Evidence (15198 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9178 claims
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Productivity
8166 claims
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Governance
7367 claims
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Human-AI Collaboration
7010 claims
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Org Design
4531 claims
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Innovation
4439 claims
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Labor Markets
3693 claims
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Skills & Training
3063 claims
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Inequality
2167 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 806 | 212 | 105 | 975 | 2164 |
| Governance & Regulation | 898 | 417 | 197 | 128 | 1671 |
| Organizational Efficiency | 865 | 210 | 132 | 88 | 1306 |
| Technology Adoption Rate | 703 | 265 | 130 | 115 | 1224 |
| Research Productivity | 474 | 140 | 65 | 350 | 1041 |
| Output Quality | 507 | 197 | 61 | 53 | 818 |
| Decision Quality | 358 | 181 | 86 | 52 | 684 |
| AI Safety & Ethics | 245 | 294 | 71 | 34 | 650 |
| Firm Productivity | 465 | 60 | 93 | 22 | 646 |
| Market Structure | 188 | 173 | 126 | 25 | 517 |
| Task Allocation | 225 | 72 | 78 | 34 | 414 |
| Innovation Output | 246 | 30 | 48 | 18 | 344 |
| Skill Acquisition | 182 | 67 | 62 | 18 | 329 |
| Employment Level | 112 | 57 | 110 | 13 | 294 |
| Fiscal & Macroeconomic | 137 | 72 | 45 | 28 | 289 |
| Firm Revenue | 175 | 50 | 28 | 5 | 259 |
| Consumer Welfare | 122 | 71 | 46 | 13 | 252 |
| Task Completion Time | 187 | 34 | 10 | 14 | 246 |
| Inequality Measures | 45 | 127 | 50 | 6 | 228 |
| Worker Satisfaction | 95 | 75 | 23 | 12 | 205 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 63 | 27 | 14 | 168 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Team Performance | 93 | 18 | 28 | 11 | 151 |
| Wages & Compensation | 79 | 39 | 25 | 7 | 150 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Job Displacement | 12 | 84 | 23 | 1 | 120 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Skill Obsolescence | 6 | 51 | 9 | 1 | 67 |
| Social Protection | 40 | 17 | 8 | 2 | 67 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Labor Share of Income | 17 | 20 | 17 | 1 | 55 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Inter-model convergence undermines product differentiation across AI providers and could accelerate commoditization of base LLM outputs.
Market-structure inference built on empirical finding of high cross-model output similarity across 70+ models and theoretical discussion of vendor differentiation; no market-level price or adoption time-series analyzed in the paper.
Homogenized AI outputs reduce the value of AI as a source of varied cognitive complements to human labor, potentially lowering productivity gains from human–AI collaboration in tasks requiring creativity and exploration.
Economic argument drawing on measured decreases in model output diversity and theoretical literature on complementarities between diverse AI outputs and human creativity; no direct measured productivity changes reported in field settings within the paper.
Reward-model and evaluation miscalibration can cause organizations to prefer models that maximize apparent evaluation scores at the expense of useful stylistic or cognitive diversity.
Comparative analyses between automated evaluation/reward-model rankings and human preference/diversity assessments reported in the paper; examples where high-scoring models produced more consensus-style outputs.
Homogenized outputs increase organizational susceptibility to groupthink and correlated errors across teams using different models.
Argument based on observed inter-model convergence (high similarity across models) implying correlated outputs and thus correlated mistakes across teams; no randomized organizational field experiment reported, this is an inferred risk from the empirical convergence data.
Homogenization of LLM outputs erodes creative diversity in AI-assisted work and reduces the variety of solutions produced.
Inference drawn from measured decreases in response diversity (entropy/distinct-n) and the observed inter-model convergence across real-world queries; argument linking lower measured diversity to fewer distinct solution proposals in AI-augmented workflows.
Current reward models and automated evaluation metrics are biased toward consensus/high-probability responses, preferring consensus-style outputs even when stylistically diverse alternatives are judged equally high-quality by humans.
Reported human preference assessments and comparisons between human judgments and automated/reward-model scores showing cases where reward models favor higher-probability/consensus outputs despite no human-quality advantage; analyses described comparing reward-model scores to human judgments on stylistically diverse outputs.
