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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AGI could strain existing governance frameworks.
Paper's policy analysis describing potential mismatches between governance capacity and AGI-induced disruptions (as stated in abstract); no empirical tests or quantification reported in the abstract.
AGI could intensify interstate competition.
Paper's geopolitical analysis and scenario-based reasoning informed by trends in AI capabilities (stated in abstract); no quantitative measures reported in the abstract.
AGI could fundamentally alter the global distribution of economic and military power.
Paper's geopolitical analysis drawing on capability trends and scenario reasoning (as stated in abstract); no empirical quantification provided in the abstract.
Existing AI-generated image detection benchmarks mainly evaluate standalone authenticity classification, cross-generator transfer, or forensic localization, leaving claim-conditioned fraudulent evidence detection underexplored.
Literature/contextual positioning in the paper contrasting prior benchmarks' focus with the proposed task.
There is a clear gap between generic AI image detection and reliable claim-conditioned refund-evidence verification.
Synthesis of experimental findings indicating that existing detectors and MLLMs are insufficiently reliable for the specific task of claim-conditioned refund-evidence verification.
Current MLLMs often recognize real-damaged evidence but fail on many fake-damaged subsets, with fake-damage detection rates (TPR) far below the 50% baseline on most generator subsets.
Experimental results reported in the paper comparing MLLM true positive rates (TPR) on real-damaged vs. fake-damaged subsets produced by multiple generators.
Human capital and technological innovation channels show weaker or even negative effects on Lae, attributed to short-term resource misallocation and skill mismatches.
Spatial mediation analysis (channel analysis) using panel data for 30 provincial regions (2012–2022) assessing mediating roles of human capital and technological innovation.
In labor-intensive industries, industrial robots shorten the backward linkage length (i.e., they reduce backward linkage length in labor-intensive sub-sectors).
Heterogeneity analysis in the paper comparing effects across labor-intensive sub-sectors within the panel of 14 manufacturing sub-sectors; reported finding of a negative effect on backward linkage length in labor-intensive industries.
Institutional inertia in property valuation poses risks to asset pricing, collateral risk modelling and investor confidence.
Analytical inference from interview findings and theoretical synthesis highlighting implications for property investment and financial market stability.
Despite advances in automation, data analytics and AI, the sector has been slow to digitise.
Background statement supported by interview data and sector observation reported in the study.
The IDOI framework provides a transferable model for understanding digital transformation in regulated, high-trust professions and highlights the market-level risks of institutional inertia in property valuation.
Development of the IDOI conceptual framework from qualitative data and theoretical integration; authors' claim about transferability and implications.
Generational divides, protectionist attitudes and fears of automation reinforce digital resistance.
Qualitative interview evidence reporting attitudes across cohorts of valuers and firm personnel; thematic analysis identifying cultural and attitudinal themes.
The Valuers Act (1948), fragmented infrastructure and sovereignty concerns limit innovation.
Interview data from practitioners, firm leaders and regulators in New Zealand citing specific regulatory and infrastructure constraints; thematic analysis.
Barriers to adoption arise primarily from institutional conservatism, outdated regulation and weak data governance rather than technical shortcomings.
Qualitative semi-structured interviews with valuers, firm leaders and regulators in New Zealand; thematic analysis guided by Rogers' diffusion of innovations and institutional theory synthesised into the IDOI framework.
LLM hallucinations are infiltrating knowledge production at scale, threatening both the reliability and equity of future scientific discovery as human and AI systems draw on the existing literature.
Synthesis/conclusion drawn from the observed prevalence, growth, distribution across fields and authorship patterns, and limited correction by moderation/publication processes described above.
Preprint moderation and journal publication processes capture only a fraction of these errors.
Comparison of hallucinated-reference prevalence in preprints versus versions that underwent moderation or journal publication, showing many errors remain uncaught.
Patent text similarity analysis confirms a 'homogenization trap' (AI-associated increases in patent-text similarity).
Text-similarity analysis of patent documents reported in the paper showing increased patent similarity associated with AI use.
Industry concentration negatively moderates the AI–innovation relationship.
Moderation analysis/interacted fixed-effects models indicating that higher industry concentration weakens the AI→innovation effect.
