Evidence (4114 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 |
Innovation
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New technologies are initially skill intensive (demand more college-educated workers) but become less so as they age (they get standardized and accessible to less-skilled workers).
Empirical descriptive evidence from novel text-based data combining patent text and job postings (building on Kalyani et al., 2025) tracking technologies and their changing demand for skills as they age.
Azar et al. (2023) show that monopsonistic employers have stronger incentives to automate and document that US commuting zones with higher labor market concentration experienced more robot adoption.
Citation reported in the paper summarizing Azar et al. (2023); empirical analysis across US commuting zones (no sample size provided here).
Acemoglu and Restrepo (2022) attribute 50–70% of the increase in US wage inequality between 1980 and 2016 to displacement of workers from tasks by automation.
Citation reported in the paper summarizing Acemoglu and Restrepo (2022)'s attribution of the rise in wage inequality to automation-driven task displacement.
Dechezleprêtre et al. (2025) exploit Germany's Hartz reforms to estimate an elasticity of automation innovation to low-skill wages of 2–5 at the firm level.
Citation reported in the paper summarizing Dechezleprêtre et al. (2025)'s empirical estimate (elasticity 2–5); the paper states this was estimated at the firm level.
Eloundou et al. (2024) predict that half of US jobs are significantly exposed to recent advances in generative AI.
Citation reported in the paper summarizing Eloundou et al. (2024)'s prediction; no sample size provided in the excerpt.
When employers have monopsony power, they choose technologies that expand this power beyond what a social planner would consider optimal.
Model results on monopsonistic employer incentives and their technological choices; discussion supported by citations.
Profit-maximizing firms pursue innovations that erode workers' market power by making them more easily replaceable, even at the expense of production efficiency; a social planner who values worker welfare would employ technologies that preserve workers' market power.
Theoretical analysis of interactions between technological choice and market power; supported by cited empirical evidence (e.g., Azar et al. 2023) in the paper.
A welfare-maximizing planner would choose to automate fewer tasks than production efficiency would dictate when workers' welfare is heavily weighted.
Model analysis of welfare-maximizing automation level compared to production-efficient automation; analytical result in the automation application.
Diminishing returns are not only a geometric flattening of the loss curve, but also rising pressure for cost reduction, system-level innovation, and the breakthroughs needed to sustain Moore-like efficiency doublings.
Analytical claim in the paper about the implications of diminishing returns for cost pressure and innovation requirements (qualitative; no sample size in excerpt).
Most of today's agents remain isolated tools or closed-ecosystem orchestrators rather than socially integrated participants in open networks.
Author claim/assessment presented as current-state analysis; no empirical breakdown or study sample provided in the text.
Prominent studies predict substantial job displacement due to automation.
Paper asserts this as background, referencing the existence of prominent studies in the literature (no specific citations or sample sizes provided in the abstract).
For organizations of n humans with AI agents, the optimal team size decreases with agent capability.
Derived implication from the stylized model's analysis of multi-human organizations interacting with AI agents.
There is no smooth sublinear regime for human effort; it transitions sharply from O(E) to O(1) with no intermediate scaling class.
Mathematical derivation from a stylized model of human-AI collaboration that assumes tasks decompose into atomic decisions, a fraction ν are novel, and specification/verification/error correction scale with task size.
The literature singles out endemic data quality issues, algorithmic bias, governance frameworks, and regulatory compliance as concerns that require trusted AI and sustainable digital finance ecosystems.
Synthesis from the reviewed literature noting recurring concerns and limitations reported across studies; the paper lists these as major challenges identified in the field.
AI can worsen financial and market performance if it crowds out normal R&D.
Paper's empirical analysis and interpretation linking AI dependence to poorer financial/market performance through displacement of standard R&D activities; presented as a study finding.
High AI dependency disclosed in financial reports does not improve firms' financial health and may even endanger it.
Empirical results drawn from the study's analysis of listed new energy vehicle and automobile manufacturers (2013–2023); statement appears in the paper's findings/conclusions.
AI dependency reduces financial safety for listed new energy vehicle and automobile manufacturers.