Feedback effects from physical capital and labor onto AI capital are weak, with only weak negative feedback observed (physical capital → AI and labor → AI small/weakly negative coefficients).
Estimated interaction coefficients from the 2016–2023 calibration showing small-magnitude, negative feedback terms from physical capital and labor onto AI.
Introducing ‘agent capital’ (AI that lowers coordination costs) reduces coordination costs inside firms (coordination compression).
Definition and central assumption of the paper's formal task-based model; analytical setup assumes agent capital parametrically reduces coordination frictions.
Extractive industries often deliver limited local employment and mainly generate rents rather than broad employment or skill spillovers.
Review of empirical studies and case evidence showing extractive FDI tends toward enclave production with low local hiring and limited upstream/downstream linkages; coverage varies by country and project.
FDI may increase within‑country wage inequality, especially when concentrated in extractive sectors or low‑skill activities.
Cross-study empirical results and theoretical arguments summarized in the review showing wage premia accruing to skilled workers and enclave effects in extractives; underlying studies vary in location, methods, and samples.
FDI may deepen labor market dualism: creating formal, higher‑paying jobs for a minority while many remain in precarious, low‑pay informal work.
Literature synthesis pointing to patterns where foreign investment produces enclave formal jobs while broader labor markets remain informal or precarious; evidence drawn from firm- and sector-level studies cited in the review.
Extremely high reported model performance (R² = 0.999) raises concerns about overfitting, data leakage, or measurement artifacts and the need for transparency, out-of-sample validation, and field trials.
Paper (or the paper's discussion/implications as summarized) notes model-risk and external validity concerns and recommends replication and validation before policy adoption.
A one standard-deviation increase in AI adoption lowers wages in the middle income quintile by 1.4%.
Panel of 38 OECD countries, 2019–2025; wage outcomes by income quintile using the AI Adoption Index and IV estimation; robustness checks reported.
Uneven inclusion in digital/AI deployments risks exacerbating digital divides and creating distributional harms.
Descriptive and case-based studies report differential access and uptake among demographic groups; limited causal quantification and varying measurement approaches across studies.
Limited auditability and explainability of AI systems increase trust and legitimacy risks.
Technical governance literature and case reports show challenges in model explainability and external audit; evidence is technical and illustrative rather than based on large-sample causal studies.
Inadequate regulatory frameworks raise privacy, accountability, and fairness concerns for AI in government.
Governance reviews and risk assessments documented in the literature highlight regulatory gaps and associated incidents/risks; empirical incident counts are not comprehensively tabulated in the review.
Procurement, budgeting rules, and siloed incentives discourage cross-cutting transformation and modular iterative deployments.
Policy and institutional analyses in the reviewed literature point to rigid procurement cycles, capital budgeting practices, and siloed funding as obstacles; examples and case narratives are provided but systematic quantification is limited.
Organizational resistance and fragmented coordination block integrated rollouts of cross-cutting digital reforms.
Qualitative case studies and governance analyses repeatedly identify intra-governmental silos, conflicting incentives, and change-resistance as implementation barriers; evidence is primarily descriptive.
Skills shortages (technical, managerial, data literacy) impede adoption and maintenance of digital and AI systems.
Multiple surveys, policy briefs and qualitative studies cited in the review report workforce capacity gaps; often based on targeted assessments or organizational audits rather than representative sampling.
Infrastructure deficits (connectivity, legacy systems) limit scale and reliability of digital/AI initiatives.
Recurring barrier documented across governance analyses and case studies; evidence includes reports of downtime, integration failures, and limited geographic reach; no unified cross-study sample provided.
Unresolved liability and regulatory uncertainty increase malpractice risk and insurance costs, leading insurers and providers to favor conservative adoption and continued human-in-the-loop safeguards.
Regulatory/legal analysis and stakeholder behavior models discussed in the review; observed cautious deployment patterns in practice noted in the literature.
Regulatory pathways and approval standards are evolving but are not yet aligned with deployment of high-autonomy clinical systems.
Review of recent policy analyses and regulatory documents showing ongoing updates and gaps between current standards and requirements for high-autonomy AI deployment.
Robust, locally appropriate data governance (privacy, interoperability, standards) is a public good that underpins trust and data-driven markets; weak governance raises risks of exclusion and foreign dependency.
