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).
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 |
Productivity
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Conditional on the rapid scenario, economists forecast the labor force participation rate falling from its current level of 62% to 55% by 2050.
Conditional forecasts in Key Findings for the economist respondent group under the rapid AI scenario (2050 horizon).
There are macroeconomic risks associated with AI-led unemployment.
Paper's macroeconomic analysis drawing on labor economics and technology adoption research; no quantitative estimates or sample sizes provided in the summary.
Managerial incentives drive premature workforce contraction during AI adoption.
Analytical claim grounded in labor economics and organizational behavior review; the summary indicates examination of managerial incentives but does not report primary empirical tests or sample sizes.
Premature workforce contraction in response to AI adoption foreshadows deeper structural challenges as AI systems mature.
Forward-looking claim based on synthesis of literature and theoretical projection; no empirical quantification or sample provided in the summary.
This pattern of premature workforce reductions reflects longstanding corporate short-termism rather than genuine technological displacement.
The paper's interpretation drawing on labor economics and organizational behavior literature; no empirical study or sample size reported in the summary.
Organizations face mounting pressure to demonstrate immediate returns on AI investments, often through workforce reductions that outpace actual automation capabilities.
Argument in paper citing accelerating AI adoption across sectors and observed managerial responses; no primary dataset or sample size reported in the text.
Applying the Auditor-Corrector methodology to ELT-Bench uncovers that most failed transformation tasks contain benchmark-attributable errors — including rigid evaluation scripts, ambiguous specifications, and incorrect ground truth — that penalize correct agent outputs.
Audit results on ELT-Bench identifying categories of benchmark errors (rigid scripts, ambiguous specs, incorrect ground truth) and attributing many failed transformation tasks to these errors; no numeric breakdown or sample count given in the excerpt.
On ELT-Bench, the first benchmark for end-to-end ELT pipeline construction, AI agents initially showed low success rates, suggesting they lacked practical utility.
Reference to initial evaluation results on ELT-Bench showing low success rates for AI agents; the provided excerpt does not give numerical success rates or sample size.
The way we're thinking about generative AI right now is fundamentally individual (this appears in how users interact with models, how models are built, how they're benchmarked, and how commercial and research strategies using AI are defined).
Author's observational/descriptive claim supported by argumentative examples (mentions user interaction patterns, model design and benchmarking practices, and commercial/research strategies); no empirical sample or quantitative analysis reported in the excerpt.
Traditional questionnaires yielded slightly higher accuracy in risk assessment.
Result reported from the two experiments comparing traditional questionnaires to adaptive ARQuest versions; no numeric accuracy or sample size provided in the excerpt.
Insurers must blindly trust users' responses, increasing the chances of fraud.
Stated as a motivating problem in the paper; presented as logical/empirical concern rather than supported by a reported study within the paper.
Insurance application processes often rely on lengthy and standardized questionnaires that struggle to capture individual differences.
Descriptive claim in paper introduction arguing limitations of standard questionnaires; no experiment or sample size reported for this assertion.
Using a stylised inpatient capacity signalling example and minimal game-theoretic reasoning, task optimisation alone is unlikely to change system outcomes when incentives are unchanged.
Theoretical analysis using a stylised inpatient capacity signalling example and game-theoretic reasoning presented in the paper (no empirical data/sample reported in the abstract).
Deployment of AI systems carries significant costs including ongoing costs of monitoring and it is unclear whether optimism of a deus ex machina solution is well-placed.
Conceptual/argumentative claim made by the authors in the paper (no empirical study or sample size reported in the abstract).
Improvements in operational resilience (OR) effectively reduce corporate operational risk.
Further analysis reported in the paper linking higher OR to lower operational risk measures for firms in the sample.
AI promotes operational resilience by reducing management agency conflicts.
Mechanism (mediation) tests reported in the paper showing AI associated with reductions in measures of agency/management conflict, which in turn relate to OR improvements.
No regulatory framework requires disclosure of machine/AI labor output.
Author's assertion in the paper (policy claim; no legislative survey or quantification reported).
No index tracks machine labor output over time.
