Evidence (16496 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
AI advances science through structurally distinct creative pathways rather than a single mechanism; the creative pathway depends on how AI is incorporated into the research process.
Interpretation synthesized from observed heterogeneity in creativity outcomes across classified AI research modes (Tool-oriented vs Adaptation-oriented) in the >1M publication analysis.
There is a long-run equilibrium (cointegrating) relationship among AI adoption, skill-disaggregated unemployment, and sustainable development in South Africa.
Empirical ARDL results reported in the paper indicating a long-run equilibrium relationship based on annual 2003–2024 time-series data.
The study uses annual time-series data from 2003–2024 and the Autoregressive Distributed Lag (ARDL) modelling approach to estimate short- and long-run coefficients.
Explicit statement in the paper: annual time-series data 2003–2024 and ARDL modelling to simultaneously estimate short- and long-run coefficients.
As a representative of new quality productive forces, brain–computer interface (BCI) technology raises high expectations but also acute concerns about brain‑privacy protection.
Statement in paper's introduction/abstract; conceptual observation based on literature and contextual analysis (no empirical study reported).
AI will have social, economic, and political impacts on work, inequality, democracy and power.
Author's projection of the domains affected by AI (stated as a subject of later chapters; no empirical evidence provided in the excerpt).
The opportunities of AI in human good are real and vast; and the opportunities in human ill, in human society, in human institutions of government, and in the longer term in the environment in which humanity thrives are real and underestimated.
Author's evaluative judgment asserting both substantial benefits and substantial underestimated harms of AI (normative claim without empirical substantiation in the excerpt).
These behavioral differences have implications for deployment of agentic AI in scientific computing workflows, such as trade-offs between speed versus auditability, silent versus transparent error handling, instruction interpretation, and the criticality of intermediate data representations in multi-model pipelines.
Authors' discussion and interpretation based on observed experimental differences between the two agents across the runs.
The autonomously generated manuscripts also diverged in length, details, and quality.
Reported qualitative comparison of the LLM-assisted manuscripts produced by each agent indicating differences in length, level of detail, and overall quality between the two agents' outputs.
The agents exhibited substantially different behaviors and computational costs.
Overall observation from the two runs: distinct behavioral patterns (silent reinterpretation vs explicit restarts), different execution times, and differing computational actions (optimization introduced by Codex).
Claude Code completed the pipeline in ~3.4 minutes with silent deviations from the specification, while Codex required ~16 minutes across explicit self-correcting restarts, including an unsolicited performance optimization of the matched filter inner loop.
Reported run-time measurements and qualitative behavior descriptions in paper: timing values (~3.4 min vs ~16 min) and observed behaviors (silent deviations for Claude Code; explicit restarts and an unsolicited optimization by Codex).
The optimal architecture is highly task-dependent.
Empirical claim in the abstract: experiments across tasks showed that different hybrid architectures perform best for different tasks.
Task accuracy, monetary cost, and edge energy consumption are tightly coupled in hybrid MAS design.
Claim made in the abstract and investigated empirically by adapting MAS architectures and measuring power, cost, and performance trade-offs.
The limitations in the audit reports reflect symbolic compliance (per institutional theory), while stewardship theory highlights potential for deeper accountability.
Theoretical interpretation using institutional theory and stewardship theory presented in the paper (argumentative rather than empirical).
Adaptive governance conditions how AI-driven capabilities translate into sustainability and risk outcomes.
Comparative analysis across the three jurisdictions (China, US, UK, 2022–2025) integrating quantitative indicators and qualitative documentary evidence, with the abstract highlighting the 'conditioning role of adaptive governance'.
Any measurement of AI brand perception must condition on the buyer persona supplying the query: the same prompt produces materially different recommendation sets depending on who the model thinks is asking, and a measurement protocol that aggregates across personas systematically obscures that variation.
Argument based on observed persona-driven variation in recommendation sets across the audit; policy/methodological recommendation derived from empirical results.
The Anthropic model shows a larger point-estimate effect than the OpenAI configurations, though clustered CIs overlap for the closer contrast (sonnet vs. OpenAI/high).
Comparison of point estimates and clustered confidence intervals across model configuration cells in the audit.
