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 |
Frontier models still make some basic mistakes that occasionally result in irreversible harm (for example, sending an email to the wrong person).
Reported observed incidents from WorkBench evaluations indicating that even top-performing models sometimes make mistakes that can cause irreversible harm; no incident counts or sample size provided in the excerpt.
In June 2026 the best agent to date, Claude Opus 4.8, took an unintended harmful action on 2.5% of tasks.
Reported evaluation result on the WorkBench benchmark (June 2026) measuring incidence of unintended harmful actions by agents; exact sample size not stated in the excerpt.
In March 2024 the best agent on WorkBench, GPT-4, took an unintended harmful action (such as emailing the wrong person) on 26% of tasks.
Reported evaluation result on the WorkBench benchmark (March 2024) measuring incidence of unintended harmful actions by agents; exact sample size not stated in the excerpt.
A lack of strategic alignment is a critical barrier that leads AI initiatives to be unused despite technical success.
Paper identifies misalignment between AI projects and organizational strategy as a failure mode in its failure analysis; methodological details not specified in the summary.
User resistance is a critical barrier that prevents AI initiatives from delivering business value.
Paper lists user resistance among critical barriers based on analysis of failed projects; no sample size or quantitative method stated in the summary.
Siloed deployments are a critical barrier causing AI initiatives to remain unused.
Identified in the paper's analysis of failure modes; presented as a key barrier (method and sample size not provided in the summary).
AI initiatives meet functional specifications yet remain unused.
Paper's examination of AI projects that passed technical/functional tests but were not adopted; method/sample size not stated in the summary.
Organizations struggle with "zombie AI investments" that succeed technically but fail to generate tangible business value.
Paper's analysis of failed AI initiatives and described observations; methods not specified in the summary (likely qualitative/case analysis).
Aligning the dimensions with the regulatory frameworks above identifies overlapping gaps neither side currently closes.
Result claimed in abstract that mapping taxonomy dimensions to listed regulatory frameworks reveals overlapping unattended gaps; no detailed counts or specific gaps listed in the abstract.
Several open-source organisations have responded with contribution policies, but the result is fragmented, and its alignment with emerging AI governance frameworks (EU AI Act, NIST AI RMF with the UC Berkeley Agentic AI Profile, ISO/IEC 42001 and 23894) is unmapped at the contribution level.
Paper states that multiple organisations adopted policies and that alignment with listed regulatory frameworks is unmapped; the paper reports a comparative study across six organisations (names given).
Autonomous and semi-autonomous AI contributors strain those assumptions
Stated in abstract as an observed tension between agent capabilities and human-focused contribution norms; no quantified incidents or metrics provided in the abstract (but paper indicates mapping of documented agent incidents).
These mechanisms produce ethical harms such as accountability deficits, epistemic injustice, labour precarity, and constrained sovereignty.
Reported synthesis finding drawing on the reviewed literature (50 articles) linking the named mechanisms to listed ethical harms.
These dynamics operate through four mechanisms: epistemic templating, governance transfer, infrastructural lock-in, and labour opacity.
The paper reports these four mechanisms as the pathways identified via the critical synthesis of the 50 articles.
The analysis identifies four interrelated dynamics—algorithmic colonialism, data colonialism, platform imperialism, and platform sub-imperialism—through which dependency and domination are reproduced across global and intra-South contexts.
Synthesis of the 50 reviewed peer-reviewed articles; these dynamics are reported as the paper's analytical findings.
AI adoption may reproduce entrenched inequalities in postcolonial contexts.
Critical synthesis (literature review) of 50 peer-reviewed articles from 2019–2025 reported by the paper.
A perfect verifier cannot substitute for taste: the unbounded stream of correct-but-worthless statements is not an engineering accident but a provable necessity, since covering unrecorded valuable mathematics requires an infinite, but asymptotically negligible, stream of certified trivia.
Theoretical proof within the model that achieving coverage of unrecorded valuable mathematics necessitates an infinite stream of verifiable-but-trivial outputs; argument that these outputs must be asymptotically negligible in rate yet unbounded in total count.
The verifier is not taste: the collections admitting generation with breadth are exactly those of the oracle-free model, characterized fiber-wise by Angluin's condition.
