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 |
Gender disparities widen significantly at the very upper end of the distribution of digital job intensity — a 'digital glass ceiling' — while lower and middle levels show more modest differences.
Distributional analysis of the Job Digital Intensity Index (JDII), constructed from ESJS digital task items, showing larger gender gaps at the upper tail of the JDII distribution.
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).
Limited reskilling opportunities and ambiguity surrounding career progression were linked to reduced confidence in future career prospects.
Survey correlations in the national sample indicating that respondents reporting limited reskilling access and ambiguous progression reported lower confidence in their future career prospects.
The findings raise ethical concerns about using such models in sensitive selection processes and highlight the need for transparency and fairness in digital labour markets.
Interpretive/concluding claim based on the observed adjective-based gendering and the broader literature on algorithmic fairness; recommendation rather than direct empirical result.
Gendered linguistic patterns emerged in the adjectives attributed to female and male candidates: GPT-5 tended to associate women with emotional and empathetic traits and men with strategic and analytical traits.
Empirical/qualitative analysis of the adjectives and descriptive language in GPT-5's outputs for the 24 simulated profiles; categories reported (emotional/empathetic vs strategic/analytical).
Large language models (LLMs) risk reproducing, and in some cases amplifying, gender stereotypes and bias already present in the labour market.
Framed as an assertion supported by prior literature and used as motivation for the study; partially evaluated empirically in this paper via the GPT-5 experiment.
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).
Compensation-based frameworks for personal data may advantage those better able to monetize data, potentially worsening inequality.
Theoretical argument and literature synthesis on distributional effects of markets and bargaining power; paper does not present empirical distributional simulations or data.
Data markets tend to concentrate benefits and rents in large platforms while externalizing harms onto individuals and society.
Argument based on descriptive facts about platform business models and literature on market concentration in digital markets; no original econometric concentration analysis provided in the paper.
Standard market-failure fixes (better information, pricing, contracting) are insufficient to address the moral and social-structural harms of commodifying privacy.
Philosophical argument drawing on noxious-markets literature and limitations of informational/contractual remedies; supported by conceptual examples rather than empirical testing.
Harms from data commodification are often externalized, diffuse, and long-term (e.g., profiling, algorithmic discrimination, chilling effects on behavior).
Normative and descriptive synthesis of existing literature on algorithmic harms and privacy externalities; no original longitudinal or causal empirical evidence presented.
Consent in data markets is frequently weak, uninformed, or coerced (due to information asymmetries, complexity, and behavioral biases), undermining the ethical legitimacy of transactions.
Argumentative claim grounded in literature on privacy notice problems, behavioral economics, and descriptive reports on digital consent practices; no new empirical study included in the paper.
Commodifying personal information poses distinctive harms to individuals and social practices, including exploitation, corruption of personal autonomy, distributional injustice, and information asymmetries.
Conceptual analysis supported by literature review across ethics, political philosophy, and descriptive facts about digital-era data practices; uses illustrative examples and secondary sources rather than original empirical data.
Creating a market for personal data is equivalent to making the right to privacy a tradeable right, and such a market should be treated as a 'noxious market' in the sense articulated by Debra Satz.
Normative, conceptual argument applying Satz's noxious-markets framework to personal data; literature review and philosophical argumentation; no original empirical sample or econometric analysis.
Family- and purpose-driven entrepreneurs (motivated by social stability) experienced larger declines in innovation following income shocks than wealth-driven entrepreneurs.
Subgroup quantitative analysis comparing self-reported post-shock innovation activity across identity-defined groups (family/purpose-driven vs. wealth-driven) within the survey sample; outcome measured conditional on reported income shocks.
Access to digital learning and credential portability could unevenly benefit those with connectivity or prior skills, creating distributional effects and digital divides that should be measured.
Conceptual risk analysis and distributional reasoning based on digital access differentials; no empirical subgroup analysis reported.
Corridor governance is fragmented, with uneven implementation capacity across sending and receiving actors.
Governance gap analysis and desk review of corridor institutional arrangements; qualitative identification of capacity and accountability shortfalls.
Current mandatory pre-departure training is typically delivered late, generically, and with weak assessment, limiting its capacity to change recruitment choices or support migrants after arrival.
