Evidence (11633 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
AI adoption affects Total Factor Productivity (TFP) of firms.
Panel regression analysis using the stated panel dataset examining relationship between AI adoption and firm-level TFP.
Overall conclusion: AI offers substantial benefits to financial institutions, but ethical considerations and strategic workforce planning are essential for sustainable integration.
Synthesis/interpretation by the authors drawing on their empirical results (positive effects on ROA, efficiency, risk-adjusted returns, customer satisfaction, reduced compliance costs/breaches) and identified challenges (algorithmic bias, workforce displacement).
Empirical analysis of cases demonstrates that diverse, and often non-ethics-related, levers motivate organizations to abandon AI development.
Analysis of cases drawn from the AI incident database and practitioner survey contrasted with the taxonomy from the scoping review; specific counts/effect measures not provided in the summary.
Three sovereignty boundaries determine whether AI remains an amplifier within a human-governed system or becomes a de facto control center: irreversible decision authority, physical resource mobilization authority, and self-expansion authority.
Conceptual model element in the paper; identification and definition of three 'sovereignty boundaries' used to analyze governance risks.
The paper formalizes this claim through decision-energy density: the rate-weighted capacity of a node to generate, evaluate, select, and execute consequential decisions.
Formal/modeling claim — the paper defines and uses a formal metric called 'decision-energy density' within its theoretical framework.
AI capabilities can be copied, invoked, embedded in workflows, and scaled across institutions at low marginal cost.
Descriptive claim about AI technology characteristics made in the paper; supported by conceptual argument and examples rather than quantified empirical data.
Earlier high-risk technologies were slowed by capital intensity, physical bottlenecks, organizational inertia, and specialized supply chains.
Historical/analytic claim presented as background context in the paper; supported by conceptual comparison rather than a specific empirical study.
These divergences carry direct implications for policy interventions.
Interpretation/conclusion drawn from the divergence between RL Feasibility Index and existing measures (policy implication claimed by authors).
Scientific institutions, distinctively, manufacture legitimate judgment, so they do not merely adapt to AI; they compete with it for the same functional role.
Conceptual/theoretical assertion in the paper describing institutional roles; no empirical data or sample size provided in the excerpt.
While Agentic AI enhances economic performance, its benefits are mediated by structural conditions and are unevenly distributed across countries (i.e., reinforcing core–periphery inequalities).
Combined findings from fixed-effects regressions, mediation analysis, and observed heterogeneity between developed and emerging economies in the 2015–2024 panel.
No single governance setting dominates across all contexts; moderate governance becomes increasingly competitive as the learner accumulates experience within the governed action space.
Empirical finding reported from experiments with the contextual-bandit learner operating under different governance constraints and learning over time; comparative performance over learning horizon described in the paper. Sample size / trial counts not provided in the excerpt.
This workload-buffering effect (governance improving performance while reducing fatigue) contradicts the usual framing of governance as pure overhead.
Interpretation and comparison of empirical manufacturing results against prior framing in literature (qualitative claim within the paper). No sample size provided.
Governance is not a binary switch but a tunable design variable: tighter constraints predictably convert autonomous AI assignments into supervised collaborations, with domain-specific costs and benefits.
Empirical finding reported from experiments using the HAAS benchmark across the two domains (software engineering and manufacturing); qualitative and/or quantitative comparisons of allocations under varying governance constraints. Paper does not state sample size in the provided text.
AI learns indiscriminately from implicit knowledge, acquiring both beneficial patterns and harmful biases.
Asserted in the paper as a conceptual point about training data and learned patterns; no empirical evaluation or quantified bias measures provided.
Workload-aware blended pricing reorders the leaderboard substantially: 7 of 10 top-ranked endpoints under the chat preset (3:1 input:output) fall out of the top 10 under the retrieval-augmented preset (20:1).
Comparison of endpoint rankings under two workload presets (chat preset 3:1 and retrieval-augmented preset 20:1); statement gives counts (7 of top 10 change).
Modeled joules per correct answer varies by a factor of 6.2 across endpoints.
Modeled energy estimate combined with task accuracy to compute joules per correct answer across 78 endpoints.
Across 78 endpoints, the same model on different endpoints differs in tail latency by an order of magnitude.
Empirical tail-latency measurements across 78 endpoints serving 12 model families.
