Evidence (2215 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Innovation
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At equilibrium prices in symmetric markets, consumer surplus is improved by cheaper search but may be decreased by more informative search, due to weakened inter-business competition.
Equilibrium price analysis within the theoretical model for symmetric firms; comparative statics showing how search cost and signal informativeness affect pricing, competition intensity, and consumer surplus. No empirical validation reported.
The market (in the model) tracks indications of fit for searched products and indications of quality for chosen products, thereby guiding subsequent searches.
Model structure and assumptions specified in the paper: an endogenous information-tracking mechanism that records signals from searches and purchases and which then influences future search behavior; presented as part of the theoretical framework rather than empirical evidence.
This advantage is contingent upon robust AI governance, ethical frameworks, and the transition from 'pilot-lite' projects to integrated, data-driven 'AI-first' business models.
Conditional claim in the paper linking success to governance, ethics, and organizational integration; appears to be normative/analytical rather than empirical in the abstract.
Energy policy uncertainty has a nonlinear effect on AI investment: moderate uncertainty fosters innovation, whereas high volatility hinders long-term investment.
Empirical analysis using nonlinear methods (WQR and WQC) on US quarterly data 2013Q1–2024Q4 (48 quarters), assessing distributional asymmetries across quantiles and time–frequency bands.
Machine-readable metrics and open scholarly infrastructure are reshaping scholarly profiles and incentives.
Conceptual and historical discussion referring to platforms and metrics (e.g., arXiv, Google Scholar, ORCID) as mechanisms changing incentives; no new empirical estimates provided.
That interconnected ecosystem is fundamentally restructuring who can do science (access), how fast discoveries propagate, and what counts as a valid scientific contribution.
Argumentative claim linking infrastructural and tool changes to changes in access, dissemination speed, and norms of contribution. The paper presents examples and narrative but no systematic empirical evaluation or sample.
The most consequential development is not any single tool but the emergence of an interconnected ecosystem—AI agents, preprint platforms, open source codebases, and citation infrastructure—that forms a feedback loop.
Synthesis/argument based on multiple examples (LLM agents, preprint servers like arXiv, open-source code repositories, citation indices). No quantitative measurement or causal identification reported.
The central tension in AI for science is between automation (building systems that replace human researchers) and augmentation (tools that amplify human creativity and judgement).
Analytical claim based on the paper's review of historical examples and conceptual discussion; no primary data or experimental design reported.
Science has repeatedly delegated its bottlenecks to machines—first inference, then search, then measurement, then the full workflow—and each delegation solves one problem while exposing a harder one underneath.
Interpretive historical argument drawing on examples across AI-for-science milestones (e.g., DENDRAL, search and inference systems, measurement automation, and contemporary end-to-end workflows). No quantitative sample or experimental method reported.
The growth effects of AI are conditional on institutional quality and organizational adaptability.
Theoretical/analytical claim in the paper's framework and supported by the stylized-facts analysis indicating heterogeneity in productivity and growth outcomes by institutional and digital capacity indicators.
Chat intent varies systematically with both the timing of chat relative to search and the category of products later purchased within the same journey.
Cross-tabulation/regression-style descriptive analysis relating classified chat intents to timing (relative to search) and subsequent purchased product categories in journey-level logs.
The paper's primary contribution is to combine established ingredients—attention scarcity, free-entry dilution, superstar effects, and preferential attachment—into a unified framework directed at claims about AI-enabled entrepreneurship.
Stated contribution and methodological description in the paper (synthesis and applied formalisation); this is a descriptive/methodological claim rather than an empirical result.
Modern pretrained time-series foundation models can forecast without task-specific training, but they do not fully incorporate economic behavior.
Statement in paper's introduction/abstract summarizing prior capabilities and limitations of pretrained time-series foundation models (no experimental sample or numeric evidence provided in the excerpt).
The governance risk-mitigation effects of AI operate through increasing financial risk exposure.
Authors' mechanism tests indicate a relationship between AI adoption and changes in financial risk exposure measures, which they interpret as a channel affecting executive behavior.
The paper draws comparisons between inference tokens and established commodities such as electricity, carbon emission allowances, and bandwidth to motivate financialization.
Theoretical comparison and historical analysis (drawing on the historical experience of electricity futures markets and commodity financialization theory) as presented in the paper.
The effects of financial digital intelligence on the innovative development of strategic emerging industries vary across regions and sectors: there are differences across central, eastern, and western regions and across capital‑intensive and technology‑intensive sectors, while no significant impact is noted in other regions and industries.
Heterogeneity analysis reported on the panel dataset (5,731 observations, 2015–2022) examining regional and industry subsamples (details of subgroup sizes and statistical tests not provided in excerpt).
Foreign direct investment (FDI) shows an insignificantly positive direct effect on local TFCP but a significantly negative indirect (spillover) effect, attributed to a 'pollution haven' effect.
