Evidence (8807 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 |
Productivity
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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.
Although AI working autonomously achieved a 37% reproduction rate, it could be useful for automated screening when human review is cost-prohibitive.
Interpretation in paper: authors note 37% autonomous reproduction rate as potentially useful for large-scale screening where human review is infeasible; based on empirical results of the experiment.
The task-based adaptive collaboration model hypothesizes that trust, explainability, and task difficulty moderate the effect of human–AI collaboration on performance.
Statement of hypothesized relationships within the model developed in the paper (theoretical hypotheses rather than reported experimental estimates).
The research is limited by the current state of AI technology and the available proxies; therefore the validity of the present optimistic findings must be continually re-evaluated.
Authors' stated limitations in the abstract noting rapid AI advancement and proxy measurement constraints.
These positive results are not supported in all contexts (i.e., the positive effects are not universally found across all specifications/contexts).
Abstract statement noting heterogeneity/robustness: preferred results hold but are 'not supported in all contexts.' Implies some specifications or subsets do not show the effects.
Employment effects follow the same timing (i.e., emerge in 2021) but diverge by exposure type.
Paper reports employment effects with temporal alignment to output effects (emerging in 2021) and heterogeneity by type of AI exposure.
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.
Approach motivation (BAS Drive) moderates whether interactive partnership benefits originality.
Moderation analysis reported from the pilot (N = 62) showing interaction between BAS Drive (a measured personality/motivation scale) and the effect of interactive partnership on originality.
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.
AI performs best in routine, data-rich situations but falls short when decisions require lived experience and contextual understanding.
Synthesis of cross-domain empirical studies and theoretical arguments showing differential AI performance by task type (routine/data-rich vs. experience-dependent/contextual).
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.
We propose 'contextuality' — the degree to which an AI system autonomously accesses a user's accumulated knowledge capital — as a dimension of AI-mediated inequality that complements, but is not reducible to, the Sharp et al. framework.
Conceptual proposal and definitional contribution in the paper presenting contextuality as a new analytic dimension.
The paper analyzes the technical basis of the Context Access Divide in Model Context Protocol (MCP) and retrieval-augmented generation (RAG) architectures.
Technical analysis and architectural examination reported in the paper (discussion of MCP and RAG as implementation-relevant architectures).
The CAD is formalized with a probabilistic model grounded in the fan effect literature in cognitive psychology.
Paper reports a formal probabilistic model drawing on the fan effect literature; model described as the formalization of CAD.
Sharp et al. (2025) introduce "agentic inequality" as a framework for analyzing disparities in access to AI agents across three dimensions: availability, quality, and quantity.
Statement and citation in the paper (reference to Sharp et al. 2025); descriptive synthesis of prior work.
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.
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).
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.
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).
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.
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 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.