Governance and policy literature synthesized in the review; conceptual arguments supported by examples but limited empirical evaluation in LMIC SME contexts.
Platform effects and supplier ecosystems associated with AI may create winner-takes-most market dynamics, so policy should monitor market concentration and enable competitive access to core AI services.
Literature on platforms and market structure combined with case examples; review notes potential for concentration but lacks broad causal studies quantifying effects in LMIC SME markets.
Fragmented or weak data governance (privacy rules, standards, interoperability, and trust) reduces SMEs’ ability to participate in data-driven markets and adopt AI.
Policy analyses and governance-focused studies in the review highlighting data governance weaknesses in LMICs and associated risks for SMEs; examples discussed rather than quantified nationally.
Scalability and rapid model improvements provided by cloud vendors are harder to capture on-premise.
Comparative discussion in TOE analysis about vendor-managed continuous model improvements and cloud scalability versus on-prem constraints; not backed by longitudinal empirical comparison in the summary.
Sanctions and supply-chain restrictions affect access to hardware and software, altering adoption paths and increasing costs; domestic substitution or international cooperation will influence future trajectories.
Institutional analysis documenting sanctions/import restrictions and their implications for hardware/software access; qualitative assessment of substitution and cooperation options.
The barriers to AI adoption in Russia’s extractive industries interact systemically (e.g., lack of data reduces demand for talent; weak infrastructure deters investment), so piecemeal measures will have limited effect.
Analytical synthesis identifying co-moving constraints across cross-country trends and qualitative firm-level evidence showing interacting bottlenecks.
Institutional failures—weak standards/interoperability, limited public–private coordination, regulatory uncertainty, and sanctions/import restrictions—exacerbate diffusion problems for AI in extractive sectors.
Institutional review of standards, procurement and public–private coordination mechanisms; documentation of regulatory uncertainty and sanctions/import restrictions affecting hardware/software access.
Infrastructure shortfalls — insufficient sensorization, limited connectivity (edge/cloud), inadequate computing hardware and immature localized software stacks — are underdeveloped in Russia relative to peers and hinder deployment.
ICT infrastructure indicators, comparative metrics on sensorization/connectivity/computing availability, and project case evidence from extractive firms.
There are human capital constraints: shortages of AI talent in industry-specific roles, limited retraining of engineering staff, and brain drain reduce the sector's capacity to absorb and deploy AI.
Workforce and education statistics, patent/activity counts, and expert commentary; qualitative case evidence showing limited retraining and talent shortages in industry-specific AI roles.
Absolute and relative AI investment volumes in the Russian extractive sector are lower than in the US, China and EU; private risk capital is limited and public support insufficiently targeted to scale-up projects.
Investment datasets and national/industry statistics comparing public and private AI investment volumes (absolute and relative to output) for extractive sectors across jurisdictions (2020–2025).
Data access is a primary bottleneck: datasets are fragmented, often proprietary or closed, ownership rules are unclear, and mechanisms for safe data sharing are weak, hindering model training and cross-firm applications.
Review of data governance frameworks across jurisdictions and firm-level case evidence documenting closed/proprietary datasets and weak sharing mechanisms.
The gap is driven not only by smaller investment flows but also by institutional constraints—limited data access, weak data governance, human capital shortages, and inadequate digital infrastructure—that together suppress diffusion and scaling of AI applications.
Institutional analysis (review of data governance frameworks, regulatory regimes, standards, market structure) plus qualitative firm-level case studies and expert commentary illustrating how these factors impede adoption and scaling.
Russia’s adoption of AI in extractive industries is both slower (lower growth rate) and shallower (lower depth of digitalization) than peer jurisdictions in 2020–2025.
Time-series comparison of digitalization/digit maturity proxies and AI investment volumes across countries for 2020–2025; synthesis of trend differences from public datasets and sectoral indices.
Between 2020–2025 Russia trails the United States, China and the EU on both digitalization indicators and AI investment volumes in the mining and oil & gas sectors.
Comparative multi-country trend analysis (2020–2025) using publicly available investment and digitalization indicators: national/industry statistics, investment datasets, and sectoral digitalization indices comparing Russia, US, China and EU over 2020–2025.
