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
Filter claims →
Productivity
8807 claims
Filter claims →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filter claims →
Inequality
2332 claims
Filter claims →
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 |
The workflow was cache-dominant, suggesting that persistent agentic environments may shift the economic unit from cost per token to cost per completed artifact.
Observed high cache-read fraction (82.9% in May subset) and interpretation by authors that caching dominates token usage, leading to the suggestion about economic-unit shifts.
Depending on operational parameters, the most time-efficient way to complete a workflow may undergo a transition between two task-processing regimes: a fully AI-assisted regime and a fully manual regime.
Analytical results derived from the paper's formal queueing model (theoretical/model-based derivation; no empirical sample reported).
AI assistance can generate a deceptive productivity signature: average completion times fall because AI tools typically supply a fast first draft, yet workflow-level performance can deteriorate when a subset of AI errors escapes review and returns as costly downstream rework.
Analytical derivation and discussion based on the paper's queueing model (theoretical/model-based evidence; no empirical sample provided).
Public data from Anthropic's Mythos Preview and Mozilla Firefox collaborations, along with public exploit-market price anchors and vulnerability reward programs, support the argument that the near-term shift is toward increased defender remediation throughput rather than simply more zero-days.
Explicit statement that the paper's argument is based on public datasets: Anthropic Mythos Preview, Mozilla Firefox collaboration records, exploit-market price anchors, and vulnerability reward program information (no sample sizes provided in the abstract).
Defender-side bugonomics already existed in vulnerability research, reward programs, and vendor remediation work; LLM-assisted systems change its scale and distribution.
Descriptive claim supported by references to vulnerability reward programs and vendor remediation practices and by public collaboration data (no numerical sample sizes provided in the abstract).
The near-term shift is not simply more zero-days; it is a move toward broader defender remediation throughput: low-signal candidates become cheaper, evidence-rich remediation become more important, and scarce capacity shifts toward maintainer review and release work.
Synthesis drawing on public data from Anthropic Mythos Preview, Mozilla Firefox collaborations, public exploit-market price anchors, and vulnerability reward program information (no numeric sample sizes provided in the abstract).
Exploits and proofs of concept remain important, but in defender workflows they primarily prove impact, guide prioritization, and justify remediation rather than serving the same role they did in high-end offensive workflows.
Conceptual argument grounded in collaboration data and public examples (Anthropic Mythos Preview and Mozilla Firefox collaborations cited); no numerical sample size provided in the abstract.
Drawing on the partial equilibrium model of Gries and Naudé (2022), existing economic frameworks may inadvertently overlook these factors.
The paper's theoretical critique referencing Gries & Naudé (2022); argument is based on model comparison and conceptual analysis rather than new empirical tests.
We identify five key moderating factors: human resource composition, baseline capability of individuals, learning curve of practitioners, incentives for fair use, and flexibility of objectives.
Explicit enumeration of proposed moderating factors in the paper (conceptual identification rather than empirical measurement).
Following the advent of high-performance generative models, AI use has been rapidly encouraged in some sectors while being restricted in others.
Descriptive claim in the paper's introduction/abstract; based on observation and literature context rather than new empirical data.
The framework does not force domains into the same shape; it surfaces each domain's actuarial geometry.
Empirical observation of differing frontier shapes and capital demands across the instantiated domains and traces.
Required reserve capital varies by 22x (Capital@50 from 289 to 6457).
Quantitative results reported in experiments across domains (Capital@50 values reported for domains; ratio computed).
The frontier exhibits a common low-reserve refusal and intermediate-release pattern across domains, with saturation only where the budget grid reaches full reserve demand.
Observed pattern reported across the four instantiated environments and the retail/airline tau-bench traces in experimental results.
AI can raise productivity and output, but its distributional effects are uncertain and mediated by institutions and access to complementary resources.
Conceptual claim in abstract synthesizing literature; supported by secondary sources and integrative framework (OECD, ILO, UNDP, WTO, WEF). No quantified sample size reported.
These findings have broader implications for productivity, equity, and capacity across the global research system.
Discussion/interpretation in paper based on causal results from randomized experiment; inference from observed behavioral changes and heterogeneous effects.