The same model on different endpoints differs in fingerprint similarity to first party by up to 12 points.
Empirical measurement of fingerprint (output-distribution) similarity to a first-party reference across the same set of endpoints (78 endpoints, 12 model families).
Across 78 endpoints serving 12 model families, the same model on different endpoints differs in mean accuracy by up to 12.5 points on math and code.
Empirical measurement across 78 endpoints and 12 model families comparing mean accuracy on math and code tasks.
Whether the futures these configurations help create remain governable and worth inhabiting will depend on leaders who can see, early enough, where and how consequential decisions are actually being shaped.
Normative/prognostic claim linking future governability to leaders' detection capabilities (conceptual; no empirical test provided in the excerpt).
These configurations will shape how power, responsibility, and trust are distributed in organizational life.
Theoretical/prognostic claim in the paper linking configurations to distribution of power, responsibility, and trust (no empirical quantification in the excerpt).
Fluent users' failures occur alongside greater success on complex tasks.
Combined analysis of task complexity, success outcomes, and failure incidence in the 27K transcripts showing that fluent users both attempt and have greater success on complex tasks even while experiencing more failures.
Fluent users adopt a fundamentally different interactional mode: they iterate collaboratively with the AI, refining goals and critically assessing outputs, whereas novices take a passive stance.
Qualitative and quantitative analysis of the same 27,000 annotated WildChat transcripts, with annotations describing interactional mode and user behavior (iteration, goal refinement, critical assessment vs. passivity).
Augmentation is bounded rather than linear (i.e., human-AI augmentation shows diminishing or negative returns past a balanced zone).
Synthesis of interview themes across 34 cases producing the bounded-augmentation / curvilinear conceptualization.
Mediators such as trust, cohesion and accountability are reshaped when AI-generated contributions enter collaboration.
Thematic evidence from interviews indicating changes in trust, cohesion and accountability dynamics associated with the introduction of AI outputs into team collaboration.
Social (leadership engagement, trust, ownership, mediation and alignment) and technical (automation, creation, reliability, distraction and integration) subsystems combine to enable or erode team effectiveness, summarized in an e-leadership–AI orientation matrix.
Analytic synthesis from thematic coding (Gioia-informed) of interview data producing a conceptual matrix mapping social and technical factors to outcomes.
Analysis identifies a curvilinear pattern of bounded augmentation, where effectiveness peaks in a zone of balanced use but declines under under-use and over-reliance.
Thematic (Gioia-informed) analysis of 34 semi-structured interviews with project managers across five UK industries; pattern emerges from cross-case coding and synthesis.
Generative AI-powered tools like ChatGPT are reshaping market skill demands while also offering new forms of on-demand learning support to meet those demands.
Framed in paper as background/motivation; asserted from prior literature and the paper's motivating claims rather than reported as a quantified result in this study.
In operational meteorology, adjoint-based methods derive value from the forecast model itself but require full data assimilation infrastructure.
Technical background in paper describing adjoint-based methods and their infrastructural requirements (methodological literature references; no new empirical data).
The rise of digital agents will transform the foundations of production, labour markets, institutional arrangements and the international distribution of economic power.
Synthesis and theoretical projection across sections of the paper; presented as a broad conclusion without reported empirical quantification in the provided text.
There is a fundamental asymmetry between economic and social reproduction: digital agents can compensate for productive functions of the population but are unable to substitute the population's functions of social reproduction.
Theoretical argument and conceptual distinction in the paper; no empirical study measuring substitution in social reproduction provided.
The retrieved sources are substantially different for each search engine (average pairwise Jaccard similarity < 0.2).
Computed average Jaccard similarity of source-domain sets returned by each engine (Google organic results, Google AIO, Gemini Flash 2.5) across the 11,500 queries; reported average similarity < 0.2.
These patterns suggest that AI adoption is associated with expected efficiency gains that shape both firms' pricing behaviour and their macroeconomic expectations.
Interpretation based on observed increases in productivity/profitability and different pricing/inflation expectations among adopters vs non-adopters in survey and DID analyses.
The rapid growth of AI and automation offers Sub-Saharan Africa economic opportunities as well as labor market challenges.
Systematic review of the literature reported in the paper; scope and number of studies not specified in the abstract/summary provided.
