Evidence (7448 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 |
AI threatens to fracture the 20th-century social contract.
Asserted in abstract as a normative/predictive claim; no empirical support described in the abstract.
Mergers are a barrier to economic growth (negative association between mergers and GDP growth).
Model results reported a negative relationship between mergers and GDP growth in the regressions described in the summary; however, the summary does not define how 'mergers' is measured, how widely it was observed across countries, or the statistical significance levels.
Without effective safeguards, the digital world can shift from a space of opportunity to one of harm.
Normative/conditional claim drawing on the book's analysis; not an empirical finding—no method or sample size applicable in the excerpt.
Unequal GenAI adoption has implications for productivity, skill formation, and economic inequality in an AI-enabled economy.
Interpretation/implication drawn from observed gendered adoption patterns in the 2023–2024 UK survey and literature on technology diffusion and labor-market impacts (no direct empirical measurement of downstream economic effects in the paper).
Preliminary evidence that inappropriate reliance on AI outputs is worse for complex information needs (complex answers).
Post-hoc/stratified analysis in the user study examining the effect of the complexity of the information need on reliance/error-detection; described as preliminary in the paper.
AI-driven productivity gains may not translate into broad-based demand if income is concentrated among capital owners, which could dampen aggregate profitability over time.
Theoretical argument grounded in Mandel-like distributional mechanics and demand-driven growth literature; speculative without empirical aggregation tests in the paper.
Concentration of curated datasets and restrictive IP can create monopolistic rents and underprovision of public‑good datasets, implying policy interventions (data sharing incentives/standards) may be required.
Economic reasoning about market formation and data as a scarce asset; no empirical market analysis provided in summary (theoretical implication).
Because deception effectiveness declines with transparency and attacker learning, strategic externalities can arise across actors (e.g., disclosures by one actor can reduce deception value for others), suggesting roles for coordination or insurance markets.
Conceptual implication and economic argument in the discussion section; not supported by explicit multi-actor modeling or empirical market analysis in the paper (argumentative/theoretical).
More granular and auditable credentials may shift signaling dynamics and risk credential inflation; regulators should monitor credential proliferation and market value.
Conceptual warning in paper (theoretical); no empirical credential-market study included.
These infrastructural and access constraints create unequal starting points that can amplify later disparities in labor-market preparedness.
Inference drawn from observed survey disparities in access, hands-on training, and preparedness; the study did not directly measure labor-market outcomes but links preparedness to potential labor-market effects in discussion.
Top-down AI guidance from institutions is common, while grassroots input from educators and students is often missing, which reduces policy relevance and uptake.
Survey items and thematic coding indicating the origin and participatory nature of institutional AI guidelines; comparative prevalence reported in open and closed responses.
Overreliance on GenAI CDS may lead to deskilling of clinicians, eroding judgment over time and increasing systemic vulnerability.
The paper cites theoretical risk and references limited longitudinal concerns; empirical longitudinal studies demonstrating deskilling are scarce per the paper’s stated evidence gaps.
Commercial structural biology services for routine solved folds may be commoditized, pushing firms toward complex validation, novel targets, or high‑value contract research.
Paper suggests this in 'Disruption of service markets' as a projected industry response; it is a strategic implication rather than an empirically demonstrated trend in the text.
Organizational compliance, governance, and transaction costs shape which AI uses are feasible, producing heterogeneity in adoption across firms; trust and accountability frictions can slow adoption even when productivity gains exist.
Workshop participants (n=15) reported compliance and governance considerations; authors infer broader organizational heterogeneity and friction effects from these qualitative data.
Designers’ expressed concerns about skill development suggest potential long-term effects on human capital accumulation; adoption that reduces learning opportunities could lower future wages or employability.
Participants' concerns captured in qualitative workshops (n=15); claim is an extrapolation to labor-market outcomes rather than direct measurement in the study.
Legacy systems and siloed incentives create switching frictions that slow diffusion of AI-enabled ISP; early adopters may achieve sustained cost and service advantages and vendors bundling technology with change management could capture large rents.
Authors' argument informed by case observations of switching costs and vendor roles; no causal market-level evidence provided.
Returns to AI investments may exhibit increasing returns to scale, reinforcing winner‑take‑most dynamics unless offset by platformization or open‑source diffusion.
Economic scenario reasoning on capital intensity and platform effects; no empirical calibration or econometric evidence provided.
