Evidence (2469 claims)
Adoption
5539 claims
Productivity
4793 claims
Governance
4333 claims
Human-AI Collaboration
3326 claims
Labor Markets
2657 claims
Innovation
2510 claims
Org Design
2469 claims
Skills & Training
2017 claims
Inequality
1378 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 402 | 112 | 67 | 480 | 1076 |
| Governance & Regulation | 402 | 192 | 122 | 62 | 790 |
| Research Productivity | 249 | 98 | 34 | 311 | 697 |
| Organizational Efficiency | 395 | 95 | 70 | 40 | 603 |
| Technology Adoption Rate | 321 | 126 | 73 | 39 | 564 |
| Firm Productivity | 306 | 39 | 70 | 12 | 432 |
| Output Quality | 256 | 66 | 25 | 28 | 375 |
| AI Safety & Ethics | 116 | 177 | 44 | 24 | 363 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 76 | 38 | 20 | 315 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 77 | 34 | 80 | 9 | 202 |
| Skill Acquisition | 92 | 33 | 40 | 9 | 174 |
| Innovation Output | 120 | 12 | 23 | 12 | 168 |
| Firm Revenue | 98 | 34 | 22 | — | 154 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 84 | 16 | 33 | 7 | 140 |
| Inequality Measures | 25 | 77 | 32 | 5 | 139 |
| Regulatory Compliance | 54 | 63 | 13 | 3 | 133 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Task Completion Time | 88 | 5 | 4 | 3 | 100 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 32 | 11 | 7 | 97 |
| Wages & Compensation | 53 | 15 | 20 | 5 | 93 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 24 | 22 | 9 | 6 | 62 |
| Job Displacement | 6 | 38 | 13 | — | 57 |
| Hiring & Recruitment | 41 | 4 | 6 | 3 | 54 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 10 | 6 | 2 | 40 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 5 | 9 | — | 26 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Org Design
Remove filter
AI raises managerial cognitive complexity and creates recurring tensions between algorithmic optimisation and systemic, ethical reasoning.
Theoretical synthesis highlighting emergent tensions from integrating computational optimisation with systems thinking and ethical considerations; conceptual, no empirical tests.
AI can augment measurement (e.g., collaboration patterns, output tracking) but if poorly designed may reinforce visibility biases that disadvantage remote workers.
Theoretical reasoning and literature citations about algorithmic bias and monitoring; illustrated with secondary examples rather than primary empirical tests.
Hybrid arrangements can exacerbate inequities in access to informal networks and career advancement, often privileging co-located or better-networked employees.
Theoretical integration of sociological and management studies with comparative case illustrations; secondary data examples referenced but no new causal empirical tests reported.
Hybrid and remote work create risks of professional invisibility, fragmented social networks, and unequal access to workplace social capital.
Literature synthesis and illustrative case studies drawn from secondary sources; qualitative/comparative case evidence rather than primary quantitative data.
Traditional STP showed a 67% performance decline after six months in unstable market conditions.
Empirical observation reported in the study—likely derived from simulation scenarios and/or longitudinal analysis of behavioral data; precise data source (simulation vs. observed field data), statistical tests, and sample framing are not specified in the summary.
The persistence of interpretive, human-in-the-loop evaluation implies ongoing labor requirements (annotation, sense-making, governance roles), affecting forecasts of automation and labor substitution in sectors adopting LLMs.
Interview reports describing continued manual work for evaluation tasks across participants; authors draw implications for labor demand.
The under‑use of external text sources in the reviewed literature may be due to privacy, legal/regulatory uncertainty, or integration costs.
Authors' interpretation linking observed low coverage of external text sources (social media, news, reviews) in the 109 articles to plausible barriers (privacy/regulation/integration); no direct empirical test in the review.
Widespread deployment of similar models could create correlated failures or fraud vectors, implying systemic risk that may warrant macroprudential attention.
Analytic caution based on model homogeneity and case/literature discussion; speculative systemic risk concern rather than empirically demonstrated.
