Evidence (3566 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
Remove filter
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.
Key technical and organizational risks include model brittleness, privacy and IP concerns in code generation (training-data provenance), and increased governance and QA burdens.
Literature review highlighting known risks and survey responses reporting practitioner concerns; no quantified incident rates provided.
Practitioners report barriers to adoption including integration costs, lack of trust/explainability, poor data quality, and skills gaps.
Thematic analysis / coding of open-ended survey responses and literature review identifying common adoption barriers; survey sample size not specified.
Signals may be gamed by providers or agents; incentive-compatible design and auditability are crucial.
Risk/limitations noted by the authors as a foreseeable strategic behavior problem; presented as a caution rather than empirically observed gaming in the current dataset.
GDP and productivity metrics that ignore interpretive labor risk understating the inputs to creative and knowledge work; RATs offer a means to measure previously invisible inputs.
Policy argument in the measurement/productivity subsection; no empirical re-estimation of GDP/productivity presented.
Algorithmic feeds and AI summarizers tend to compress or automate interpretive traces, potentially erasing signals of reasoning, context, and tacit knowledge.
Conceptual claim supported by argumentation and examples in the paper; no empirical comparison between RATs and existing summarizers is presented.
Human ratings and preference-trained metrics reward visually vivid but exaggerated color and contrast, which leads to outputs that are less photorealistic when photorealism is the intended objective.
Reported experiments in the paper comparing human preference ratings and preference-trained evaluators against a color-fidelity-focused ground truth (CFD). The authors state these existing evaluators favor high saturation/contrast and qualitatively and quantitatively select images that are 'too vivid' relative to photographic realism (paper reports qualitative examples and quantitative comparisons; exact sample sizes and statistical values are described in paper but not provided in the summary).
Adoption complementarities (AI tools + developer skill + organizational processes) favor larger incumbents and well‑funded firms, possibly increasing concentration in tech sectors.
Theoretical argument about complementarities and returns to scale; illustrative examples; lacks firm‑level empirical testing.
In the near term, displacement risks concentrate on junior or highly routine roles; mobility and retraining will determine realized unemployment impacts.
Task automatability mapping indicating routine tasks more automatable and qualitative reasoning on labor mobility; no empirical unemployment projections.
Adoption will be heterogeneous: larger firms and well‑resourced teams will capture more gains earlier, producing competitive advantages.
Theoretical argument about adoption complementarities (AI tools + developer skill + organizational processes) and illustrative examples; no cross‑firm empirical analysis.
Differential adoption across firms (due to modular, scalable designs and data advantages) may create winner‑takes‑most effects and increase market concentration, benefiting early adopters with rich data/integration capabilities.
Market-structure claim supported by economic reasoning about scale and data advantages; no cross-firm empirical adoption study or market concentration time‑series is provided.
Initial investment, integration, and ongoing maintenance/compliance costs can be substantial and affect short-term ROI.
Interviewed administrators and implementation reports citing upfront and recurring costs (integration, model maintenance, compliance); quantitative budget figures not standardized across sites in the paper.
Risk of deskilling or reduced empathy if human roles are overly automated.
Thematic analysis of staff interviews and surveys reporting concerns about loss of practice, reduced patient contact, and potential diminishment of empathetic skills; no longitudinal measures of skill loss presented.
Technical and organizational integration with legacy hospital IT systems is nontrivial.
Implementation reports and interviews describing integration work, time, and resource needs; descriptive accounts of technical and organizational barriers (no universal timelines/costs reported).
Algorithmic bias in NLP models can misclassify complaints from underrepresented groups.
Observations from system classification error analyses (disparities reported by demographic group) and corroborating qualitative concerns from staff and administrators; specific subgroup sample sizes and effect magnitudes not provided.
Data privacy and security risks arise from centralizing complaint text and metadata.
Stakeholder interviews, thematic coding of concerns, and risk assessment commentary based on centralized logs and metadata aggregation; no measured breach incidents reported here.
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.
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.
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.
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.
Regulatory fragmentation increases compliance costs and stifles cross-border scale economies; international coordination and mutual recognition of standards can lower trade costs.