Spatial Durbin Model estimates for FDI on panel (30 provinces, 2010–2023): direct coefficient positive but not significant; indirect coefficient significantly negative; interpretation given as pollution-haven mechanism.
Industrial intelligence exhibits regional heterogeneity: a significantly negative direct effect in the east, a significantly positive direct effect in the central region, an insignificant direct effect in the west, and positive indirect (spillover) effects in the east and west.
Regional/subsample Spatial Durbin Model analyses dividing the sample into east, central, and west regions (30 provinces, 2010–2023); reported region-specific direct and indirect coefficients and significance levels.
Industrial intelligence has an insignificantly negative direct effect on local TFCP, but its positive spatial spillover effect is significant at the 1% level, producing a significantly positive total effect.
Spatial Durbin Model results for industrial intelligence on panel (30 provinces, 2010–2023): direct coefficient negative and not statistically significant; indirect coefficient positive and significant at 1%; total effect positive and significant.
China's TFCP rose overall from 2010 to 2023 but exhibited a widening regional gap of 'higher in the east, lower in the west'.
Panel data of 30 Chinese provincial-level regions (2010–2023); TFCP measured using an undesirable-output super-efficiency SBM model and summarized temporal and spatial patterns.
The study identifies the main AI-enabled mechanisms advancing CE principles in smart manufacturing, waste valorisation, supply-chain transparency, and sustainable design.
Bibliometric network analysis of 196 peer-reviewed articles (2023–2024) and systematic review of 104 studies, per the abstract; identification is presented as a product of these analyses.
AI is not an inherent instrument of justice but a malleable socio-technical force whose equitable outcomes depend on policy design and institutional context.
Interpretation and synthesis of empirical results showing conditional and heterogeneous effects of AI; normative conclusion drawn by authors from observed heterogeneity and mediating channels.
Governmental structures, labor supply and demand, and incorporation of financial measures act as key intervening variables affecting achieved ROI from GenAI implementations.
Qualitative synthesis and theoretical analysis reported in the paper identifying contextual/intervening variables.
There is an evident tension between privacy and security in existing AI governance approaches.
Thematic synthesis and co-occurrence network from the reviewed studies identify trade-offs and tensions reported between privacy-preserving approaches and security requirements.
The fragility of 'Pax Silica' has implications for global capitalism, technological governance, and geopolitical stability.
Analytical inference and concluding assessment based on theoretical framework and comparative analysis; no empirical quantification provided in the abstract.
The paper proposes new mechanisms through which big data affects individual welfare (beyond simple productivity gains), linking privacy costs, multiplier effects, and R&D transformation patterns.
Theoretical/mechanism development: the paper articulates new channels in its macro theoretical framework describing how data sharing impacts welfare via multiple mechanisms (model construction and analytic discussion; no empirical/sample validation).
Consumption is affected by the multiplier effect and the transformation patterns of R&D.
Theoretical: model analysis links consumption dynamics to a multiplier effect and to how R&D transforms inputs/outputs (comparative statics/dynamics in the theoretical framework).
Individuals’ welfare is influenced by both the privacy cost of big data sharing and their consumption levels.
Theoretical: welfare in the model is specified as a function of consumption and a privacy cost term arising from big data sharing; result follows from analytic derivation within the model (no empirical/sample data).
Capability and trust formally diverge beyond a critical scale (Capability-Trust Divergence).
Claim of a formal proof in the paper (mathematical / theoretical demonstration). No empirical sample size reported in the excerpt.
The Institutional Scaling Law shows that institutional fitness -- jointly measuring capability, trust, affordability, and sovereignty -- is non-monotonic in model scale, with an environment-dependent optimum N*(ε).
Theoretical derivation / analytic model presented in the paper (formal derivation of an 'Institutional Scaling Law'). No empirical sample size reported in the excerpt.
Regional analysis shows inland regions remain capital-dependent, with an estimated (capital) elasticity of approximately 0.43.
Regional decomposition/estimation reported in the study comparing inland regions to coastal ones using the extended production function.
The paper is primarily theoretical and historical; empirical validation is needed to quantify the irreducible component of LLM value, and practical degrees of rule‑extractability may exist even if some capabilities remain tacit.
Stated limitations section acknowledging the theoretical nature of the work and the need for empirical follow‑up.
If an LLM's full capability were reducible to an explicit rule set, that rule set would be an expert system; because expert systems are empirically and historically weaker than LLMs, this leads to a contradiction (supporting non‑rule‑encodability).
Logical proof‑by‑contradiction presented in the paper, supported by conceptual mapping between rule sets and expert systems and qualitative historical comparisons.
HindSight has limitations: it depends on citation and venue proxies for impact, uses a finite forward window (30 months), and may undercount delayed-impact research and be domain-specific to AI/ML.
