Evidence (4004 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 |
Labor Markets
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Robustness checks across the capital share, shock persistence, and the utility specification show that only an empirically implausible labor–AI elasticity reverses the wage and fertility signs.
Sensitivity/robustness analysis of model results by varying parameters (capital share, shock persistence, utility functional form) and the labor–AI elasticity, reporting conditions under which sign flips occur.
A forecast-error variance decomposition attributes most aggregate volatility to the longevity shock, while the AI shock dominates the variance of the return to AI capital.
Model-based forecast-error variance decomposition implemented on the simulated stochastic model to apportion variance of aggregate variables and the return to AI capital across shocks.
The two shocks move fertility in opposite directions: the AI shock raises fertility modestly through an income effect, while the longevity shock lowers fertility by strengthening life-cycle saving motives and increasing the cost of childrearing.
Endogenous-fertility overlapping-generations model with counterfactual simulations for AI and longevity shocks; comparative statics and simulation results regarding fertility responses and their mechanisms.
The AI shock reallocates investment from physical to AI capital.
Model simulation showing changes in investment allocation across capital types following the AI technology shock.
These patterns suggest a commoditization effect of AI on labor, with implications for online labor market design, workers' incentives to invest in human capital, and labor welfare.
Interpretation synthesized from the three empirical findings above (decline in human-capital importance, rise in price importance, decline in demand premium for high-human-capital workers, and reallocation toward lower-priced workers). This is presented as the paper's conceptual/mechanistic conclusion and policy implication rather than a separately tested causal estimate. (Empirical basis: Upwork analysis and difference-in-differences; sample size not reported in abstract.)
The macroeconomic significance of AI-induced productivity depends not only on technological efficiency, but also on the distributive transmission of productivity gains through labour income, disposable income, prices, investment, public expenditure, transfers and external demand.
Theoretical argument and synthesis of literature in the conceptual review (no new empirical estimation reported).
Returnees face a short-run employment penalty after returning from cross-border work, but this penalty fades with cross-border tenure and with time since return.
Chapter 4: causal analysis using linked Belgian administrative registers comparing returnees to stayers; reported short-run employment penalty and dynamic fade-out with tenure and time since return.
Random-forest models (Belgian administrative registers) reveal sharply nonlinear transition patterns predicting entry and exit into cross-border work, with commuting time, prior employment instability, earnings, and household cross-border exposure as strong predictors.
Chapter 4: linked Belgian administrative registers identifying cross-border spells in Luxembourg; predictive analysis using random-forest models; individual-level predictors and nonlinear patterns reported.
The effect of AI development on firms' labor educational structure is substantially larger in high-technology industries: the effect in high-technology industries is approximately 2.5 times as large as that in non-high-technology industries.
Industry heterogeneity analysis reported in the paper comparing coefficients for high-technology vs. non-high-technology industry subsamples using firm-level data (Chinese A-share firms, 2014–2024); reported ratio ≈ 2.5.
The substitution (for low-educated labor) and complementarity (with high-educated labor) effects of AI on firms' labor educational structure exhibit significant regional heterogeneity: the substitution effect is stronger in developed regions, while the complementarity effect is more pronounced in less developed regions.
Subgroup/heterogeneity analysis across regions using the firm-level panel (Chinese A-share firms, 2014–2024); reported differences in coefficients by regional development level.
Firms' technological innovation capability significantly mediates the effect of AI development on labor educational structure: by enhancing technological innovation capability, AI reduces demand for low-educated labor and increases demand for high-educated labor.
Mediation/causal pathway analysis reported in the study using firm-level data and mediation regressions on Chinese A-share listed firms (2014–2024); the paper reports that technological innovation capability is a significant mediating variable linking AI development to changes in labor education composition.
AI-induced changes are displacing existing labor jobs while also creating new jobs that require high technological skills.
Summary claim from the SLR reporting that reviewed empirical studies report both displacement of existing jobs and creation of new, high-skill jobs; no quantified displacement/creation rates provided in the excerpt.
Between 2017 and 2025, studies identified current trends of AI-induced changes affecting both blue-collar and white-collar occupations.
Synthesis statement in the paper reporting that reviewed empirical studies identified trends across blue- and white-collar jobs (timeframe 2017–2025). Specific studies or counts not provided in the excerpt.
AI's rapid evolution has profound effects on the labor market, influencing the levels, skills needed for jobs, and overall jobs content.
Statement from the paper's synthesis/introduction summarizing reviewed empirical studies (systematic literature review covering studies from 2017–2025). Number of underlying studies not reported in the excerpt.
Embodied intelligence is driving the human-machine relationship from a "human-dominated" model toward "collaborative co-creation," which, while boosting productivity, also triggers deep-seated contradictions in production relations.
