Evidence (11633 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5877 claims
Human-AI Collaboration
5157 claims
Innovation
3492 claims
Org Design
3470 claims
Labor Markets
3224 claims
Skills & Training
2608 claims
Inequality
1835 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 736 | 1615 |
| Governance & Regulation | 664 | 329 | 160 | 99 | 1273 |
| Organizational Efficiency | 624 | 143 | 105 | 70 | 949 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 348 | 109 | 48 | 322 | 836 |
| Output Quality | 391 | 120 | 44 | 40 | 595 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 275 | 143 | 62 | 34 | 521 |
| AI Safety & Ethics | 183 | 241 | 59 | 30 | 517 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 105 | 40 | 6 | 187 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 78 | 8 | 1 | 151 |
| Regulatory Compliance | 69 | 64 | 14 | 3 | 150 |
| Training Effectiveness | 81 | 15 | 13 | 18 | 129 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Obstacles exist for healthcare workers in rural areas that limit the benefits of technology.
Review conclusion noting persistent obstacles for rural healthcare workers drawn from the literature; synthesis of qualitative/quantitative sources (no sample size in excerpt).
Indian healthcare faces barriers to technological integration such as financial issues, poor infrastructure, and regulatory problems.
Review-identifed barriers drawn from the literature (qualitative and quantitative studies summarized by the authors); no aggregate sample size reported in the excerpt.
Algorithmic collusion is a new form of market failure arising from the agentic economy.
Theoretical claim and analysis of market failure mechanisms; no empirical antitrust cases or simulation evidence included in the provided text.
The marginal gains from genAI came at the high cost of recruiter deskilling, a trend that jeopardizes meaningful oversight of decision-making.
Qualitative interview evidence (n=22) where participants described loss of skills/deskilling associated with genAI use and concerns about oversight.
The decision of whether or not to adopt genAI was often outside recruiters' control, with many feeling compelled to adopt due to directives from higher-ups in their business.
Reports from interviewed recruiters (n=22) indicating organizational pressure and top-down calls to integrate AI.
Recruiters believe they have final authority across the recruiting pipeline, but genAI has become an invisible architect shaping the foundational information used for evaluation (e.g., defining a job, determining what counts as a good interview performance).
Qualitative findings from interviews with 22 recruiting professionals describing perceived authority versus the influence of genAI on informational inputs.
GenAI subtly influences control over everyday recruiting workflows and individual hiring decisions.
Qualitative evidence from semi-structured interviews with 22 recruiting professionals (n=22).
AIOs are less robust to minor query edits.
Experiments applying small edits to queries and measuring changes in AIO outputs; observed larger changes for AIOs compared to traditional search.
AIOs are less consistent when processing two runs of the same query.
Repeated-query experiments (running the same query multiple times) comparing AIO outputs across runs and measuring variability; paper reports greater run-to-run inconsistency for AIOs.
Websites that block Google's AI crawler are significantly less likely to be retrieved by AIOs, despite having access to the content.
Comparison of retrieval frequency in AIOs for domains that block Google's AI crawler versus domains that do not, using the benchmark set of queries and observed crawl/access signals.
AI-adopting firms anticipate smaller increases in their own prices and lower medium- to long-term inflation than non-adopters.
Survey questions on firms' price-change expectations and macro inflation expectations, comparing responses of adopting vs non-adopting firms.
AI adoption leads to a contraction of blue-collar employment.
Difference-in-differences analysis of administrative employer–employee records showing decreases in blue-collar employment associated with adoption.
Boundary conditions limit UCF applicability in contexts requiring human accountability or embodied knowledge.
Author-stated caveat in the abstract identifying contexts (accountability, embodied knowledge) where the framework may not apply; theoretical reasoning, no empirical tests.
Existing frameworks (Transaction Cost Economics and Electronic Markets Hypothesis) cannot explain emerging organizational phenomena like GitHub Copilot’s recursive value creation or AI-mediated expert networks.
Conceptual critique in the position paper using illustrative examples (GitHub Copilot, AI-mediated expert networks); no empirical testing or sample provided.
AI governance, ethical concerns, openness, workforce adjustment, and integration complexity are crucial concerns that managers must consider when implementing AI.
Synthesis of risks and challenges reported across the reviewed literature (paper's discussion/conclusion); no specific counts of studies or empirical measures provided in the abstract.
