Evidence (4333 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 |
Governance
Remove filter
Wage polarization is likely: middle-skill wages will be compressed while high-skill wages rise; some low-skill service roles may persist or expand.
Synthesis of skill-biased technological change literature and task substitution/complementarity arguments; paper references empirical patterns of polarization in prior studies.
Firms with better data infrastructure and higher initial IT investment will adopt AI faster, potentially widening performance gaps across firms and industries.
Theory-informed assertion and literature synthesis; no empirical heterogeneity analysis is specified in the abstract.
Complementarity between AI and skilled accountants may raise wages for analytical roles while compressing demand for routine clerical roles, contributing to wage polarization.
Prediction grounded in economic theory and prior literature; the paper does not report direct wage-change estimates in the abstract.
AI will automate routine accounting tasks, reducing demand for low-skill bookkeeping work while increasing demand for higher-skilled roles (data interpretation, advising, oversight), creating occupational reallocation and upskilling needs.
Projection based on task-based labor economics literature and the paper's synthesis; not supported by specific longitudinal labor-market estimates in the abstract.
Treating privacy as non-tradeable (or tightly constrained trade) will change incentives for firms that monetize personal data, affecting the supply of training data for AI and the trajectory of AI development.
Policy-analytic inference drawing on market-incentive logic and descriptive accounts of firms’ data practices; no quantitative modeling of data supply or AI development provided.
Implication for AI economics: scholars should be alert to epistemic capture—funding, institutional incentives, and geopolitical context can shape which AI governance and market theories gain traction.
Analogy and inference from the historical Cold War case study applied to contemporary AI economics; conceptual argument rather than direct empirical test in AI context.
Europe, Japan, and South Korea occupy intermediate positions between China and the United States in terms of AI–robotics integration and actor composition.
Comparative country-level decomposition of patent series and actor-type shares (1980–2019) reported in the paper; metrics for integration and actor composition place these regions between the stronger China pattern and the more market-driven U.S. pattern.
AI can enable new revenue streams (platforms, personalized pricing, automation-as-a-service) and increase market concentration, producing 'winner-takes-most' dynamics that raise profit rates for leading adopters and compress margins for laggards.
Literature synthesis on platforms and winner-take-all effects applied to AI; conceptual argument without firm-level causal testing in the paper.
AI adoption exerts downward pressure on routine labor costs while raising capital and recurrent costs (R&D, computing infrastructure, data, cybersecurity); higher fixed and lower marginal costs favor scale and incumbents with access to data and capital.
Conceptual cost-structure analysis drawing on automation and platform literature; no microdata or empirical cost estimates presented.
AI is a Schumpeterian general-purpose technology that can increase aggregate productivity potential but will do so unevenly across firms and sectors, producing heterogeneous effects on profitability.
Theoretical application of general-purpose technology and Schumpeterian literature to AI; literature-based claims without original empirical validation in the paper.
Firms' profitability and sustainability are shaped both by technological adoption (which can raise productivity and market power) and by structural pressures (trade wars, labor relations, supply constraints) that can erode margins.
Synthesis of firm-level implications from innovation and political-economy literatures; no firm-level causal estimates presented in the paper.
Contemporary crises change firms' cost structures (logistics, inputs, financing) and revenue prospects (demand shifts, market access).
Interpretive synthesis of observed firm-level impacts from pandemic, inflation episodes, and geopolitical events reported in secondary literature (no primary firm-level panel used).
Supply-chain fragilities and trade conflicts (emphasized by Mandel) mediate distributional and macroeconomic outcomes during long waves and crises.
Qualitative historical interpretation and literature references on supply-chain disruptions and trade conflicts (no systematic empirical identification in the paper).
New technological waves—most notably artificial intelligence (AI) and the green transformation—act as Schumpeterian forces that can alter productivity, competition, and profitability.
Conceptual mapping of Schumpeterian innovation-cluster theory to contemporary technologies (literature synthesis; no firm-level causal estimates reported).
Contemporary shocks (COVID-19, global inflation, geopolitical tensions) interact with long-wave mechanisms to reshape firms' cost and revenue structures.
Interpretive application of the comparative framework to recent historical episodes and macro trends; qualitative evidence from literature on pandemic and recent shocks (no primary microdata presented).
Patients classified as high‑risk by CDRG‑RSF had higher TMB, lower NK and CD8+ T cell infiltration, and model‑predicted resistance to Erlotinib and Oxaliplatin but sensitivity to 5‑fluorouracil.
CDRG‑RSF study reported immune deconvolution and TMB comparisons across risk groups and used pharmacogenomic prediction methods to infer drug sensitivity/resistance patterns for high‑risk vs low‑risk groups.
Both DNNs and LASSO correlated well at the individual‑sample level, but linear models (LASSO) struggled to recover cross‑study DEA log2FCs despite good sample‑level fits.
