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 |
Possible tax instruments for AI include corporate income and rent-based taxes, consumption taxes on AI-related services, and excise instruments tied to specific AI activities.
Author survey of tax instruments presented in the paper; descriptive listing rather than empirical claim (paper states these instruments are discussed/surveyed).
The mandate acted as a catalyst rather than a direct driver: because adoption and usage intensity were not randomly assigned, the evidence strongly implicates an adoption-and-use channel rather than exact causal attribution.
Authors' methodological caveat based on observational (non-randomized) adoption and usage intensity; interpretation of DiD estimates as indicative of channels rather than definitive causal estimates.
Coding agents are capable; human oversight is the bottleneck.
Authors' high-level claim/argument in the paper, supported conceptually and motivated by the reported experiment showing reviewer limits.
Macroeconomic evidence remains cautious because AI diffusion is still uneven across industries and many firms are in early adoption stages.
Paper's synthesis of macroeconomic and industry-level sources (OECD, IMF, BLS, McKinsey, etc.) reporting uneven diffusion and early-stage adoption.
The productivity effect of AI is not automatic; it depends on firm-level adoption, worker skills, complementary investment in software and data systems, managerial readiness, task suitability, and the ability of organisations to redesign workflows around AI.
Paper's conceptual argument and synthesis of secondary literature highlighting conditional factors for realizing productivity gains.
Optimal tax and regulatory policies that achieve Pareto-improvements differ depending on whether there is competition in AI production.
Policy analysis within the theoretical model deriving optimal tax/regulatory prescriptions under different market structures (competitive vs monopolistic). No empirical sample reported.
The impact of productivity gains differs depending on whether AI production is competitive or monopolistic.
Comparative theoretical analysis in the model contrasting competitive vs monopolistic AI production. No empirical sample reported.
Improvements in AI productivity trigger labor reallocation and changes in absolute and relative wages for different types of labor.
Analytical economic model / comparative statics in the paper (theoretical result). No empirical sample reported.
Agentic AI differs from human organisations because these patterns are not sustained by motivation, identity, trust, employment, socialisation, or moral accountability; they are sustained by context architecture: prompts, memory, traces, schemas, tools, validators, and permissions.
Theoretical argument in the paper contrasting sustaining mechanisms for organisational behaviour; based on conceptual analysis and description of system-level affordances (no sample size reported).
Across four high-stakes domains, assigning different personas is sufficient for AI agents to report divergent, often opposing, conclusions from the same data and question, with findings systematically aligned with those beliefs.
Experimental manipulation across four domains where AI agents were assigned different personas and produced analyses from the same data/question; comparison of resulting conclusions showing divergence and alignment with persona beliefs.
The SCR-enhancing effect of GAI is conditional: it is not automatic but depends critically on alignment between technological deployment and organizational adaptation.
Empirical heterogeneity/conditionality findings from the panel analysis (2017–2024), implying the positive effect of GAI on SCR varies with organizational alignment and adaptation measures.
The net effect of AI on work is better described as displacement than wholesale elimination.
Author's conceptual argument and synthesis of literature/reports (qualitative argumentation in the paper).
Hybrid (human-AI) performance, analyzed at the individual forecaster level, is trimodal: most people either deferred to the model (matching it) or rubber-stamped a prior guess (performing worse than the model alone), while a minority engaged in genuine complementary reasoning and reached accuracy matching or even exceeding the market.
Pilot empirical analysis comparing individual forecasters' hybrid forecasts to both the model and the Polymarket benchmark; claims reported at individual level in the paper.
These findings do not necessarily generalize to more sophisticated schemes that simulate human conversation.
Cautionary/qualitative statement in the abstract noting limitation of the experimental manipulation (symbolic awards) and that more sophisticated conversational agents might have different effects; not an empirical finding from this study.
The research contributes by connecting AI adoption to inclusive economic modernization and proposing a governance-based framework for managing its risks in low- and middle-income contexts.
Originality / Value section claims conceptual contribution and a proposed governance framework; based on the paper's synthesis of comparative data and theoretical discussion (not an empirically validated framework in this study).
In established open-source projects, adopting an AI coding agent makes code modestly more complex but does not crowd out the human newcomers that a project depends on.
Synthesis of the paper's DiD results: no significant decline in newcomer inflow, unchanged onboarding/retention, correlational beginner-task measure unchanged, and measured modest increases in complexity metrics.
