Evidence (3308 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 |
Skills Training
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Low-information AI neither improves immediate performance nor preserves performance after AI assistance is removed, and is linked to weaker learning overall.
Within-study comparison of low-information AI assistance versus other conditions in the controlled logical reasoning task; immediate and post-AI performance measured (sample size not reported in abstract).
Greater AI usage is associated with weaker skill development: heavy AI users underperform relative to comparable peers, whereas light AI users perform similarly to matched users who do not use AI.
Controlled experiment using a logical reasoning task with on-demand AI assistance; comparison between heavy users, light users, and matched non-users reported in the study (sample size not stated in abstract).
In those same benchmarks, 16 of 84 tasks suffered negative deltas when Skills are introduced.
Reference to the same prior benchmark aggregation that reported task-level deltas (count of tasks with negative deltas = 16 out of 84).
Prompting and AI literacy alone may be insufficient to ensure epistemically independent AI support; system-level approaches are needed to better promote critical engagement in human–AI collaboration.
Authors' interpretation and implication drawn from the experiment's preliminary results: partial improvement from prompt-based training but persistent propagation of contextual errors.
An intervention (prompting training—either general or sycophancy-focused) did not eliminate the propagation of contextual errors from users into AI responses.
Participants received either general or sycophancy-focused prompting training in a pre/post within-subject design; authors report that the propagation of contextual errors persisted after the intervention.
The propagation of user errors into AI responses significantly reduced final user task performance.
Same experiment (n=60); authors report that user errors propagated into AI responses and that this propagation was associated with lower final participant performance on the analytical survival ranking tasks.
The propagation of user errors into AI responses significantly reduced the quality of AI feedback.
Same controlled mixed-design experiment (n=60) with multi-turn human–AI interactions; authors report that when users supplied lower-quality initial responses, those errors were propagated into the AI's responses, reducing AI feedback quality.
LLMs are highly sensitive to user input: lower-quality initial responses lead to poorer AI advice, suggesting that the model mirrors or incorporates user reasoning rather than correcting it or offering better alternatives.
Controlled mixed-design experiment with 60 participants performing multi-turn analytical survival ranking tasks; participants generated individual rankings and then made final decisions after collaborating with an AI assistant. Reported as a preliminary result in the paper.
Challenges including algorithmic bias, data privacy concerns, high costs, and skill gaps persist across contexts.
Cross-study synthesis of barriers and challenges reported in the 21 included studies spanning multiple contexts.
SMEs face unique resource constraints yet lag in AI-HRM adoption.
Synthesis conclusion from the systematic review of 21 included studies (published 2019–2026) comparing adoption patterns and barriers for SMEs.
Selective displacement from AI is concentrated among older and lower-mobility workers.
Explicit claim in chapter summary, stated to be traced from labour market data and emerging workplace evidence (no numeric breakdown in excerpt).
Analysis indicates a significant negative relationship between perceived opportunities and challenges related to AI (i.e., higher perceived opportunities are associated with lower perceived challenges).
Correlation and regression analyses performed in SPSS on primary survey data showed a statistically significant negative association between measures of perceived opportunities and perceived challenges.
There exists employee resistance to change in response to AI adoption.
Survey-based measures of resistance included in the questionnaire and analyzed (descriptive/correlation/regression) using SPSS.
Employees identify ethical issues—particularly transparency and accountability of AI systems—as a notable challenge.
Survey items on ethical concerns analyzed with SPSS (descriptive and reliability analyses).
Employees have concerns regarding data privacy related to AI systems.
Primary survey data using a Likert-scale questionnaire; findings summarized with descriptive statistics and reliability analysis.
Employees report lack of AI-related skills (skill gaps) as a significant challenge to human–AI collaboration.
Survey responses from employees in AI-enabled organizations collected via a structured questionnaire and analyzed (descriptive/correlation).
Employees report fear of job displacement as a notable challenge associated with AI adoption.
Primary survey data (structured questionnaire) capturing perceived challenges; descriptive statistics reported.