Conceptual/theoretical argumentation in the paper, drawing on Marx's theory of reproduction; no empirical sample or quantitative data reported.
With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.
Extended model incorporating endogenous capital accumulation: analytical solution/characterization showing unbounded (explosive) growth in aggregate variables while real wages remain stagnant in the long run (model derivation).
Along the transition path of automation, data simultaneously augments the productivity of already-automated tasks and expands the automation frontier (dual role).
Analytical results from the dynamic model showing two mechanisms: (i) data increases productivity of tasks already automated; and (ii) data enables automation of additional tasks (model derivations).
Perkembangan AI mengotomatisasi tugas rutin sekaligus menciptakan peluang pekerjaan baru berbasis digital.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
The comparative evaluation shows differences in economic inclusiveness between ML, DL, and Generative AI.
Abstract states differences in economic inclusiveness found in the review; no quantitative inclusiveness metrics or sample sizes provided in abstract.
The comparative evaluation shows differences in explainability among ML, DL, and Generative AI.
Abstract notes comparative differences in explainability as part of review findings; no empirical measures of explainability included in abstract.
The comparative evaluation shows differences in patterns of substituting labor across ML, DL, and Generative AI.
Abstract states comparative differences in labor-substitution patterns based on the systematic review of literature; no empirical counts or sizes in abstract.
The comparative evaluation shows differences in scale of impact across ML, DL, and Generative AI.
Abstract reports a comparative evaluation highlighting scale differences across AI phases; no quantitative scale measures given in abstract.
Generative AI brings innovative disruption with profound effects on the structure of employment, knowledge-based ecosystems, and high-skill industries.
Synthesis claim in abstract based on reviewed peer‑reviewed literature; no specific studies, sample sizes, or quantitative effects reported in abstract.
Although the geometry (bipolar structure) is stable, its content is not: across a decade the polarity has inverted relative to Frey and Osborne (2013).
Comparison of macro-level placements between the paper's LLM-era OAI and the Frey-Osborne (2013) rankings; authors report inversion and supporting correlation statistics.
Tool-Mediated Physical (M2) and Planning & Design (M7) are separated by Cohen's d = 2.41 (H = 172.88, p = 6.21e-34).
Statistical comparison reported in the paper (Cohen's d, H-statistic, p-value) between the two macro clusters' OAI distributions.
Projecting the DWA-level Occupational Automation Index (OAI) onto a 7-macro semantic typology produces a bipolar structure (two poles separated by a low-contrast middle band).
Authors' projection of previously computed DWA-level OAI onto a 7-cluster semantic typology and subsequent analysis of cluster structure.
There is a significant U-shaped relationship between AI application and employees' job insecurity: moderate AI application reduces insecurity, whereas excessive application heightens it.
Empirical analysis of cross-sectional self-reported questionnaire data collected from employees (411 valid responses) using regression-type analyses reported as showing a significant U-shaped relationship between AI application intensity and job insecurity.
The prominence of machine learning, Internet of Things (IoT), and cybersecurity varies depending on organisational context and role requirements within the wind sector.
Paper reports variation across data sources and organisational contexts based on interviews, surveys, and job-posting patterns; no subgroup sample sizes or statistical tests reported in summary.
The paper provides a consolidated, theory‑driven synthesis of the mechanisms through which AI‑mediated platforms simultaneously create opportunities and reproduce disadvantage for women.
Originality/value statement in the paper describing its contribution as a consolidated, theory‑driven synthesis and actionable insights for researchers, policymakers, and platform designers.
There is significant cross-national, cross-industry, and cross-regional heterogeneity in AI's impact.
Conclusion from the systematic literature review indicating variation across countries, industries and regions in the effects reported by prior studies.
Research has shown that artificial intelligence is primarily driven by substitution effects in the short term, but will generate complementary and creative effects in the long term.
Synthesis claim from the literature review; the paper reports this as an aggregate finding from prior studies (no single-study sample size provided).
The paper analyzes the direct impact of artificial intelligence on employment structure, occupational tasks, and skill demand, as well as its indirect effects on job mobility, cross-border and industry differences, and policy interventions.
Descriptive claim of scope drawn from the systematic literature review conducted by the authors; no single empirical sample reported.
The rapid development of artificial intelligence is profoundly reshaping the global labor market landscape.
Statement in paper based on a systematic literature review synthesizing prior studies; no single empirical sample reported.
Collective practices that emerge in response (from shared prompt strategies to jailbreaking techniques) represent vernacular knowledge formations that, while often exhibiting magical thinking, contain resources for 'revolutionary prompting' and the transformation of individual prompt anxiety into collective political critique.