There is regulatory uncertainty around AI-generated filings and responsibility/liability for automated outputs.
Analysis and literature review discuss unclear regulatory positions and legal risks noted in case organizations' deployment considerations.
Integration complexity with legacy ERP/financial systems and sharing-center processes is a significant implementation challenge.
Case study narratives describe integration work and friction points; analytic framing highlights ERP compatibility issues.
Model hallucinations, lack of explainability, and limited audit trails limit safe adoption.
Paper cites literature and case observations about model reliability and explainability issues; examples and discussion are qualitative.
Data privacy, confidentiality, and cross-border data transfer concerns are important barriers to deployment.
Challenges enumerated from case studies and literature; specific organizational concerns cited in cases (Xiaomi, Deloitte) and in regulatory discussion.
Explainability, auditability, or data-localization requirements could favor larger vendors with compliance capacity, increasing market concentration and affecting competition among AI suppliers.
Market-structure argument grounded in regulatory-compliance burden analysis and comparative examples; not supported by empirical market data in the study.
Legal uncertainty and strict procedural requirements increase compliance costs and regulatory risk, which can slow AI adoption by firms and public agencies.
Theoretical economic implications drawn from legal analysis and comparative observations; no empirical measurement of costs or adoption rates in the study.
AI can restrict or reshape human administrative discretion in legally sensitive ways.
Doctrinal analysis of statutory specificity and formal procedural requirements in civil-law contexts, illustrated with Vietnam as the exemplar case; comparative observations.
Physical constraints (power grid reliability, water consumption for cooling, and data-center capacity) together with diminishing marginal returns on scaling make continued monolithic scaling economically and environmentally risky.
Conceptual argumentation using known infrastructure constraints and economic reasoning about diminishing returns; no new empirical assessment or quantified risk analysis included.
Reasoning-augmented models (e.g., models using chain-of-thought, multi-step reasoning, or external retrieval/looping) can inflate per-query compute by orders of magnitude, exacerbating sustainability problems.
Argument based on architectural patterns (multi-step reasoning, retrieval augmentation, multiple model passes) and reported per-query compute multipliers in auxiliary literature (referenced anecdotally); the paper provides no new benchmarked per-query compute measurements.
The energetic burden of generative AI is shifting from one-time training to recurring, potentially unbounded inference costs as models become productized and high-traffic.
Synthesis of industry observations and early/anecdotal quantitative reports on operational workloads; no original empirical time-series or workload measurements provided in this paper.
Scaling monolithic LLMs toward artificial general intelligence (AGI) is colliding with hard physical and economic limits (energy, grid stress, water use, diminishing returns).
Conceptual synthesis and argumentation drawing on observed industry trends (training/inference cost growth), infrastructure constraints (grid reliability, data-center cooling/water use) and theoretical diminishing marginal returns on model/data scaling. No new empirical dataset or controlled experiments reported in the paper.
Field observations from an enterprise deployment demonstrate production failure modes traceable to missing identity propagation, timeout/budgeting policies, and machine-readable error semantics.
Empirical context described as field lessons from an enterprise agent platform integrated with a major cloud provider's MCP servers; production failure vignettes and operational log analysis (client redacted).
MCP lacks three protocol-level primitives needed for reliable, production-scale agent operation: identity propagation, adaptive tool budgeting, and structured error semantics.
Observational analysis and classification of production failures from an enterprise agent deployment; taxonomy of failure modes identifying gaps in these specific areas.
Agents that attempt to infer others' reasoning depth may be vulnerable to strategic misrepresentation (partners could behave to induce incorrect ToM estimates).
Conceptual analysis in the paper and discussion of strategic incentives; paper also identifies the risk and suggests potential mitigations (e.g., conservatism, verification, meta-reasoning).
Both too little and too much recursive reasoning (i.e., too shallow or too deep ToM) can produce poor joint behavior — miscalibrated anticipation harms coordination.
Observed non-monotonic effects in the reported experiments where fixed-order agents at either low or high ToM orders performed worse in mismatched pairings; evidence comes from the same multi-environment evaluation using joint-payoff / success-rate metrics.
