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 |
AI coding assistants expand the volume of code requiring review, turning code review into a growing bottleneck.
Authors' analytical claim linking increased code production from AI assistants to increased review workload; presented as an observed/trend claim in the paper rather than supported by a quantified study in the abstract.
Code review has evolved for decades, from informal peer checking to today's pull request (PR) workflows, yet it remains a largely manual, uneven, and cognitively demanding process.
Authors' literature review and historical synthesis of code review practices presented in the paper (conceptual / review-based evidence). No empirical sample or experiment reported in the abstract.
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.
Greater automation can obscure rather than eliminate failure modes.
Analytical claim in paper arguing that increased automation hides failures; presented as an interpretive finding rather than a quantified experimental result in the excerpt.
End-to-end autonomous systems have not yet consistently reached major-venue acceptance standards.
Paper's statement based on review of acceptance/peer-review outcomes and standards as of April 2026; no numeric acceptance-rate data presented in the excerpt.
Research code lags far behind pattern-matching benchmarks.
Paper's evaluative claim from its experiments/coding analysis indicating code produced for research tasks is weaker than benchmark performance on pattern-matching tasks; excerpt contains no numerical comparison.
Generated ideas often degrade after implementation.
Paper statement about the gap between idea generation and implemented results reported in the Creation-phase analysis; no quantified follow-up study reported in the excerpt.
AI remains fragile for genuinely novel ideas, research-level experiments, and scientific judgment.
Summary claim from the paper's end-to-end lifecycle analysis indicating limitations on novelty and experimental rigor; no numeric performance metrics provided in excerpt.
Frontier LLMs fail to judge novelty reliably.
Paper's claim from its Validation-phase analysis that models do not reliably assess novelty; excerpt contains no underlying experimental sample or validation metrics.
Frontier LLMs miss hidden errors.
Qualitative statement from paper indicating models fail to detect some latent or subtle errors in research artifacts; no numeric evaluation provided in excerpt.
Under scientific pressure, even frontier LLMs still fabricate results.
Reported observation in paper about model behavior under scientific-use conditions; no specific quantitative experiments or sample sizes given in the excerpt.
Diagnostics also reveal a small tail of extreme errors for the Random Forest model.
Model diagnostic analyses reported in the paper indicating error distribution and presence of extreme prediction errors (tail).
Unrestricted frontier-scale checkpoint synthesis remains open (i.e., not yet solved).
Authors' assessment in the abstract noting current limits; asserts that unrestricted synthesis at frontier/model-scale has not been achieved.
Real-world trajectory data can provide highly accurate insights but collecting it is costly and often infeasible for many retailers.
Author claim about practical constraints of data collection for retailers; argued contextually in the paper rather than presented as a quantified empirical finding in the excerpt.
Actual customer trajectories deviate by an average of 28% from shortest paths.
Empirical measurement reported in the paper comparing real-world trajectory data to shortest-path (TSP-like) routes; exact sample size not stated in the provided text.
In the context of search retrieval, current cold-start models suffer from the misalignment between training objectives and online business metrics, and they lack effective mechanisms to measure an item's growth potential.
Claim made in paper as motivation/background; no empirical details provided in the excerpt.
Existing systems tend to prioritize presenting users with already popular items, a phenomenon often referred to as the "Matthew effect".
Statement/observation in the paper; presented as background/motivation (no empirical evidence or sample size reported in the excerpt).
AI agents deployed into SRE workflows currently derive their understanding of environment state from raw observability telemetry at query time, paying a semantic-interpretation tax in tokens, latency, and inferential reliability.
Author statement / problem framing in the paper (no quantitative experiment reported for this general claim).
Both major deployed systems and designed mechanisms concentrate on the most observable and easiest-to-govern tier, while the forms of commercial influence most consequential for user autonomy remain poorly understood and lack frameworks for detection, measurement, or disclosure.
Critical review of deployed system design choices and governance mechanisms; authors assert that attention and tooling focus on observable product-mention-level interventions while higher-tier influences lack measurement and disclosure frameworks.
These tiers instantiate across modalities and system architectures, including retrieval-augmented generation and agentic pipelines where upstream decisions can sharply constrain downstream outcomes.
Analytical claim supported by examples and discussion of system architectures (e.g., RAG, agentic pipelines) showing how interventions at different stages map to the taxonomy; no quantitative evaluation reported in excerpt.
Generative AI fundamentally changes advertising: rather than placing products into discrete slots, it enables interventions on the generative process itself, which induce commercial influence through less observable channels.
Conceptual argument backed by analysis of how generative models produce outputs and how interventions can operate on latent variables of generation; illustrated via taxonomy in the paper rather than quantified empirical tests.
Empirical research shows that ads woven directly into large language model (LLM) outputs often go undetected by users.
Reference to prior empirical studies (unspecified in the excerpt) showing user failure to detect embedded ads in LLM outputs; presented as an empirical finding rather than new experimental data in this paper.
Management shareholding and analyst attention amplify the debt-cost penalty faced by AI washing firms.
Heterogeneity/interaction analyses showing larger post-shock financing-cost increases for AI washing firms with higher management shareholding and greater analyst attention (descriptive of moderator effects; no sample sizes in abstract).
Difference-in-differences estimations reveal that AI washing firms experience a 12.5 basis point relative increase in debt financing cost afterward.
Difference-in-differences estimations comparing AI washing firms to others before and after the FYP shock; effect reported as 12.5 basis points increase in debt financing cost (sample size not stated in abstract).
Standard health system digital transformation policy, which typically addresses only the threshold failure through individual incentives, is predicted to systematically produce the partial adoption trap.
