Evidence (3308 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 |
Skills Training
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The model yields two limits on the speed of learning and adoption: a structural limit determined by prerequisite reachability and an epistemic limit determined by uncertainty about the target.
Theoretical result stated in the paper (model-derived identification of two distinct limiting factors on learning speed).
Teaching is modeled as sequential communication with a latent target.
Modeling assumption explicitly stated in the paper (formalization of teaching in the theoretical framework).
The paper models the learner as a mind: an abstract learning system characterized by a prerequisite structure over concepts.
Modeling assumption explicitly stated in the paper (definition of the 'mind' in the theoretical model).
This Article presents the results of an experiment in which a transcript of a hypothetical client interview involving potential disability discrimination, retaliation, and wrongful termination claims was submitted to each AI system, with prompts requesting identification and assessment of viable legal theories.
Methodological description of the experiment: one hypothetical client interview transcript fed to each of four AI engines with prompts to identify and assess legal theories.
Despite fears of mass unemployment, aggregate labor-market data through 2025 show limited labor-market disruption from generative AI.
Review of aggregate employment and labor-market studies and macro-level data through 2025 cited in the brief; methods include analyses of employment statistics and macro labor indicators (no single sample size reported).
Open research challenges that define the research agenda include scaling beyond benchmarks, achieving compositionality over changes, metrics for validating specifications, handling rich logics, and designing human-AI specification interactions.
Authors' explicit enumeration of open problems and a proposed multi-disciplinary research agenda; presented as expert opinion rather than empirical finding.
Self-concordance did not mediate the AI-over-questionnaire effect on goal progress.
Preplanned mediation model reported in the paper found no evidence that self-concordance mediated the AI vs questionnaire effect on goal progress; reported as non-significant in the preregistered analysis.
Compared with the matched written-reflection questionnaire, the AI did not significantly improve overall goal progress.
Preplanned comparison within the preregistered RCT; reported non-significant difference between AI and written-reflection condition on overall goal progress at two-week follow-up (no significant p-value reported in the summary).
We conducted a preregistered three-arm randomized controlled trial (RCT) comparing an AI career coach ('Leon,' powered by Claude Sonnet), a matched structured written questionnaire, and a no-support control.
Preregistered RCT reported in the paper; three arms as described; total sample size N = 517; participants randomized to AI coach, written-reflection questionnaire, or no-support control; outcomes assessed at two-week follow-up.
Research agenda: empirical microdata on managerial time use, task-level automation, performance outcomes, and wage impacts are needed to quantify substitution versus complementarity and to evaluate human-in-the-loop designs' effects on firm performance and distributional outcomes.
Explicit methodological recommendation within the paper; identifies gaps due to the paper's conceptual (non-empirical) approach.
Practical recommendations for firms and policymakers include investing in training for AI curation/evaluation/coordination, experimenting with decentralised decision rights and governance safeguards, and monitoring competitive dynamics related to model/platform providers.
Policy and practitioner takeaways explicitly presented in the discussion/implications sections, deriving from the conceptual framework and mapped literature.
The paper recommends a research agenda for AI economists: causal microeconometric studies (DiD, IVs, RCTs), structural models with hybrid human–AI agents, measurement work on GenAI use, distributional analysis and policy evaluation.
Explicit recommendations listed in the implications and research agenda sections; logical follow‑on from bibliometric findings about gaps in causal and measurement evidence.
Bibliometric mapping profiles the intellectual structure and evolution of the field but does not establish causal effects of GenAI on organisational outcomes.
Methodological limitation explicitly stated in the paper; bibliometric approach (co‑word, citation, thematic mapping) is descriptive and historical in scope.
Co‑word and thematic analyses reveal six coherent conceptual clusters that bridge technical AI topics (e.g., LLMs, GANs) with managerial themes (e.g., autonomy, coordination, decision‑making).
Thematic mapping and co‑word network analysis performed on the 212‑paper corpus; identification of six clusters reported in results.
Bibliometric and conceptual tools (VOSviewer, Bibliometrix) were used to identify performance trends, co‑word structures, thematic maps, and conceptual evolution in the GenAI–organisation literature.
Methods section: use of VOSviewer for network visualization and Bibliometrix for bibliometric statistics, co‑word analysis, thematic mapping and Sankey thematic evolution.
The study analysed a corpus of 212 Scopus‑indexed publications covering 2018–2025 to map emergent literature on Generative AI and organisational change.
Bibliometric dataset constructed from Scopus; sample size = 212 peer‑reviewed articles; time window 2018–2025; analyses performed with Bibliometrix and VOSviewer.
