Evidence (4892 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).
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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 |
Org Design
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The AI-investment paradox persists because firms govern AI as a broad technology program rather than as a set of discrete, investable decision opportunities embedded within workflows.
Argument/theoretical claim developed by the authors as the central explanatory hypothesis of the paper; presented conceptually rather than tested empirically within this work.
Despite enterprises continuing to invest heavily in AI, many initiatives fail to scale or generate sustained business value (the 'AI-investment paradox').
Background claim stated in the paper's introduction/abstract and presented as motivating fact; supported implicitly by citations to prior literature and industry reports (no original empirical sample or quantitative analysis reported in this paper).
Temporary accommodation has become a major fiscal and administrative pressure for English local authorities, particularly in London, where demand and costs have risen sharply.
Statement in paper introduction/background; contextual claim based on administrative observations and cited motivation for building DOMUS (no specific sample size or numerical data reported in the provided text).
The increase in ICD risk at higher levels of AI investment is weaker among firms with above-normal external audit attention.
Moderator (heterogeneity) tests reported in paper showing the ICD-risk increase with AI investment is attenuated for firms receiving above-normal external audit attention.
The increase in ICD risk at higher levels of AI investment is weaker among firms with CIO presence.
Moderator (heterogeneity) tests reported in paper showing attenuated ICD-risk increase for firms that have a Chief Information Officer (CIO).
The increase in ICD risk at higher levels of AI investment is weaker among IT-industry firms.
Moderator (heterogeneity) tests reported in paper showing smaller upward slope of ICD risk with AI investment for firms in the IT industry.
At lower levels of AI exposure, AI investment is associated with lower ICD risk.
Substantive component of the reported U-shaped relationship estimated in regressions on the 41,725 firm-year sample.
Claude Haiku 4.5 exhibits an idle-drift failure mode, repeatedly choosing inaction despite producing coherent assessments and plans.
Qualitative observation from agent trajectories showing that Claude Haiku 4.5 repeatedly selects no-action decisions over time while still generating coherent internal assessments and plans.
Secure attachment further moderated the indirect effect of organizational AI adoption on employees' turnover intentions via identity threat (i.e., it attenuated the mediated effect).
Moderated mediation (conditional indirect effect) analysis reported on three-wave survey data of 312 employees; secure attachment reported to weaken the indirect AI adoption → identity threat → turnover intentions pathway.
Secure attachment negatively moderated the relationship between organizational AI adoption and identity threat (i.e., higher secure attachment reduced the AI adoption → identity threat effect).
Moderation analysis (interaction effect) reported in the three-wave survey data (N=312); secure attachment reported to negatively moderate the AI adoption to identity threat path.
A lack of strategic alignment is a critical barrier that leads AI initiatives to be unused despite technical success.
Paper identifies misalignment between AI projects and organizational strategy as a failure mode in its failure analysis; methodological details not specified in the summary.
User resistance is a critical barrier that prevents AI initiatives from delivering business value.
Paper lists user resistance among critical barriers based on analysis of failed projects; no sample size or quantitative method stated in the summary.
Siloed deployments are a critical barrier causing AI initiatives to remain unused.
Identified in the paper's analysis of failure modes; presented as a key barrier (method and sample size not provided in the summary).
AI initiatives meet functional specifications yet remain unused.
Paper's examination of AI projects that passed technical/functional tests but were not adopted; method/sample size not stated in the summary.
Organizations struggle with "zombie AI investments" that succeed technically but fail to generate tangible business value.
Paper's analysis of failed AI initiatives and described observations; methods not specified in the summary (likely qualitative/case analysis).
Aligning the dimensions with the regulatory frameworks above identifies overlapping gaps neither side currently closes.
Result claimed in abstract that mapping taxonomy dimensions to listed regulatory frameworks reveals overlapping unattended gaps; no detailed counts or specific gaps listed in the abstract.
Several open-source organisations have responded with contribution policies, but the result is fragmented, and its alignment with emerging AI governance frameworks (EU AI Act, NIST AI RMF with the UC Berkeley Agentic AI Profile, ISO/IEC 42001 and 23894) is unmapped at the contribution level.
