Evidence (3231 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5921 claims
Human-AI Collaboration
5192 claims
Org Design
3497 claims
Innovation
3492 claims
Labor Markets
3231 claims
Skills & Training
2608 claims
Inequality
1842 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 738 | 1617 |
| Governance & Regulation | 671 | 334 | 160 | 99 | 1285 |
| Organizational Efficiency | 626 | 147 | 105 | 70 | 955 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 349 | 109 | 48 | 322 | 838 |
| Output Quality | 391 | 121 | 45 | 40 | 597 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 277 | 145 | 63 | 34 | 526 |
| AI Safety & Ethics | 189 | 244 | 59 | 30 | 526 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 106 | 40 | 6 | 188 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 79 | 8 | 1 | 152 |
| Regulatory Compliance | 69 | 66 | 14 | 3 | 152 |
| Training Effectiveness | 82 | 16 | 13 | 18 | 131 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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AI has generated new employment opportunities that require advanced technical, analytical, and managerial skills.
Reported from analysis of existing studies and sector trends indicating creation of new roles and skill demands (literature review).
The integration of AI technologies such as machine learning, automation, chatbots, and predictive analytics has significantly improved efficiency and productivity in areas like retail, marketing, finance, and supply chain management.
Systematic analysis of existing literature and sectoral trends reported in the paper (literature review; no original primary sample or experiment reported).
Coding is one of the most LLM-exposed tasks.
Authors link O*NET task measures of LLM exposure to occupational data (motivating selection of programming-intensive occupations).
The review integrates fragmented literature into a cohesive framework and offers implications for managers and policymakers to pursue more balanced, inclusive, and context-sensitive AI adoption strategies.
Author-stated contribution of the review based on synthesis of the 40 included studies; normative recommendations derived from the review.
Generative AI adoption is associated with mixed employee perceptions: some studies report increased efficiency and higher job satisfaction.
Aggregate finding from included studies in the review that report positive employee-reported outcomes (efficiency, satisfaction).
There is consistent evidence of productivity improvements from generative AI in workplace settings, driven by task automation, decision support, and knowledge augmentation.
Synthesis of findings across the 40 included empirical and conceptual studies (review-level conclusion summarising multiple studies reporting productivity effects).
Ireland’s high levels of educational attainment offer a strong foundation for benefiting from AI adoption, but targeted educational support (especially for older workers or those with lower formal qualifications) and investment in lifelong learning and retraining will be essential.
Policy assessment based on Ireland's workforce characteristics and the report's scenario findings about which groups face disruption; presented as a recommendation/interpretation.
Increases in returns to capital as a result of AI adoption, while modest in percentage terms, benefit households at the very top of the income distribution, where the vast majority of Ireland’s capital income is concentrated.
Simulated changes in returns to capital combined with income distribution data showing concentration of capital income among top households; reported in the report.
For those who remain in work, AI is expected to increase productivity. We estimate that workers who are not displaced may see modest but broadly shared wage gains.
Scenario assumptions and international evidence on productivity effects of AI, incorporated into the report's simulations of wages for non-displaced workers.
There is an urgent need for targeted workforce planning, investment in human capital, and collaboration between industry, government, and educational institutions to manage AI-driven labour market transformations.
Policy conclusion drawn from the paper's theoretical framing (SBTC, Human Capital Theory) and the empirical patterns identified in secondary data and official reports (2020–2024).
Comparative insights from the United Kingdom show that more systematic AI adoption and structured training programs mitigate workforce displacement.
Cross-country comparison using secondary data and official reports (2020–2024) highlighting the UK's more systematic AI adoption and structured training, which the paper presents as reducing displacement risk.
AI adoption is increasing demand for new competencies.
Secondary sources and official reports (2020–2024) cited in the paper document emerging skill requirements and employer demand for new competencies.
AI adoption is driving growth in high-wage occupations.
Analysis of secondary data and official reports (2020–2024) reporting expansion of high-wage occupational categories in India.
AI adoption disproportionately benefits high-skilled workers.
The paper cites theoretical frameworks (Skill Biased Technological Change and Human Capital Theory) and analyses of secondary data and official reports from 2020–2024 showing relative gains for high-skill occupations.
All data, code, and model responses are open-sourced.
Statement in the paper asserting that data, code, and model outputs are publicly released.
78.7% of observed AI interactions are augmentation, not automation.
Empirical classification of AI interactions (from cross-referenced Anthropic Economic Index interactions/tasks) reported as a percentage in the paper.
