Evidence (4857 claims)
Adoption
5586 claims
Productivity
4857 claims
Governance
4381 claims
Human-AI Collaboration
3417 claims
Labor Markets
2685 claims
Innovation
2581 claims
Org Design
2499 claims
Skills & Training
2031 claims
Inequality
1382 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 417 | 113 | 67 | 480 | 1091 |
| Governance & Regulation | 419 | 202 | 124 | 64 | 823 |
| Research Productivity | 261 | 100 | 34 | 303 | 703 |
| Organizational Efficiency | 406 | 96 | 71 | 40 | 616 |
| Technology Adoption Rate | 323 | 128 | 74 | 38 | 568 |
| Firm Productivity | 307 | 38 | 70 | 12 | 432 |
| Output Quality | 260 | 71 | 27 | 29 | 387 |
| AI Safety & Ethics | 118 | 179 | 45 | 24 | 368 |
| Market Structure | 107 | 128 | 85 | 14 | 339 |
| Decision Quality | 177 | 75 | 37 | 19 | 312 |
| Fiscal & Macroeconomic | 89 | 58 | 33 | 22 | 209 |
| Employment Level | 74 | 34 | 78 | 9 | 197 |
| Skill Acquisition | 98 | 36 | 40 | 9 | 183 |
| Innovation Output | 121 | 12 | 24 | 13 | 171 |
| Firm Revenue | 98 | 35 | 24 | — | 157 |
| Consumer Welfare | 73 | 31 | 37 | 7 | 148 |
| Task Allocation | 87 | 16 | 34 | 7 | 144 |
| Inequality Measures | 25 | 76 | 32 | 5 | 138 |
| Regulatory Compliance | 54 | 61 | 13 | 3 | 131 |
| Task Completion Time | 89 | 7 | 4 | 3 | 103 |
| Error Rate | 44 | 51 | 6 | — | 101 |
| Training Effectiveness | 58 | 12 | 12 | 16 | 99 |
| Worker Satisfaction | 47 | 33 | 11 | 7 | 98 |
| Wages & Compensation | 54 | 15 | 20 | 5 | 94 |
| Team Performance | 47 | 12 | 15 | 7 | 82 |
| Automation Exposure | 27 | 26 | 10 | 6 | 72 |
| Job Displacement | 6 | 39 | 13 | — | 58 |
| Hiring & Recruitment | 40 | 4 | 6 | 3 | 53 |
| Developer Productivity | 34 | 4 | 3 | 1 | 42 |
| Social Protection | 22 | 11 | 6 | 2 | 41 |
| Creative Output | 16 | 7 | 5 | 1 | 29 |
| Labor Share of Income | 12 | 6 | 9 | — | 27 |
| Skill Obsolescence | 3 | 20 | 2 | — | 25 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
Productivity
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AI may alter firms' competitive dynamics by amplifying scale advantages and platform effects, making antitrust, data portability, and competition policy relevant to preserve contestability and innovation.
Synthesis of industrial organization theory and empirical observations of platform markets and data-driven firms cited in the literature review; no primary empirical study included in this paper.
If quantum advantages accrue initially to well-capitalized incumbents (cloud providers, financial firms, pharmaceuticals), we should expect increased market power and higher rents.
Scenario analysis and historical analogs where early compute advantages concentrated market power; qualitative market-structure modeling.
Benefits of quantum diffusion are likely to be uneven across countries, firms, and workers—boosting regions with strong innovation ecosystems and possibly increasing market concentration among compute-capable incumbents.
Multi-region/sectoral modeling with heterogenous adoption and capability parameters; historical analogs showing concentration following early compute advantages; scenario comparisons.
Without coordinated investments and governance, large theoretical gains may remain unrealized or be very unevenly distributed.
Policy counterfactual scenarios in which underinvestment, fragmented governance, or restrictive export regimes reduce adoption elasticities and infrastructure readiness, producing lower and more concentrated macro gains compared with coordinated-investment scenarios.
High executive digital cognition on its own tends to weaken the policy's positive effect on energy utilization efficiency (interpreted as short-run adjustment costs from digital transformation).
Interaction tests between policy treatment and an executive-level digital-cognition measure show a negative interaction coefficient in DID regressions; authors interpret this as evidence of short-run adjustment costs.
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.
Absent interoperability, divergence in data and AI rules will raise transaction costs, reduce trade gains, and create opportunities for regulatory arbitrage.
Economic reasoning and scenario-based projections; asserted as an outcome of mechanism analysis rather than demonstrated with quantitative estimates.
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.
Capabilities and data advantages for certain vendors could lead to market concentration and platform dominance in AI-driven educational feedback.
Expert concern synthesized from the workshop of 50 scholars about market dynamics; theoretical warning without empirical market-structure analysis in the report.
Differential access to high-quality AI feedback systems and bias in training data can exacerbate educational inequalities and harm marginalized groups.
Expert consensus and thematic analysis from the 50-scholar workshop, raising equity and bias risks; no empirical subgroup effectiveness estimates included.
Learners may over-rely on AI feedback or game systems to obtain desirable responses, reducing effortful learning.
Workshop participant concerns synthesized qualitatively; cited as risk and an open empirical question—no experimental data provided.
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.
There is a risk of manipulation and misinformation if argument mining/synthesis is unregulated or misaligned with social incentives, creating externalities that may justify public intervention.
Conceptual risk assessment combining known misinformation dynamics and AI capabilities; no empirical incident data provided.
Increased error risk and weaker explainability from GLAI will raise malpractice and liability exposure for firms and lawyers, driving up insurance and compliance costs.
Legal-risk analysis and economic reasoning connecting explainability/liability to insurance costs; no empirical cost studies presented.
