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
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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 |
Org Design
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Evidence also includes pattern matching with documented agentic R&D deployments.
Methodological statement in the paper claiming pattern matching with documented agentic R&D deployments (unspecified number/source).
The study includes a foresight scenario analysis projecting four plausible 2040 R&D futures to stress-test design choices.
Methodological statement in the paper describing a four-scenario foresight analysis.
Empirical evidence for the design is triangulated from four semi-structured expert interviews with senior R&D leaders across industrial, healthcare, and academic settings.
Methodological statement in the paper specifying four semi-structured expert interviews.
Because all observations come from a single practitioner, the inferential statistics are exploratory and hypothesis-generating rather than confirmatory; portability across the full portfolio awaits multi-practitioner replication.
Explicit limitation stated in the paper about the single-practitioner design and its implications for inference.
The framework is illustrated with an accounts-payable simulation and a companion spreadsheet.
Empirical illustration: the paper includes (or accompanies) an accounts-payable simulation and a spreadsheet to demonstrate the model and estimation approach.
The note starts from a compact dashboard expression, expands it into a fuller structural model, defines all variables and parameters, and shows how each cost category can be estimated from operational data.
Methodological description in the paper: construction of dashboard, expansion to structural model, full variable/parameter definitions, and stated procedures for estimating cost categories from operational data; accompanied by worked examples.
Agentic Technical Debt is a stock of accumulated design and governance liability.
Definition provided in the paper as part of the conceptual framework that labels Agentic Technical Debt as a stock (accumulated) liability tied to design and governance.
This note develops a formal and managerially usable model that distinguishes Agentic Technical Debt from Stochastic Tax.
Author states development of a formal, managerially usable model and explicit distinction between the two constructs; supported by model construction in the paper (structural model and dashboard).
Agentic AI systems combine probabilistic reasoning with delegated action through tools, context, memory, orchestration, and external workflow integration.
Conceptual/definitional statement in the paper; presented as the working characterization of 'Agentic AI systems' within the model specification.
(i, continued) The counterfactual toll has explicit non-uniqueness (i.e., non-uniqueness of the toll is demonstrated).
Mathematical argument in the paper identifying conditions or constructions that lead to multiple valid tolls (formal counterexample or theorem on non-uniqueness).
The study contributes a taxonomy of AI workforce impact, a Workforce Resilience Readiness Score (WRRS), an AI Workforce Trust Index (AWTI), an Ethical Automation Boundary concept, and a pilot empirical validation design.
Declared methodological and conceptual contributions in the paper (these are presented as deliverables of the study; no validated results reported in the excerpt).
The International Labour Organization's 2025 update highlights the need to assess the exposure of generative AI at the task level using task data, expert input, and AI model predictions.
Reference to ILO 2025 update recommendation described in the paper (policy/technical guidance rather than primary empirical data in the excerpt).
A path analysis was used to trace structural relationships between HR quality, effectiveness perceptions, and AI readiness.
Paper reports a path analysis linking composite HR quality indices, perceived HR effectiveness, and AI readiness measures; uses same survey sample.
A binary logistic regression modelling active AI adoption was estimated with McFadden R² = 0.032.
Reported logistic regression model fit (McFadden R² = 0.032) for AI adoption outcome using the survey data.
An OLS regression was estimated explaining perceived HR effectiveness with R² = 0.446.
Reported OLS model fit statistics in the paper (R-squared = 0.446); model explains perceived HR effectiveness using survey data.
Constructed and validated a composite index of external HR quality factors with Cronbach's α = 0.959.
Measurement validation reported in the paper; Cronbach's alpha reported for external HR factors.
Constructed and validated a composite index of internal HR quality factors with Cronbach's α = 0.924.
Measurement validation reported in the paper; Cronbach's alpha reported for internal HR factors.
A large-scale empirical survey of 12,562 public servants was conducted in June 2025 in Kazakhstan.
Statement in paper specifying survey sample and date; sample of public servants N = 12,562, June 2025.
A strict May 2026 trajectory subset captured 627 model-completed events and 73.95 million recorded tokens, of which 82.9% were cache reads.
Subset analysis of telemetry for a May 2026 trajectory reported by authors; counts of model-completed events and token logs, with cache-read classification.
Memory-derived records identified 482 output-proxy events and 889 failure, verification, correction, or protocol-proxy events.
Analysis/parsing of memory-derived records from the persistent environment yielding categorized event counts.
Active system time was 579.7 hours (30-minute capped-gap estimate).
Computed runtime activity metric from system telemetry/logs over the study period; authors report a 30-minute capped-gap estimate to compute active system time.
The workspace included 502 memory-related files, 17 configured agent directories, and 57 skill files.
Inventory of the implemented persistent agent workspace reported by authors as part of the case study (counts extracted from workspace metadata/filesystem).
Recoverable main-agent telemetry contained 75,671 de-duplicated records across 96 active days, with 8,059 user-role and 23,710 assistant-role messages.
Structured self-observed implementation case study (unit: a single persistent human-agent environment) conducted Jan 31–May 25, 2026; authors report recoverable telemetry logs totaling these counts.
We compare and benchmark strategy profiles adopted by open and proprietary state-of-the-art language models deployed in AgentSociety against best response.
Empirical benchmarking experiments comparing multiple language models' strategy profiles to best-response strategies (experimental evaluation / benchmarking).
Historically, the most visible high-end bugonomics was offense-priced because production-grade zero-days and exploit chains were expensive specialist outputs for governments, brokers, and offensive vendors.