Author's assertion in the paper (stated lack of existing indices; no systematic review/sample reported).
This labor force is entirely invisible to the economic infrastructure humanity has built to measure work: no standardized unit of measurement exists.
Author's assertion/diagnosis in the paper (argumentative/observational, no empirical survey or sample reported).
Agent contributions are associated with more churn over time compared to human-authored code.
Longitudinal comparison between agent-generated and human-authored contributions reported in the paper (churn/survival estimates described; association between agent contributions and higher churn asserted).
Unbalanced or poorly governed adoption of Big Data and AI contributes to increased systemic risk, cybersecurity vulnerability, regulatory fragmentation and third-party dependence on BigTech platforms.
Argument based on qualitative literature review and synthesis of international empirical studies and comparative sector analysis; no single-sample empirical study in this paper.
Extreme automation (high AI intensity) causes employment decline.
Part of the U-shaped relationship reported by the paper's empirical results; described qualitatively in the abstract/summary.
Task orchestration is the most under-researched dimension among the five workplace-design components.
Finding from the PRISMA-guided systematic review of 120 papers, which mapped coverage across the five dimensions and identified task orchestration as having the least research attention.
Decision authority allocation emerges as the binding constraint for Society 5.0 transitions.
Result synthesized from the systematic review and theoretical analysis mapping the five workplace-design dimensions; stated as the binding constraint in the paper's findings.
The literature shows persistent gaps in empirical validation, standardized evaluation methods, and sector-specific comparative analyses of agentic AI in financial services.
Review-level assessment noting limited empirical studies, heterogeneous evaluation metrics, and few direct cross-sector comparisons up to mid-2024.
Significant implementation barriers persist, notably workforce transformation challenges, legacy system integration difficulties, and trust deficits.
Thematic synthesis across empirical and conceptual papers in the review reporting implementation barriers and change management issues.
Ethical concerns—including bias, lack of transparency, and regulatory compliance risks—remain critical for agentic AI in financial services and necessitate layered governance and human-AI collaboration.
Collation of ethical, legal, and governance issues reported across the reviewed multidisciplinary studies and normative discussions.
Insurance is comparatively underrepresented in the literature and in reported agentic AI deployments compared with banking and investment.
Review finding (counts/themes across included studies indicating fewer studies/applications in insurance relative to banking and investment).
A weak manager directing a weak worker achieves a 42% success rate, performing worse than the weak agent alone which achieves 44%.
Empirical comparison across the same 200 SWE-bench Lite instances and pipeline configurations, comparing weak-manager+weak-worker pipeline to weak single-agent baseline.
Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation.
Calibration of the model to O*NET tasks + expert survey + GPT-4o decompositions; implementation results reported for computer vision showing substitution varies with task complexity.
AI systems exhibit predictable but diminishing returns to data, compute, and model size (scaling-law experiments), implying the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly.
Scaling-law experiments estimating performance as a function of data, compute, and model size; described experimental estimation of production function.
The common claim that generative AI simply amplifies the Dunning–Kruger effect is too coarse to capture the available evidence.
Paper's synthesis of heterogenous empirical findings from human–AI interaction, learning research, and model evaluation used to critique the uniform-amplification interpretation; no single empirical countertest reported.
LLM use degrades metacognitive accuracy and flattens the classic competence–confidence gradient across skill groups (i.e., reduces calibration and narrows differences in self-assessed confidence by skill level).
Synthesis of studies from human–AI interaction and learning research reported in the paper that document worsened calibration and a reduction in the competence–confidence gradient when users rely on LLM outputs; the paper does not report a single combined sample size.
The agent team topology exhibits higher operational fragility due to multi-author code generation.
Reported empirical observation from experiments comparing architectures, attributing increased fragility/errors to multi-author code generation in the agent team setup (stated qualitatively; no quantitative failure rates provided in the abstract).
Azar et al. (2023) show that monopsonistic employers have stronger incentives to automate and document that US commuting zones with higher labor market concentration experienced more robot adoption.
Citation reported in the paper summarizing Azar et al. (2023); empirical analysis across US commuting zones (no sample size provided here).
