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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Current simulation practice is insufficiently integrated with enabling technologies (digital twins, data analytics, AI/ML) and with relevant government policy constraints.
Synthesis of literature and gap analysis in the paper; assertions are conceptual and not empirically tested within the paper.
Current simulation practice has limited strategic orientation, often focusing more on tactical and operational questions than on firm strategy.
Literature review and analysis in the paper highlighting the emphasis in existing studies on tactical/operational problems.
Current simulation practice lacks contextualization to firm‑ and industry‑specific realities.
Findings from the paper's literature review and critique sections; no new empirical measurement provided.
Current manufacturing and supply‑chain simulation practices are insufficiently contextualized, strategically focused, or integrated with modern technologies and policy considerations.
Literature review and critique of existing simulation practice presented in the paper; no original empirical data or case studies.
Personalization raises distributional concerns and risks of manipulation or biased treatment; regulators may need to set transparency, fairness, and data-use standards.
Policy analysis and normative recommendation based on known risks of personalization systems; not empirically demonstrated in robotic deployments here.
LLM-based personalization generates context-aware responses but often fails to model long-term preferences and fine-grained user/item relations needed for consistent, proactive personalization.
Conceptual critique based on surveyed limitations of LLM-based approaches; no new experimental data reported.
Trade policy (trade openness) should be modeled as a moderating factor when estimating technology-driven urban outcomes because openness can dampen local price effects of digital trade.
Inference based on the reported negative moderation effect of trade openness on the digital-trade → house-price relationship from interaction regressions.
Greater trade openness weakens (attenuates) the positive effect of digital trade on city-level house prices.
Interaction terms between digital trade and a measure of trade openness in the panel regressions; reported negative moderation effect (exact openness measure and sample details not provided).
Policy and regulatory vacuum (data governance, interoperability, safeguards) limits scale and inclusive diffusion of AI in agriculture.
Authors' thematic finding from reviewed literature and institutional reports noting weak policy frameworks and governance gaps.
Limited digital literacy and human capacity among smallholders is a key barrier to adoption and effective use of AI solutions.
Multiple studies and reports in the review documenting low digital literacy, limited extension capacity, and training needs among target users.
Scalable adoption of AI in developing-country agriculture is constrained by infrastructure gaps (connectivity, power, data platforms).
Thematic synthesis across reviewed studies and reports identifying recurring infrastructure constraints limiting deployment and scale-up.
Data governance, privacy, and cybersecurity risks can create negative externalities and raise adoption costs, requiring governance frameworks that affect social welfare outcomes.
Recurring risk themes across reviewed papers (conceptual analyses, case reports) that highlight governance and cybersecurity concerns associated with DT data.
Principal barriers to DT adoption include paper‑based or legacy regulatory/compliance processes that slow digitisation.
Findings from reviewed studies noting regulatory and compliance processes as impediments to digital handover and automated workflows.
Principal barriers to DT adoption include misaligned stakeholder incentives and fragmented project delivery models.
Synthesis of conceptual and case literature describing contractual and incentive misalignments that impede lifecycle data continuity.
Principal barriers to DT adoption include low digital maturity and uneven capabilities across supply chains.
Recurring observations in the literature review about heterogeneous digital skills and maturity across firms in the supply chain.
Principal barriers to DT adoption include data quality and continuity problems at handover.
Thematic synthesis across reviewed literature reporting frequent issues with data quality and handover continuity between project phases.
Principal barriers to DT adoption include interoperability gaps and lack of standards.
Thematic findings from qualitative synthesis of the 160 reviewed studies (recurring theme across conceptual papers, case studies and pilots).
ANN analysis ranks need-for-human-interaction barriers as the most important predictor of GAICS adoption outcome.
ANN feature-importance analysis reported in the paper that ranks predictors for adoption outcome and finds the human-interaction barrier as the top predictor; paper abstract does not include details on ANN implementation or sample characteristics.
Platformization and data moats in digital lending can increase concentration risks: firms with richer data histories gain sustained access to cheaper finance, potentially raising market concentration.
Market structure analysis and conceptual synthesis of two‑sided platform economics applied to fintech; argued via theoretical mechanisms and qualitative observations rather than new empirical measurement of concentration effects.
Contemporary financing alternatives introduce new risks including data/privacy vulnerabilities, regulatory compliance gaps, and lender heterogeneity.
Synthesis of regulatory and institutional context and qualitative assessment of financing models; supported by discussion of practical risks observed in case studies and literature on digital finance governance.
Lowered cost and faster design cycles increase biosecurity and dual‑use concerns, and therefore economic policy should consider regulation, liability, and monitoring.
