Evidence (7560 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 |
Human Ai Collab
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Using LLMs led to fewer creative moments observed in participants (p=0.002).
Within-subject comparison between LLM-assisted and unassisted conditions with reported p-value p=0.002. Study sample N=20.
Participants using LLMs had significantly shorter idea-generation periods (p=0.0004).
Within-subject comparison between LLM-assisted and unassisted conditions reported in paper; p-value reported as p=0.0004. Sample size N=20.
Existing AI assistants (e.g., ChatGPT, Copilot) utilize pre-defined user preferences and chat interaction histories and are therefore confined to reactive exchanges lacking sufficient adaptability to users' psychophysiological states.
Authorial characterization/argument about current AI assistant behavior; no empirical data reported in abstract to substantiate beyond description.
Small-scale retail businesses remain structurally excluded from these advancements due to configuration complexity, technical overhead, and limited digital capabilities.
Asserted as a problem statement in the paper; no empirical evidence, sample size, or quantitative analysis provided in the excerpt.
Producing hardened, production-grade agent workflows may require extra compute and time, and these costs must be amortized through reuse across a broad user community.
Argument in paper reasoning that added rigor entails higher compute/time costs and that reuse across users is needed to amortize these costs; no empirical cost estimates provided.
By focusing on rapid, real-time synthesis, AI agents are effectively delivering users improvised prototypes rather than systems fit for high-stakes scenarios in which users may unwittingly apply them.
Conceptual argument presented in the paper asserting a qualitative mismatch between on-the-fly agents and high-stakes production needs; no empirical validation reported.
The on-the-fly paradigm short-circuits disciplined software engineering processes—iterative design, rigorous testing, adversarial evaluation, staged deployment, and more—that have delivered relatively reliable and secure systems.
Argumentative claim in paper linking the on-the-fly loop to reduced application of standard SE processes; no empirical study, sample, or quantitative evidence provided.
These findings underscore the insufficiency of current agents for interdependent workflows, positioning ComplexMCP as a critical testbed for the next generation of resilient autonomous systems.
Synthesis of empirical results (low agent success rates, identified bottlenecks) presented by authors to make a broader claim about agent readiness and the benchmark's relevance.
(3) strategic defeatism, a tendency to rationalize failure rather than pursuing recovery.
Qualitative/quantitative trajectory analysis indicating agents often choose rationalization/explanatory actions over recovery or retry strategies after failures.
(2) over-confidence, where agents skip essential environment verifications;
Trajectory analyses showing agents often omit verification steps leading to failed interactions; reported as an identified failure mode.
Granular trajectory analysis identifies three fundamental bottlenecks: (1) tool retrieval saturation as action spaces scale;
Trajectory analyses of agent interactions with the benchmark reported by authors; observational claim from analysis of agent action sequences as action space increases.
We evaluate various LLMs across full-context and RAG paradigms, revealing a stark performance gap: even top-tier models fail to exceed a 60% success rate, far trailing human performance 90%.
Empirical evaluation reported by authors comparing multiple LLM agents (full-context and RAG) against human performance on benchmark tasks; specific reported success rates: <=60% for top models, 90% for humans.
Without parallel investment in digital literacy, organizational culture, and inter-firm networks, AI will reproduce rather than reduce employment inequalities.
Authors' conclusion drawn from thematic analysis of interviews and conceptual framing; predictive statement based on qualitative findings.
AI adoption in peripheral economies is not a purely technological or financial challenge but a social and human capital challenge, embedded in a biocultural environment shaped by brain drain, institutional thinness, and weak civic intermediation.
Synthesis of interview findings using Bitsani's Biocultural City framework; qualitative evidence from 12 interviews supports this argument.
Knowledge deficits and financial constraints emerge as primary barriers [to AI adoption].
Thematic analysis of the twelve semi-structured interviews reporting these themes as primary barriers.
Disclosure banners, conversion A/B testing, UI dark-pattern taxonomies, and generic LLM safety scores were built for older interfaces and miss the prose-recommendation surface where the steering happens.
Argument in paper that existing governance/audit tools designed for ranked-list or older UIs do not cover the new single-sentence prose-recommendation surface; no empirical test reported in excerpt.
Common failures include replacing essential operations such as sweeps, lofts, and twist-extrudes with simpler sketch-and-extrude patterns.
Error-mode analysis described in the paper/abstract showing that models substitute complex CAD operations (sweep, loft, twist-extrude) with simpler sketch-and-extrude sequences.
Common failures include misinterpreting industrial design parameters.
Reported error analysis in the paper/abstract indicating models often misinterpret engineering/design parameters when generating CAD programs.
Common failures include missing fine 3D structure.
Qualitative and quantitative analysis of model outputs on BenchCAD reported in the paper/abstract noting missing fine 3D structural details as a frequent error mode.
Current AI development trajectory reflects value choices that prioritize conversational generality over domain specificity, accountability, and long-term social sustainability.