The paper contributes by providing a structured synthesis that bridges efficiency-driven and labor-oriented perspectives on AI-driven manufacturing.
Authors' stated contribution in the paper: a structured thematic synthesis integrating two perspectives from the reviewed literature.
While new high-skill roles emerge from AI adoption, their limited accessibility constrains workforce transition.
Literature synthesis indicating emergence of high-skill roles alongside barriers to access (skills, education, hiring practices) reported in reviewed studies.
This study analyzes three key dimensions: labor displacement as a structural risk, the limitations of job transformation, and the emergence of human-centered AI.
Explicit methodological statement in the paper: systematic literature review and thematic synthesis focusing on three named dimensions.
AI redefines job roles.
Authors' thematic analysis of secondary sources and peer-reviewed literature (qualitative synthesis). No sample size reported.
Artificial Intelligence (AI) has changed how people work across various fields and businesses, especially in the Indian Information Technology (IT) industry.
Authors' qualitative synthesis of peer-reviewed literature and thematic evaluation of secondary data (literature review). No sample size reported.
Digital transformation has expanded connectivity and participation, but the benefits remain unevenly distributed due to asymmetries in data ownership, algorithmic governance, platform control, and value capture.
Argument supported by a literature review / conceptual synthesis of recent studies on digital transformation, data ownership, platform governance and value capture (no original empirical sample reported).
Completion time itself is not sufficient to characterize efficiency gains.
Authors' inferential conclusion in the abstract based on observed dissociation between completion time (no difference) and subjective effort (lower with AI) in their preregistered study (N = 1237).
Decomposition analysis reveals that wage benefits are concentrated among employees aged 45 and above, managers, and white-collar workers; other worker categories experience stagnant wages, and no group shows a negative wage effect.
Decomposition of wage effects by worker groups (age, occupation/type) using the integrated dataset and the DiD/other regression analyses.
Wage increases at small firms primarily explain the positive adoption effect, while wages at medium and large firms remain stagnant after adoption.
Heterogeneity analysis by firm size within the DiD framework showing differential post-adoption wage trajectories for small versus medium/large firms.
Key mechanisms of AI's impact on employment structure were identified: automation of routine processes, formation of new professional profiles, and changes in requirements for employees' competencies.
Qualitative analysis of statistical data, industry reviews, and regulatory legal documents described in the paper (no experimental or survey sample size reported).
Including narrative explanations with AI predictions may involve tradeoffs for decision-making performance.
Synthesis and conclusion based on the experiment's findings (null effect on accuracy, increased reliance, and exploratory detrimental effects on response time and discrimination).
Recent Chinese regulatory initiatives addressing anthropomorphic and emotionally interactive AI services illustrate emerging governmental responses to the social and psychological risks associated with relational AI.
Cited as an illustrative example in the recommendations; the text references Chinese initiatives but does not provide specific citations, legal texts, or empirical evaluation within the document.
Regulatory approaches to advanced AI systems are evolving differently across major jurisdictions.
General observation in the recommendations; no cross-jurisdictional comparative analysis or dataset provided in the text.
Widely used conversational systems increasingly function as interfaces through which users access information, digital services, and online markets.
Descriptive claim presented in the recommendations; no quantitative metrics (e.g., usage statistics, market share) or empirical study cited in the text.
Conversational AI evolves into systems capable of shaping users’ emotions, behaviour, and social engagement.
Stated as a descriptive premise in the policy recommendations; no empirical study, sample size, or quantitative data provided in the text.
AutoResearch autonomy is domain-conditioned: more credible in structured, executable, and rapidly verifiable settings but limited in embodied, delayed, heterogeneous, ethical, or institutionally accountable contexts.
Authors' synthesis of system capabilities and application domains from the surveyed literature; qualitative assessment of where autonomy is plausible vs limited.
Emerging AI-led systems coordinate larger portions of the discovery loop without achieving robust autonomy.
Survey of recently proposed AI scientist and AI-led systems showing increased coordination across workflow steps but lacking evidence of fully autonomous, robust operation; qualitative synthesis.
Algorithmic authority may both strengthen and undermine legitimacy of decisions in AI-enabled organizations.
