practice.
Additional empirical tests on the same sample (27,400 firm-years, 2013–2021) comparing firms' earnings management measures before/after or between adopters and non-adopters of BT (earnings management measured by standard accrual-based metrics—details in paper).
The inability of models to reliably self-author useful Skills implies that models typically cannot produce the procedural knowledge they would benefit from consuming.
Interpretation based on the empirical finding that self-generated Skills provided no average benefit; inferred conclusion about model-authored procedural content quality. The paper's claim is supported by the comparative experimental results but the inference about broader capabilities is derived from those results rather than a direct separate measurement.
In some tasks, curated Skills worsened performance: 16 of 84 tasks showed negative deltas.
Per-task delta analysis reported in the paper: authors report 16 tasks with negative deltas where curated Skills reduced pass rate. (Note: the paper elsewhere reports 86 tasks in the benchmark; the negative-task count is reported as 16 of 84 in the paper's per-task summary.)
Developing economies face heightened risks from AI due to large informal sectors, limited reskilling infrastructure, weaker labor mobility, and constrained social protection.
Comparative institutional analysis and application of structural-transformation theory; argument is qualitative and no explicit cross-country regression or representative sample of developing countries is provided in the paper.
Displacement often occurs faster than job creation and worker reallocation, producing transitional unemployment and skills gaps.
Temporal-mismatch argument based on historical patterns of technological adoption and task-based substitution theory; paper synthesizes prior theoretical work rather than presenting new time-series microdata or measured reallocation speeds.
Developing economies are more vulnerable where employment is concentrated in routine or informal tasks and where reskilling, mobility, and institutional buffers are limited.
Comparative consideration of advanced vs developing economies drawing on macro/sectoral indicators, labor market structure discussions, and existing empirical studies cited conceptually.
Creation of new jobs often lags displacement, producing transitional unemployment and reallocation frictions in the short- to medium-term.
Dynamic/task-based theoretical framing and synthesis of empirical evidence on technology adoption episodes showing delayed job creation relative to displacement.
AI disproportionately automates routine and many middle-skill tasks (both manual and cognitive), displacing corresponding occupations.
Synthesis of occupation- and task-level exposure studies and task-based automation literature referenced in the paper (no new empirical sample provided).
Stronger internal corporate governance weakens the AI → executive pay relationship, consistent with governance limiting managerial rent capture during technological change.
Moderation analysis in the paper interacting the firm AI indicator with corporate governance measures; results show a smaller AI effect on pay in firms with stronger governance (same sample and regression framework).
Inflation and geopolitical fragmentation can raise the cost of AI deployment (hardware shortages, supply constraints) and complicate cross-border data flows, slowing diffusion or creating regionalized AI ecosystems.
Conceptual argument linking macroeconomic and geopolitical constraints to AI deployment costs; no empirical cost-accounting or cross-country diffusion analysis provided in the paper.
Mandel's account—that capitalist production relations, class struggle, and global imbalances shape the course and consequences of waves—implies that crises expose and amplify supply-chain fragilities and bargaining conflicts that affect profitability.
Theoretical interpretation of Mandel's political-economy literature and historical examples (qualitative).
Platforms optimized for engagement can produce externalities that distort lived temporality (loss of presence and meaning) beyond standard attention‑capture harms.
Argument synthesizing platform literature and phenomenological concerns; no new empirical analysis of platform effects provided.
Contemporary transhumanist and neurotechnology developments (BCIs, neural digital twins, human–AI collaboration) have advanced technologically but lack a robust conceptual core focused on lived experience and temporality.
Survey and synthesis of existing literatures reported in the paper (conceptual review); no systematic empirical content analysis or coded sample size provided.
High PIGRS scores associate with genomic instability (higher tumor mutational burden and MATH heterogeneity scores) and immune‑escape signatures.
Association analyses within the PIGRS study linking high risk scores to higher TMB, elevated MATH scores, and immune evasion markers (multi‑omics and immune gene set analyses reported).
High upfront and maintenance costs create scale advantages for larger institutions or centralized providers, potentially concentrating market power among well-resourced curriculum developers.
Economic inference from cost structure described in paper; no market concentration empirical data provided.
Disadvantages and risks include significant resource investment, complexity aligning multiple standards, and a high demand for continuous updates and audits.
Paper's risks section (author assertion); no quantified cost or burden data.
Implementing this program requires substantial resources and ongoing governance.
Author assertions in disadvantages/risks section; no cost accounting or empirical costing data provided.
Workforce adoption barriers and the need for reskilling are obstacles to implementing the hybrid cloud financial framework.
Paper identifies workforce/reskilling challenges in its discussion; no empirical measurement of training needs or adoption rates provided in the summary.
On-premise ERPs create delays in reporting, security vulnerabilities, and regulatory/compliance inefficiencies for EPC firms.
Paper asserts these as problems motivating the hybrid approach. The summary provides no empirical comparison metrics between on-premise and cloud systems.
Proprietary models trained on large clinical datasets can create high entry barriers, concentrating market power among a few platform firms and increasing prices for hospitals.
Market-structure and platform economics analysis in the paper; empirical evidence of concentration in GenAI healthcare is limited and no firm-level market-share data are provided.
Liability and accountability gaps exist for AI-suggested errors: it is unclear whether vendors, hospitals, or clinicians are responsible for harms resulting from GenAI CDS recommendations.
Policy and legal analysis discussed in the paper; this is a structural/legal observation rather than an empirical finding and no case-law sample size is provided.