Historical observation corroborated by reference to public exploit-market price anchors (market price data referenced; no specific figures included in the abstract).
There is a significant deficiency in India-centric qualitative investigations on human-AI collaboration in the IT sector.
Authors' review of peer-reviewed literature and secondary data concluding a gap in India-focused qualitative studies (literature gap analysis). No numeric count provided.
We interviewed 24 product-focused individuals at a large technology firm about how AI has impacted their own work, their work within their product team, and their professional interactions.
Qualitative semi-structured interviews with 24 product-focused employees at a single large technology firm; sample size = 24.
This scoping review adhered to the PRISMA-ScR guidelines and encompassed 29 peer-reviewed empirical studies published from 2020 to 2025.
Methods statement in the paper (explicit methodological description).
AI capability is conceptualized/measured as having sub-dimensions including technical infrastructure and management.
Measurement/model description in paper: AI capability broken into sub-dimensions (technical infrastructure, management); supported by survey instrument and measurement model using PLS-SEM on 251 firms.
The mixed-method approach, combining partial least squares–structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA), was used for analyzing the survey data of 251 firms.
Methods statement in paper: authors report using a mixed-method approach (PLS-SEM and fsQCA) on survey data; sample size explicitly stated as 251 firms.
Prior productivity does not predict AI use.
Analysis linking prior productivity measures to reported AI adoption in the Census Bureau survey data; finding of no predictive relationship reported.
The analysis uses a mandatory, purpose-designed Census Bureau survey of approximately 28,500 establishments.
Census Bureau mandatory survey specifically designed for this study; sample size stated as approximately 28,500 establishments.
The study extends the Technology Acceptance Model (TAM), Dynamic Capabilities Theory, and the Technology-Organisation-Environment (TOE) framework into the qualitative, emerging-economy entrepreneurial context.
Authors' stated theoretical contribution based on mapping thematic results to TAM, Dynamic Capabilities, and TOE frameworks within analysis and discussion sections.
This study employed an interpretivist, qualitative research design using sixteen in-depth semi-structured interviews with entrepreneurs across fintech, edtech, health-tech, logistics, retail, and SaaS in Delhi/NCR, India, and used Braun & Clarke's (2006) six-phase thematic analysis framework.
Explicit methodological description in the paper: interpretivist qualitative design; n=16 in-depth semi-structured interviews across specified sectors in Delhi/NCR; thematic analysis following Braun & Clarke (2006).
Using a qualitative approach with 17 expert interviews from employees at startups.
Methods statement in paper specifying qualitative study design and sample size of 17 interviews.
Process-related insights into how GenAI transforms startups are limited.
Authors' literature positioning / gap statement in paper (no empirical metric provided).
We map that space through six interconnected elements: sociotechnical context, decision-making frameworks, human decision participants, AI capabilities, interaction, and holistic evaluation.
The paper's proposed analytical/framework contribution listing six elements (descriptive of the authors' mapping work).
Most current work treats human-AI combination as an engineering problem and concentrates on interpretability, trust calibration, or interface design.
Authors' characterization of the existing literature and dominant research foci (qualitative literature assessment; no quantitative breakdown provided).
We call this persistent shortfall the 'synergy gap.'
Terminology/definition introduced by the authors in the paper (conceptual claim, not an empirical finding).
Current evidence does not support the simple claim that autonomous code generation automatically improves engineering outcomes.
Synthesis of mixed results from controlled studies, meta-analyses, and benchmarks reported in the paper (no single sample size given in abstract).
All [the listed orchestration frameworks] follow the same pattern: an external orchestrator above the LLM, injecting instructions and routing decisions every turn.
Author assertion based on architectural analysis of the listed frameworks (observation of orchestration pattern in the named projects).
The study used established measurement scales to assess AI-driven learning culture, knowledge orchestration, organisational intelligence and innovation performance.
Methods: authors report use of established scales for AIDLC, KO, OI and IP in the questionnaire.
Structured questionnaires were distributed between March and October 2025 to employees involved in innovation, learning and project management roles in Karachi, Lahore and Islamabad.
Methods section description of data collection period, target respondent roles, and cities covered.
Most respondents held undergraduate or postgraduate degrees in computer science, engineering or business-related disciplines.
Sample demographic summary from the survey (N=348).
After screening the data, 348 valid responses were analyzed.
Structured questionnaires distributed March–October 2025 to employees in medium and large IT firms in Karachi, Lahore and Islamabad; screening produced 348 valid responses (sample description in methods).
The paper draws on empirical studies from 2024–2026.
Methodological statement in the paper specifying the time window of empirical studies used in the analysis.
We compared the traits causing the incidents with the traits that 197 developers building AI systems for those tasks would have preferred.
Study design: comparison between trait set responsible for incidents (from incident reports) and stated developer preferences collected from a sample of 197 developers working on those tasks.
We compared the extracted traits with the traits that 202 workers highly familiar with those tasks would have preferred.
Study design: a comparison between LLM-extracted traits from incident reports and stated preferences from a sample of 202 workers familiar with the tasks.
We used an LLM-as-an-expert approach to extract the main traits of the AI systems involved in those incidents using an established framework of twelve traits.
Methods statement: applied a Large Language Model to code/extract AI system traits from the incident reports using an established 12-trait framework.
We analyzed 1,524 reports of incidents in which AI systems were used to perform 171 occupational tasks across 12 industry sectors.
Descriptive statement in paper: dataset comprised 1,524 incident reports, covering 171 occupational tasks and 12 industry sectors (dataset construction / corpus used for analysis).