Our findings show qualitative and enduring differences between hyperscaler-based platforms and non-hyperscaler providers.
Stated as a conclusion based on the paper's taxonomy and comparative analysis; phrasing indicates interpretive/qualitative evidence rather than longitudinal empirical demonstration (no temporal sample or size reported in abstract).
Non-hyperscaler providers embody distinct value-creation logics beyond hyperscaler efficiency.
Claim arises from the taxonomy and comparative analysis contrasting hyperscaler-based platforms with non-hyperscaler alternatives; evidence appears qualitative and conceptual as presented in the paper summary (no empirical sample size reported in abstract).
No single LLM dominates across engine types, highlighting the importance of specific tasks and tradeoffs between speed and accuracy.
Empirical observation from cross-engine evaluations reported in the paper; descriptive conclusion without numeric dominance metrics or sample sizes in the excerpt.
The evaluations implemented by the initiative demonstrate that AI enabled modeling tools perform better at discussion and basic qualitative tasks than with causal reasoning and quantitative error fixing.
Result reported from the implemented evaluations comparing relative performance across task categories (discussion/qualitative vs causal reasoning/quantitative error fixing); no quantitative effect sizes or sample sizes provided in the excerpt.
When engines from the sd ai project are coupled with different LLMs, their performance on these evaluations reveals variability across different AI tools.
Empirical statement in the paper based on applying the implemented evaluations to different engine+LLM combinations; no numeric performance metrics or sample sizes reported in the excerpt.
We illustrate this transition through examples in consumer markets, education, news, and coding.
Authors state they use sectoral examples to illustrate the framework; this is a claim about the paper's contents rather than an empirical finding.
We offer a three-stage lens: Augmentation, Automation, and Reconstruction.
Conceptual framework proposed by the authors; presented as a taxonomy in the paper (no empirical validation reported in the excerpt).
There is a suggestive non-linear relationship between embodiment and team performance.
Analysis reported in the paper indicating a non-linear (not strictly monotonic) association between degree of agent embodiment (Box, Avatar, humanoid) and measured team performance; described as 'suggestive' in the abstract, without quantified functional form or statistics included there.
Artificial agents have an uneven impact on team outcomes, with some mixed human–AI teams performing exceptionally well and others markedly worse.
Observed performance outcomes across mixed human–AI teams in the escape room experiment, showing high between-team variability; exact sample size and statistical details not provided in the abstract.
Acquiescent silence (resignation-based) is motivationally distinct from defensive (fear-driven) silence.
Theoretical distinction advanced using organisational silence literature (conceptual claim referencing existing theory).
Adverse employment and compensation effects are concentrated among workers in non-AI tasks and non senior-level positions, indicating an asymmetric distribution of gains from AI adoption.
Heterogeneity analysis / subgroup results showing larger negative employment/compensation responses for workers in non-AI tasks and for non senior-level positions across the sample.
Human capital structure moderates the relationship between AI application and enterprise innovation efficiency.
Moderation analysis on A-share listed firms (2012–2023) indicating significant interaction effects between AI application and measures of human capital structure.
Fiscal support intensity moderates the impact of AI application on enterprise innovation efficiency.
Empirical moderation tests using firm-level panel data (2012–2023) showing interaction between AI application measures and fiscal support intensity.
Market segmentation exerts a moderating effect on the relationship between AI application and enterprise innovation efficiency.
Moderation analysis in the empirical framework applied to the 2012–2023 panel of Shanghai and Shenzhen A-share firms showing interaction effects between AI application and market segmentation measures.
These findings demonstrate the feasibility and current limits of automated expertise mapping.
Synthesis/conclusion based on model performance (e.g., MAE results) and observed limitations reported across evaluations.
AI maturity moderated the effects of governance exposure on adaptation (p ≤ 0.035).
Reported moderation analysis: 'with AI maturity moderating these effects (p ≤ 0.035)'.
Reward-level intervention (via equity-aware LLM refinement) significantly improves equity, but demographic disparities in AI-driven controllers persist.
Overall conclusion drawn from reported experimental results (improvements in group satisfaction metrics but acknowledgment that disparities remain).
