Evidence (9875 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
Filtered →
Productivity
8807 claims
Filter claims →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filter claims →
Inequality
2332 claims
Filter claims →
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 |
Adoption
Remove filter
Human-AI interaction factors influence people’s reliance on AI advice.
Synthesis claim from the analytical review indicating that interaction design and other human-AI interaction variables affect reliance (specific factors not enumerated in the abstract).
The AAITCF treats context as constitutive of intervention effectiveness and highlights underexplored causal pathways from AI deployment to long-term institutional change, taxpayer trust, and equitable fiscal governance.
Framework description and identification of research gaps in the paper based on the literature synthesis using the CIMO framework.
The study finds that prior reviews tended to focus narrowly (e.g., on detection metrics, behavioral dynamics, or ethical deficits) without integrating institutional boundary conditions, governance capacity, or an overarching theoretical framework.
Critical comparison and gap analysis of existing review literature as reported in the paper's introduction and synthesis sections.
Effectiveness of AI in tax compliance is contingent on data quality, governance capacity, and organizational readiness.
CIMO-structured synthesis of contextual factors across the 68 reviewed articles highlighting data, governance, and organizational readiness as moderators of AI effectiveness.
Policy implications derived from the literature include interventions spanning labor transition (reskilling/transition support), competition regulation, and digital governance.
Narrative synthesis of policy recommendations across the 78 studies and institutional reports included in the SLR.
Firm-level productivity gains from AI are contingent on complementary organizational investment.
Synthesis finding from the SLR: multiple studies report that complementary investments (e.g., organizational change, worker training, data infrastructure) are necessary for realizing productivity benefits.
AI policies' carbon outcomes depend on regional economic structures, implying the need for spatially differentiated governance.
Interpretation/implication drawn from heterogeneous and spatial analyses showing region-specific effects; result is policy recommendation based on study findings (supporting analyses referenced but not detailed in abstract).
Heterogeneous effects: emissions decreased in the Pearl River Delta and increased in the Chengdu–Chongqing region and in resource-based cities (these heterogeneous findings are statistically marginal).
Subgroup/regional heterogeneity analysis comparing policy effects across regions (Pearl River Delta, Chengdu–Chongqing, resource-based cities); statistical significance described as marginal in the paper (no sample sizes or exact p-values provided in abstract).
The remaining clicks from ChatGPT are not a scaled-down Google stream: they skew toward specialized destinations and away from ad-supported sites.
Categorical analysis of destinations clicked from ChatGPT versus Google using URL-level Comscore U.S. desktop clickstream; comparison of destination types (specialized sites vs. ad-supported sites).
Although SMEs anchor employment and output across Sub‑Saharan Africa, their uptake of AI lags global benchmarks, and prevailing explanations emphasize capital, infrastructure, and institutional voids while overlooking leadership competencies.
Background/introductory claim made by the authors to motivate the study (presented as context rather than an empirical finding from this study).
Firm profitability shows a "J-curve" as firms move from no adoption to deep adoption.
Reported relationship between adoption intensity and firm-level profitability (authors' empirical comparison/regression of profitability across adoption categories).
Adoption is slowly accelerating among non-technology firms but very aggressive adoption in the technology sector which accounts for two-thirds of deeply integrated enterprise adoption.
Reported sectoral breakdown and temporal trend in adoption (authors' sector analysis of SEC 10-K–based adoption measure; statement that tech sector comprises two-thirds of deep adopters).
Empirical claims across the reviewed literature vary in methodological rigor and should be viewed with caution before standardized replication.
Meta-level assessment presented in the review of peer‑reviewed literature (2020–2025); no formal quality-assessment statistics provided in the excerpt.
The Twin Transition is macro-feasible, but its adjustment costs fall unevenly on the manufacturing workforce.
Distributional outcomes and sectoral labor adjustment results from the CGE model (S4) showing heterogeneous effects across manufacturing sectors and implied labor reallocation costs.