Rather than restoring stability, this cycle intensifies anxiety, undermines mastery, and erodes professional confidence.
Theoretical claim about psychological outcomes from the conceptual reskilling loop; paper provides argumentation but no empirical measurements.
Based on Job Demands–Resources (JD-R) theory and Conservation of Resources (COR) theory, the paper conceptualizes an AI-induced reskilling loop in which ongoing technological change leads to skill erosion, continuous reskilling demands, cognitive and emotional depletion, and reinforced learning as a defensive response to perceived obsolescence.
Theoretical model/loop derived from applying JD-R and COR frameworks; no empirical test or sample reported in the paper.
The paper introduces the concept of 'reskilling fatigue' to explain the human consequences of persistent skill volatility among Established Knowledge Professionals (EKPs).
Conceptual/theoretical contribution presented by the authors; definition and argumentation rather than empirical validation.
Continuous reskilling is widely promoted as a solution to AI-driven disruption, but little attention has been paid to its cumulative psychological costs.
Argument from literature review/observation in the paper; no empirical measurement or sample reported in the paper.
Employees experience technostress, anxiety and micro-political negotiation around AI tools in everyday work.
Reported experiences from semistructured interviews with 28 managers/professionals across 12 organizations; thematic analysis highlighting technostress and anxiety as themes.
Increased levels of AI assistance may degrade productivity, leading to potentially significant shortfalls under the model's identified conditions.
Model-based comparative-statics and steady-state analysis showing scenarios where marginal increases in AI assistance reduce expected task output; examples/parameter illustrations provided in the paper (theoretical, no empirical sample).
Introducing AI unreliability (errors/noise in AI outputs) in the model can also generate a productivity paradox: greater AI assistance may lower productivity.
Analytical/theoretical model incorporating AI unreliability; model derivations and examples demonstrating conditions under which unreliability leads to reduced productivity (no empirical data).
Incorporating endogeneity in skill development into the model can induce a productivity paradox where increased AI assistance reduces productivity.
Analytical/theoretical model of human-AI interaction with utility-maximizing human agents and endogenous skill development; steady-state and comparative-static analysis reported in the paper (no empirical sample).
AI integration simultaneously increases labor concerns about skill obsolescence by 33%.
Reported as a survey/result in the paper; the study includes surveys of 800 marketers (self-reported concerns about skill obsolescence are likely derived from that survey sample).
Rising data velocity renders legacy systems obsolete—threatening approximately $3.4 trillion in global marketing spending.
Paper reports an estimate/claim about threatened global marketing spending tied to legacy systems becoming obsolete (derivation likely from the study's quantitative analysis or economic estimate described in the paper).
62% of teams suffer from "AI paralysis," unable to scale pilot initiatives beyond isolated implementations.
Reported as a finding in the paper's mixed-methods study (paper states AI adoption audits of 120 organizations and surveys of 800 marketers as part of the study).
Using LLMs led to fewer creative moments observed in participants (p=0.002).
Within-subject comparison between LLM-assisted and unassisted conditions with reported p-value p=0.002. Study sample N=20.
Participants using LLMs had significantly shorter idea-generation periods (p=0.0004).
Within-subject comparison between LLM-assisted and unassisted conditions reported in paper; p-value reported as p=0.0004. Sample size N=20.
AI-assisted engineering teams concurrently face a 19% risk of skills obsolescence.
Empirical finding reported by the study, presumably based on the mixed-methods data (survey/Delphi/case studies) described in abstract.
Forecasts indicate that automation may supplant as much as 45% of traditional tasks by 2030.
Statement in paper referencing external forecasts (no specific source or sample reported in abstract).
Credential erosion is evident in the aggregate pattern (credentials losing signaling value relative to AI-augmented skill demonstrations).
Synthesis statement from included studies noting credential erosion alongside skill signaling changes; not quantified in the excerpt.
Developing economies reliant on cognitive services outsourcing face disproportionate disruption through both direct exposure and indirect demand-erosion channels.
