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Home Papers Evidence Explore Trends Syntheses Digests About 🎲 Workforce Futures
Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review. How this is built →

Evidence (16496 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
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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
There exists a finer-grained divide at the level of individual interaction — the Context Access Divide (CAD) — whereby two users with nominally equivalent agent access may experience qualitatively different AI utility depending on whether the system can autonomously retrieve context (Dynamic Context Retrieval) or requires manual document attachment (Manual Attachment).
Conceptual argument and definitional framing in the paper introducing the CAD as a novel dimension of inequality; comparison of two interaction modalities (Dynamic Context Retrieval vs Manual Attachment).
high negative The Context Access Divide: Interaction-Level Architecture as... AI utility experienced by the user (qualitative usefulness)
Free overrides also cut sales by 1.19%.
Randomized field experiment comparing free-overrides arm to control; effect reported as 1.19% reduction in sales.
high negative A Simple Solution to Improving Human Supervision of Algorith... Sales
Free overrides reduce inventory by 1.95%.
Randomized field experiment comparing free-overrides arm to control; effect reported as 1.95% reduction in inventory.
high negative A Simple Solution to Improving Human Supervision of Algorith... Inventory level
The testing tool raised cost by 42 to 68 percent without improving functional score or reliability, even on interface visible criteria.
Comparison between runs with and without the testing tool showing reported cost increase (42–68%) and no improvement in functional score or reliability.
high negative Reasoning effort, not tool access, buys first-try reliabilit... development cost, functional score, reliability (interface-visible criteria)
Container deployment was the dominant defect, failing first try in 44 percent of runs.
Criterion-level analysis of failure modes across the 90 runs reporting first-try failure frequency for container deployment.
high negative Reasoning effort, not tool access, buys first-try reliabilit... first-try container deployment failure rate
AI development and deployment can shift costs onto others, including systemic risks from rapid frontier development.
Author assertion that rapid frontier development of AI creates systemic risks; no empirical quantification in excerpt.
high negative Taxing Artificial Intelligence systemic risks from frontier AI development
AI development and deployment can shift costs onto others, including labor and creative displacement.
Author assertion identifying labor and creative displacement as an externality of AI; no empirical evidence or sample provided in excerpt.
high negative Taxing Artificial Intelligence labor and creative displacement
AI development and deployment can shift costs onto others, including environmental pressures on local communities.
Author assertion listing environmental pressures as an externality of AI development and deployment; no empirical data or sample provided in excerpt.
high negative Taxing Artificial Intelligence environmental pressures on local communities
Aligning AI systems with human teams remains a major challenge to realizing AI's full potential in organizations.
Authors' statement in abstract framing the motivation for the study; supported by literature cited in full paper (abstract asserts this as a core challenge).
high negative Mapping Human–AI Relationships: Intellectual Structure and C... difficulty of human–AI alignment in teams
A substantial and persistent gap below expert level reliability therefore remains.
Inference in abstract based on reported model accuracies (~82.7%) compared with the informal expert reference (~95%).
high negative Pre-Flight: A Benchmark for Evaluating Large Language Models... gap between model accuracy and expert accuracy on domain-specific tasks
Against an informal expert reference of around 95%, obtained from a low sample quiz of aviation professionals at a conference, even the strongest model evaluated (released in 2026) reaches 82.7%.
Reported comparison in abstract between informal expert reference (~95%) and top model accuracy (82.7%) on the Pre-Flight benchmark; expert reference described as a 'low sample quiz' (sample size not reported). Model accuracy implicitly measured across benchmark questions (300).
high negative Pre-Flight: A Benchmark for Evaluating Large Language Models... accuracy on Pre-Flight benchmark (multiple choice)
The high stakes, regulated nature of the aviation domain makes the gap (in domain-specific evaluation) consequential.
Argumentative statement in abstract emphasizing domain characteristics; no quantification or external citation provided in the abstract.
high negative Pre-Flight: A Benchmark for Evaluating Large Language Models... consequential risk from lack of domain-specific evaluation
General purpose benchmarks do not measure whether a model reasons safely and correctly about aviation specific operational knowledge.
Argument/assertion in abstract about limitations of general-purpose benchmarks; no formal empirical comparison presented in the abstract.
high negative Pre-Flight: A Benchmark for Evaluating Large Language Models... coverage of aviation-specific operational reasoning by general-purpose benchmark...