Same cross‑omics comparative study: reported good sample‑level prediction correlations for both model classes, but DNNs more faithfully reproduced differential expression signals across independent studies while LASSO did not recover DEA log2FCs robustly.
Fidelity gains from prompt engineering, model selection, or participant/environment modeling have been limited and context-dependent.
Synthesis of studies that tested prompt/model/participant modeling interventions and reported mixed or modest fidelity improvements; aggregated conclusion in the review.
Defender returns depend critically on attacker rationality and information-processing; economic/security models should incorporate strategic heterogeneity and bounded rationality for accurate valuation.
Computational sensitivity analyses varying attacker rationality/modeling assumptions with reported impact on metrics (simulations; details of attacker models and number of runs not specified).
Computational results highlight tradeoffs among decoy realism, defender budget, and attacker rationality (attacker model), affecting deception value.
Simulated parameter sweeps varying decoy realism, budget levels, and attacker rationality with reported sensitivity analyses (computational experiments; exact experimental grid not specified).
Compliance costs and audit requirements create regulatory barriers to entry but also incentives for standardized metadata and interoperable systems; policy can encourage open standards to reduce lock-in.
Policy analysis and recommendation in paper (theoretical); no regulatory cost quantification provided.
Algorithmic lesson planning, automated audits, and data-driven competency mapping are natural targets for AI augmentation and can reduce recurring resource burdens but require quality-labelled data, strong governance, and transparency.
Paper's discussion of AI complementarity (conceptual); no implementation trials or performance metrics presented.
The taxonomy clarifies where substitution versus complementarity are likely: AI-assisted tasks imply partial substitution of routine work; AI-augmented applications generate complementarities that increase demand for higher cognitive skills; AI-automated systems shift labor toward monitoring, exception handling, and governance.
Inference from mapping the three interaction levels to observed case features (n=4) and application of the Bolton et al. framework in cross-case synthesis.
AI-augmented systems support real-time medical tasks (e.g., decision support during procedures), amplifying human judgment and speed but raising required cognitive skills and changing training and coordination practices.
Findings from the case(s) labeled AI-augmented in the four-case qualitative sample and cross-case interpretive analysis using the service-innovation framework.
Levels of familiarity and use of AI tools vary widely by role, discipline, and region.
Quantitative survey items (Likert-scale, multiple-choice) measuring familiarity and use of AI tools; subgroup comparisons (role, discipline, region) using descriptive statistics; thematic support from open-ended responses.
There are large disparities in AI engagement and preparedness across roles (students vs. educators), academic disciplines, and world regions.
Descriptive statistics from the survey comparing subgroups by role, discipline, and region; sample of >600 respondents; measures include self-reported awareness, familiarity, use, and confidence mapped to UNESCO competency frameworks.
Task-based labor effects: GenAI will substitute routine tasks (documentation, triage) and complement complex decision-making; net employment effects are ambiguous and vary by role.
Task-based model of labor and early observational/pilot studies; the paper highlights heterogeneity by specialty and role, but presents no comprehensive empirical employment-impact studies.
GenAI can reduce clinician time per case (productivity gains) but may increase utilization (more tests/treatments) if it lowers thresholds for intervention or aligns with revenue incentives.
Economic reasoning supported by early empirical and simulation work; the paper notes the possibility based on task substitution and induced demand literature; direct causal empirical evidence from large-scale deployments is limited.
AI-enabled credit scoring and dynamic pricing can expand access but also entrench algorithmic bias, affecting distributional outcomes.
Literature synthesis and conceptual discussion calling for research to evaluate distributional impacts; examples of mechanisms (credit scoring, dynamic pricing) cited but no empirical bias quantification provided in the summary.
The benefits of digital financial ecosystems are strongest where supporting infrastructure (broadband, identity systems, payment rails) and enabling policies exist.
Comparative case-study synthesis and descriptive comparisons across national/regional implementations showing conditional variation by infrastructure and policy context; no standardized cross-country regression evidence presented in the summary.
High-quality labeled IoT traffic is scarce and valuable, and data-sharing mechanisms (federated learning coalitions, data marketplaces) could emerge but require privacy and legal frameworks.
Survey notes about dataset scarcity and potential economic models for data sharing; recommendation that privacy/legal frameworks are prerequisites.
There is a strong commercial opportunity for deployable ML-IDS tailored to IoT and edge deployments, but development and operational costs (data collection, compression, privacy, pipelines) are substantial.
Economic implications and market analysis drawn from the survey: unmet deployment needs, scarce labeled data, and additional engineering requirements imply market demand and higher costs.
Improved personalization via RS techniques can increase consumer surplus by better matching robot behaviors to user needs, but it also creates the potential for finer-grained price or content discrimination if monetized.
Economic reasoning and implications section; conceptual analysis without empirical measurement.
Sector-specific characteristics (regulation, competition intensity, product tangibility) shape the feasibility and design of VBP systems.
Thematic cluster from the SLR where sectoral factors were repeatedly cited as influencing VBP design across included studies.