AI exposure is more positive for occupations performing nonroutine interactive work and more negative for occupations concentrated in analytical, scientific, and operations-control skills.
Occupation-level analysis mapping skill content (interaction-and-communication vs. analytical/scientific/operations-control) to market-implied AI premium; comparison across occupational skill categories.
The study reveals an 'AI Competency Paradox'—AI raises technical skills while increasing demand for meta-competencies that established frameworks fail to assess.
Synthesis of empirical findings reported in the paper linking measured increases in technical skills with unmet assessment needs for meta-competencies.
There are two distinct regional catch-up trajectories: Digital Leapfrogging in the Baltic States and Industrial Deepening in the Visegrad Group.
Systematic empirical documentation across the Visegrad Group and Baltic States (2022–2025) using the paper's assessment approach; patterns labeled and interpreted by the author.
Important gaps remain in the literature and warrant further research.
Paper's abstract statement that the review identifies important gaps that warrant further research (based on review of 194 articles).
The existing literature on AI and economic development remains fragmented, with limited integration across development dimensions.
Conclusion drawn in the abstract from the systematic review of 194 peer-reviewed articles noting fragmentation and limited cross-dimension integration.
AI's effects are often uneven and highly context-dependent.
Summary statement in the abstract based on the systematic review of 194 articles noting heterogeneity in AI impacts across contexts and dimensions.
The LCCP effect on AI industry development varies across local contexts, with stronger effects observed in established innovation hubs and in some follower regions undergoing industrial transition.
Heterogeneity analyses in the staggered DID framework on the 285-city panel (2007–2022) that split the sample by city type/region (innovation hubs vs. followers/industrial-transition regions) and report differential policy coefficients.
Code detected as likely to be generated by LLMs shows substantial intra-repository code clones.
Code-clone analysis applied to code flagged by LLM-detection tools within the same repositories (detector-based proxy approach).
The cooperative effects of the prosocial AI interventions were short-lived, fading after the first few rounds.
Temporal analysis of contributions over rounds in the iterated game showing decay of the prosocial AI effect after the initial rounds (reported in the experiment with N = 1,283).
In coding tasks, low agreeableness leads to large communication shifts that have little effect on milestone completion.
Experimental manipulation of agreeableness in LLMs on structured coding tasks; observed large changes in communication but little change in milestone completion rates. No quantitative effect sizes or sample counts given in the abstract.
Personality effects depend critically on task structure.
Authors compared the impact of personality manipulation across three distinct task domains (structured coding, open-ended research collaboration, competitive bargaining) and report differing outcomes by domain. Abstract does not provide numeric sample sizes or statistical details.
Prior work shows that agents prompted with low agreeableness produce adversarial language, while those prompted with high agreeableness become cooperative.
Citation to prior literature (not specified in the abstract) reporting correlations/causal effects of agreeableness prompts on generated language (adversarial vs cooperative). No sample size or study details provided in the abstract.
Other refugee groups saw meaningful gains in job placements, but increases were concentrated among males and in low-skilled jobs, with only limited effects for females.
Subgroup difference-in-differences analyses by origin group, gender, and skill level using administrative placement data.
Key human factors—trust calibration, output-quality sensemaking, expertise depth, feedback latency, cognitive load, and metacognitive skill development—serve as performance-shaping mechanisms within AI-enabled systems.
Presentation of a socio-technical evaluation model synthesizing prior research across several disciplines (conceptual synthesis; no empirical sample reported).
Overall conclusion: AI plays a dual role — fostering productivity and inclusion (through employment and some gender balance gains) while posing risks of increased within-firm inequality.
Synthesis of empirical findings from fixed-effects regressions, mediation and moderation analyses on the firm panel showing employment and wage gains alongside increased pay dispersion.
AI’s impact on university-educated labour cannot be understood through technological capability alone; it requires analysing the rentier dynamics of contemporary capitalism.
Theoretical argument and conceptual framework drawing on political economy and sociology (no empirical sample reported).
The U-shaped relationship between AIIA and APCRS remains significantly U-shaped across grain strategic zones.
Subsample/region-specific tests reported in the paper showing the U-shaped relationship persists in grain strategic zones using the provincial panel.
The effect of AIIA on APCRS is more pronounced in regions with higher levels of marketization and industrialization.