Misalignment in Theory-of-Mind (ToM) order between agents (i.e., agents using different recursive reasoning depths) degrades coordination performance.
Empirical experiments using LLM-driven agents with configurable ToM depth across four coordination environments (a repeated matrix game, two grid navigation tasks, and an Overcooked task); comparisons of matched (same-order) vs mismatched (different-order) pairings using task-specific joint payoffs and success rates as metrics.
Human–AI chats contain fewer emotional and social messages compared with human–human chats.
Content coding of chat transcripts comparing frequencies of emotional/social message categories across human–AI (n = 126) and human–human (n = 108) conditions; reported lower counts/proportions of social/emotional content in human–AI dialogs.
Public‑interest concerns (bias, misuse, systemic risk) may be harder to mitigate via simple transparency rules; policies should emphasize outcome‑based regulations, mandatory behavioral testing, and marketplace disclosure obligations for stressed scenarios.
Policy implication derived from the non‑rule‑encodability thesis; no empirical policy evaluation included.
Standard contracts and regulatory audits that rely on inspection of rule sets or source code will be insufficient to assess model behavior or risk; regulators and buyers must rely more on behavior‑based testing, standards, and outcome measures.
Policy and regulatory argument derived from the main theorem about non‑rule‑encodability; no empirical regulatory studies presented.
Full interpretability via rule extraction may be impossible for the most valuable parts of LLM competence, limiting the utility of some transparency approaches for safety and auditing.
Argumentative consequence of the main theoretical claim and structural mismatch; supported by historical limitations of rule‑based systems; no empirical tests reported.
There is a structural mismatch between explicit human cognitive tools (rules, checklists) and the pattern‑rich, high‑dimensional competence encoded in LLMs.
Theoretical/structural argument about distributed statistical representations in LLMs versus discrete rules; no experimental quantification provided.
Historical expert systems failed to generalize or scale to complex, ambiguous tasks, contrasting with LLMs' broader empirical successes.
Historical case analysis and literature review-style discussion of expert systems versus contemporary LLM performance; no new quantitative historical dataset provided.
High governance costs in regulated/high-risk domains can slow adoption of agentic systems, concentrating deployment in less regulated uses or among large firms that can afford governance infrastructure.
Economic reasoning about fixed and marginal governance costs and firm-level adoption decisions; no empirical adoption data presented.
Path-dependent behavior increases the complexity of principal–agent contracting and moral hazard between platforms, enterprise customers, and downstream users, requiring richer contract terms (acceptable paths, logging, audit rights).
Economic theory reasoning and applied contract/design implications discussed; no empirical contract-study data.
Path-dependent policies complicate ex post auditing and simple rule-based regulation; regulators may prefer standards requiring runtime evaluation and logging to be enforceable in practice.
Conceptual argument about limits of auditing when important state is ephemeral and about how runtime logging enables ex post review; illustrative policy examples mapping to runtime requirements.
Current models appear to internalize preferences as persistent, high‑priority rules rather than conditional behavioral signals contingent on conversational norms and context.
Behavioral patterns observed across BenchPreS scenarios (preference application persisting in inappropriate contexts) and ablation results; interpretive claim based on empirical behavior rather than direct model internals inspection.
BenchPreS detects a pervasive context‑sensitivity failure: models often treat stored preferences as globally enforceable rules rather than conditional, context‑dependent signals.
Pattern of results across the benchmark showing high MR alongside cases where preference application should have been suppressed; qualitative interpretation of model behavior across varied interaction partners and normative contexts in the dataset.
Modern frontier LLMs frequently misapply stored user preferences in contexts where social or institutional norms require suppression (third‑party communication).
Empirical evaluation using the BenchPreS benchmark: models were provided stored preferences and asked to generate responses across contexts requiring either application or suppression; Misapplication Rate (MR) computed as fraction of instances where preferences were applied despite required suppression. Multiple state‑of‑the‑art models were tested (described generically as “frontier models”) across the scenario set.