Empirical analysis of a sample of listed new energy vehicle and automobile manufacturers covering 2013–2023; the paper reports data analysis showing AI dependency reduces financial safety.
More informative search can degrade both learning and consumer surplus unless the market learns as much as consumers (for example, by "reading the transcripts" of agentic conversations).
Analytical comparative statics in the paper's theoretical model showing how increasing the informativeness of consumer-side signals affects learning dynamics and welfare; relies on model assumptions about what information the market collects versus consumers.
Technological proximity has a noteworthy negative effect on collaboration, underscoring the importance of complementary knowledge in AI innovation.
SAOM estimates from longitudinal patent collaboration data (2013–2024) showing a statistically negative coefficient for technological proximity (implying organizations closer in technology space are less likely to form ties).
Sentiment signals derived from sparse news are commonly used in financial analysis and technology monitoring, yet transforming raw article-level observations into reliable temporal series remains a largely unsolved engineering problem.
Framing statement in the paper's introduction/abstract describing the problem motivation; conceptual argument rather than empirical test.
Current closed models are generally ill-suited for scientific purposes (with some notable exceptions).
Argumentative and evaluative reasoning in the paper comparing features of closed models to scientific needs; no empirical sample size reported in abstract.
Restrictions on information about model construction and deployment threaten reliable inference in research that involves those models.
Conceptual argument and analysis presented in the paper (no empirical sample or randomized evaluation reported in abstract). The paper analyzes how specific types of information restrictions (about model construction and deployment) create threats to inference.
This inefficiency directly undermines UN Sustainable Development Goals 13 (Climate Action) and 10 (Reduced Inequalities) by hindering equitable AI access in resource-constrained regions.
Normative/analytic claim in the paper linking energy inefficiency to negative impacts on specific UN SDGs (argumentative, not empirically quantified in the abstract).
Current paradigms indiscriminately apply computation-intensive strategies like Chain-of-Thought (CoT) to billions of daily queries, causing LLM overthinking that amplifies carbon emissions and operational barriers.
Claim/assertion in the paper framing the problem (conceptual/observational argument; no specific empirical backing provided in the abstract).
There is a potential for exclusion due to limited digital footprints, which can limit who benefits from AI-driven finance.
Abstract explicitly identifies potential exclusion of people with limited digital footprints as a challenge, based on qualitative interviews and case-study evidence.
Data privacy concerns are a notable challenge in deploying AI-driven financial solutions.
Abstract lists data privacy concerns among identified challenges drawn from interviews and analysis across the three case studies.
Infrastructure limitations pose a barrier to adoption and effective use of AI-enabled financial services.
Abstract identifies infrastructure limitations as a challenge, based on qualitative interviews and case-study evidence.
Digital literacy gaps are a challenge limiting the effectiveness and inclusion of AI-driven financial solutions.
Abstract lists digital literacy gaps among identified challenges, based on qualitative insights from the 1,500 interviews and case-study observations.
Triangulation with market data and sentiment analysis confirms that public enthusiasm often outpaces actual technological readiness.
Paper states market data and sentiment analysis were used to triangulate findings and reports this systematic gap; no numeric effect sizes or sample counts provided.
The main risk is not merely copying, but the possibility that useful capability can be transferred more cheaply than the governance structure that originally accompanied it.
Conceptual threat model articulated in the paper; argued on normative/theoretical grounds without reported empirical measurement or sample.
Distillation becomes less valuable as a shortcut when high-level capability is coupled to internal stability constraints that shape state transitions over time.
Theoretical argument presented as the paper's core claim; introduces a conceptual mechanism (capability-stability coupling) and argues why this would reduce the usefulness of distillation. No empirical data, experiments, or sample are reported.
Hallucination and content filtering are the most common frustrations reported across all platforms.
Qualitative and/or survey-coded responses about user frustrations aggregated across platforms (overall N=388); paper reports these two issues as the most common.
Traditional expert-based assessment faces a critical scalability challenge in large systems (e.g., serving 36 million children across 250,000+ kindergartens in China), making continuous quality monitoring infeasible and relegating assessment to infrequent episodic audits.