Normative/critical analysis in the paper highlighting design priorities and trade-offs; no empirical measurement provided.
Sustained investment in large-scale chatbot infrastructures increases environmental costs.
Paper asserts environmental impacts from infrastructure investment (energy, resource use) as part of systemic critique; no quantified environmental measurements or sample size reported.
Chatbot-driven AI development contributes to concentration of economic power.
Argumentation about industry dynamics and infrastructure centralization in the paper; no empirical market-concentration metrics or sample provided.
The normalization of chatbots contributes to labor displacement.
Theoretical argument linking widespread chatbot adoption to changes in work and employment; no empirical displacement estimates provided.
Normalization of chatbot-mediated interaction alters patterns of work, learning, and decision-making, contributing to deskilling, homogenization of knowledge, and shifting expectations of expertise.
Analytical reasoning and literature-informed claims in the paper; no quantitative measurement or sample reported.
Chatbot-based systems often fail to adequately meet user needs, particularly in complex or high-stakes contexts, while projecting confidence and authority.
Qualitative argumentation and illustrative examples in the paper; no reported controlled empirical study or sample size.
The chatbot paradigm is not a neutral interface choice, but a dominant sociotechnical configuration whose widespread adoption reshapes social, economic, legal, and environmental systems.
Conceptual argument and synthesis in the paper (theoretical analysis); no empirical sample or quantitative data reported.
This reliance frequently leads to an excessive reliance on mechanistic interpretability to address a deployment challenge beyond its intended scope.
Author argument drawing on conceptual critique and cited empirical distinctions (paper's argumentative content).
AI deployment in sensitive domains (health care, credit, employment, criminal justice) is often treated as unsafe to authorize until model internals can be explained.
Author assertion based on observed regulatory and institutional tendencies described in the paper (argumentative / contextual evidence within the paper).
A scoping review found that only 9.0% of FDA-approved AI/ML device documents contained a prospective post-market surveillance study.
Paper references a scoping review that examined FDA-approved AI/ML device documents and reported the 9.0% figure.
A 53-percentage-point gap between internal representations and output correction shows that understanding may not translate into action.
Paper cites a recent empirical finding reporting a 53 percentage-point gap between models' internal representations and their ability to correct outputs (described as 'recent evidence').
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.
Even access to the true conditional vulnerability probability cannot eliminate misallocation: aleatoric uncertainty over individual vulnerability status is irreducible, and probabilistic targeting inevitably misallocates some resources.
Theoretical argument in the paper (conceptual/theoretical result about irreducible aleatoric uncertainty and its implications for probabilistic targeting).
Consequently, generated artifacts may exhibit brittle behavior and limited deployability.
Paper asserts that lack of production awareness leads to brittle artifacts and limited deployability; no quantitative measures or sample sizes provided in the abstract.
AI-assisted development tools often lack awareness of architectural constraints, infrastructure dependencies, and organizational standards required in production environments.
Asserted observation in the paper arguing limitations of general-purpose AI code generation when targeting production-ready systems; no empirical sample size or methodological details provided in the excerpt.
Current AI tools are not yet mature enough to replace developers.
Conclusion drawn from the controlled experiment and participant feedback comparing AI-assisted vs traditional task-splitting.
Breaking down user stories into actionable tasks is a critical yet time-consuming process in agile software development.
Background/introductory statement in the paper describing the problem motivation; no experimental sample size reported for this claim.
There are three practical failure modes produced or amplified by AI-assisted causal analysis: (1) method-data mismatch, where AI bypasses expertise at execution; (2) confidence laundering, where AI amplifies the credibility of formatted output; and (3) invisible forking, which spans both.
Taxonomy created and justified in the paper via conceptual argument and illustrative discussion; no empirical classification study or prevalence estimates provided.
AI industrializes the packaging of existing inferential failure modes: the barrier between naming a method and executing it has collapsed, allowing weak foundations, dressed as rigorous analysis, to reach audiences at a scale, speed, and polish that previously required expertise.
Conceptual claim supported by narrative reasoning and illustrative examples; no empirical data on scale, speed, or reach are given.
AI changes the incidence, observability, and persuasive force of inferential failures enough to create a practically distinct governance problem (even if it does not invent previously nonexistent inferential failures).
Argumentative/theoretical reasoning in the paper; no empirical measurement of incidence, observability, or persuasiveness provided.
When AI assists with methods whose validity depends on assumptions that cannot be verified from the output alone ("vibe inference"), the failure surface is structurally different: the output does not reliably signal invalidity, and when it does, recognizing the signal requires the expertise the workflow bypasses.
Logical/qualitative argument and definition development in the paper (no empirical validation or measured instances provided).
AI-assisted methodology ("vibe methodology") democratizes the failure modes specific to each domain.
Conceptual/theoretical argument presented in the paper; no empirical sample, quantitative data, or experiments reported.
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.