Qualitative/interpretive claim based on observed user practices and collective responses to LLM behaviour; no systematic survey or sample sizes reported in the abstract.
Overall, STARA technologies can both enhance skill development, thriving and career opportunities and concurrently produce identity threats, pressures, and contextual complexities that shape long-term career trajectories—requiring integrated organisational and labour-market perspectives to design supportive approaches.
Editorial synthesis and summary of contributions in the special issue; draws on multiple cited empirical and conceptual studies included in the issue and prior literature.
In the platform economy, performance and career success are increasingly captured through alternative, often real-time metrics, diverging from traditional indicators and raising challenges for integrating conventional and non-traditional measures of career outcomes.
Synthesis of literature on platform work and algorithmic management cited in the editorial (multiple references to platform economy research and contributions to the special issue).
Algorithmic systems for productivity and performance monitoring generate efficiencies but also create new pressures in technology-mediated work environments, including the tracking of employees’ emotional and physiological responses at work and during non-work time.
Literature synthesis and citations (e.g. Giermindl et al., 2022; McCartney and Fu, 2022; Norlander et al., 2021; Downie et al., 2025).
AI usage at work can simultaneously enhance employees' thriving and induce identity threat; employees’ learning and performance goal orientations drive career growth in this context (Yuan et al., 2026, in this special issue).
Reported empirical finding from a paper in the special issue (Yuan et al., 2026) cited in the editorial.
Significant advancements in smart technology, AI, robotics and algorithms (STARA) are changing how organisations design and implement work for the current and future workforce.
Statement in the editorial supported by references to prior literature and reviews (e.g. Brougham and Haar, 2018; Raisch and Krakowski, 2021; Tang et al., 2023; Ulfert et al., 2024; Yam et al., 2023). This paper is an editorial/literature-synthesis rather than a primary empirical study.
Grounding the concept of defensive AI governance in organisation-level evidence from the Global South contributes to debates on platform power, journalistic agency, and AI governance in journalism.
Theoretical/interpretive claim based on the study's case of Al-Masry Al-Youm and its empirical insights; presented as a contribution to scholarly debates. Sample size not reported in the excerpt.
The authors introduce the concept of 'defensive AI governance' to describe how AI adoption is managed through organisational practices of limitation, supervision, and infrastructural self-protection.
Conceptual contribution grounded in organisation-level qualitative evidence from interviews and analysis of Al-Masry Al-Youm's practices; the concept is derived from the study's empirical findings. Sample size not reported in the excerpt.
The newsroom adopts, adapts, and governs AI across data journalism, fact-checking, and generative applications.
Empirical observations and interview data from Al-Masry Al-Youm detailing specific domains of AI integration (data journalism, fact-checking, generative tools). Sample size not reported in the excerpt.
There is a long-run equilibrium (cointegrating) relationship among AI adoption, skill-disaggregated unemployment, and sustainable development in South Africa.
Empirical ARDL results reported in the paper indicating a long-run equilibrium relationship based on annual 2003–2024 time-series data.
The study uses annual time-series data from 2003–2024 and the Autoregressive Distributed Lag (ARDL) modelling approach to estimate short- and long-run coefficients.
Explicit statement in the paper: annual time-series data 2003–2024 and ARDL modelling to simultaneously estimate short- and long-run coefficients.
AI will have social, economic, and political impacts on work, inequality, democracy and power.
Author's projection of the domains affected by AI (stated as a subject of later chapters; no empirical evidence provided in the excerpt).
The opportunities of AI in human good are real and vast; and the opportunities in human ill, in human society, in human institutions of government, and in the longer term in the environment in which humanity thrives are real and underestimated.
Author's evaluative judgment asserting both substantial benefits and substantial underestimated harms of AI (normative claim without empirical substantiation in the excerpt).
The limitations in the audit reports reflect symbolic compliance (per institutional theory), while stewardship theory highlights potential for deeper accountability.
Theoretical interpretation using institutional theory and stewardship theory presented in the paper (argumentative rather than empirical).
Adverse employment and compensation effects are concentrated among workers in non-AI tasks and non senior-level positions, indicating an asymmetric distribution of gains from AI adoption.
Heterogeneity analysis / subgroup results showing larger negative employment/compensation responses for workers in non-AI tasks and for non senior-level positions across the sample.
AI is changing skill requirements—some skills become obsolete and new skills are required.
Paper identifies changing skill requirements as a key area of examination (abstract). This is stated as an asserted trend based on the paper's review rather than a quantified empirical finding in the provided text.
AI has changed how work is executed (work processes and execution).
Explicit statement in the paper's abstract; presented as a qualitative/general finding from the paper's evaluation and literature synthesis (no numerical sample provided).