Comparative governance analysis and economic reasoning about cross-border trade and compliance; no cross-country causal estimates provided in the report.
Large incumbents with data/network advantages may entrench market power.
Policy and literature review noting data/network effects, observed tendencies in tech markets; sectoral examples discussed in the report.
Without targeted policy, AI can amplify winner-take-all dynamics (market concentration, superstar firms) and spatial inequalities (urban vs. rural).
Theoretical economic arguments and review of literature on data/network effects and concentration; comparative policy analysis that raises distributional concerns.
There is a persistent gap between policy intent (promises of ethical protection and economic opportunity) and lived experience, producing new forms of social exposure—especially for vulnerable groups.
Synthesis of qualitative findings from documents, ethics guidelines, industry statements, and stakeholder commentary indicating aspirational policy language contrasted with limited enforceable protections; specific lived-experience case data are not provided.
Lack of enforceable data-rights and accountability mechanisms strengthens incumbent platforms’ control over data markets, potentially reducing competition and hindering entry by smaller firms.
Qualitative review of regulatory texts and industry positioning showing limited enforceable data-rights provisions; theoretical market-structure inference without empirical market-share analysis.
Weak or non‑enforceable rules create conditions for negative externalities (data exploitation, discriminatory automation) that markets alone may not correct.
Argumentative synthesis from document analysis and theoretical framing (communication rights, market-failure logic); supported by examples in policy and industry discourse but not by empirical market-level measurement in the paper.
The dominant framing privileges economic imaginaries of competitiveness and development over communication rights, producing regulatory blind spots and reinforcing existing inequalities.
Interpretive analysis using communication-rights theory and SCOT applied to policy and industry discourse; comparison of economic-oriented language versus rights-oriented provisions in reviewed documents.
Regulatory attention typically overlooks vulnerable and marginalized populations (low-wage workers, women, rural communities), whose mobile communication practices and data are disproportionately exposed to harm.
Document-based qualitative analysis identifying patterns of inclusion/exclusion in regulatory texts and public debate; stakeholder commentary reviewed indicates limited consideration of these groups. (Sample count not provided.)
Indonesia’s governance of mobile-AI rests largely on soft‑law, aspirational instruments (guidelines, non‑binding ethics codes), which limits enforceability and accountability.
Qualitative discourse- and document-based analysis of key policy documents, national ethics guidelines, industry statements, and public stakeholder commentary related to mobile-AI in Indonesia. (The paper identifies dominant use of non‑binding instruments; exact number of documents reviewed is not specified.)
Widespread adoption of LLMs without adequate verification increases systemic cybersecurity risks with potential economic spillovers.
Synthesis of security incident case studies and risk analyses revealing vulnerabilities in generated code and potential downstream impacts.
Models lack deep contextual reasoning and may fail on tasks requiring long-term design thinking or deep domain knowledge.
Benchmark failures and user studies in the reviewed literature demonstrating degraded performance on complex architectural/design tasks and domain-specific reasoning problems.
Use of these tools can mask gaps in foundational computational skills among novices.
Pedagogical case studies and assessments indicating reliance on AI can produce superficial solutions and lower demonstrated understanding of core concepts.
Negative externalities from synthetic media (misinformation, reputational harm, verification costs) may justify public interventions such as provenance standards, mandatory labeling, penalties for malicious misuse, and public investment in verification infrastructure.
Policy analysis and normative recommendations based on identified externalities in the reviewed literature; no empirical policy evaluation in paper.
Compliance with IP, privacy and liability regimes will impose costs (monitoring, licensing, disclosure) that may raise barriers for smaller entrants and affect prices and diffusion of generative audiovisual models.
Regulatory and economic literature synthesized in the narrative review; policy/legal case citations included but no new cost estimates provided.
Proliferation of generated content may increase information supply but lower per-item attention and willingness-to-pay, potentially reducing monetization unless intermediaries solve discoverability and trust issues.
Theoretical arguments using attention-economy literature and secondary studies; narrative reasoning without new empirical quantification.
Platforms and firms that control model training data and deployment infrastructure will gain strategic advantage, increasing risks of vertical integration and market concentration.
Market-structure and firm-strategy analysis drawn from secondary literature and conceptual arguments in the paper.