Preliminary empirical evidence across included studies indicating larger negative impacts for economies dependent on cognitive-services exports; described as preliminary but material.
Observable labor market data already document patterns consistent with AI-driven displacement rather than mere transformation—concentrated among routine cognitive tasks and junior roles.
Synthesis of observed labor market indicators from retained empirical studies since 2020 showing concentration of declines in routine cognitive tasks and junior roles.
Evidence from online labor markets shows a 2%–21% reduction in posting volumes for automatable creative tasks following ChatGPT's release.
Empirical analyses of online labor market posting volumes reported in multiple studies included in the review; range reported across studies.
Across synthesized studies, there was a 14–41% reduction in postings for entry- and mid-level software development and content-creation roles in high-income economies between 2022 and 2024 (range across individual studies: −14% to −41%; median: −23%).
Synthesis of empirical studies retained in the systematic review (numerical range and median reported across non-overlapping study designs and geographies); no pooled meta-analytic estimate provided.
Without parallel investment in digital literacy, organizational culture, and inter-firm networks, AI will reproduce rather than reduce employment inequalities.
Authors' conclusion drawn from thematic analysis of interviews and conceptual framing; predictive statement based on qualitative findings.
AI adoption in peripheral economies is not a purely technological or financial challenge but a social and human capital challenge, embedded in a biocultural environment shaped by brain drain, institutional thinness, and weak civic intermediation.
Synthesis of interview findings using Bitsani's Biocultural City framework; qualitative evidence from 12 interviews supports this argument.
Knowledge deficits and financial constraints emerge as primary barriers [to AI adoption].
Thematic analysis of the twelve semi-structured interviews reporting these themes as primary barriers.
Firms do not internalize the congestion externality they impose on the retraining queue, the irreversibility of permanent exit, or the wage depression borne by non-routine incumbents — explaining why market adoption speed exceeds the social optimum.
Model-based mechanism: normative/comparative analysis showing omitted externalities in firm-level optimization relative to social planner, leading to divergence between private and social adoption speeds.
Social welfare is strictly concave in adoption speed and is maximized at an interior optimum below the market rate of adoption.
Analytical welfare optimization in the theoretical model: social-welfare function as a function of adoption speed yields strict concavity and an interior social optimum; comparison with market equilibrium adoption speed indicates market rate exceeds social optimum.
Faster adoption causes a sustained compression of the labor share throughout the transition window.
Model result showing time-path of labor's income share under varying adoption speeds in the theoretical framework.
Faster adoption produces a steeper and more persistent decline in labor force participation.
Dynamic model trajectories and comparative statics showing time path of labor force participation under different adoption-speed parameters.
Faster adoption overwhelms the retraining pipeline and generates permanent labor-force exit through worker discouragement.
Model mechanism: finite-capacity retraining queue in the dynamic model leads to queue congestion, producing a discouraged stock of permanently exited workers (analytical result in the theoretical model).
Current AI development trajectory reflects value choices that prioritize conversational generality over domain specificity, accountability, and long-term social sustainability.
Normative/critical analysis in the paper highlighting design priorities and trade-offs; no empirical measurement provided.
Sustained investment in large-scale chatbot infrastructures increases environmental costs.
Paper asserts environmental impacts from infrastructure investment (energy, resource use) as part of systemic critique; no quantified environmental measurements or sample size reported.
Chatbot-driven AI development contributes to concentration of economic power.
Argumentation about industry dynamics and infrastructure centralization in the paper; no empirical market-concentration metrics or sample provided.
The normalization of chatbots contributes to labor displacement.
Theoretical argument linking widespread chatbot adoption to changes in work and employment; no empirical displacement estimates provided.
Normalization of chatbot-mediated interaction alters patterns of work, learning, and decision-making, contributing to deskilling, homogenization of knowledge, and shifting expectations of expertise.
Analytical reasoning and literature-informed claims in the paper; no quantitative measurement or sample reported.