Unconstrained agents introduce security risks, erode codebase scalability, and make human review increasingly costly.
Authors' argumentative claim supported by the controlled experiment showing lower recall (more missed backdoors) in unconstrained condition and discussion of costs and scalability.
high negative Steerability via constraints: a substrate for scalable overs... security risk (missed backdoors) and cost/effort of human review
Monopoly production of AI restricts its deployment, slowing the transition and impact of AI.
Theoretical model comparing monopolistic AI producer behavior to competitive deployment; result is derived analytically. No empirical sample reported.
high negative The Economic Benefits and Costs of AI and Policies to Mitiga... AI deployment / transition speed
Wages of labor that is substituted for by AI decrease in both absolute and relative terms.
Analytical economic model / comparative statics predicting wage declines for labor substituted by AI. No empirical sample reported.
high negative The Economic Benefits and Costs of AI and Policies to Mitiga... wages of labor substituted by AI
Computational theorising, synthetic task simulations, real LLM agent traces, and robustness analyses show that human-imitation forms often underperform when they add lossy handoffs, correlated deliberation, and verification burdens.
Empirical and simulation-based methods listed in the paper (computational theorising, synthetic task simulations, analysis of real LLM agent traces, robustness checks). The excerpt does not report sample sizes, numeric effect sizes, or statistical tests.
high negative The Organizational Behavior of Agentic AI: Collective Intell... performance (relative underperformance) of human-imitation organisational forms
Applied to the human immigration study, 13.5% of reported human analyses fell in the most extreme 5% of the analysis space (m<0.05).
Application of Agentic Bootstrap / m-value estimation to the dataset analyzed by 42 human research teams; result that 13.5% of those reported analyses had m-values below 0.05.
high negative The Agentic Garden of Forking Paths proportion of reported human analyses that are extreme relative to the plausible...
AI agents may amplify this longstanding problem by making such exploration inexpensive and scalable.
Argument based on observed ability of AI agents to generate many plausible analysis paths and the lower cost/effort of producing such analyses using AI.
high negative The Agentic Garden of Forking Paths risk of amplification of selective reporting due to AI-enabled scalability of an...
These findings suggest that the central challenge is often not flawed analyses, but selective exploration and reporting from a large space of methodologically defensible analyses.
Interpretation based on empirical results showing that divergent/opposing conclusions can be produced without clear methodological flaws and that many analyses pass review despite divergence.
high negative The Agentic Garden of Forking Paths primary source of problematic scientific claims (selective exploration/reporting...
GAI adoption produces a suppression (negative) effect on SCR via increased upstream supplier concentration, indicating a trade-off between flexibility gains and coordination stability losses.
Mechanism analysis of the panel data showing upstream supplier concentration operates as a suppressor (negative mediator) in the relationship between GAI adoption and SCR.
high negative How Generative Artificial Intelligence Adoption Enhances Fir... Supply chain resilience (SCR) suppressed via upstream supplier concentration (me...
Public discourse still focuses heavily on job losses while paying less attention to the opportunities that AI creates.
Author's observation/argument in the paper (qualitative commentary comparing public discourse emphasis).
high negative AI-Driven Workforce Transformation: Displacement, Opportunit... media/public discourse emphasis on job losses vs. opportunities
The report identifies 'AI washing,' a practice in which companies mention AI as justification for what are really financially motivated layoffs.
Identification/term introduced in the paper based on examples or synthesis of corporate reporting and layoff cases (as described).
high negative AI-Driven Workforce Transformation: Displacement, Opportunit... misuse/mislabeling of AI as justification for layoffs
Roughly 92 million jobs might face displacement by 2030.
Projection synthesized from cited external reports (WEF/PwC/MGI/Gartner/IMF) as reported in the paper.
high negative AI-Driven Workforce Transformation: Displacement, Opportunit... number of jobs projected to face displacement by 2030
The review identifies attention bias as a focal mechanism, particularly salient for retail investors.
Synthesis from the literature review highlighting attention bias as a recurring mechanism with emphasis on retail investor susceptibility (no quantitative sample size reported).
high negative Mitigating Attention Bias in Index-Fund Investment: AI-Enabl... attention bias (mechanism affecting investment behavior)
In information-intensive and high-noise stock markets, investors often face information overload and rely on heuristics and selective attention, which can lead to cognitive biases and reduced decision quality.