Under S3 alone the Electricity sector expands only +0.10% by 2030 despite a 14.87% per year IT investment surge, indicating binding generation capacity that the Green AI productivity shock relaxes in S4.
CGE simulation of S3 (exogenous IT investment surge at 14.87% per year) and comparison with S4 results in the 23-sector model calibrated to 2019 I-O table.
Green AI’s export surge causes real exchange rate appreciation that displaces output in Textiles by 5.5% and in Leather and Footwear by 16.1%, while Heavy Manufacturing expands by 12.9% and IT Hardware by 10.9%.
Sectoral output changes reported from the CGE model under scenario S2 (Green AI) calibrated to Vietnam 2019 I-O table.
Brown AI (S3) is macro-neutral in GDP terms but imposes a consumption cost of 0.42% by 2030 as infrastructure investment crowds out household expenditure.
Model simulation of scenario S3 (Brown AI) in the 23-sector recursive dynamic CGE calibrated to Vietnam 2019 I-O table; S3 modelled as an exogenous IT hardware and services investment surge.
Traditional jobs based on manual work are transforming into collaborative management and exception-handling roles that demand new cognitive and ethical skills from employees.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No specific sample size reported.
The model yields propositions on threshold effects, productivity J-curve dynamics, distributional stress, and policy sequencing.
Model-derived propositions and theoretical implications presented in the paper (analytical derivations and theory-building).
The DIAC model identifies three regimes of AI adoption and absorption: adoption without absorption, constrained complementarity, and adaptive complementarity.
Taxonomy and regime definitions derived in the paper's theoretical model (analytical/theory-building).
The same AI shock can produce divergent outcomes in small open economies.
Core theoretical claim derived from the Dynamic Institutional Absorptive Capacity (DIAC) model developed in the paper (analytical/theory-building).
Artificial intelligence is widely expected to raise productivity, yet its macroeconomic gains remain uncertain, uneven, and institutionally mediated.
Statement and literature-motivated framing in the paper's introduction; supported by analytical theory-building (DIAC model) rather than empirical data.
The organizing claim of the theory is that review is the control point through which a coding agent's effect on software is decided, and that AI does not fix the sign of that effect: the team sets it, through the expertise its humans bring and how it structures the review process.
Synthesis of practitioner discourse coded into a causal model derived from the LLM-assisted analysis of 3,100 sampled documents; presented as the central theoretical claim.
Practitioners sharply disagree about how coding agents change code review: whether review becomes the bottleneck, whether human review remains necessary, and whether agents erode the understanding that review once built.
Synthesis of practitioner discourse at scale via collected grey-literature (engineering blogs and Reddit threads) and a coded sample; claim summarizes observed disagreement in practitioner sources.
The direction of these observed trends (review frequency, merge speed, discussion) flips under different but equally defensible analysis choices.
Authors' sensitivity/robustness checks on the observational GitHub analysis indicating that trend direction depends on analysis choices; reported in abstract without numeric detail.
A backdating exercise on the synthetic difference-in-differences yields larger absolute estimates than the actual treatment date across most age bands.
Robustness/check: synthetic DiD backdating experiment reported in the paper produced larger absolute estimates when using earlier (backdated) treatment dates.
The paper identifies four systemic tensions generated by embodied AI adoption: openness versus control; scaling versus local fit; automation ambition versus reliability constraints; and monetization versus trust.
Explicit listing of four tensions in the abstract as theoretical findings (conceptual analysis).
Data generated through physical use of embodied AI travels beyond the adopting firm (i.e., data flows cross firm boundaries).
Explicit conceptual claim in the abstract about data movement across ecosystems (theoretical observation).
Embodied AI implies a double learning loop: a closed learning loop inside the adopting firm (transforming situated use into operational feedback and workflow changes) and an external learning loop across the ecosystem of technology providers, component suppliers, software firms, platform orchestrators, and users.
Conceptual model/argument presented in the abstract describing intra-firm and inter-organizational learning loops (theoretical development).
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.
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.
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 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.
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.
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).
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).
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.
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.