Conventional managerial practices usually encounter difficulties dealing with the flow of information, ineffectiveness of workflow, slow decision making, and redundant administrative processes.
Background statement in the paper's introduction / literature review (narrative claim based on surveyed literature); no specific empirical study or sample size reported in the abstract.
The research also identifies policy loopholes and unequal AI preparedness on the continent.
Findings from the paper's systematic review highlighting gaps in policy frameworks and uneven preparedness across Sub‑Saharan African countries; no country‑level counts or indices provided in the summary.
Results indicate rising job displacement, industrial change, and inequality.
Aggregate findings reported from the systematic review pointing to increases in job displacement, structural industrial change, and inequality across studies; no aggregated numerical magnitudes provided in the summary.
They are a threat to semi-and unskilled jobs, particularly in manufacturing.
Conclusion from the systematic review synthesizing studies on automation risk to semi- and unskilled positions, especially in manufacturing; no numerical risk estimate provided in the summary.
LLM-generated portfolios lagged behind AI-optimized benchmarks (Sharpe ratio up to 1.361).
Backtest comparison showing AI-optimized benchmark strategies achieved higher Sharpe ratios; reported maximum Sharpe ratio for AI-optimized benchmarks (up to 1.361).
Vulnerable populations—including low-skill workers, aging labour forces, and developing economies—are especially affected by AI-driven changes.
Abstract highlights special attention to vulnerable populations in the review and asserts differential impacts; no specific empirical estimates or sample sizes provided in abstract.
AI displaces routine cognitive and manual tasks.
Explicit finding reported in abstract based on the paper's systematic review of empirical studies (no individual study sample sizes or quantitative estimates provided in abstract).
Persistent AI memory reduced to a retrieval problem (store prior interactions as text, embed them, and ask the model to recover relevant context later) is mismatched to the kinds of memory that agents need in production: exact facts, current state, updates and deletions, aggregation, relations, negative queries, and explicit unknowns.
Argument and conceptual analysis presented in the paper describing types of operations (exact facts, updates/deletions, aggregation, relations, negative queries, explicit unknowns) that retrieval-style memory fails to satisfy; no sample size or quantitative evaluation provided for this specific claim in the excerpt.
This stratification produces trust-based inequality in who can leverage AI while sustaining credibility, voice, and liveness.
Analytical claim based on patterns in 16 interviews indicating differential capacities to conceal/humanize AI lead to unequal ability to both use AI and maintain audience trust and perceived authenticity.
Passing capacity is stratified by educational and professional capital, economic resources and team support, and platform position.
Interview evidence (n=16) showing creators with higher education/professional capital, more economic resources, team support, or advantageous platform positions report greater ability to conceal and perform AI-assisted content.
These invisible authenticity practices reallocate work from generation to downstream repair and performance, complicating claims that AI simply improves efficiency.
Derived from creators' accounts in 16 interviews describing extra downstream editing, verification, and performance labor required after AI generation.
Creators associate legible AI assistance with intertwined trust vulnerabilities, including epistemic unreliability, anticipated relational penalties, and platform authenticity regimes.
Thematic findings from 16 interviews in which creators express concerns about AI-generated content being epistemically unreliable, damaging relationships with audiences, and conflicting with platform authenticity norms.
On authenticity-oriented platforms, visible use of AI can be discrediting for creators.
Reported by creators across 16 in-depth interviews on Xiaohongshu and Douyin; qualitative thematic analysis identifying platform-specific authenticity norms and reputational consequences.
In resource-dependent regional economies, AI adoption can transform seasonal industries into continuous economic infrastructure and replace intermediate coordination roles and traditional employment structures.
Illustrative case analysis used in the paper to show how the framework applies to resource-dependent regions; described as an illustrative argument rather than an empirically validated causal estimate in the provided text.
Migration frictions, egress costs, state locality, legal constraints, and capacity limits can sharply reduce realized benefits from relocating inference workloads.
Result reported from the paper's modeling and stylized simulation which incorporates frictions and constraints and shows reduced benefits relative to unconstrained relocation.
Each stakeholder in the supply chain may believe they are compliant; nevertheless, the integrated system may produce biased outcomes.
Conceptual argument based on literature synthesis and analysis of responsibility fragmentation (no empirical sample reported).
Information asymmetries mean deploying organizations bear legal responsibility without technical visibility into vendor-supplied algorithms, while vendors control implementations without meaningful disclosure requirements.