Theoretical model and proofs in the paper comparing a verifier-equipped nested-language generation model to an oracle-free model; characterization via Angluin's condition (formal, fiber-wise).
Those valuable signals are entangled with framework churn, naming drift, generated-source ambiguity, dependency rituals, CI dialects, weak proof routes, and human-oriented review customs.
Qualitative claim/analysis in the paper describing entanglement of signal and accidental complexity; no empirical quantification provided.
Frontier coding models may spend substantial capacity learning not only program behavior, but also accidental entropy in human repositories.
Conceptual/theoretical assertion presented in the paper (no empirical sample or experiment reported for this claim).
The review highlights critical challenges related to privacy, emotional surveillance, algorithmic bias, and employee trust associated with emotional AI in the workplace.
Aggregated observation from the systematic review; these concerns are reported as recurring themes across the surveyed literature (specific counts/examples not given in the abstract).
The study identifies specific retention issues including rigid work practices, a predominantly masculine culture, and occurrences of bullying and harassment.
Findings from thematic analysis of 23 interviews using NVivo 13; participants' accounts raised these specific themes as retention-related issues.
Women in UK construction continue to face major retention challenges driven by structural biases that lead to feelings of disrespect, insufficient support, and being undervalued.
Thematic analysis of 23 qualitative interviews with women involved in digitally enabled projects; participants reported experiences and perceptions related to retention and workplace culture.
Women make up less than 15% of the UK construction workforce.
Statement in the paper likely citing national labour/industry statistics or prior literature (not primary data from this study).
Interactive effects and dynamic vicious cycles exist among the three mechanisms: temporal loss of control amplifies the physiological effects of temporal predation, while temporal acceleration intensifies the psychological effects of temporal loss of control.
Theoretical interaction hypotheses articulated in the framework based on cross-model synthesis and literature discussion; no empirical interaction tests presented in the abstract.
Temporal loss of control is expected to contribute to depression and to heighten occupational injury risk, with learned helplessness and the depletion of cognitive resources as key mediating processes.
Theoretical claim derived from integrating Karasek’s demand-control model and job demands-resources literature; proposed mediators and outcomes come from conceptual argument and cited studies rather than new empirical tests.
Temporal acceleration and discipline are theorized to undermine mental health, giving rise to anxiety and burnout via time panic and emotional exhaustion.
Framework/theoretical argument grounded in integration of Rosa’s social acceleration and psychological job-stress models; claim supported by referenced literature but no new empirical data reported in the abstract.
Temporal predation primarily damages physiological health—manifesting as cardiovascular strain and musculoskeletal injuries—through the mediating pathway of chronic fatigue.
Theoretical proposition based on literature synthesis and mediation logic presented in the framework; no primary empirical data or sample size reported in the article text provided.
Algorithmic time politics damages occupational health through three interconnected mechanisms—temporal predation, temporal acceleration and discipline, and temporal loss of control—which form a progressive chain from 'the quantity of time' through 'the quality of time' to 'the sovereignty over time.'
Theoretical multilevel framework developed by the article combining disciplinary theory, social acceleration theory, job demand-control and job demands-resources models and literature review; no empirical testing reported.
In platform labor, algorithms reshape workers’ perception and control of time through mechanisms such as dynamic pricing, compulsory task assignment, time-limit compression, and real-time surveillance, giving rise to a novel power formation—“algorithmic time politics.”
Conceptual/theoretical claim constructed by the article via literature integration and argumentation (synthesis of Foucault, Rosa, Karasek, Bakker & Demerouti); no empirical sample or quantitative study reported.
A rise in firm age by one standard deviation reduces the share of AI workers by 5.2%.
Quantitative estimate reported in abstract based on the paper's empirical analysis of the novel dataset (Babina et al., 2024).
Older firms often encounter difficulties integrating AI talent, possibly due to entrenched practices, outdated systems, and resistance to change.
Empirical analysis described in abstract using the novel resume/job-posting dataset for U.S. firms; mechanism explanations provided in text.
The economy is generically inefficient (under the laissez-faire equilibrium) and a planner can optimally tilt the direction of data accumulation to improve outcomes.
Welfare analysis within the model: comparison of decentralized equilibrium and planner's problem, demonstrating inefficiency and characterizing planner's optimal policy for directing data accumulation (analytical welfare results).