The study cross-references the SAFI benchmark with real-world AI adoption data from the Anthropic Economic Index covering 756 occupations and 17,998 tasks.
Data linkage described in the paper: use of Anthropic Economic Index as real-world AI adoption dataset (numbers reported in text).
The benchmark covers 263 text-based tasks spanning all 35 skills in the U.S. Department of Labor's O*NET taxonomy.
Reported dataset construction in the paper: 263 tasks mapped to 35 O*NET skills.
We present the Skill Automation Feasibility Index (SAFI), benchmarking four frontier LLMs -- LLaMA 3.3 70B, Mistral Large, Qwen 2.5 72B, and Gemini 2.5 Flash -- across 263 text-based tasks spanning all 35 skills in the U.S. Department of Labor's O*NET taxonomy (1,052 total model calls, 0% failure rate).
Empirical benchmark executed by the authors: 263 text-based tasks mapped to 35 O*NET skills, 4 LLMs, 1,052 total model calls reported, and reported 0% failure rate.
China's 'Global Community of Shared Future' white paper and Putin's 2024 Valdai address provide empirical evidence for an articulated alternative vision to the Western‑led global order.
Qualitative textual/readings of the cited official documents (the white paper and the Valdai address) used in the paper as empirical support; no quantitative content analysis or sample coding is reported.
Technical workers' potential for progressive transformation lies not just in their strategic importance and specialized knowledge but in their ability to build solidarity across the broader ecosystem of AI labour while operating between otherwise incommensurable philosophical and infrastructural systems.
Normative/theoretical claim combining philosophical analysis (Chinese Marxism, Bauman) with empirical literature on hidden AI labour and infrastructure competition (Muldoon et al., 2024); offered as an interpretive synthesis rather than empirically validated causal finding.
Technical workers occupy a strategic position at the intersection of competing infrastructural systems and alternative visions of global order, making them potentially crucial actors in determining the outcome of the current interregnum.
Argumentative claim supported by secondary empirical literature cited in the paper (Muldoon, Graham, and Cant, 2024) on hidden labour supporting AI systems and on geopolitical competition over digital infrastructure; presented as qualitative/interpretive evidence rather than primary quantitative measurement.
The semi-core's challenge to Western hegemony creates unique conditions for systemic transformation.
The paper advances this as a theoretical argument synthesizing World‑Systems theory, Demirel (2024), Bauman's philosophical work, and interpretive readings of official Chinese and Russian documents; no quantitative causal test is reported.
The emergence of a 'semi-core' is represented most prominently by China and Russia.
The paper cites Ege Demirel (2024) as the primary conceptual source and draws on textual evidence from China's 'Global Community of Shared Future' white paper and Putin's 2024 Valdai address; presented via World‑Systems theoretical framing and qualitative/discourse analysis.
We hypothesize the emergent necessity of a 'Compliance Premium,' indicating wage resilience increasingly tied to risk-absorption capacity.
Hypothesis proposed by authors based on observed institutional/business risk differentials from HITL validation and OAI patterns; framed as a forward-looking interpretation rather than demonstrated empirical result.
Non-routine cognitive roles highly dependent on symbolic manipulation (e.g., Data Scientists) face unprecedented exposure, with OAI ≈ 0.70.
Reported OAI value for example occupation(s) (Data Scientists) derived from the algorithmic aggregation across DWAs; claim presented as a key empirical finding.
We utilize a multi-agent LLM ensemble to score both technical feasibility and business risk for DWAs.
Method description: deployment of a multi-agent LLM ensemble to produce scores on technical feasibility and business risk per DWA. Specific ensemble composition and hyperparameters not provided in the excerpt.
We introduce a Tech-Risk Dual-Factor Model that jointly scores technical feasibility and business risk to re-evaluate occupational exposure to LLMs.
Methodological contribution described in the paper (model specification). Implementation details described elsewhere in paper (see multi-agent scoring and aggregation), but claim itself is the introduction of the model.
The study introduces 'career reconfiguration' as a framework explaining intra-role task transformation, extending existing career mobility and job transition theories.
Theoretical/conceptual contribution presented in the paper (framework proposition; not an empirical effect).
Mediation analysis confirms that training and organizational support significantly mediate the relationship between AI adoption and career shifts.
Mediation analysis reported in the study (method stated; no mediation coefficients or sample size provided in abstract).
Together, these variables explain 61% of the variance in adaptive outcomes (R² = 0.61).
Multiple regression model summary reported in the paper (R-squared value provided; sample size not stated).