Argument and synthesis from the paper's literature review describing investor behavior in noisy, information-intensive markets (no specific sample size reported).
high negative Mitigating Attention Bias in Index-Fund Investment: AI-Enabl... decision quality (reduced)
Many participants used the model to rubber-stamp a prior guess and, as a result, performed worse than the model alone.
Pilot analysis comparing hybrid forecasts to model-alone forecasts and observing a subset whose forecasting error exceeded the model's error; described qualitatively in the paper.
high negative Human Capital, Not Model Benchmarks, Predicts Hybrid Intelli... forecasting accuracy relative to model (worse performance)
Awards from bots with the lottery rationale can in fact reduce user activity and downstream impact.
Reported negative effect found in the Reddit field experiment: awards administered by apparent bot accounts with a lottery rationale were associated with reduced subsequent user activity and downstream impact. Sample size not reported in the abstract.
high negative A field experiment of social influence and behavioral contag... user activity (volume) and downstream impact
Uneven regulatory capacity in developing economies is a structural risk that can undermine systemic stability during rapid AI adoption.
Findings and Discussion note uneven regulatory capacity as a key challenge; based on comparative country assessments and qualitative policy analysis (no sample size or quantified impact).
high negative Economic and Financial Implications of Artificial Intelligen... regulatory capacity and its effect on systemic stability
AI introduces cybersecurity risks that may undermine systemic stability if not managed.
Findings identify cybersecurity risk as a structural vulnerability; supported by qualitative institutional analysis and financial stability indicators (no quantitative effect reported).
high negative Economic and Financial Implications of Artificial Intelligen... cybersecurity risk to financial system stability
AI introduces algorithmic bias that poses a risk to financial systems if left unaddressed.
Findings list algorithmic bias among main structural risks; based on qualitative analysis of institutional readiness and policy frameworks (no empirical quantification provided).
high negative Economic and Financial Implications of Artificial Intelligen... presence/impact of algorithmic bias
The AI premium is absent in emerging markets, including China.
Geographic cross-sectional analysis indicating no significant AI premium in emerging market firms (explicitly mentioning China).
high negative AI Premium AI premium presence/absence in emerging markets (e.g., China)
The identified gap is the price of non-credible oversight communication.
Theoretical argument and formal analysis linking the existence of the slab (gap) to non-credible communication/signaling constraints in the oversight interface; the paper interprets the gap as arising from inability to credibly communicate oversight-relevant information.
high negative A Contextual-Bandit Oversight Game with Two-Sided Informatio... decision_quality / oversight_effectiveness
There is a slab (region) of avoidable harm: cases where the AI privately knows the proposed action is harmful and shutdown would help, yet a myopic human, trusting her prior, declines to oversee.
Analytic characterization of the gap between the team-optimal policy and the myopic human rule in the one-shot model; the paper identifies parameter-region (the 'slab') where the myopic rule fails to oversee while oversight would reduce harm.
high negative A Contextual-Bandit Oversight Game with Two-Sided Informatio... avoidable_harm / error_rate (harm occurrence due to non-oversight)
The relational value of workplace AI companions remains underexplored.
Claim motivated by the authors' systematic interdisciplinary literature review (paper states relational value is underexplored); no numeric count of studies provided in excerpt.
high negative Thinking, Feeling, Becoming: A Relational Competency Framewo... extent of research attention to relational value
Unemployment, idle labour and weakening domestic demand are holding back growth.
Empirical results reported in the paper indicate a negative relationship between unemployment (and related indicators) and economic growth in the 27-country panel (2008–2020).
high negative AI Readiness, Renewable Energy, and Industrial Development: ... economic growth (country-level) / unemployment rate
The existing international competency indices fail to capture the structural differentiation in AI-driven educational transformation across EU moderate innovator economies, rendering evidence-based policy design inadequate.
Stated as a motivating assertion in the paper; based on the author's critique of existing indices and the subsequent focused evaluation of selected EU moderate innovator economies (Visegrad and Baltic states). No specific quantitative comparison of indices is reported in the abstract.
high negative AI-Education and Innovation Competitiveness: EU Moderate Inn... adequacy of international competency indices to capture structural differentiati...
AI poses environmental challenges.
The abstract lists environmental challenges as one of the potential trade-offs identified by the systematic review of 194 articles.
high negative Artificial Intelligence and Economic Development: A Systemat... environmental_sustainability / environmental_impact
AI can contribute to widening inequality.