If left unchecked, managerial short-termism combined with AI adoption can create a feedback loop where firms cut labor to boost short-term profits, undermining aggregate demand and eroding the market that sustains those profits.
Conceptual macroeconomic and organizational synthesis drawing on theory and historical patterns; no new empirical time-series demonstrating this loop in current AI-driven layoffs.
Work-time reduction policies carry distributional and implementation risks (heterogeneous effects by occupation, firm size, capital intensity; risk of hidden wage cuts) that require careful compensation rules and monitoring.
Theoretical reasoning and references to heterogeneous outcomes in prior work-hour studies; no new empirical quantification of heterogeneity in AI-era implementations.
Lower household demand resulting from payroll cuts can precipitate further cost-cutting and automation, creating a self-reinforcing feedback loop that risks persistent demand shortfalls and higher structural unemployment.
Theoretical models of demand-driven adjustment and cited historical patterns; conceptual argument rather than empirical causal identification in contemporary AI contexts.
AI-justified layoffs are driven more by managerial short-termism and misaligned executive incentives than by immediate technological necessity.
Interdisciplinary conceptual synthesis drawing on labor-economics theory, organizational behavior literature linking executive compensation/short-termism to layoffs, and selected prior empirical studies; no new firm-level causal identification or large-scale dataset provided.
Passive monitoring and predictive models are insufficient for governing the complex dynamics of a tech-driven economy.
Conceptual critique based on economic cybernetics literature and the author's expert assessment; no empirical test comparing governance regimes is provided.
Digitalization is deepening digital inequality (unequal access to digital tools, skills, and benefits) across social groups and regions.
Qualitative analysis and expert assessment; the paper calls for new metrics but does not present systematic empirical measures of inequality.
Digital transformation can generate technological unemployment if not managed with appropriate retraining and social protection measures.
Expert assessment and literature-informed argumentation in the paper; no empirical longitudinal analysis isolating technology-driven job losses presented.
Forced or poorly regulated digitalization risks exacerbating social stratification.
Conceptual argument supported by qualitative analysis of policy documents and expert assessment; no empirical causal estimates provided.
Industry-level AI substitution risk moderates the AI–ECSR relationship: higher substitution risk sharpens the inverted U and shifts its peak left (firms in high-substitution-risk industries reach the turning point earlier and suffer stronger negative effects at high AI adoption).
Interaction terms between AI (and AI^2) and an industry AI substitution-risk measure in panel regressions show heterogeneity consistent with a leftward shift and steeper decline in high-risk industries; results reported across the 2,575-firm panel with controls and robustness checks.
Beyond a certain threshold of AI embedding, deeper AI adoption shifts managerial attention toward AI systems and away from employees, reducing ECSR (AI attention shift mechanism).
Negative AI^2 coefficient in quadratic panel regressions indicates declining ECSR at high AI adoption; supported by theoretical dual-agent model arguing attention shift; robustness checks reported. (Sample: same 2,575 firms, 2013–2023.)
Trust, verification costs, and legal/governance requirements remain consequential even with AI mediation and may limit or shape adoption.
Theoretical discussion of governance and verification costs; no empirical measurement of these costs in adopter firms provided.
AI-mediated interpretation and action carry risks related to quality, bias, and misalignment, which can produce miscommunication or incorrect automated actions.
Paper's discussion section raising caveats; conceptual risk analysis without empirical incident data; references to general concerns in AI safety literature (no new empirical evidence provided).
Organisations struggle to optimise human–AI collaboration in knowledge‑intensive decision‑making.
Statement based on a systematic synthesis of human–AI interaction and knowledge management literature presented in the paper; no primary empirical sample or dataset reported in the abstract.
Despite increased deployment, the field lacks a principled framework for answering when a team is helpful, how many agents to use, how team structure impacts performance, and whether a team is better than a single agent.
Authors' assessment of the literature and gaps; presented as a motivation for their work (no empirical count of missing frameworks given in excerpt).