Authors' stated limitations in the paper noting reliance on observable downstream signals (citations/venues), the finite forward window, field heterogeneity, and measurement noise.
Demand for labor will shift toward data scientists, ML engineers, and interdisciplinary scientists, while wet-lab expertise and translational teams remain crucial.
Workforce trend analysis and employer hiring patterns summarized in the paper; interviews/case studies indicating changes in team composition.
AI excels at hypothesis generation but cannot replace scientific reasoning and experimental validation; human expertise remains essential.
Argument and case examples in the paper showing AI-generated hypotheses requiring human-led experimental design, interpretation, and validation.
Net gains from AI are not automatic nor evenly distributed; benefits depend on translation rates to clinical success and on addressing non-technical enablers.
Synthesis and conditional argument informed by sector observations; not backed by empirical distributional analysis in the paper.
Alignment with evolving regulatory expectations (evidence standards, auditing, liability) is necessary to translate AI capabilities into products and reduce adoption risk.
Policy-focused argument referencing regulatory uncertainty; no empirical measures of regulatory impact included.
Realized, sustained impact ('democratized discovery') from AI depends on non-technological enablers: high-quality interoperable data, rigorous validation, transparency/auditability, workforce upskilling, ethical oversight, and regulatory alignment.
Synthesis and prescriptive argument in editorial grounded in observed constraints; no empirical testing of causal dependence provided.
Reward mechanisms reviewed include up-front token sales, milestone-triggered payouts, bounties, and royalties/licensing revenue distribution.
Synthesis of literature and case-study descriptions documenting available reward/payment mechanisms used by DAOs in decentralized science contexts.
Decision models in DAO governance include token-weighted voting, quadratic voting, reputation/stake-based delegation, and multisig/DAO councils for off-chain execution.
Theoretical review of governance mechanisms and survey of existing DAO practices as reported in secondary sources and project documentation.
The review synthesizes cross-domain evidence on the use of AI across the continuum from target identification to regulatory integration and critically evaluates existing limitations including data bias, interpretability discrepancy, and regulatory ambiguity.
Statement about the scope and content of the review (literature synthesis and critical evaluation). This is a description of the paper's methods/content rather than an empirical finding; the excerpt indicates these topics are discussed.
Major actors such as the United States, China, and the European Union pursue distinct models of AI development and regulation.
Comparative policy analysis and qualitative document review of national/regional AI strategies and regulatory proposals for the United States, China, and the EU (specific documents and sample size not specified).
The study identifies the emergence of three competing governance paradigms: the innovation-driven liberal model, the ethics-oriented regulatory model, and the state-controlled authoritarian model.
Finding from the paper's comparative policy analysis and qualitative review of policy documents across major actors (United States, European Union, China); underlying document sources referenced qualitatively but not enumerated as a quantitative sample.
The pandemic produced a 1.5% increase in people identifying as potential entrepreneurs but a 2.3% contraction in emerging entrepreneurs, indicating a breakdown in converting aspiration into formal entrepreneurial activity (pipeline disruption).
Reported percentage changes in pipeline stages (potential entrepreneurs and emerging entrepreneurs) measured in the survey before/after (or during) the pandemic within the >27,000 respondent sample; comparison of identification and transition rates along the entrepreneurial pipeline.
Long-run integration (degree of long-run association) between core AI and AI-enhanced robotics differs systematically across national innovation systems.
Country-level decomposition of patent filing series and time-series econometric tests for long-run relationships / cointegration between core AI and AI-enhanced robotics patent series for each country/region (China, U.S., Europe, Japan, South Korea).
Core AI, traditional robotics, and AI-enhanced robotics follow distinct historical trajectories over 1980–2019 and do not move together uniformly.
Time-series analysis using annual patent filing counts (1980–2019) for each domain; tests for common long-run relationships / co-movement across the three patent series (as reported in the paper). Country-aggregated and domain-specific patent time series were analyzed; exact sample size (total patents) not specified in the summary.
Kondratieff, Schumpeter, and Mandel each highlight different drivers of capitalist long waves: Kondratieff emphasizes regular technological-driven renewal, Schumpeter emphasizes entrepreneurship and innovation-led creative destruction, and Mandel emphasizes class relations and production structures.
Comparative theoretical analysis and literature synthesis across the three schools; conceptual summary of canonical positions (no original dataset; qualitative interpretation).
XChronos reframes transhumanist technology evaluation in experiential terms, creating both market opportunities and measurement/regulatory challenges for AI economics.
Synthesis and concluding argument in the paper summarizing proposed implications; conceptual reasoning without empirical tests.
RL and adaptive methods are good for real-time adaptation but can be myopic, require large amounts of interaction data, and struggle to incorporate long-term preference structure and ethical constraints.
Surveyed properties of reinforcement learning and adaptive methods in HRI/RS literature; no new empirical evaluation in this paper.