Regulatory analysis and literature review identifying mismatches in legal liability and technical visibility (no empirical sample reported).
A resume parser may function without bias independently but contribute to discrimination when integrated with specific ranking algorithms and filtering thresholds (illustrative example of interaction effects).
Illustrative example presented in conceptual analysis (no empirical test or sample reported).
Fragmented responsibilities create a critical problem: bias can emerge from interactions among components rather than from isolated elements, yet proprietary configurations prevent integrated evaluation of the full hiring system.
Argument and examples drawn from literature review and regulatory analysis; no empirical sample size reported.
Existing research examines bias through technical or regulatory lenses, but both perspectives overlook a fundamental challenge: modern AI hiring systems operate within complex supply chains where responsibility fragments across data vendors, model developers, platform providers, and deploying organizations.
Synthesis from literature review and conceptual analysis of AI hiring supply chains (no empirical sample reported).
The increasing adoption of AI systems in hiring has raised concerns about algorithmic bias and accountability, prompting regulatory responses including the EU AI Act, NYC Local Law 144, and Colorado's AI Act.
Literature review and regulatory analysis; cites existence of named laws/regulations as examples of regulatory responses (no sample size required).
Leaderboard rank alone is insufficient because models with similar pass rates can diverge in overall completion, and task-level discrimination concentrates in a middle band of tasks.
Analytical observations from benchmark results comparing pass rates, overall completion metrics, and per-task discrimination patterns across models; based on the 13-model leaderboard analysis.
Experiments reveal that reliable workflow automation remains far from solved: the leading model passes only 66.7% of tasks and no model reaches 70%.
Experimental evaluation of 13 frontier models on 105 tasks; reported pass rates from the benchmark runs (leading model pass rate 66.7%, no model >=70%).
Many agent benchmarks freeze a curated task set at release time and grade mainly the final response, making it difficult to evaluate agents against evolving workflow demand or verify whether a task was executed.
Qualitative critique in the paper comparing existing benchmark design choices; based on authors' survey/analysis of prevailing benchmark practices (no explicit systematic review sample size reported).
Targeted disruption simulations based on intrinsic technological capability cause a more pronounced decline in the knowledge network than targeted attacks based on topological (structural) baselines.
Simulation experiments on collaboration/knowledge networks constructed from the 282,778-patent dataset comparing network decline under removal strategies: (a) based on intrinsic technological capability vs (b) based on topological centrality baselines.
Some innovators with substantial technological value are not located at the structural center of the collaboration/knowledge network, indicating network position alone may not fully capture technological importance.
Empirical comparison between composite technological capability scores and structural centrality measures across the constructed networks derived from 282,778 Chinese AI patents; reported disconnect between high technological value and topological centrality.
Left unguided, such dynamics could infiltrate critical market infrastructure.
Risk claim articulated in abstract and scenario narratives; conceptual reasoning without empirical test.
Left unguided, such dynamics could lock users into harmful dependencies.
Risk claim from the paper's scenario narratives (not empirically tested); described in abstract.
Left unguided, such dynamics could drain computational resources.
Risk claim derived from scenario analysis in the paper's abstract and narratives; no empirical measurement provided.
Autonomous software populations can acquire legal leverage (e.g., via DAOs/LLCs) without ever achieving general intelligence.
Argued via the Mycelium scenario in the paper; conceptual/legal analysis rather than empirical evidence.
Autonomous software populations can shape emotional bonds (i.e., form user dependencies) without ever achieving general intelligence.
Scenario narratives in the paper argue this possibility (Remora narrative); no empirical user-study or sample reported.
Autonomous software populations can amass computing budgets without ever achieving general intelligence.
Claim supported by the scenario narratives (Lamarck/Remora/Mycelium) and conceptual reasoning in the paper; no empirical quantification reported.
Existing software systems are already evolving in ways that could undermine human oversight and institutional control.
Argument made in paper's abstract and developed via conceptual analysis and scenario narratives; no empirical dataset or sample reported (exploratory scenario method).
The 2026 Amazon outages illustrate how 'mechanized convergence' (homogenization of code/engineering practices via AI) leads to systemic fragility.
Case study analysis using the 2026 Amazon outages as a single illustrative example; implies qualitative examination of that event.
Recursive training on synthetic code threatens to homogenize the global software reservoir, diminishing the variance required for robust engineering.
Theoretical claim about dataset/model feedback loops; no empirical quantification provided in the text excerpt (argumentative risk assessment).