Theoretical analysis in the paper presenting dual possibilities for algorithmic authority's impact on legitimacy, supported by conceptual reasoning and literature (no empirical test reported).
We propose the Shannon Scaling Law, a unified theoretical framework that models LLM training as information transmission over a noisy channel, grounded in the Shannon-Hartley theorem, mapping model parameters to channel bandwidth and training tokens to signal power.
Theoretical formulation presented in the paper, grounded on Shannon-Hartley theorem and a mapping between model/data quantities and communication-theoretic quantities (bandwidth, signal power).
The effects of digital transformation on labor demand vary substantially across types of digital technologies.
Analysis across different digital technology categories reported in the paper showing heterogeneous effects on labor demand (data: Chinese A-share manufacturing firms, 2011–2024). (Sample size not stated in provided text.)
The impact of digital transformation on labor demand differs across firms with different ownership structures, factor intensity, and asset sizes.
Heterogeneity analysis reported in the paper using subsample or interaction regressions by firm ownership, factor intensity, and asset size (Chinese A-share manufacturing firms, 2011–2024). (Sample size not stated in provided text.)
The capability-level theory explains when digital modularization extends to organizational disaggregation and when accountability keeps capabilities integrated.
Author claim about the explanatory scope of the developed theory; supported by conceptual argumentation and illustrative examples across several domains rather than empirical tests.
Seven propositions link agentic assembly-cost reductions, accountability assets, appropriability, orchestrator intent capture, and boundary misconfiguration to boundary strategy, value appropriation, and rule debt.
Theoretical development consisting of seven formal propositions in the paper; propositions are reasoned and illustrated but not empirically validated.
Verification cost and responsibility transferability determine whether the execution and accountability boundaries can move together.
Propositional/theoretical argument within the capability-level theory; supported by conceptual reasoning and illustrative cases, not by empirical estimation.
Labor-market adjustment to generative AI is a process of organizational reconfiguration, in which firms reshape both hiring demand and the task architecture of work.
Synthesis/conclusion drawn from the paper's empirical findings (decomposition results, heterogeneity analyses).
Adjustment to generative AI differs across the job ladder: senior jobs adjust earlier and mainly through reallocation, whereas junior jobs adjust through a broader mix of reallocation, redesign, and their interaction.
Heterogeneity analysis by job seniority reported in the paper (timing and margin composition of adjustment by seniority).
Generative AI exposure is dynamic rather than fixed, changing substantially over time.
Empirical application of the dynamic posting-level exposure measure to the nationwide job-postings data showing substantial temporal change (as stated in the paper's findings).
The authors construct a dynamic, posting-level measure of generative AI exposure using a two-stage large language model pipeline that identifies tasks in each posting and classifies the extent to which generative AI can perform or assist them.
Paper methodology description: two-stage LLM pipeline to identify tasks and classify generative AI perform/assist capacity at the posting level.
The study uses a nationwide dataset of job postings in the United States covering all sectors of the economy.
Paper statement: 'Using a nationwide dataset of job postings in the United States, covering all sectors of the economy.' (dataset description)
Models with near-identical overall strength show qualitatively different capability profiles.
Observed differences in capability-profile axes for models with similar aggregate scores in the tournament.
Managerial traits, such as risk tolerance and patience, play a role in shaping firms' AI adoption decisions.
Inclusion of manager-level trait measures (risk tolerance, patience) in the ifo Business Survey and analysis showing associations between these traits and reported AI adoption.
Drivers and barriers to AI adoption include firm-specific characteristics and industry dynamics.
Survey-based analysis linking firm characteristics and industry-level factors to reported AI adoption decisions in the ifo Business Survey (likely correlational/regression analysis).
AI adoption/diffusion varies across firm sizes.
Analysis of adoption patterns by firm size using ifo Business Survey firm-level responses (comparison across size categories).
AI is changing informal cultural practices like professional mentoring that are key to helping professionals settle in their positions, stay engaged with their work, and grow their careers.
Participant reports from the 24 interviews indicating changes to informal practices such as mentoring, onboarding, and informal feedback.
AI is changing formal role responsibilities and collaborations between those roles.
Qualitative interview data from 24 product-focused employees describing shifts in formal responsibilities and inter-role collaboration.