Structured desk review of policy and program materials and corridor process mapping identifying timing, actors, and touchpoints; qualitative/administrative evidence rather than quantitative outcome measurement.
The emergence and promotion of these theories acted as a 'Trojan horse' of ideological persuasion: technically framed economic scholarship advanced political messages that ran counter to the expected normative defense of markets and democracy.
Interpretive synthesis from archival and textual analysis showing alignment between the technical content of certain economic arguments and political narratives; analysis of institutional and funding contexts that plausibly facilitated persuasive deployment.
A strand of influential 20th‑century Western economic theory concluded that democracy and market institutions are dysfunctional.
Case‑study historical and textual analysis of Cold War‑era economic literature and influential works (including canonical publications and writings by prominent economists); close reading of papers/books and contemporaneous debates as reconstructed from archival and publication materials.
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).
Policy levers matter: increasing openness/shared ownership of AI, strengthening rent-sharing (higher ξ), and reducing concentration of complementary assets (antitrust, data portability) can reduce the probability that AI widens aggregate inequality.
Model counterfactuals and policy experiments in the calibrated framework that vary ownership/access parameters, ξ, and asset concentration to show distributional outcomes shift accordingly.
Traditional extrapolation-based employment forecasting (as used in current BLS/standard practice) is inadequate for capturing AI-driven labor market change.
Conceptual argument in the paper highlighting limitations of extrapolation methods (failure to distinguish automation vs augmentation, inability to capture rapid nonlinear adoption dynamics and demographic heterogeneity). No empirical test or sample is reported; critique is supported by theoretical considerations and examples rather than an applied dataset.
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).
LLM-generated participants are particularly risky in strategic and game-theoretic settings because they may misrepresent incentives, dynamic strategic thinking, and bounded rationality.
Review highlights examples and theoretical concerns from multiple studies indicating misrepresentation of strategic behavior; grouped under risks for strategic settings.
The price-of-transparency quantifies how increased observability (e.g., from disclosure or regulation) can reduce the effectiveness of deception-based defenses, informing policy tradeoffs.
Formal definition of price of transparency and analytical results showing its effect; policy implication drawn in discussion (theoretical analysis, no empirical policy case studies).
The absence of level‑4 evidence (organizational/patient outcomes) limits the ability of health systems and payers to conduct cost‑benefit or return‑on‑investment analyses for upskilling investments in AI.
No included study reported level‑4 outcomes; the paper reasons that without organizational/patient outcome data, economic evaluation is hampered.
Because most programs were short, introductory, and assessed only short‑term learner outcomes, they likely produce modest increases in individual AI literacy but are insufficient to build advanced clinical AI competencies that would change clinical task allocation or productivity.
Synthesis combining program characteristics (short duration, introductory content, academic delivery) and outcome mapping to only Kirkpatrick levels 1–3 in the 27 studies; interpretation drawn in the paper.
Workplace stress is associated with reduced job performance.
PLS-SEM analysis on the same N = 350 sample. Reported direct path: Stress → Performance, β = 0.158, p < 0.001. (Note: the study interprets this as stress reducing performance; sign/coding conventions are not detailed in the summary.)
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.
One-size-fits-all AI competency approaches fail to account for local labor markets, pedagogical traditions, and resource realities; respondents favor context-aware frameworks allowing discipline-specific adaptation.
Thematic analysis of open-ended responses expressing preferences for context-aware, flexible frameworks; survey items mapped to UNESCO competency frameworks asking about adaptability and local relevance.
Infrastructural limitations (bandwidth, computing resources, licensing costs) disproportionately affect respondents in the Global South and smaller institutions.
Comparative descriptive analysis by region (Global South vs Global North) and institution size/type within the >600 respondent sample; survey items on infrastructure and costs; thematic coding supporting differential impact.
Practical barriers—software access, available datasets, and lab time—limit experiential learning that builds AI competency.
Survey items listing barriers to AI learning and training; thematic coding of open responses highlighting software, dataset, and scheduling constraints.
Respondents cite limited opportunities for applied, project-based learning with AI tools; where AI appears in curricula, coverage is more theory-oriented than hands-on.
Quantitative items and open-ended responses about types of training and curricular integration; thematic analysis of qualitative data indicating prevalence of theory-focused instruction versus applied opportunities.