Research agenda: causal studies (panel data, quasi-experiments) are needed to estimate effects of AI exposure on employment outcomes and to evaluate retraining/income-support interventions for pre-retirement populations.
Authors’ stated recommendation based on limits of cross-sectional regression results from the n=889 survey and the identified need to move from association to causation.
Study limitations: cross-sectional design, self-reported intentions, potential unobserved confounders, and limited generalizability to only three cities (Beijing, Guangzhou, Lanzhou).
Explicit methodological statements in the paper describing data and design: cross-sectional survey of 889 respondents from three cities and reliance on self-reported employment intentions.
Outcomes reported are primarily self-reported psychological measures rather than objective productivity metrics.
Paper reports measurement instruments focused on self-reported self-efficacy, psychological ownership, meaningfulness, and enjoyment/satisfaction; no primary objective productivity metrics reported.
The experiment was pre-registered, used occupation-specific writing tasks, and employed a between-subjects design with three conditions (No-AI, Passive AI, Active collaboration).
Study design reported in the paper: pre-registration statement, N = 269, between-subjects assignment to three conditions using occupation-specific writing tasks.
Active, collaborative AI use preserves perceived meaningfulness of work at levels comparable to independent work and does not produce the lasting psychological costs seen with passive use.
Pre-registered experiment (N = 269) with post-manipulation and post-return measures; Active-collaboration condition matched No-AI on meaningfulness and showed no persistent declines after returning to manual tasks.
Active, collaborative AI use preserves psychological ownership of outputs at levels comparable to independent work.
Pre-registered experiment (N = 269); Active-collaboration condition reported ownership levels similar to No-AI condition on self-report scales.
Active, collaborative AI use (human drafts first, then uses AI to refine) preserves self-efficacy at levels comparable to independent (no-AI) work.
Pre-registered experiment (N = 269) comparing Active-collaboration and No-AI conditions; no statistically meaningful differences in self-efficacy between them (self-reported measures).
The work is qualitative and exploratory — presenting naturalistic phenomena rather than causal empirical estimates, and is intended to be hypothesis-generating rather than definitive.
Methodology explicitly stated: naturalistic, qualitative daily observations over one month across multiple platforms; comparative observational documentation without experimental manipulation or causal identification.
Results are from role-play contexts and short-term interventions; economic estimates of benefit require validation in field settings, across diverse populations, and with different LLM models.
Authors' caveats and limitations stated in the paper noting external validity concerns and the experimental context (role-play, short-term follow-up).
Outcome measures included alignment to the normative taxonomy (coding/automated), recipient-rated perceptions of being heard/validated, and blinded empathy judgments.
Methods section description listing primary and secondary outcomes used in the trial and evaluations.
A data-driven taxonomy was derived mapping common idiomatic empathic moves (e.g., validation, perspective-taking, emotional labeling, offers of support) used in naturalistic support conversations.
Textual analysis of the collected corpus (33,938 messages) produced an operational taxonomy of idiomatic empathic expressions used in the role-play dialogues.
The Lend an Ear platform collected a large conversational corpus: 33,938 messages across 2,904 conversations with 968 participants.
Dataset description reported in the paper specifying counts of participants, conversations, and messages used to build and analyze communication patterns.
Key empirical metrics introduced and used are: AI adoption rates (sector-level intensity), Skill shift index, Hybrid job share, and employment levels/net changes by sector.
Methods description listing the constructed metrics used in the simulated dataset and subsequent analyses (definitions and calculation procedures provided in the paper).
The study's main limitations include reliance on a simulated dataset rather than exhaustive administrative microdata, literature limited to selected publishers/years, and correlational (not causal) identification of some effects.
Authors' explicitly stated limitations in the paper's methods and discussion sections describing data choices (simulated dataset, selected publishers 2020–2024) and the observational/correlational nature of several analyses.
This work is conceptual/theoretical and reports no original empirical dataset; it explicitly calls for mixed-methods empirical validation (case studies, field experiments, longitudinal studies), measurement development, and multi-level data collection.
Explicit methodological statement in the paper describing its nature as a theoretical synthesis and listing empirical needs; no empirical sample provided.
Four autonomous agents were benchmarked on the same fresh CTF challenge set alongside human teams.
Benchmarking experiment described in the study: four autonomous AI agents evaluated on the identical fresh challenge set used in the live onsite CTF.
The study's empirical base consists of 40 semi-structured interviews with cross-industry project practitioners in the UK, analyzed using thematic qualitative methods.
Stated data and methods in the paper: sample size (40), interview method, cross-industry sampling, and thematic analysis.