Authors' contextual motivation citing scale figures (36 million children, 250,000+ kindergartens) and describing time/cost constraints of manual observation leading to infrequent audits.
AI-enabled, democratised production is more likely to intensify competition and produce winner-take-most outcomes than to generate broadly distributed entrepreneurial success.
Synthesised theoretical prediction based on the unified framework (attention scarcity + free-entry dilution + superstar/preferential attachment dynamics) developed in the paper; no empirical validation provided.
When the framework is extended to include quality heterogeneity and reinforcement dynamics, equilibrium outcomes exhibit declining average payoffs.
Analytical extension of the baseline formal model to incorporate heterogeneous quality and reinforcement (preferential attachment) dynamics; theoretical derivation in the paper; no empirical sample.
In markets with near-zero marginal costs and free entry, increases in the number of producers dilute average attention and returns per producer.
Formal theoretical model introduced in the paper (Builder Saturation Effect) that assumes near-zero marginal costs, free entry, and finite human attention; no empirical sample or experimental data reported.
Agent memories currently remain private and non-transferable because there is no way to validate their value.
Descriptive assertion in the paper about current state of agent memories; no empirical survey or measurement cited.
Measuring only technical model performance (such as predictive accuracy) is insufficient for assessing the strategic impact of AI in drug discovery.
Argued in the paper as a critique of current evaluation practices; presented as a conceptual point rather than supported by new empirical data in the excerpt.
Pressure remains high to increase the probability of success to improve the effectiveness of pharmaceutical R&D.
Asserted in the paper as motivational context for the work; framed as an industry pressure point rather than backed by a specific empirical sample or quantified survey in the excerpt.
Increasing cost and failure rates in the pharmaceutical R&D process have not fundamentally improved over the last decade.
Stated as a contextual observation in the paper's opening paragraph; presented as a summary of industry trends (no specific dataset, sample size, or citation included in the excerpt).
Current (pay-upfront) models impose a financial barrier to entry for developers, limiting innovation and excluding actors from emerging economies.
Analytical argument in the paper based on cost-structure reasoning and literature on barriers to entry; no empirical sample or causal estimate provided.
Only 12% of AI market value is used in physical activities.
Descriptive aggregate: authors categorize and report that 12% of estimated AI market value maps to physical activities.
Coal-based energy consumption structure and a secondary-industry-dominated industrial structure significantly inhibit regional TFCP and have strong negative spatial spillovers.
Control-variable coefficients from Spatial Durbin Model on panel data (30 provinces, 2010–2023) showing statistically significant negative direct and indirect effects for coal-dominant energy structure and secondary-industry share.
Adoption barriers exist, particularly for small and medium-sized enterprises and firms in emerging economies, where capability and data constraints limit impact.
Findings reported from the systematic review and mixed-methods assessment (abstract references barriers observed across reviewed studies); number of studies reported in abstract is 104 for the systematic review.
AI can initially exacerbate distributional injustice.
Dimension-level analysis indicating negative (or initially negative) effects of AI on the distributional component of the energy justice index.
There are few integrated frameworks (bridging ethics and technical controls) in the current AI governance landscape.
Result of the literature review and cluster analysis showing limited coverage of frameworks that integrate ethical principles with auditable technical controls.
Findings reveal a fragmented landscape dominated by ethics/privacy-centric and compliance/risk-focused approaches.
Synthesis of the reviewed literature and results of PCA/k-means clustering indicate thematic dominance of ethics/privacy and compliance/risk orientations across frameworks.
The article argues that the idea of a “Pax Silica” is fragile.
Conclusion drawn from the paper's theoretical framework and comparative analysis; presented as an assessment rather than empirical measurement.
Contemporary struggles over semiconductor supply chains represent not a new hegemonic order but a logistical adaptation of Pax Americana.
Stated thesis supported by comparative/historical analysis and theoretical argumentation (comparative analysis of historical Pax orders and U.S. techno-security architecture); no quantitative sample size reported in abstract.
Past machine learning applications to pricing have produced models that adapt slowly to real-time changes, depend heavily on historical data, and struggle to handle multi-agent scenarios.
Stated as literature/related-work critique in paper; no new empirical evidence or sample size provided in the excerpt.