The utility-aware framework preserves inverse U-shaped demand patterns for attributes such as aesthetics and uniqueness, improving demand-based performance while preserving fidelity and semantic consistency.
Empirical claim from the paper that their method maintains observed inverse U-shaped demand relationships for certain attributes in their experiments while improving demand-related metrics.
The UPCT framework offers a unified explanation for varied phenomena: pandemic resilience patterns, divergent digital transformation outcomes, and emerging risks of AI-driven organizational rigidity.
Synthesis claim by the author asserting explanatory scope of the theoretical framework; no empirical cross-case synthesis or formal validation included.
The paper's Universal Phase Crystallization Theory (UPCT) reconceptualizes organizations as recursive generative cycles (Φ→R→S→Φ′) and asserts organizational existence is better described as E = ΦR rather than E = S.
Theoretical/model claim introduced and developed in the paper; purely conceptual without empirical testing.
Resilience should be redefined not as reserve magnitude (accumulated buffers) but as recoverability of generative relational capacity.
Normative/theoretical redefinition proposed by the paper; no empirical validation provided.
AI is changing skill requirements—some skills become obsolete and new skills are required.
Paper identifies changing skill requirements as a key area of examination (abstract). This is stated as an asserted trend based on the paper's review rather than a quantified empirical finding in the provided text.
AI has changed how work is executed (work processes and execution).
Explicit statement in the paper's abstract; presented as a qualitative/general finding from the paper's evaluation and literature synthesis (no numerical sample provided).
AI has changed who works in jobs (i.e., workforce composition).
Stated in the paper's abstract as an asserted effect of AI on employment composition; presented as part of the paper's review rather than a specific empirical estimate.
The penetrating utilization of AI-based methods to perform tasks has drastically changed how jobs are performed.
Claim asserted in the paper (abstract) as a descriptive conclusion from the paper's review/analysis; no empirical sample or quantified effect reported in the provided text.
AI is altering nearly every aspect of human interaction—such as work and society.
Statement in the paper's abstract/intro; presented as a general observation in the paper (literature review/qualitative synthesis implied). No primary sample size or empirical estimate reported in the provided text.
Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping both answer quality and serving cost.
Paper's conceptual description of retrieval pipelines and configuration dimensions (LLM, retriever, number of documents, number of hops, synthesis strategy). No empirical sample size reported for this descriptive claim.
Comparative analysis of Japanese, European, and United States legal frameworks shows differing treatments of translation data and points toward the need for redistributive design to remedy unequal attribution and capture.
Comparative legal analysis across jurisdictions (Japan, EU, US) and normative argument proposing redistributive design directions; no experimental or quantitative evaluation provided.
Emotion is a strategic action channel rather than a surface style.
Interpretation based on experimental results (GoEmotions prompting and subsequent analyses) demonstrating that adding emotional framing changes negotiation outcomes in systematic ways.
cBCI synergy is heavily contingent on the temporal dynamics of trust, providing a critical framework for designing dynamically gated Human-AI systems.
Interpretive/concluding claim based on experimental results (timing-dependent failure modes, Oracle gating, Hybrid Fusion effects) reported in the study.
AI timing dictates the mechanism of team failure: high-speed AI interventions risk inducing reflexive blind compliance while delayed interventions can induce ambiguous cognitive conflict.
Synthesis claim derived from experimental contrasts between Fast/Less-Accurate and Slow/Accurate AI conditions and observed human/team behaviors (blind compliance vs. delayed conflict).
AI's future impact on employment will depend not only on automation capabilities but also on how responsibly enterprises manage workforce transitions.
Paper's concluding claim synthesizing arguments and proposed governance approach (normative conclusion rather than an empirically tested causal estimate in the excerpt).
AI-induced workforce disruption is not only a labor market issue but also an enterprise governance challenge.
Argument/position advanced in the paper highlighting governance responsibilities for firms implementing AI.
Artificial intelligence, especially generative AI, is transforming enterprise operations by automating tasks, enhancing decision-making, and redefining job roles.
Conceptual statement in the paper describing observed/expected effects of generative AI on enterprise operations (no specific empirical sample or experiment reported in the excerpt).