Acemoglu and Restrepo (2022) attribute 50–70% of the increase in US wage inequality between 1980 and 2016 to displacement of workers from tasks by automation.
Citation reported in the paper summarizing Acemoglu and Restrepo (2022)'s attribution of the rise in wage inequality to automation-driven task displacement.
Dechezleprêtre et al. (2025) exploit Germany's Hartz reforms to estimate an elasticity of automation innovation to low-skill wages of 2–5 at the firm level.
Citation reported in the paper summarizing Dechezleprêtre et al. (2025)'s empirical estimate (elasticity 2–5); the paper states this was estimated at the firm level.
Eloundou et al. (2024) predict that half of US jobs are significantly exposed to recent advances in generative AI.
Citation reported in the paper summarizing Eloundou et al. (2024)'s prediction; no sample size provided in the excerpt.
When employers have monopsony power, they choose technologies that expand this power beyond what a social planner would consider optimal.
Model results on monopsonistic employer incentives and their technological choices; discussion supported by citations.
Profit-maximizing firms pursue innovations that erode workers' market power by making them more easily replaceable, even at the expense of production efficiency; a social planner who values worker welfare would employ technologies that preserve workers' market power.
Theoretical analysis of interactions between technological choice and market power; supported by cited empirical evidence (e.g., Azar et al. 2023) in the paper.
A welfare-maximizing planner would choose to automate fewer tasks than production efficiency would dictate when workers' welfare is heavily weighted.
Model analysis of welfare-maximizing automation level compared to production-efficient automation; analytical result in the automation application.
Observed declines in browsing time due to ChatGPT adoption are concentrated in website categories such as search and news, which are highly exposed to substitution by generative AI.
Category-level browsing time changes across website classification; concentration of declines in categories identified as highly overlap-exposed to chatbot capabilities using web-scraping and LLM site-level overlap classification.
High-income and younger households adopt generative AI substantially faster than low-income and older counterparts, and this gap is widening over time ('generative AI divide').
Descriptive heterogeneity analysis using Comscore household demographics (income and age bins) and observed adoption trajectories across 2021–2024; authors report widening gap rather than convergence.
Diminishing returns are not only a geometric flattening of the loss curve, but also rising pressure for cost reduction, system-level innovation, and the breakthroughs needed to sustain Moore-like efficiency doublings.
Analytical claim in the paper about the implications of diminishing returns for cost pressure and innovation requirements (qualitative; no sample size in excerpt).
Prominent studies predict substantial job displacement due to automation.
Paper asserts this as background, referencing the existence of prominent studies in the literature (no specific citations or sample sizes provided in the abstract).
For organizations of n humans with AI agents, the optimal team size decreases with agent capability.
Derived implication from the stylized model's analysis of multi-human organizations interacting with AI agents.
There is no smooth sublinear regime for human effort; it transitions sharply from O(E) to O(1) with no intermediate scaling class.
Mathematical derivation from a stylized model of human-AI collaboration that assumes tasks decompose into atomic decisions, a fraction ν are novel, and specification/verification/error correction scale with task size.
So far the maintenance and migration work was done largely manually by human experts.
Background assertion in the paper's introduction/abstract; no empirical backing provided in abstract.
The regime divide deepens under AI capital concentration, admits a permanent displacement attractor in shallow markets, and generates equity market participation hysteresis in which the ERP remains elevated after employment has normalised.
Model-based assertions: analysis shows capital concentration magnifies regime separation, yields a permanent displacement attractor in shallow-market parameterizations, and produces hysteresis in participation leading to persistently elevated ERP after employment recovery.
The alignment risk channel is specific to agentic AI: correlated misalignment in AI objectives generates aggregate output shocks with fat left tails; formalised via Hansen-Sargent multiplier preferences, the resulting alignment risk premium (ARP) enters the equilibrium ERP decomposition as a priced factor additively separable from the participation wedge.
Theoretical formalisation in the paper: uses Hansen-Sargent multiplier preferences to capture model uncertainty/robustness and defines an ARP that is additively separable in the ERP decomposition.