Evidence (7870 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 |
Governance
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AI has changed how work is executed (work processes and execution).
Explicit statement in the paper's abstract; presented as a qualitative/general finding from the paper's evaluation and literature synthesis (no numerical sample provided).
AI has changed who works in jobs (i.e., workforce composition).
Stated in the paper's abstract as an asserted effect of AI on employment composition; presented as part of the paper's review rather than a specific empirical estimate.
The penetrating utilization of AI-based methods to perform tasks has drastically changed how jobs are performed.
Claim asserted in the paper (abstract) as a descriptive conclusion from the paper's review/analysis; no empirical sample or quantified effect reported in the provided text.
AI is altering nearly every aspect of human interaction—such as work and society.
Statement in the paper's abstract/intro; presented as a general observation in the paper (literature review/qualitative synthesis implied). No primary sample size or empirical estimate reported in the provided text.
Comparative analysis of Japanese, European, and United States legal frameworks shows differing treatments of translation data and points toward the need for redistributive design to remedy unequal attribution and capture.
Comparative legal analysis across jurisdictions (Japan, EU, US) and normative argument proposing redistributive design directions; no experimental or quantitative evaluation provided.
AI's future impact on employment will depend not only on automation capabilities but also on how responsibly enterprises manage workforce transitions.
Paper's concluding claim synthesizing arguments and proposed governance approach (normative conclusion rather than an empirically tested causal estimate in the excerpt).
AI-induced workforce disruption is not only a labor market issue but also an enterprise governance challenge.
Argument/position advanced in the paper highlighting governance responsibilities for firms implementing AI.
Artificial intelligence, especially generative AI, is transforming enterprise operations by automating tasks, enhancing decision-making, and redefining job roles.
Conceptual statement in the paper describing observed/expected effects of generative AI on enterprise operations (no specific empirical sample or experiment reported in the excerpt).
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.
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.
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).
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).
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).
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.
Artificial Intelligence (AI) has caused massive changes in nature of workplaces in healthcare sector.
Asserted in paper's introduction and supported by a scoping review (PRISMA-ScR) of 29 peer-reviewed empirical studies published 2020–2025.
The paper examines the macroeconomic impact of AI (drawing on the cited institutional projections) to understand sectoral and aggregate economic implications for Georgia.
Method: macroeconomic synthesis of external projections (Goldman Sachs, McKinsey, Penn Wharton, IMF) and application to Georgia; no reported experimental sample size.
Consumer decision-making is shifting from linear to nonlinear patterns under intelligent technologies.
Synthesis from the paper's systematic review and content analysis of literature (2010–2025); no sample size or primary empirical study reported in the summary.
Scaling helps but does not solve the accumulated-message effect (Anthropic models: Haiku -0.22 to Opus -0.17; OpenAI models: Nano -0.34 to GPT-5.2 -0.17).
Comparison of effect magnitudes (Cohen's d values) across model families and sizes reported in the experiments.
The accumulated-message effect concentrates on items where the model is genuinely uncertain at baseline (d = -0.34 for high-entropy items, vs d = -0.15 when the baseline is deterministic).
Subset analysis partitioning items by baseline model entropy/uncertainty; reported Cohen's d for high-entropy vs deterministic-baseline items (no separate sample counts reported in the abstract).
Models shift toward the conversation's prevailing polarity (accumulated message effect on LLM judgments, AMEL).
Experimental comparison where identical test items were presented either in isolation or following histories saturated with predominantly positive or negative evaluations, across the full dataset (75,898 API calls to 11 models). Reported effect: d = -0.17, p < 10^-46.
Comparative analysis reveals significant institutional differences between EU and Ukrainian legal systems that are relevant to regulatory stability, the cost of innovation, data accessibility, the balance of market power, and guarantees for consumers and employees.
Qualitative comparative examination of institutional and cultural/procedural differences between EU and Ukraine as presented in the paper (method: comparative approach; no quantitative metrics provided).
Most Ukrainian laws relevant to the digital economy are based on existing legal structures and systems, and Ukraine currently lacks a unified regulatory system specifically designed for artificial intelligence.
Comparative analysis of Ukrainian and EU legal frameworks as described in the paper (method: comparative approach; legal document review referenced qualitatively).
Digitalisation is making data and algorithmic systems increasingly important economic resources, thereby changing the way markets operate, how labour is organised, how productivity is measured and how income is distributed.
Conceptual analysis and theoretical model developed via literature synthesis and comparative approach (no empirical sample reported).
Through case studies and architectural illustrations, the paper highlights both the innovation potential and governance challenges posed by agentic systems.
Case studies and architectural illustrations cited in the abstract as the basis for highlighting benefits and challenges. No numeric evaluation provided in the abstract.
The integration of artificial intelligence (AI) agents into payment systems signals a profound shift in the architecture of financial transactions.
Conceptual and technical analysis presented in the paper (argumentative claim in abstract). No empirical sample or quantitative data reported in the abstract.
Techno-sovereignty is a mode of authority grounded in control over data, computation, and AI infrastructures, exercised through state, corporate, and community or Indigenous configurations.
Conceptualization and normative-theoretical analysis drawing on political theory and community/Indigenous approaches (qualitative, no quantitative data).
AI alters strategizing practices (Strategy-as-Practice) by making strategy processes continuous and AI-augmented rather than episodic and purely human-driven.
Conceptual synthesis of Strategy-as-Practice literature; theoretical claim about process change to continuous, AI-augmented strategizing; no empirical sample.
AI redistributes resource control to stakeholders, challenging the Stakeholder Resource-Based View by changing who holds and controls strategically valuable resources.
Theoretical argument within the Stakeholder Resource-Based View stream; conceptual synthesis asserting redistribution of resource control to external stakeholders and algorithmic actors; no empirical evidence reported.
AI reconfigures ecosystems and platforms around foundation models, shifting how complementary actors interact and altering platform/ecosystem structure.
Analytical review of Ecosystems and Platforms literature; conceptual claim that foundation models act as central coordinating technologies; no empirical data or sample.
AI embeds algorithmic actors into the microfoundations of strategy, altering the role and behavior of individual-level actors that underlie firm-level phenomena.
Conceptual analysis of Microfoundations literature; theoretical proposition that algorithms act as actors at micro levels; no empirical sample provided.
AI creates hybrid cognitive architectures by integrating algorithmic cognition with human cognition, thereby changing how strategic decisions are made.
Theoretical argument drawing on literature in Behavioral Strategy and cognitive theory; conceptual synthesis without reported empirical tests or sample.
AI introduces a theoretical discontinuity that challenges core assumptions of strategic management (specifically those rooted in industry-structure and resource-based perspectives).
Conceptual/theoretical analysis across literatures in strategic management; the paper synthesizes prior debates and argues AI undermines prior assumptions. No empirical sample or quantitative data reported.
"General knowledge application" is the second most popular category among highlighted benchmarks, yet it is vaguely defined.
Categorization results from applying the paper's taxonomy to the Benchmarking-Cultures-25 dataset (counts/rankings reported by category). The paper comments on the vagueness of the label.
Benchmarks are attributed different competencies by different builders, depending on their narrative.
Qualitative and comparative analysis mapping benchmark labels and builders' claims in the Benchmarking-Cultures-25 dataset (139 model releases); the paper documents instances where the same benchmark is presented as evidence of different capabilities by different builders.