Because feedbacks from capital and labor onto AI are weak, AI can grow rapidly and may lead to lock-in, concentration, and distributional risks that warrant monitoring and possible redistributive or competition policies.
Empirical finding of weak negative feedbacks to AI in estimated interaction coefficients combined with theoretical interpretation about growth and lock-in risks.
Job insecurity rises when FDI is short‑term, footloose, or concentrated in capital‑intensive extractive projects.
Conceptual arguments and empirical examples in the review linking investment temporariness and capital intensity to higher job instability; empirical evidence less comprehensive and context-specific.
Private governance and firm-level solutions (internal standards, bargaining with unions) may proliferate, but these can entrench firm-specific norms and increase market power asymmetries.
Conceptual argument drawing on governance and industrial organization literature; no empirical measurement of prevalence or market-power effects included.
Inadequate protections reduce public trust in mobile-AI services, which can slow diffusion and undercut the growth trajectories that policy narratives anticipate.
Inferred from stakeholder commentary and policy discourse combined with communication-rights theory; the paper does not present survey or adoption-rate data.
Low-wage and platform workers are particularly exposed to algorithmic management and surveillance, with potential downward pressure on wages, bargaining power, and job quality.
The paper's qualitative analysis of stakeholder comments and policy omissions, combined with literature-based inference about platform labor dynamics; no primary labor-market survey or quantitative wage data provided.
Soft‑law governance and growth-first narratives risk concentrating benefits (investment, productivity gains) while externalizing costs (privacy harms, biased decisioning) onto vulnerable populations, exacerbating inequality and reducing inclusive economic development.
Analytic inference from qualitative review of governance instruments and policy narratives combined with communications-ecology and political-economy reasoning; not based on quantitative economic measurement in the paper.
Legal liability and cyber-insurance markets will need to adapt as machine-generated code becomes pervasive, with pricing internalizing risk from inadequate verification processes.
Speculative legal/economic implication discussed in the paper; no actuarial or legal-case data provided.
Individual developers or firms may underinvest in verification because defect accumulation imposes external costs on downstream actors, creating market failures that can justify standards, certifications, or regulation mandating interlocks or minimum verification practices.
Policy and market-failure argument based on externalities presented conceptually; no modeling or empirical evidence of such externalities provided.
Short-run productivity gains from generative AI may be offset by longer-run increases in maintenance, security breaches, and reliability costs if verification lags.
Economic reasoning and forward-looking implications discussed in the paper; no empirical cost-benefit or longitudinal data presented.
Small, unverified errors, insecure patterns, and brittle interactions accumulate over time (latent accumulation), increasing operational fragility and long-run maintenance costs.
Theoretical argument and illustrative examples in the paper; no longitudinal defect accumulation studies or empirical cost analysis provided.
Time pressure and productivity incentives lead developers to accept plausible AI outputs without full validation, a behavioral/institutional failure mode called the 'micro-coercion of speed' that effectively reverses the burden of proof.
Behavioral diagnosis and incentive analysis presented conceptually in the paper; no behavioral experiments, surveys, or observational data reported.
Hallucination and error risk introduce potential liabilities in client engagements and may change contracting, insurance, and pricing practices in consulting services.
Derived from practitioner concerns reported in interviews and authors' normative discussion; no contractual or insurance-market data presented.
Effective deployment requires governance, verification processes, and liability management to manage hallucination risk, creating adoption costs that may advantage larger firms and affect market concentration and pricing power.
Argument based on interviews about necessary organizational safeguards and the resource requirements to implement them; speculative market-structure implications are not empirically tested in the paper.
Widespread GenAI use may accelerate skill obsolescence for routine competencies and increase the premium on monitoring, critical evaluation, and AI‑integration skills, shifting investment toward retraining and upskilling.
Projection based on qualitative interviews and the authors' economic interpretation of TGAIF; no longitudinal or wage/skill data provided.
Uncertainty about long-run agentic behavior increases option value and downside risk of investing in agentic systems, which may raise discount rates and required returns.
Economic argument applying risk/return logic to agentic uncertainty; no quantitative empirical evidence provided.
Economic rents and advantages may accrue to agents who control large datasets, computing resources, and organizational processes that effectively integrate AI as a co-pilot, potentially increasing market concentration among AI providers.
Economic theory on scale economies and platform effects combined with observed industry patterns; reviewed literature provides conceptual arguments and case examples rather than broad empirical market-structure measurement.