Loss of control over research data impedes local capture of value (knowledge, IP, downstream services) and can create externalities when data are repurposed or commercialized without equitable benefit sharing.
Conceptual argument grounded in case observations about data flows and provider practices; no quantitative measures of value capture provided.
Dominant AI/cloud providers become de facto gatekeepers of data processing and storage; researchers and institutions, particularly in lower‑capacity jurisdictions, have limited bargaining power to enforce data‑sovereignty or transparency terms.
Mapping of third‑party dependencies and interview/observational evidence of institutional procurement constraints in the Chile case; normative discussion of market power implications.
Algorithmic opacity and cross‑border regulatory fragmentation raise monitoring, compliance, and contractual costs for collaborative research, effectively increasing the transaction costs of data‑intensive science.
Analytical inference from qualitative findings (opacity, legal fragmentation) and normative economic reasoning presented in the implications section; no quantitative transaction‑cost measurement reported.
Inequalities in infrastructure (local compute, storage, institutional procurement power) amplify these problems: researchers in weaker jurisdictions face higher risks and fewer mitigation options.
Case study observations about local infrastructure capacity, procurement practices, and institutional constraints in Chile; qualitative reports of limited mitigation choices.
Rather than shifting liability away from researchers, AI systems increase researchers' ethical responsibilities: researchers must assess third‑party tools, negotiate data flows, and manage risks despite having limited contractual leverage.
Qualitative interviews and institutional observations reporting researchers' roles in assessing tools and managing data flows; normative analysis of accountability responsibilities in the case study.
Algorithmic opacity (hidden models, undocumented data flows, proprietary cloud stacks) reduces researchers' ability to control or even know how participant data are used, transferred, or monetized.
Interview data and mapping of third‑party dependencies showing opaque provider practices and limited transparency about model/data flows in the Chile case study.
Everyday AI services used in research introduce new, diffuse points of data capture and processing that complicate informed consent and privacy management.
Observations and documented mappings of tool use and data flows (e.g., transcription services, cloud platforms, meeting assistants) reported in the case study; supported by qualitative interviews with researchers/administrators.
AI tools embedded in everyday research infrastructures intensify — rather than reduce — ethical accountability burdens: they constrain researcher autonomy and undermine data sovereignty, especially in cross‑national settings where legal protections are fragmented or weaker.
Qualitative case study centered on environmental science research in Chile that uses GDPR as a normative framework; methods reported include interviews, observation, and mapping of data flows and third‑party dependencies (sample sizes not reported).
Insufficient regulation increases risks of negative externalities (privacy harms, biased hiring/management) that can reduce labor supply attachment or lower human capital investments.
Theoretical reasoning and synthesis of documented case studies and reports referenced in the commentary; not supported by new causal empirical analysis in the paper.
Absent strong worker voice or mandated impact assessments, AI-driven surveillance, algorithmic management and task reallocation are more likely, increasing risks of deskilling, displacement, and discriminatory outcomes.
Policy synthesis identifying plausible channels from AI system use to worker harms; supported by case-study reports in the symposium but no systematic empirical quantification in this commentary.
Weakening of organized labor and stalled worker-protection legislation raises the probability that AI adoption will increase employer bargaining power, potentially depressing wages and worsening job quality for affected occupations.
Analytic inference from labor economics theory and policy review; commentary does not present causal microdata linking AI adoption to wage or job-quality outcomes.
Export controls may constrain access to advanced models and hardware, affecting productivity gains unevenly across firms and sectors.
Policy analysis of current export control instruments and their potential economic effects; no firm- or sector-level quantitative analysis presented.
A conservative Supreme Court majority increases the risk of rulings that could further constrain organized labor and weaken labor’s power to negotiate AI-related workplace rules.
Legal analysis connecting Supreme Court composition and recent jurisprudence to possible effects on labor law and collective bargaining; predictive inference rather than empirical testing.
The incoming second Trump administration is dismantling many Biden-era worker-protection initiatives (notably rescinding or undercutting the Biden Executive Order intended to hold employers accountable for AI impacts).
Policy/legal analysis referencing recent executive actions and reported rollbacks of Biden-era frameworks; synthesis of documents and news/administrative actions reviewed in the commentary; no original empirical sample.