The relative importance of AI-related equities as shock transmitters diminishes over time.
Time-varying estimates from the TVP-VAR showing a decline in the net transmitter contribution of the AI equities group across the sample.
The overall level of connectedness declines modestly following the public release of ChatGPT by OpenAI in November 2022.
Time-series comparison of aggregate connectedness measures derived from the TVP-VAR, with a reported modest post-November 2022 decline (event reference: ChatGPT release).
A policy irreversibility result: there exists a critical time before the singularity after which redistribution becomes politically impossible because wealth concentration makes feasible tax rates vanishingly small.
Proof/argument in the paper showing that as time approaches the singularity the set of tax rates that satisfy political-feasibility constraints (workers' budget / feasibility) shrinks to zero, implying a latest feasible intervention time.
Financialization amplifies the exponent of the super-exponential divergence by a factor γ_F/η.
Mathematical derivation in the paper showing that the exponent in the asymptotic growth rate near the singularity is multiplied by γ_F/η when including the financialization term γ_F K_f^2 and its coupling parameter η.
Near the singularity, the wealth ratio between capital owners and workers diverges super-exponentially.
Asymptotic analysis near the finite-time singularity showing that the ratio of capital-owner wealth to worker wealth grows faster than exponential (super-exponentially) as time approaches the blow-up time.
AI adoption deepens the negative indirect effect of CEO–TMT faultlines on green innovation via reduced eco-attention (moderated mediation).
Reported moderated mediation analysis on the panel dataset (35,347 firm-year observations) showing that AI moderates the indirect path from CEO–TMT faultlines to green innovation through eco-attention, making the indirect effect more negative when AI is greater.
AI technology strengthens the negative relationship between CEO–TMT faultlines and eco-attention (AI exacerbates the adverse effect of faultlines on eco-attention).
Moderation/interaction analysis reported in the paper using the same panel dataset (35,347 firm-year observations) indicating a significant interaction between AI adoption and CEO–TMT faultlines on eco-attention.
CEO–TMT faultlines reduce eco-attention (organizational attention to environmental issues).
Direct association reported in the paper from regression/mediation models using the panel dataset (35,347 firm-year observations) showing a negative relationship between CEO–TMT faultlines and eco-attention.
CEO–TMT faultlines negatively affect green innovation through reduced eco-attention.
Empirical mediation analysis on the panel dataset (35,347 firm-year observations, 2010–2023) testing CEO–TMT faultlines -> eco-attention -> green innovation.
AGI (Artificial General Intelligence) is problematic both conceptually and definitionally.
Authorial assertion in the paper stating AGI is problematic as a concept and definition; framed as a conditioning assumption that shapes the subsequent analysis.
The paper argues we should avoid assuming the inevitability of the current situation relating to AI (i.e., the current commercial AI development trajectory is not inevitable).
Authorial methodological claim in the paper's framing/introductory text; presented as a normative methodological stance rather than empirical evidence.
Across short stories, marketing slogans, and alternative-uses tasks, three frontier LLMs fall below parity across crowding kernels.
Empirical experiments reported in the paper evaluating three frontier large language models on three task domains (short stories, marketing slogans, alternative-uses) and finding ρ < 1 (below parity) across crowding kernels. The abstract specifies three models but does not report the number of generated samples per model or other sample-size details.
This creates an evaluation blind spot, as AI can improve individual outputs while increasing population-level crowding.
Theoretical/ conceptual claim in the paper arguing that improvements at the individual-output level can still increase similarity (crowding) at the population level; no empirical numbers given in the abstract.
Creative AI systems are typically evaluated at the level of individual utility, yet creative outputs are consumed in populations: an idea loses value when many others produce similar ones.
Conceptual argument presented in the paper's introduction motivating a population-level perspective on creative outputs (no empirical sample size reported).
The reform reduces industrial wastewater discharge, which improves agricultural production conditions (mechanism linking the reform to higher grain yield).
Mechanism analysis in the paper reporting reductions in industrial wastewater discharge following the reform (mediation channel analysis).