Paper raises these concerns in 'Externalities, regulation, and biosecurity'; it is a policy recommendation based on reduced barriers to design rather than empirical incidents presented in the text.
High compute requirements favor incumbents with capital and cloud access, increasing barriers to entry and potential for market concentration in biotech AI.
Paper argues this in 'Capital, compute, and concentration', linking compute intensity to entry barriers; no quantitative thresholds or firm‑level data are presented.
Economic value and competitive advantage will concentrate around entities that control large sequence/structure datasets, compute resources, and refined models (platform effects).
Paper states this as a likely market outcome in 'Market structure and value capture' and 'Capital, compute, and concentration' sections; no quantitative market analysis is provided.
Students raised concerns about ChatGPT producing factual errors, the risk of overreliance that could reduce independent thinking, and functional constraints of free ChatGPT versions.
Qualitative analysis of open-ended student survey responses; concerns consistently reported across responses in the sample of 254 students.
Biased or unrepresentative AI outputs produce negative externalities, including maladaptation and inefficient investments in vulnerable regions.
Conceptual analysis and illustrative cases linking misleading model outputs to maladaptive decisions; the paper notes risks rather than providing quantified incidence or cost estimates.
Returns to scale in compute and data favor incumbents; without intervention this dynamic can entrench inequality in the global climate-information market.
Economic theory of returns to scale combined with observed compute concentration; no empirical elasticity or returns-to-scale estimates provided.
Concentration of compute and model development creates market power for Northern institutions and companies, likely leading to unequal pricing, control over standards, and capture of high-value climate services.
Descriptive mapping of concentration plus economic analysis of market structure and returns to scale; illustrative rather than quantitatively proven across markets.
Rapid AI adoption without a shift from model-centric to data- and equity-centric development risks producing systematically worse performance and misleading recommendations for the most climate-vulnerable, data-sparse regions.
Synthesis of domain-specific case studies (weather/climate, impact models, LLMs) and conceptual causal tracing demonstrating how infrastructure asymmetry can degrade outputs in vulnerable regions; evidence illustrative rather than causal-estimate based.
Large language models (LLMs) that rely on dominant, textualized climate knowledge tend to foreground Northern epistemologies and marginalize local or indigenous knowledge, reinforcing biases in climate narratives and recommendations.
Case studies and analysis of training-corpus composition and output examples illustrating the dominance of Northern textual sources and examples of sidelining local knowledge; no large-scale audit results provided.
In climate impact modelling, sparse and unrepresentative exposure and vulnerability data combined with inadequate validation generate high uncertainty and risk of misleading interventions and maladaptation in vulnerable locales.
Targeted case studies and literature synthesis showing gaps in exposure/vulnerability datasets and validation failures; argument is illustrated rather than quantified across all systems.
In weather and climate modelling, historically and spatially biased observational data produce systematic performance gaps in under-observed tropical and low-income regions, reducing forecast fidelity where adaptive capacity is lowest.
Comparative, domain-specific case studies and literature review documenting observational data sparsity and illustrative empirical performance gaps; no single cross-system statistical estimate provided.
The geographic concentration of compute and model development creates path dependence: model design, training datasets, and validation reflect Northern priorities and contexts.
Conceptual analysis supported by cross-disciplinary synthesis and illustrative case studies showing dataset selection, validation practices, and model design choices aligned with Northern contexts rather than global representativeness.
Creators who systematize high-throughput AI workflows or control distribution channels may capture outsized returns, potentially increasing winner-take-most dynamics on platforms.
Theoretical implication extrapolated from observed high-throughput practices and monetization strategies in the 377 videos; not directly measured or quantified in the dataset.
Widespread unverifiable income claims and promotional framing create noisy signals about viable earnings, complicating entrants’ investment decisions and labor market expectations.
Analytical inference based on the documented prevalence of unverifiable earnings claims in the 377 videos and theory about market signaling; not quantitatively tested in the paper.
GenAI lowers the time and skill cost of producing many types of creative outputs, which can increase content supply and exert downward pressure on wages for routine creative tasks.
Inference drawn as an implication from observed practices (e.g., mass production workflows) in the 377 videos and existing literature; not directly measured in this study.
Creators and the community knowledge base document shifting norms around authorship and attribution: GenAI blurs who is considered the creator and complicates labor recognition and rights.
Coding captured explicit discussion and contested norms about authorship, attribution, and creator identity across the 377 videos.
Some creators recommend or describe synthetic engagement practices (e.g., automated posting, synthetic comments/engagement) as tactics to inflate visibility.
Thematic coding noted advice or descriptions of engagement-inflating tactics across videos in the 377-video corpus.
Creators surface and often employ practices that raise content misappropriation concerns (use of copyrighted or third-party material in synthetic outputs).