Model prediction contrasting full policy architecture vs. conventional policies that focus solely on individual incentives; analytical conclusion that such limited policies leave other failure modes unaddressed and therefore lead to stable partial adoption. Theoretical model; no empirical sample.
The barrier-lowering benefit of failed attempts is offset when trust erosion is rapid.
Model analysis combining cost-ratchet dynamics and trust erosion parameters; results showing interaction where fast trust erosion negates barrier reductions. Theoretical simulations/derivations; no empirical sample.
These failure modes are most severe precisely for the technologies with the greatest systemic value: the Value-Adoption Paradox.
Analytical result from the model showing failure-mode severity as a function of systemic value; theoretical identification of a paradox where higher systemic-value technologies face stronger coordination/trust/cultural barriers. Theoretical derivation; no empirical sample.
The basin of attraction of the partial adoption trap is enlarged by a cultural failure arising from negative coordination norms among doctors.
Model analysis including cultural coordination norms; theoretical demonstration that negative norms exacerbate partial adoption equilibria. Theoretical model; no empirical sample.
The basin of attraction of the partial adoption trap is enlarged by a trust failure arising from the organisation's inability to credibly commit to sharing productivity gains.
Model extension incorporating organisational commitment/transfer of gains; analytical results showing trust/commitment constraints increase stability of partial adoption. Theoretical model; no empirical sample.
The basin of attraction of the partial adoption trap is enlarged by a threshold coordination failure arising from the non-appropriable nature of systemic benefits.
Model analysis showing how non-appropriable systemic benefits (externalities) change payoff structure and enlarge the basin of attraction for partial adoption. Theoretical derivation; no empirical sample.
Current monolithic architectures struggle to enforce rigid brand constraints, frequently hallucinating unapproved visual assets.
Asserted critique of existing architectures in paper; no specific empirical metrics, datasets, or sample sizes provided.
Integration of generative video models into enterprise environments is restricted by temporal inconsistencies and severe brand misalignment.
Statement in paper describing deployment limitations; no empirical study, dataset, or sample size provided to quantify these restrictions.
Deterministic copy collapses uncertainty (i.e., copying deterministically collapses the learner's uncertainty over actions).
Ablation/diagnostic comparisons reported in the paper showing deterministic-copy policies reduce or collapse uncertainty compared to stochastic or trace-informed policies in the benchmark tasks.
Reward-only PPO variants miss trace alignment (they achieve reward/KPIs but do not align with benchmark trace/behavior).
Empirical comparison across the two-hotel benchmark and a compact hidden-budget bidding task showing reward-only PPO variants fail to match trace-based diagnostics.
Observed failures in the pilot were localized primarily to external integrations.
Pilot outcome summary in the paper stating failure localization was mainly due to external integrations (no numeric breakdown provided).
Agentic systems plan at inference time, making behavior sensitive to prompt variation and difficult to audit.
Author statement characterizing agentic (planning) AI systems and their inference-time sensitivity and auditability challenges.
Existing workflow platforms offer few semantic correctness guarantees.
Author statement contrasting current platforms' observability/durability with lack of semantic correctness guarantees.
Under an idealized model of independent steps, a ten-step process with 90% per-step reliability completes successfully only 35% of the time.
Analytic, idealized independence model reported in the paper (mathematical calculation: 0.9^10 ≈ 0.3487).
Distributing deliberation tools across a hierarchy degrades performance relative to hierarchy alone for all five model families, reaching up to 3.4× worse mean return while using 1.8–2.7× more tokens.
Empirical comparisons across the twelve configurations showing distributed deliberation vs. hierarchy-alone across five model families and six models; measured mean returns and token consumption over 3,475 episodes with token-level accounting.
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).
Our results show that multi-resource stranding materially changes deployable capacity, effective capital expenditure, and delivered performance.
Empirical/modeling results from the paper's framework (simulation results using projection models + Azure operational data); the abstract claims material effects but does not report numeric sample sizes or effect sizes in the excerpt provided.
Designing an efficient power delivery hierarchy for the long run is difficult because rack placement feasibility, workload impact, and cost depend jointly on electrical topology, deployment granularity, placement policy, power oversubscription, and workload mix.
Analytic/methodological claim enumerating interacting factors; stated as a complexity motivating the modeling framework.
Power utilization is particularly important as grid power capacity is a scarce resource in the AI era.
Contextual claim in the paper linking increased AI demand to constrained grid power capacity; supported by the paper's framing rather than reported empirical measurements in the abstract.
As power densities increase, a datacenter designed for a different target density may strand power, i.e., may be unable to use all the power that its delivery hierarchy has provisioned.
Conceptual/mechanistic claim supported by the paper's modeling framework that examines mismatches between provisioned power and deployed demand; no numeric sample size provided in the abstract.
This poses a major challenge for datacenter power delivery designers.
Argument based on the projected rise in rack power density and resulting engineering constraints; asserted in the paper's introduction/contextual framing rather than an experimental result.
Yapay zekâ gelişmekte olan ekonomiler için hem fırsatlar hem de tehditler yaratmaktadır: AI işgücü maliyeti avantajını törpüleyebilir.
Kavramsal değerlendirme; mekanizma temelli argüman (otomasyon işgücü maliyeti avantajını azaltır); ampirik veri ya da örneklem belirtilmemiştir.
Bu dönüşüm mevcut küresel değer zinciri yapılarını ve ülkelerin bu zincirlerdeki konumlarını doğrudan sorgulamaktadır.
Kavramsal tartışma; yazarın analitik çerçevesiyle GVC (küresel değer zinciri) yapılarının AI ile yeniden değerlendirilebileceği ileri sürülmektedir; ampirik örneklem yok.
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.