Limitation: Implementation heterogeneity — the costs and feasibility of the recommended HR changes vary by context and may affect generalisability.
Explicit limitation acknowledged in the paper; drawn from theoretical reasoning about contextual heterogeneity and practitioner variability.
Limitation: The framework is conceptual and requires empirical validation across sectors, firm sizes and AI‑intensity levels.
Explicit limitation acknowledged by the authors; based on the paper's method (theoretical synthesis, no original data).
The paper generates empirically testable propositions (e.g., how leader practices affect AI adoption speed, task reallocation, productivity, error rates, employee well‑being and turnover) and suggests natural‑experiment settings for evaluation.
Stated methodological output of the conceptual synthesis; the paper lists candidate empirical tests and research opportunities but contains no original empirical tests.
The available evidence consists mainly of promising empirical studies and case studies, but there are few long-run, generalized ROI or productivity estimates; results are heterogeneous across therapeutic areas.
Self-described limitation of the narrative review: heterogeneity of study designs and outcomes precluded pooled quantitative estimates and long-run ROI assessment.
AI applications span the full drug development pipeline, including target discovery, in silico screening and de novo design, preclinical safety models, clinical trial design and patient selection/monitoring, and post-marketing surveillance.
Comprehensive literature synthesis across preclinical, clinical, and post-marketing sources in the narrative review summarizing documented uses across these stages.
Current evidence is illustrative rather than systematic; there is a lack of long-run, quantitative measures of AI’s effect on late-stage clinical outcomes in the literature reviewed.
Explicit methodological statement in the paper: study is an expert/opinion synthesis and narrative review with no new causal econometric estimates or primary experimental data.
Suggested metrics for researchers and investors to monitor include R&D cycle time, cost per IND/NDA, proportion of projects using AI, success rates at development stages, market concentration measures, and investment flows into AI-enabled biotech vs incumbents.
Recommendations made in the Implications section as metrics to watch; no empirical tracking or baseline measures provided.
Limitations of the analysis include limited empirical validation of archetypes or impacts and potential selection bias toward prominent firms and technologies.
Explicit limitations stated in the Data & Methods section of the paper.
The paper is an editorial/conceptual synthesis rather than a primary empirical study: it uses qualitative analysis and illustrative examples, and reports no new quantitative estimates.
Explicit statement in the Data & Methods section of the paper describing document type, approach, evidence base, and limitations.
Ethical oversight and governance (addressing bias, consent, downstream risks) are critical constraints that must be addressed for AI to generate sustained benefits.
Normative synthesis referencing common ethical concerns; no empirical evaluation of oversight mechanisms in the paper.
Transparency and auditability for model behavior, provenance, and decisions are essential for trustworthy deployment and regulatory acceptance.
Policy and governance synthesis drawing on regulatory dynamics; no empirical study of regulatory outcomes included.
Rigorous model validation and reproducibility across datasets and settings are necessary constraints for successful AI deployment.
Normative claim in the editorial based on reproducibility concerns in ML and biomedical research; no reported validation trials within the paper.
The paper is primarily discursive and invitational: it opens a dialogue and proposes a research agenda rather than providing definitive empirical answers.
Stated methodological stance and limits: conceptual/philosophical analysis, interdisciplinary literature synthesis, qualitative/illustrative examples, and explicit note of no systematic empirical evaluation.
The paper identifies three core mechanisms underlying calibrated trust and complementarity: (1) calibrated trust balancing reliance and oversight, (2) complementarity–trust interaction for optimal performance, and (3) dynamic feedback loops producing reinforcing learning cycles.
Explicit identification of mechanisms claimed in the paper's synthesis; this is a descriptive claim about the paper's content rather than an empirical finding—no sample or empirical test reported in the abstract.
It remains unclear how developers' general programming and security-specific experience, and the type of AI tool used (free vs. paid), affect the security of the resulting software — motivating this study.
Paper's stated research gap/motivation: the authors identify uncertainty in the literature regarding interactions between developer experience, AI tool tier (free vs. paid), and resulting code security.
Participants were assigned a security-related programming task using either no AI tools, the free version, or the paid version of Gemini.
Experimental design described in the paper: random/conditional assignment of participants into three groups (no AI, free Gemini, paid Gemini) performing the same security-related programming task.
We conducted a quantitative programming study with software developers (n = 159) exploring the impact of Google's AI tool Gemini on code security.
Explicit methodological statement in the paper: a quantitative study with 159 participating software developers assigned to experimental conditions to evaluate Gemini's impact on security-related programming tasks.