Instances and discussions captured in the 377-video sample where creators show or recommend synthesizing, transforming, or repurposing third‑party content.
Many videos advertise earnings or income claims that are unverifiable within the content, producing noisy market signals.
Qualitative observations from coding the 377 videos noting frequent asserted earnings without reproducible evidence or transparent accounting.
Numerical simulations using calibrated parameter sets produce phase diagrams and time-paths that show when gradual adjustment transitions into explosive demand collapse and financial stress under different combinations of capability growth, diffusion speed, and reinstatement rate.
Calibrated numerical simulation experiments described in the methods and results sections, using FRED, BLS, and occupational AI-exposure inputs and varying key model parameters.
Because consumption is concentrated and top incomes have high AI exposure, shocks to top-income labor/income disproportionately affect aggregate consumption and thereby threaten private credit and mortgage markets — the paper maps plausible exposures to roughly $2.5 trillion of global private credit and about $13 trillion of mortgages.
Calibration exercise linking household-level demand shocks (based on concentration and AI-exposure mapping) to aggregate credit and mortgage aggregates; reported dollar-amount mappings in the paper's scenarios.
Top-quintile households are also the cohort with the highest measured AI exposure (i.e., incomes/occupations most exposed to AI substitution), increasing the concentration of AI-driven demand risk.
Mapping occupation-level AI-exposure indices to household income quantiles using BLS occupation employment and wage data; used in calibration and scenario analysis.
Intermediation collapse: AI agents reduce information frictions and automate advice/coordination tasks, compressing intermediary margins toward logistics/execution costs and repricing business models across SaaS, payments, consulting, insurance, and financial advisory, with knock-on effects for firm valuations and collateral values that underpin credit markets.
Modeling of intermediary margins and information rents within the macro-financial framework; calibrated scenarios and sectoral discussion mapping margin compression to valuation and collateral effects.
Ghost GDP: AI output that replaces labor-intensive output can create a wedge between measured GDP (which may rise) and consumption-relevant income (which can fall) because a declining labor share reduces monetary velocity absent proportionate transfers — producing hidden demand shortfalls.
Formalization in the paper linking labor share to monetary velocity and thus to consumption-relevant income; calibration using FRED macro time series and monetary-aggregate/velocity proxies.
When firms rationally substitute AI for labor, aggregate labor income can fall and lower demand, which accelerates further AI substitution — a 'displacement spiral' whose net feedback is either self-limiting (convergent) or explosive (runaway adoption + demand collapse) depending on AI capability growth rate, diffusion speed across firms/sectors, and the reinstatement rate (rate at which new paid human roles or demand reappear).
Formal model derivations that identify key parameters and inequalities separating convergent vs explosive regimes; calibrated simulations that vary capability growth, diffusivity, and reinstatement elasticity to produce different phase outcomes.
Rapid AI adoption can create a macro-financial stress scenario not primarily through productivity collapse or existential risk but via a distribution-and-contract mismatch: AI-generated abundance reduces the need for human cognitive labor while institutions (wage contracts, credit, consumption patterns, financial intermediation) remain anchored to the scarcity of human cognition, producing a self-reinforcing downward spiral in labor income, demand, and intermediary margins that can tip into an explosive crisis unless offset by sufficiently fast reinstatement of human-paid demand or deliberate policy/market responses.
Analytical macro-financial model coupling firm-level substitution decisions, aggregate demand mapping, and financial-sector balance-sheet propagation; calibrated numerical simulations using U.S. macro time series (FRED), BLS occupation-level employment and wages, and published occupation-level AI-exposure indices; phase diagrams and scenario time-paths reported in the paper.
Distributional shifts and regime changes require periodic revalidation or TSFM updates to maintain reliable performance.
Paper discussion of limitations and recommended operational procedures (revalidation and periodic TSFM updates) to handle non-stationarity and regime shifts; rationale based on time-series modeling risks.
If the TSFM produces biased or poor forecasts in certain regimes, those errors can propagate into the downstream regression and harm performance.
Stated caveat in the paper (theoretical/empirical rationale); logical consequence of using TSFM-generated features as inputs—error propagation risk discussed in analysis/limitations section.
These methodological adaptations reduce but do not eliminate validity threats; they often increase complexity and cost while leaving unresolved issues of generalizability and time-dependence.
Practitioner accounts (n=16) describing limits/tradeoffs of adaptations; authors' synthesis concluding residual threats remain despite adaptations.
External validity is limited: results from a given trial may not generalize across model versions, populations, tasks, or to temporally distant deployments.
Interview-derived themes (16 practitioners) and authors' analytic mapping to external validity concerns; supported by examples of model/version dependence discussed in interviews.