Evidence (4892 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
Filtered →
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 |
Org Design
Remove filter
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.
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.
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.
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.
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)
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.
AI adoption is allowing professionals to blur and extend the boundaries of their corporate roles.
Reported by interview participants (qualitative evidence) from the 24 interviews at one large technology firm.
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 results of fsQCA demonstrate how the combination and roles of strategic resources (e.g. AI capabilities and decision-making agility) shift in response to varying organizational and environmental conditions.
fsQCA configurational analysis reported in paper showing multiple causal pathways and differing configurations of AI capabilities, decision-making agility, and contextual conditions associated with performance; based on the same survey of 251 firms.
Environmental dynamism and complexity differently moderate the relationship between decision-making agility and firm performance.
Reported moderation analyses in the PLS-SEM results indicating interaction effects of environmental dynamism and environmental complexity on the decision-making agility → performance path; based on survey of 251 firms.
AI adoption correlates with more-recent digital infrastructure—cloud computing and predictive analytics—rather than legacy on-premises IT or descriptive analytics.
Correlational analysis using variables from the Census Bureau survey that measure presence of cloud computing, predictive analytics, on-premises IT, and descriptive analytics; sample derived from ~28,500 establishments.
Acceleration in the Generate/Take Action phase translates into durable performance only when Analyze/Prioritize is de-biased by individuals and teams, and Measure/Review converts results into reusable knowledge with appropriate inference discipline.
Thematic conclusions from the 17 interviews and cross-case analysis (Gioia methodology) identifying conditional relationships across stages of the seven-stage growth pipeline.
GenAI enables small teams to expand capacity while creating new dependencies and coordination logics.
Empirical finding from 17 interviews indicating both expanded capacity and emergent dependencies/coordination needs.
GenAI drives structural recomposition across four domains: shifting roles, AI-embedded workflows, evolving capability expectations, and leaner work architectures.
Empirical finding from thematic analysis of 17 expert interviews reported in the results.
Interpretability, trust calibration, and interface design matter, but they cover only part of what determines whether human-AI combination works.
Authors' argumentative claim based on their analysis and mapping of broader factors; presented as an evaluative conclusion rather than an empirical estimate.
Meta-analytic evidence shows moderate but heterogeneous effects of agentic/code-generation tools on productivity.
Reference to meta-analytic synthesis across studies reported in the paper (meta-analytic details not provided in abstract).
AI changes the traditional relationship between learning and performance: in AI-intensive environments, learning must be supported by systems that coordinate knowledge and build intelligence rather than relying on learning alone.
Authors' synthesis and interpretation of their cross-sectional mediation results (AIDLC → KO → OI → IP) and comparison with prior management models.
The study evaluates contemporary mitigation frameworks for algorithmic bias in HR settings.
Statement of the paper's evaluative aim; implies review/assessment of mitigation strategies but no specific methods or metrics provided in excerpt.
The paper analyses three primary vectors of AI bias in hiring: data bias, interaction bias, and evaluation bias.
Stated analytic framework in the paper (categorization of bias vectors); descriptive content rather than quantified empirical result.
This study examines the dual role of AI in the workplace: as a tool for bias reduction and as a potential vehicle for systemic discrimination.
Statement of the paper's research aim / framing; descriptive claim about the paper's scope rather than empirical finding.
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.
We examine algorithmic co-supervision (ACoS) as a hybrid control mode in which supervisors and AC systems jointly direct, evaluate, and discipline workers.
The paper's stated empirical and conceptual focus; supported by the authors' analysis of 14 real-world ACoS settings (as reported in abstract).
Managerial authority is shifting from human supervisors alone toward varying hybrid arrangements in which humans and algorithms jointly control workers.
Claim drawn from prior literature and the authors' conceptual framing; the paper also analyzes real-world settings (14) to illustrate hybrid arrangements.
Classical categories (labour, capital, firm, market, productivity, trust) remain necessary but are incomplete for describing economic action when technologies prepare decisions, coordinate workflows, support tasks, verify transactions, and reshape responsibility.
Conceptual analysis supported by diagnostic indicators showing distributed decision/action capacity across humans, AI agents, robots, protocols, compute and energy systems; argumentative/theoretical evidence rather than causal inference.
Labour projections are more consistent with task reallocation than labour disappearance.
Analysis of labour-market reallocation data and labour projections (public sources) interpreted under a task-reallocation framework rather than full employment loss, using relative growth and reallocation indicators.
High-AIC participants realized outsized gains from GenAI access; low-AIC participants saw limited or even negative marginal returns.
Subgroup analysis of the randomized experiment comparing treatment effects by AIC level; authors report large positive treatment effects for high-AIC subgroup and small or negative effects for low-AIC subgroup.
The distribution of gains from GenAI access was highly uneven across users.
Experimental results showing heterogeneous effects across participants (variance/heterogeneity analyses reported in the paper).