Regional heterogeneity analysis in the paper comparing subsamples or interacting AIIA with measures of marketization and industrialization across the 30 provinces (2016–2024).
Agricultural labor productivity strengthens the curvature of the estimated nonlinear (U-shaped) relationship between AIIA and APCRS.
Heterogeneity/moderation tests reported in the paper indicating that higher agricultural labor productivity makes the U-shaped pattern more pronounced, based on the 30-province panel.
Artificial intelligence industry agglomeration (AIIA) has a U-shaped relationship with agricultural pollution–carbon reduction synergy (APCRS) in the full sample.
Full-sample empirical analysis using panel regressions on data for 30 provinces (2016–2024) showing a nonlinear (U-shaped) estimated relationship between AIIA and APCRS.
A 2025 forecasting study of experts reveals an apparent disconnect between expectations of significant AI capability improvements and modest near-term economic projections.
2025 forecasting study / expert elicitation involving 69 leading economists and 52 AI experts, plus additional expert panels; comparison of experts' expectations about AI capability progress versus their near-term economic projections.
The effect of AI adoption on inequality is heavily moderated by a country's educational infrastructure and baseline economic development.
Reported moderation analysis / subgroup comparisons using OLS regression and Random Forest on the World Bank/OECD cross-country dataset indicating that the AI–inequality relationship varies with measures of education and development.
From a sociomaterial perspective, auditor reconfiguration depends both on the evolution of technological capabilities (material agency) and on professionals' engagement and adaptation (social agency).
Theoretical framing and interpretive synthesis in the SLR of 43 studies; application of sociomateriality theory to the empirical patterns identified in the literature.
The introduction of AI reconfigures the auditor’s role through an ongoing, dynamic process: as technology evolves, organizational practices and arrangements transform, rebalancing functions and responsibilities between auditors and tools.
Interpretive synthesis from the SLR of 43 studies using a sociomateriality theoretical lens; cross-study observations about changing tasks, responsibilities and human–machine interactions.
Micro-level efficiency improvements often come at the cost of heightened macro-level fragility.
Theoretical trade-off derived from the dual analytical framework and conceptual argumentation in the paper (no empirical validation reported).
When candidate quality is heterogeneous, prompt injection is less effective on average, but can occasionally allow lower-quality candidates to outrank higher-quality ones, raising fairness concerns.
Controlled experiments comparing homogeneous vs. heterogeneous candidate quality conditions and tracking ranking outcomes; specific experimental counts not included in the abstract.
The paper develops a task-to-firm conversion framework explaining why task-level GenAI productivity gains do not automatically translate into firm-level improvements.
Theoretical and conceptual contribution presented in the review, integrating multiple literatures (GPT theory, digital economics, task experiments, China studies).
Despite task-level gains, GenAI produces uneven or limited firm-level productivity effects in many settings.
Review synthesizing discrepancies between task-level experiments and firm-level outcome studies, and discussion of conversion frictions in the paper.
Generative AI (GenAI) should not be treated as a standalone productivity shock; its economic value depends on the interaction between model capability, task fit, human-AI calibration, organizational complementary assets, and regional digital infrastructure.
Conceptual framework developed in this review synthesizing literature from AI research, task-level productivity experiments, general-purpose technology theory, digital economics, and China-focused digital transformation studies; no new firm-level empirical analysis in this paper.
Existing user-role frameworks (e.g., the BTP User Type Matrix) require adaptation because the workforce is undergoing significant role-specific changes.
Authors' analysis based on 20 expert interviews and a 24-person workshop that uncovered mismatches between current role taxonomies and emergent AI-influenced responsibilities.
There is a growing reliance on agentic AI systems within the platform context.
Qualitative evidence from the 20 interviews and the 24-participant workshop reporting increased dependence on AI agents for tasks and decision support.
There is increasing automation of operational tasks in the development domain.
Participant reports and workshop discussions from 20 interviews and a 24-person workshop indicating automation of operational activities; qualitative thematic evidence.
The results reveal substantial shifts in day-to-day tasks and roles in the development domain.
Reported findings from 20 expert interviews and a 24-participant participatory workshop; claim based on participants' reported changes to responsibilities and observed themes in the data.
AI is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms.
Qualitative study reported in the paper combining 20 expert interviews and a participatory workshop with 24 participants; findings derive from thematic analysis of participant accounts and workshop outputs.