Paper states that multiple organisations adopted policies and that alignment with listed regulatory frameworks is unmapped; the paper reports a comparative study across six organisations (names given).
Autonomous and semi-autonomous AI contributors strain those assumptions
Stated in abstract as an observed tension between agent capabilities and human-focused contribution norms; no quantified incidents or metrics provided in the abstract (but paper indicates mapping of documented agent incidents).
Those valuable signals are entangled with framework churn, naming drift, generated-source ambiguity, dependency rituals, CI dialects, weak proof routes, and human-oriented review customs.
Qualitative claim/analysis in the paper describing entanglement of signal and accidental complexity; no empirical quantification provided.
Frontier coding models may spend substantial capacity learning not only program behavior, but also accidental entropy in human repositories.
Conceptual/theoretical assertion presented in the paper (no empirical sample or experiment reported for this claim).
The study identifies specific retention issues including rigid work practices, a predominantly masculine culture, and occurrences of bullying and harassment.
Findings from thematic analysis of 23 interviews using NVivo 13; participants' accounts raised these specific themes as retention-related issues.
Women in UK construction continue to face major retention challenges driven by structural biases that lead to feelings of disrespect, insufficient support, and being undervalued.
Thematic analysis of 23 qualitative interviews with women involved in digitally enabled projects; participants reported experiences and perceptions related to retention and workplace culture.
Women make up less than 15% of the UK construction workforce.
Statement in the paper likely citing national labour/industry statistics or prior literature (not primary data from this study).
A rise in firm age by one standard deviation reduces the share of AI workers by 5.2%.
Quantitative estimate reported in abstract based on the paper's empirical analysis of the novel dataset (Babina et al., 2024).
Older firms often encounter difficulties integrating AI talent, possibly due to entrenched practices, outdated systems, and resistance to change.
Empirical analysis described in abstract using the novel resume/job-posting dataset for U.S. firms; mechanism explanations provided in text.
At min-cost, Brick incurs 11.85 points accuracy loss.
Empirical evaluation on the 5,504-query benchmark reporting accuracy loss at the min-cost operating point.
Frontier models cost ten to one hundred times more than local open-weight models.
Cost comparison statement in the paper (asserted market/commercial cost multiples).
Existing LLM routers rely on surface features such as domain labels, keywords, and token count, ignoring the within-domain variance that actually determines model success.
Claim about prior work / existing systems presented in the paper; no explicit empirical test shown in the abstract.
Research on embedded finance emphasizes modularity but offers limited insight into how systems evolve when AI-driven inference and platform environments jointly structure financial action.
Literature-based critique in the paper's theoretical review of embedded finance research; no empirical sampling reported.
An unconstrained multi-agent baseline produced critical failures in 72% of runs.
Reported experimental result from the 2x4 factorial experiment (failure rate for the unconstrained multi-agent baseline reported as 72%).
Xie et al. (2026) show experimentally that job candidates are less satisfied with firms using AI evaluators than with human experts due to perceived loss of control; the negative effect is stronger for individuals with an internal locus of control.
Experimental study on recruitment using control theory as described (sample size not provided).
In the healthcare sector, Chou et al. (2026a, 2026b) identify AI anxiety as a multifaceted hurdle to adoption; emotional affect and outcome expectations are essential influences on usage intentions (two-stage SEM-ANN approach).
Two-stage SEM–ANN modeling grounded in social cognitive theory as reported; empirical data specifics not provided in text.
Liu et al. (2026a, 2026b) find experimentally that the severity of AI service failure in hotel contactless services significantly decreases customers' forgiveness willingness, but high levels of brand attachment mitigate this negative effect.
Experimental studies in hotel contactless service contexts (details and sample sizes not provided in the text).
Allowing AI to take the lead in strategic decision-making without human wisdom may be inappropriate due to AI's inability to navigate tacit knowledge and ethical nuances in Chinese management wisdom.
Argumentative claim based on cited literature (e.g., De Cremer and Kasparov, 2021; Del Giudice et al., 2023) and authors' synthesis.