Readiness to change is a significant predictor of career adaptation (beta = 0.298, p = 0.011).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Openness to technology is a significant predictor of career adaptation (beta = 0.367, p = 0.003).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Organizational support is a significant predictor of career adaptation (beta = 0.389, p = 0.005).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Skills training is the strongest predictor of career adaptation (beta = 0.412, p = 0.002).
Multiple regression analysis reported in the paper (predictors of career adaptation; sample size not stated).
Overcoming the structural skill deficit through deliberate investment in tertiary education reform and strong private-public partnerships for continuous vocational learning is mandatory for Nigeria to successfully leverage the AI revolution for inclusive economic growth and ensure long-term workforce resilience.
Study conclusion synthesizing survey results (150 firms) and qualitative policy/workforce analysis to make policy recommendations.
The rate of new job creation hinges critically on the immediate implementation of targeted, scalable reskilling programs.
Paper's projections and analysis drawing on the survey of 150 firms and qualitative interviews; presented as a conditional/projection based on current skills gap and training initiatives.
Azar et al. (2023) show that monopsonistic employers have stronger incentives to automate, and US commuting zones with higher labor market concentration experienced more robot adoption.
Citation to Azar et al. (2023) empirical evidence reported in the paper.
Noy and Zhang (2023) and Brynjolfsson et al. (2025) provide emerging empirical evidence that AI can function as a labor-complementary technology when designed to do so.
Cited empirical studies referenced in the paper arguing that certain AI applications complement human labor.
Eloundou et al. (2024) predict that half of US jobs are significantly exposed to recent advances in generative AI.
Citation to Eloundou et al. (2024) empirical study reported in the paper's introduction.
Firms may not sufficiently account for non-monetary aspects (safety, meaning of work) when choosing technologies; a planner would include these non-monetary considerations in steering technological progress.
Theoretical argument and model extension in Section 6 on monetary vs non-monetary aspects of technology choices.
In multi-good economies, a planner can raise poor agents' real incomes not only by affecting factor incomes but also by focusing technological progress on making goods cheaper that are disproportionately consumed by poorer agents.
Extension of the baseline model to multiple goods (Section 5) identifying distributional consumption-channel effects.
When capital and labor are gross complements, a planner concerned with workers' welfare would favor capital-augmenting innovations to raise wages.
Analytical result from a factor-augmenting application of the paper's model examining complementarity conditions between capital and labor.
A welfare-maximizing planner will impose positive robot taxes when robots substitute for human labor, with the optimal tax rate increasing in the planner's concern for workers' welfare.
Model application to robot taxation presented in the paper; comparative statics on planner weights.
When redistribution is costly or incomplete, production efficiency is no longer optimal and a planner will distort technology choice to improve distribution (i.e., engage more in steering).
Theoretical derivation extending Atkinson-Stiglitz framework with endogenous technology and costly redistribution; comparative statics on redistribution cost.
The welfare benefits of steering technological progress are greater the less efficient social safety nets are.
Theoretical result derived in the paper's baseline and extended models analyzing a planner who can shape technology choices and faces costly/incomplete redistribution.
In the short run, with fixed human capital, wages, and job boundaries, AI raises productivity by reducing the time required to perform steps.
Model distinction between short-run (fixed job design and skills) and long-run horizons; short-run optimization shows AI reduces expected execution times for steps, thereby raising productivity.
Aggregating heterogeneous firms that deploy a commonly available AI technology yields an aggregate production function that admits a constant elasticity of substitution (CES) representation with three inputs: aggregate manual labor, aggregate AI-assisted labor, and aggregate capital.
Theoretical aggregation argument drawing on Houthakker (1955) and Levhari (1968), deriving a macro-level CES representation from a microfounded algorithmic cost function defined by firms' joint optimization over AI deployment and job design.
Improvements in AI quality generate non-linear effects on labor demand and wages because firms' cost-minimizing AI deployment and job designs change discretely at particular AI quality thresholds (microfoundation for the productivity J-curve).
Theoretical analysis of discrete switches in the cost-minimizing arrangement as AI success probability and execution times change; characterization of threshold effects and discussion linking to the J-curve phenomenon (model results and comparative statics).
Adjacency to AI-executed steps increases the likelihood that a given step is executed by AI (local complementarities): a step is more likely to be AI-executed in occupations where its neighboring steps are also AI-executed.
Empirical comparisons of conceptually similar steps across occupations paired with workflow adjacency information and realized AI execution outcomes from Anthropic’s Economic Index; statistical tests reported in the paper.