Generative AI poses substitution risk for entry-level or routine cognitive work focused on generation or drafting without evaluative responsibility.
Task-based analyses and case studies indicating automation potential for routine generation tasks; empirical demonstrations of AI-produced drafts/outputs that could replace such work, but longer-run displacement evidence is limited.
Upfront integration and recurring governance costs mean smaller firms may face higher relative costs — potentially increasing scale advantages for larger incumbents.
Deployment case studies and cost reports indicating significant fixed integration and governance costs; inference to market structure is speculative.
There is a risk of deskilling through excessive reliance on AI, implying a need for continuous training and certification to preserve human judgment.
Qualitative interview evidence and observed concerns about overreliance; authors recommend training/governance based on identified risks; no direct longitudinal measurement of deskilling provided in summary.
Recommendation algorithms and widespread automated advice can induce herding or increase common exposures across retail investor portfolios, with potential macroprudential implications.
Theoretical discussion supported by examples from retail trading episodes and algorithmic amplification literature referenced in the review (conceptual and anecdotal evidence; limited systematic empirical quantification).
Insurance markets may price AI-specific fraud risk, raising premiums or creating new products (AI-fraud insurance).
Speculative economic implication suggested by the authors; no market data or insurer statements cited.
Vendors offering integrated governed hyperautomation stacks may capture premium pricing and increase switching costs, potentially widening adoption gaps between large incumbents and SMEs.
Market-structure and competitive dynamics discussed theoretically in the Implications section; no market-share or pricing data provided.
There are risks that concentration of modeling capability around well-funded actors could create inequality in capture of downstream economic gains despite open data.
Risk analysis in the discussion section; argued qualitatively without empirical testing in the paper.
Higher compliance and liability costs may be passed to districts, potentially affecting the affordability of EdTech for underfunded schools unless federal guidance or subsidies offset costs — a distributional concern.
Economic distributional reasoning (theoretical), not supported by empirical pricing or budget impact data in the Article.
Exposure to AI and platform work produces psychosocial effects for workers, including increased job insecurity, stress, and changing task content in surviving occupations.
Surveys, qualitative case studies, and workplace studies summarized in the review reporting worker‑reported insecurity and stress; the review also highlights inconsistent measurement and limited systematic evidence on psychosocial outcomes.
Regulators and standard-setters who value transparency and auditability will need to account for the gap between evaluation results and actionable fixes; firms may require incentives or rules to ensure evaluation leads to remediation, not just documentation.
Authors' policy implication derived from the study's finding of a results-actionability gap and discussion of auditability concerns; speculative recommendation rather than empirical finding.
Delegation of oversight and reallocation of monitoring tasks due to AI integration changes transaction costs and affects organizational design and governance needs (e.g., more verification/audit effort or specialist oversight roles).
Based on participants' reported shifts in who performed monitoring/oversight tasks in the 40 interviews and the authors' interpretation of those shifts in organizational/economic terms.
Standardized, high-quality data will concentrate competition on modeling, compute, and algorithmic innovation, favoring actors with greater compute resources.
Economic argument presented in the discussion; not evaluated with empirical market data in the paper.
The paper is the first systematic integration of XAI-based predictive modeling with counterfactual policy simulation specifically targeted at sustainability-oriented HR (Green HRM).
Authors' novelty claim stating this combination is novel in the Green HRM literature; no systematic literature review evidence provided in the summary to independently verify primacy.
The paper likely includes ablation studies and standard metrics (task success rate, step-wise error, plan coherence) to isolate contributions of the two training stages and to evaluate performance.
Summary states these analyses as 'likely additional methods' (i.e., typical but not fully detailed in the abstract); no direct confirmation or results provided in the provided text.
This study represents the first attempt to conduct a comprehensive evaluation of artificial intelligence (AI) and its influence on job displacement based on the existing body of literature.
Author assertion in the paper; the excerpt provides no external verification (no citation of prior reviews/meta-analyses to justify the 'first attempt' claim).
We currently lack an understanding of how political parties perceive the potential impact AI has on employment, the role of regulations in protecting workers from AI-related job losses, and the importance of AI educational and training programs.
Statement of a literature/knowledge gap motivating the study (assertion by the authors; no empirical basis provided in the excerpt).
This research is one of the first large-scale quantitative studies to empirically validate the mediating pathways through which GenAI influences business performance in the UK market.
Positioning/originality claim in the paper's literature review and contribution statement asserting relative novelty and sample size (n = 312) compared to prior studies.