Developers reject fixes for (a) incorrect implementation (e.g., incomplete, wrong approach), (b) fixes that do not pass CI pipelines and fail tests, (c) fixes for which the agent is unable to perform the implementation (e.g., no code generated, sessions lost), and (d) fixes whose priority is low.
Observed categories from the qualitative analysis of the 306 non-merged PRs described in the study.
The qualitative findings identify 14 reasons divided into four high-level categories for rejecting AI-agent fixes.
Result of the paper's qualitative analysis on the representative sample (306 non-merged PRs).
From a first exploration of the AIDev dataset, 46.41% of the fixes proposed by the agents Copilot, Devin, Cursor, and Claude are rejected.
Empirical analysis of the AIDev dataset reported by the authors; agents named explicitly (Copilot, Devin, Cursor, Claude).
Existing evaluation frameworks mask critical architectural gaps and inefficiencies of complex MAS by failing to account for the marginal utility of increased computational cost.
Comparative analysis of performance versus computational cost across evaluated systems showing limited marginal gains despite higher cost (authors' analysis across experiments).
Across traditional reasoning datasets and tasks with interactive multi-step workflows (e.g., BrowseComp-Plus), automatically generated MAS consistently underperform Chain-of-Thought with Self-Consistency (CoT-SC) despite being up to 10x more expensive.
Systematic empirical evaluation comparing automatically generated MAS to CoT-SC across multiple task suites including traditional reasoning datasets and interactive multi-step workflows such as BrowseComp-Plus (experimental comparisons reported in the paper).
Empirical support for MAS superiority relies primarily on comparisons with SAS baselines using benchmarks that prioritize isolated reasoning tasks, which do not adequately assess MAS advantages.
Critical literature review and analysis of prior empirical evaluations (authors' claim about the composition and limitations of existing benchmarks).
Datasets are rarely standardized or shared.
Review synthesis and commentary across included studies and supplementary documents indicating limited data standardization and sharing.
Neither MCP nor A2A defines the shared workspace in which humans and agents perform accountable work together.
Analytical claim by the authors contrasting existing standards with the missing specification of a shared human-agent workspace; no empirical evaluation provided.
In current practice the human judgement is recorded, if at all, in application code, chat threads, ticket comments, and tribal memory.
Descriptive statement about current recording practices; presented without empirical study or counts in the provided text.
The technical surface for this collaboration remains weakly specified.
Asserted by the authors as an assessment of current technical standards and interfaces; no audit or measurement cited in the provided text.
This regulatory pressure creates a direct conflict between multi-stakeholder transparency and corporate data privacy.
Paper's conceptual argument describing a tension between transparency requirements and proprietary data protection; no empirical study provided.
Regulatory compliance demands have surpassed the capacity of manual corporate reporting.
Assertion in paper (conceptual observation about reporting capacity); no empirical measurement or sample size reported.
The convergence of the 2026 European Union Safe and Sustainable by Design (SSbD) framework, Corporate Sustainability Due Diligence Directive (CSDDD), and Carbon Border Adjustment Mechanism (CBAM) introduce a severe governance bottleneck for advanced semiconductor manufacturing facilities ("Smart Fabs").
Declarative claim in paper based on policy convergence analysis; no empirical dataset or sample size reported (conceptual/analytical argument).
Transformational leadership negatively moderates the relationship between AI application and employees' job insecurity, buffering employees' insecurity responses across varying levels of AI application.
Moderation analysis reported in the study using the same employee survey dataset (411 valid responses), indicating a statistically significant buffering (negative) moderating effect of transformational leadership on the AI–job insecurity relationship.
Self-efficacy negatively moderates the relationship between AI application and employees' job insecurity by strengthening the insecurity-reducing effect of moderate AI application and weakening the insecurity-enhancing effect of excessive application.
Moderation analysis on the same cross-sectional survey data (411 valid employee questionnaires), reporting a statistically significant negative (buffering) interaction of self-efficacy with AI application intensity on job insecurity.
Employees currently lack clear guidance on appropriate use of GenAI within organizations.
Background claim in paper motivating the study (statement that employees 'lack clear guidance on appropriate use').