In the fully automated long-run case, short-run dynamics depend on the pattern of data spillovers, but automation is always slow in the long run: the share of tasks produced by labor decays asymptotically as a power law in time.
Analytical asymptotic result from the dynamic model showing that, under full automation, the labor-produced task share follows a power-law decay; short-run behavior is shown to depend on spillover structure (model derivation and asymptotic analysis).
At min-cost, Brick incurs 11.85 points accuracy loss.
Empirical evaluation on the 5,504-query benchmark reporting accuracy loss at the min-cost operating point.
Frontier models cost ten to one hundred times more than local open-weight models.
Cost comparison statement in the paper (asserted market/commercial cost multiples).
Existing LLM routers rely on surface features such as domain labels, keywords, and token count, ignoring the within-domain variance that actually determines model success.
Claim about prior work / existing systems presented in the paper; no explicit empirical test shown in the abstract.
AI adoption is significantly hampered by a lack of workforce skills and supporting infrastructure in these accounting organizations.
Qualitative interview findings and questionnaire responses synthesized via thematic analysis and inferential/statistical analysis (sample size not reported).
Accounting organizations in the study are still in the early stages of AI adoption.
Synthesis of questionnaire and interview findings with thematic analysis indicating limited breadth/depth of AI use (sample size not reported).
AI is used mainly for repetitive and routine accounting tasks, with very little use for higher-level work.
Questionnaire responses and interview data summarized with descriptive statistics and thematic analysis (sample size not reported).
As the "Twin Transition" of green and digital transformation accelerates, the industry faces technology gaps - including Scope 3 emissions and e-waste recycling - that impede sustainable scaling and lead to social tensions.
Author's synthesis and argument drawing attention to technology gaps (Scope 3 emissions, e-waste) as barriers; presented as a conceptual finding rather than empirical measurement.
Current scaling trajectories for Generative AI, typified by linear supply-side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs.
Author's stated analysis/observation in the paper (conceptual critique of current industry scaling practices); no empirical sample or quantitative study reported.
The absence of standardized data governance policies and localized, language-accessible software platforms exacerbates the technological divide in digital agriculture.
Review synthesis identifying governance and software-localization as structural barriers; no empirical governance-audit sample sizes provided in the abstract.
In India, where the sector is dominated by smallholder farmers with fragmented landholdings, the transition to digital agriculture is significantly hindered by severe economic constraints, a lack of robust rural digital infrastructure, and pervasive digital illiteracy.
Targeted review analysis focusing on the Indian agricultural context; claim draws on country-specific literature but the abstract does not report specific empirical sample sizes or quantified barriers.
Despite these proven agronomic and environmental benefits, the global diffusion of digital agriculture remains highly uneven.
Review assertion based on cross-study synthesis that diffusion/adoption is not uniform globally; abstract provides no country-by-country adoption statistics or sample sizes.
Synthetic data can be biased, noisy, and misspecified.
Background claim in the paper describing failure modes of synthetic data; motivated as a fundamental concern motivating the methodological work. No empirical quantification provided in the excerpt.
Existing evaluations of autonomous penetration capabilities often employ opaque methodologies, rely on unrealistic or overly simplified penetration-testing scenarios, or provide LLMs with excessive prior knowledge and task-specific guidance, and cannot accurately capture the extent to which modern AI systems can autonomously perform this capability in high-impact scenarios.
Statement in paper's introduction/abstract summarizing limitations of prior work (literature review / critique of evaluation practices).
API-based approaches struggle with heterogeneous protocols and inaccessible commercial interfaces.
Author assertion contrasting API-based approaches with GUI and COM approaches (conceptual/architectural argument rather than specific experiment).
GUI-based agents suffer from fragile visual grounding and long-horizon error accumulation.
Author assertion in paper introduction describing limitations of GUI-based agents (conceptual analysis / literature-grounding rather than new experimental data).
Research on embedded finance emphasizes modularity but offers limited insight into how systems evolve when AI-driven inference and platform environments jointly structure financial action.
Literature-based critique in the paper's theoretical review of embedded finance research; no empirical sampling reported.
An unconstrained multi-agent baseline produced critical failures in 72% of runs.
Reported experimental result from the 2x4 factorial experiment (failure rate for the unconstrained multi-agent baseline reported as 72%).