Abstract reports the review identifies widening inequality as a potential trade-off of AI, based on synthesis of 194 articles.
high negative Artificial Intelligence and Economic Development: A Systemat... income_distribution / inequality
AI can give rise to job displacement.
The abstract states the review finds potential trade-offs including job displacement across the surveyed literature (194 articles).
high negative Artificial Intelligence and Economic Development: A Systemat... job_displacement
Only a small percentage of the human-labelled bugs are detected as being likely associated with LLM-generated code.
Comparison between a set of human-labelled bugs and detector-flagged LLM-generated code to assess co-occurrence (human-labelled bug sample size not provided).
high negative An Exploratory Study on LLM-Generated Code and Comments in C... percentage of human-labelled bugs associated with code detected as likely LLM-ge...
Comments exhibit a relatively low proportion of grammatically correct sentences.
Linguistic/grammatical analysis of comments flagged as likely LLM-generated (detector-based proxy analysis).
high negative An Exploratory Study on LLM-Generated Code and Comments in C... proportion of grammatically correct sentences in comments detected as likely LLM...
Code detected as likely to be generated by LLMs decreased over time (2021–2025).
Detector-based proxy analysis on active company- and community-maintained repositories from 2021 to 2025 using various tools and techniques to detect LLM-generated code.
high negative An Exploratory Study on LLM-Generated Code and Comments in C... proportion of code detected as likely LLM-generated over time
This asymmetry highlights dual-use risks of AI systems designed to influence group behavior in collective action settings.
Interpretation and implication drawn from experimental findings showing that antisocial personalization can more strongly and persistently undermine cooperation.
high negative AI Persuasive Framing in Collective Dilemmas policy/ethical risk (dual-use potential of persuasive AI)
There is an asymmetry between prosocial and antisocial persuasion: antisocial (selfish) persuasion produces larger and more persistent reductions in cooperation than prosocial persuasion produces increases.
Direct experimental comparison of prosocial versus antisocial AI persuasion treatments in the iterated Collective Risk Game showing differential magnitudes and temporal persistence of effects (reported results from N = 1,283).
high negative AI Persuasive Framing in Collective Dilemmas relative magnitude and persistence of changes in contributions and group success...
The larger and more persistent negative effects of antisocial AI persuasion were particularly pronounced for personalized interventions.
Subgroup or interaction analysis in the experiment indicating that personalization (targeting by Social Value Orientation) amplified the persistence and magnitude of antisocial framing effects (reported within the N = 1,283 sample).
high negative AI Persuasive Framing in Collective Dilemmas interaction of personalization with antisocial framing on contributions and grou...
When AI treatments were reconfigured to promote selfish behavior through exculpatory framing, the negative effects on contributions and group success were larger and substantially more persistent.
Experimental comparison between prosocial and antisocial (exculpatory/selfish) AI treatments in the iterated Collective Risk Game showing larger and longer-lasting reductions in contributions and lower group success rates under antisocial framing (reported across N = 1,283).
high negative AI Persuasive Framing in Collective Dilemmas player contributions and group success rate under antisocial framing
In open-ended collaboration and bargaining, the same manipulation substantially degrades performance.
Experimental manipulation of agreeableness in LLMs on open-ended research collaboration and competitive bargaining tasks; authors report substantial performance degradation in these domains. Abstract lacks numeric metrics, sample sizes, and statistical significance details.
high negative When Does Personality Composition Matter for Multi-Agent LLM... team performance (quality/success in open-ended collaboration and bargaining)
In the same repositories, agent-authored contributions concentrate repository-level friction roughly twice as much as human ones (intraclass correlation 0.30 versus 0.16); this gap holds after controlling for codebase size, age, task shape, process maturity, and merge path.
Comparison of intraclass correlations (ICC) between agent-authored and human-authored pull requests using multilevel models with controls for codebase characteristics and process variables. Dataset includes >930,000 agent-authored PRs (human sample size not specified in excerpt).
high negative Govern the Repository, Not the Agent: Measuring Ecosystem-Le... concentration of repository-level integration friction (measured by intraclass c...
The paper reframes humans not as passive users, but as core system components whose competencies, limitations, and adaptive capacities constrain the performance envelope of optimized AI systems.
Framing/interpretive claim derived from the paper's perspective and literature synthesis (conceptual; no empirical support provided in text).
high negative Optimizing Human Capital in AI-Enabled Architectures: A Syst... constraining effect of human competencies and limitations on AI system performan...