The combination of hallucination and professional overreliance strains existing regulatory goals (e.g., explainability, human oversight) within European AI governance frameworks.
Legal and regulatory analysis mapping technical and behavioral risks onto European AI governance goals; references to statutory/regulatory texts and policy debates. Qualitative argumentation rather than empirical test.
Fabricated or opaque intermediate data and reasoning in GLAI weaken explainability, making it difficult to provide meaningful explanations about how outputs were produced.
Conceptual analysis of token-prediction architectures, literature on explainability limits of LLMs, and legal/regulatory analysis referencing explainability requirements. No empirical measurement.
Hallucinated content produced by GLAI is often linguistically fluent and persuasive, increasing the risk that legal professionals will accept it without verification.
Literature synthesis on model fluency and behavioral literature on trust in coherent authoritative outputs, plus illustrative vignettes. No original experimental data or sample size.
This architectural mismatch (token-prediction vs. formal legal reasoning) contributes to confident but factually incorrect outputs (hallucinations) in GLAI.
Technical/conceptual analysis plus synthesis of existing literature on hallucinations in generative models; illustrative examples and vignettes provided. No primary empirical measurement in the paper.
Observed failure modes during the workflow included hypothesis creep, definition-alignment bugs (mismatch between informal and formal definitions), and agent avoidance behaviors (agents delegating or failing to complete tasks).
Qualitative analysis and post-mortem reported in the paper based on the single project workflow and logs; specific failure modes enumerated by authors from their process observations.
Absence of governance and observability could increase social costs of accidents and induce conservative regulation that stifles beneficial adoption.
Policy reasoning and historical regulatory responses to systemic risks; conceptual projection without quantitative modeling of regulatory impact.
Strong proprietary stacks and incompatible protocols could create winner‑take‑all or oligopolistic market outcomes due to network effects and switching costs.
Market‑structure theory and historical platform examples (e.g., dominant tech platforms); argument is conceptual and not backed by new empirical market analysis in the paper.
Without these architectural commitments, the economic costs — stranded assets, safety incidents, reduced innovation, and high coordination costs — will be substantial.
Predictive economic argument built from historical IoT/Internet lessons and systems reasoning; no quantitative cost estimates or econometric analysis in the paper.
Poor governance and observability in agent networks would make accountability, certification, and regulation difficult.
Policy and governance reasoning with illustrative domain examples; conceptual argument without empirical governance case studies or metrics.
Weak or brittle security and trust mechanisms across distributed agent ecosystems will pose serious risks.
Lessons drawn from IoT security failures and conceptual threat analysis; no new penetration testing or security metrics presented.
Lifecycle mismatch — rapidly evolving AI software embedded in long‑lived physical assets — risks premature ossification or expensive retrofits.
Systems engineering reasoning and historical analogies to embedded systems/IoT lifecycles; no quantitative lifecycle modeling or case study data in the paper.
Misalignment or poor meta-control could produce persistent unsafe behaviors in autonomous learners; governance and oversight mechanisms will be crucial.
Risk analysis based on conceptual failure modes for meta-control; no empirical incidents reported in the paper.
Current models transfer poorly across domains, are brittle in nonstationary environments, and are inefficient in physical/embodied tasks.
Synthesis of known challenges from prior literature and practical experience; paper cites these as motivating observations rather than reporting new data.
Current models have limited meta-control and do not autonomously decide when to explore, imitate, consult prior knowledge, or consolidate.
Conceptual critique based on typical ML training pipelines and limited on-line decision-making modules; no empirical tests in paper.
There is weak integration between passive observation (supervised/representation learning) and active experimentation (reinforcement/exploratory learning) in current systems.
Observation of methodological separation in current literature and systems; conceptual discussion in the paper.
Current AI models lack the architectures and control mechanisms required for sustained, autonomous learning in dynamic real-world settings.
Conceptual/theoretical analysis presented in the paper; synthesis of limitations observed in existing literature and practices (no new empirical data provided).
Attribution (labeling responses as AI) can alter perceived empathy and therefore matters for product design, branding, and disclosure policy decisions.
Findings from the attribution effect experiment showing reduced feelings of being heard/validated when replies are labeled AI despite identical content; authors discuss implications for product design and disclosure.
Existing idea-evaluation approaches (LLM judges or human panels) are subjective and disconnected from real research outcomes.
Framing and motivation in the paper arguing current approaches rely on subjective judgments and do not directly tie to later publication/citation outcomes; supported implicitly by the empirical mismatch (LLM-judge vs HindSight).
LEAFE's benefits depend on informative, actionable feedback; environments with noisy or adversarial feedback may limit improvements.
Limitations stated in the paper noting sensitivity to feedback quality; conceptual reasoning that the method relies on extracting actionable signals from environment feedback.
Outcome-driven post-training (optimizing final rewards) underutilizes rich environment feedback and causes 'distribution sharpening' — policies overfit a narrow set of successful behaviors and fail to broaden problem-solving/recovery capacity in long-horizon settings.
Problem diagnosis in the paper supported by comparison of outcome-driven RL (GRPO) performance versus LEAFE and by conceptual argument about how optimizing final success signals can narrow behavioral support; supported by empirical observations of poorer recovery/generalization in baselines.
Rotation-based PTQ methods (designed for integer formats) fail on MXFP4 because global orthogonal rotations move outlier energy across quantization blocks, creating new outliers and often producing bimodal activations that underutilize the limited MXFP range.
Analytical argument backed by empirical observations reported in the paper: activation-distribution analysis demonstrating cross-block outlier propagation and bimodality when applying global orthogonal rotations to MXFP4-blocked layouts; comparisons to performance collapse under those methods.
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.