Tabular data does not have a foundation model that understands it natively; every approach to tabular AI today (from gradient-boosted trees to the latest tabular foundation models) requires a preprocessing pipeline before any model can consume the data.
Paper's survey/positioning statement asserting the current state of tabular AI approaches and their reliance on preprocessing pipelines (no specific empirical dataset given).
DePAI entails risks including security, centralization, incentive failure, legal exposure, and the crowding-out of intrinsic motivation, requiring value-sensitive design and continuously adaptive governance.
Risk analysis and conceptual argument in the paper identifying possible failure modes and recommended design/governance responses; no empirical incidence data provided.
Mechanism tests indicate innovation stagnation in mature firms with redundant AI is a pathway that limits productivity gains (i.e., AI can be associated with stagnant innovation in mature firms).
Mechanism analysis reported in the paper showing signs of reduced innovation-related gains or stagnation in mature, advanced firms using AI (interpreted as redundant AI leading to limited incremental innovation).
Of these four, integration capacity is the least developed for scientific institutions and the most binding: no improvement in AI tooling can buy it.
Normative/diagnostic claim in the paper about relative scarcity and irreducibility of integration capacity; no empirical measures or sample provided in the excerpt.
Four complements then become scarce and load-bearing for AI-augmented science: verified signal, legitimacy, authentic provenance, and integration capacity (the community's tolerance for delegated cognition).
Theoretical framework proposed by the paper; list of four complements presented as an argument without empirical quantification in the excerpt.
Frontier software engineering agents have saturated short-horizon benchmarks while regressing on the work that constitutes senior engineering: long-horizon, multi-engineer, ambiguous-specification deliverables.
Position asserted in the paper based on literature/benchmark trends and authors' field observations; no original empirical dataset or quantified analysis provided in the paper text excerpt.
The most valuable AI capabilities (reasoning, judgment, intuition) are precisely those we cannot verify with current methods.
Argumentative claim in the position paper linking capability value to unverifiability; no empirical validation or measurement of 'value' or verifiability included.
Current reliability methods can only verify explicit knowledge against sources, creating a fundamental gap in verifying AI's implicit knowledge.
Conceptual critique in the paper of existing verification/validation approaches; no systematic review or empirical comparison provided.
Implicit knowledge remains unexternalized because documentation cost exceeds perceived value.
Presented as an economic/theoretical explanation in the paper; no empirical study, sample, or cost estimates provided.
Whether it is the periodic compulsory recoinage in medieval Europe or Gesell's stamp scrip, both are essentially mechanisms for taxing money holdings.
Interpretive/historical claim presented by the authors; no empirical testing or sample reported in the excerpt.
The devaluation of money runs through almost the whole process of history, from the weight reduction and purity decrease of metallic coin to the unanchored over-issuance of paper currency.
Historical summary/claim by the authors referencing long-run monetary history; no specific empirical study or sample size given in the excerpt.
Current AI agents implement only the first half of CLS (fast exemplar/hippocampal-style storage) and lack the slow weight-consolidation half.
Analytic claim in paper comparing current AI agent designs to CLS; no empirical evaluation reported in abstract.
Agents that rely only on lookup are structurally vulnerable to persistent memory poisoning as injected content propagates across all future sessions.
Theoretical/security argument presented in paper; claims about propagation of injected content across sessions; no empirical attack experiments detailed in abstract.
Conflating the two produces agents that face a provable generalization ceiling on compositionally novel tasks that no increase in context size or retrieval quality can overcome.
Formal claim asserted in paper (formalization of limitations and proofs claimed); no empirical sample detailed in abstract.
Conflating retrieval and weight-based memory produces agents that accumulate notes indefinitely without developing expertise.
Theoretical argument/formalization presented in paper; claim based on analysis of how lookup-only systems fail to consolidate abstract knowledge; no empirical sample reported in abstract.
Treating lookup as memory is a category error with provable consequences for security.
Theoretical/formal argument and formalization in paper; security consequences (e.g., persistent poisoning) claimed; no empirical sample reported in abstract.
Treating lookup as memory is a category error with provable consequences for long-term learning.
Theoretical/formal argument asserted in the paper, drawing on formalization and Complementary Learning Systems theory; no empirical sample reported in abstract.