Implementation challenges and pricing dynamics differ between B2B and B2C settings.
SLR thematic coding that separated findings and implementation considerations for B2B versus B2C contexts within the included literature.
Technology and AI are increasingly integrated into pricing processes, but this integration is uneven across contexts and the literature.
Thematic cluster from the SLR indicating growing but uneven mentions and treatments of technology/AI across included studies.
Green-technology innovation acts as a threshold moderator: DE produces direct carbon-reduction effects (reducing PCE) only after green-technology innovation exceeds a critical threshold; below that threshold DE does not reduce PCE.
Threshold-regression models (panel threshold estimation) using a measured index of green-technology innovation as the threshold variable on the 278-city panel (2011–2022). Results show different coefficient regimes for DE on PCE depending on whether green-innovation is below/above the estimated threshold.
The digital economy (DE) exhibits a U-shaped relationship with carbon emission efficiency (CEE): at early stages of DE development CEE worsens (declines) with DE, but beyond a certain DE level CEE improves as DE expands further.
Panel fixed-effects regressions using the same sample of 278 cities (2011–2022) with DE and DE^2 terms; the estimated coefficients on DE and DE^2 are statistically significant and imply a U-shaped relationship.
The digital economy (DE) exhibits an inverted-U relationship with per capita carbon emissions (PCE): at low levels of DE, PCE initially rises with DE, but after a turning point further DE expansion is associated with falling PCE.
Panel fixed-effects regressions on a balanced panel of 278 Chinese prefecture-level cities observed annually from 2011–2022. Models include DE and DE^2 terms; coefficients on DE and DE^2 are statistically significant in the pattern consistent with an inverted-U and a turning point is estimated from those coefficients.
Evidence of labour reallocation within rural economies following AI-driven productivity changes was observed in the reviewed literature.
Reported findings across several reviewed studies noting shifts in labour allocation and task composition on farms and in related value-chain activities.
Paper‑based regulatory environments slow DT diffusion; digitised compliance and standardised data schemas can accelerate adoption and enable AI‑driven oversight.
Findings in the review noting regulatory friction and proposed solutions; supported by case evidence where digitisation of compliance facilitated digital workflows.
DT adoption is a socio‑technical transformation that requires governance, standards, collaborative delivery models, and workforce capability building — not just technology deployment.
Conceptual synthesis and cross‑study recommendations in the reviewed literature emphasizing organizational, contractual, and governance changes alongside technology.
Better predictive models can shrink asymmetric‑information wedges and potentially reduce interest spreads for high‑quality but thin‑file borrowers; however, model errors or biased features can systematically exclude certain groups.
Conceptual analysis of model performance, bias risk, and implications for pricing; supported by literature on algorithmic bias and selective case evidence but not empirical causal tests within the paper.
Blockchain applications (tokenization, smart contracts) have potential for transparent, programmable financing and lower transaction costs but remain nascent and face legal and market adoption barriers.
Qualitative synthesis of emerging blockchain use cases and legal/regulatory analysis; characterization is forward‑looking and based on current maturity levels rather than empirical measurement of outcomes.
Crowdfunding is useful for market validation and early‑stage capital but has limited ticket sizes and is not scalable for growth capital needs.
Comparative assessment of financing models and illustrative examples; conclusion based on typical crowdfunding ticket sizes and market practice rather than new representative data.
Revenue‑based financing offers flexible repayments tied to cash flow and suits startups with recurring revenues, but can be more expensive over time and is less regulated.
Qualitative evaluation of product features in the comparative framework and literature synthesis; based on product design characteristics rather than primary quantitative pricing analysis in the paper.
FinTech lending platforms provide high accessibility and speed through alternative data and automated underwriting, with variable costs and scalability but raise regulatory and data‑privacy concerns.
Comparative qualitative assessment and illustrative case studies demonstrating faster approvals and broader reach for thin‑file borrowers; evidence is descriptive and not nationally representative or causally identified.
Traditional sources (bank loans, government schemes) offer lower nominal cost for creditworthy borrowers and regulatory protections, but suffer from collateral requirements, slow processes, and limited outreach to informal/small firms.
Comparative framework evaluation across five variables and institutional/regulatory synthesis; findings are qualitative and built on established banking characteristics rather than new representative quantitative data in the paper.
AI‑driven protein structure prediction will reallocate economic value across the biotech R&D stack—compressing early discovery costs, increasing returns to downstream validation/optimization, and favoring actors combining data, compute, and domain expertise.
Paper summarizes this as an overarching implication in the 'Overall' paragraph, integrating prior methodological and economic arguments; no quantitative economic model or empirical measurement is provided.
Labor demand will shift away from low‑throughput experimental structure determination toward ML model engineers, computational biologists, and integrative experimentalists, requiring retraining in experimental groups.
Paper states this in 'Labor and skill shifts'; it is an inferred labor market consequence without workforce surveys or models in the text.