LLMs are able to extract signals from unstructured text (financial news headlines) but have limitations without explicit quantitative optimization.
Interpretation in discussion/conclusion: empirical finding that LLM-based portfolios beat naive diversification but underperform AI-optimized strategies, implying LLMs extract signals from text yet lack full optimization capability.
Statistical tests confirmed significant performance differences (p ≤ 0.01).
Reported inferential statistics in results: statistical tests comparing strategy performances produced p-values at or below 0.01.
Susceptibility to visual priming varies across state-of-the-art VLMs.
Comparative experiments run across multiple state-of-the-art vision-language models showing differential changes in IPD behavior when exposed to the same visual primes and color cues. (Paper notes variation in susceptibility and mitigation effectiveness across models; specific model list and per-model sample sizes not given in the abstract.)
Color-coded reward matrices alter VLM decision patterns.
Experimental condition varying the visual presentation of the IPD payoff matrix (color-coding of rewards) and measuring resulting decision patterns of multiple VLMs in IPD trials. (Reported as part of the experimental setup across models; exact counts not provided in abstract.)
VLM behavior can be influenced by image content depicting behavioral concepts (kindness/helpfulness vs. aggressiveness/selfishness).
Experimental manipulation in the Iterated Prisoner's Dilemma (IPD): VLMs were exposed to images labeled/connoting 'kindness/helpfulness' versus 'aggressiveness/selfishness' and subsequent choices in IPD rounds were recorded across multiple state-of-the-art VLMs. (Paper reports experiments across multiple VLMs; exact sample sizes per model/condition not stated in the abstract.)
AI adoption leads both to job displacement and job creation, including the emergence of new occupational categories.
Abstract states the review examines empirical evidence on both job displacement and creation and the emergence of new occupations; no numeric counts or sample sizes provided in abstract.
The study identifies short-term transitional risks and long-term productivity gains associated with AI integration in the workforce.
Abstract states the paper evaluates both short-term risks and long-term productivity gains from AI integration based on the reviewed literature; no empirical quantification given in abstract.
AI-driven automation and augmentation are reshaping employment landscapes, with emphasis on sector-level disruption, skill transformation, and socioeconomic consequences.
Abstract states this as a conclusion of the review drawing on interdisciplinary empirical literature; no specific studies or sample sizes cited in abstract.
The accelerating deployment of artificial intelligence across industries has fundamentally altered the structure of global labour markets.
Statement in abstract summarizing a systematic review of interdisciplinary literature (economics, computer science, organizational behaviour, public policy); no specific sample size reported in abstract.
The magnitude of AI’s effect on potential GDP varied across industries and depended on the level of digital maturity, human resources, and institutional conditions.
Decompositional analysis across aggregated industry data and scenario-based modeling drawing on sectoral sources and reviews.
Firms may continue to exist as legal and physical entities, but their coordinating function will be displaced as they become data nodes within regionally governed AI infrastructure.
Predictive/conceptual claim within the framework; no empirical sample reported in the excerpt and presented as a theoretical outcome of Interface Internalization.
The Structural Dissolution Framework challenges the Coasian view that organizational boundaries are determined by transaction cost minimization, arguing that AI makes such boundaries economically obsolete.
Theoretical critique of transaction-cost-based explanations for firm boundaries presented in the paper; argumentative and conceptual rather than supported by empirical tests in the provided summary.
Regional data sovereignty entities will emerge as organizational forms that replace the coordinating role of firms and markets.
Normative/predictive claim within the paper's framework arguing for new organizational forms (regional data sovereignty entities); illustrated conceptually (e.g., through resource-dependent regional economies) rather than empirically tested in the provided text.
Domain-specific data refinement infrastructure will become the new basis of positional control in industries.
Theoretical claim in the framework asserting a shift in positional control to data refinement infrastructure; presented as a predicted structural outcome rather than supported by empirical data in the provided text.
AI adoption moves value creation away from physical resources and human collaboration toward continuous token flows produced through data refinement loops.
Theoretical/analytical claim within the Structural Dissolution Framework and illustrative discussion; no empirical quantification provided in the text excerpt.
The mechanism driving this restructuring is 'Interface Internalization', through which inter-agent coordination is absorbed into intra-system computation.
Conceptual mechanism defined and argued in the paper; presented as the central theoretical mechanism rather than as an empirically validated finding.