Evidence (7395 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 |
Adoption
Remove filter
Faster workflows and lower transaction costs due to AI may increase publication rates, change authorship practices, and affect incentives for replication and robustness.
Raised in Incentives and Research Behavior as a predicted effect. This is a theoretical prediction grounded in observed workflow changes; the abstract does not supply longitudinal or causal evidence documenting these behavioral changes.
Firms that integrate LLMs effectively (tooling, testing, governance) could capture outsized productivity gains, raising firm-level dispersion.
Case studies, practitioner reports, and economic reasoning about adoption and governance advantages; empirical cross-firm causal evidence lacking.
Use of GenAI can reduce demand for lower‑value routine work while increasing demand for higher‑skill oversight, synthesis, and relationship tasks.
Authors' interpretation of interview data and framework implications; no labor-market or demand-side empirical data provided in the paper.
Hysteresis bands and safe-exit timers may become regulated design choices in contexts where rapid authority oscillations lead to harm.
Speculative policy projection in the discussion of regulatory implications; rationale based on safety concerns, not empirical legal analysis or observed regulatory actions.
Access to diverse interaction data and the ability to train and maintain adaptive models create scale economies and barriers to entry, potentially consolidating advantage for large incumbents.
The paper provides economic reasoning and qualitative case discussion about data as a strategic asset; this is a theoretical/empirical hypothesis rather than a directly measured claim within the paper.
Superior AI integration and oversight capabilities can create competitive differentiation; if quality failures are widespread, providers with stronger human-AI blends may gain market advantage.
Market-structure reasoning and illustrative case examples; speculative without systematic empirical validation.
Policy responses (disclosure requirements, liability for misinformation, auditability) will affect deployment costs and firm strategy; transparent AI use and human escalation pathways lower regulatory and reputational risk.
Regulatory analysis and reasoning; supported by case examples where disclosure/controls reduced reputational exposure; no comprehensive causal evidence.
Improved availability and personalization can increase consumer welfare for routine interactions, but trust failures can reduce long-term demand or increase churn; net welfare depends on governance quality.
Conceptual welfare reasoning backed by case studies of improved availability and separate case reports of trust-related churn; lacks long-run welfare quantification.
Wages may diverge: downward pressure on routine-role wages and a premium for supervisory and relational skills.
Theoretical labor-economics arguments and tentative early evidence from organizational changes; acknowledged as speculative with limited empirical support.
Expect labor reallocation from routine frontline tasks toward higher-skill supervision, escalation handling, and customer experience design; demand for prompt engineering and AI oversight rises.
Economic reasoning supplemented by early observational reports from firms (role changes, new hiring patterns); no long-run labor market causal estimates provided.
Human–AI collaboration is more likely to augment rather than replace skilled finance workers, leading to task reallocation toward higher-value judgment and oversight.
Interpretation based on interview accounts and observed adoption/use patterns indicating complementary roles for humans and AI; the claim is inferential rather than directly causally estimated in the quantitative analysis summarized.
The market for HR analytics platforms and tailored AI services is expanding, with potential for vendor lock-in effects and platform concentration.
Market implication synthesized in the review from literature noting growing demand for HR AI tools; largely inferential rather than empirically proven within the reviewed studies.
Automation of administrative HR tasks may reduce demand for lower-skilled HR roles while increasing wages and demand for analytics-capable workers, contributing to within-firm wage reallocation.
Review implication synthesizing literature trends on automation and skill demand; not based on causal longitudinal evidence (review highlights evidence gaps).
Heterogeneous adoption of data-driven HRM may widen productivity dispersion across firms and affect market competition.
Implication drawn in the review based on heterogeneous adoption patterns discussed in included studies and economic interpretation of productivity effects.
Centralized governance architectures can favor integrated platform vendors (bundled low-code + RPA + AI + policy engines) or create opportunities for governance-layer specialists, affecting competition and lock-in.
Market-structure implication argued through economic and industry reasoning; supported by observations of vendor dynamics in practitioner examples but not by systematic market analysis.
Enabling safer deployment of higher-risk automations may increase displacement of routine cognitive tasks while creating demand for governance, compliance, and AI oversight roles.
Projected labor-market effect based on task composition reasoning and practitioner expectations; suggested as a likely outcome but not empirically measured in the paper.
Insurers may revise underwriting, raise premiums, or exclude certain AI-related exposures until risk assessments improve; new insurance products may emerge for AI governance failures.
Policy and market impact speculation based on perceived risk; no empirical insurer responses or underwriting data provided.
Firms will reallocate resources toward AI governance, monitoring tools, and skilled auditors (increasing compliance and labor costs), and demand for products/services (prompt-provenance tools, watermarking, AI forensic services, certified-safe LLMs) will rise.
Market/economic projection based on the identified threat and presumed demand for mitigations; speculative without market-data support in the paper.
Policy implication: policymakers seeking to balance openness and security should consider layered, adaptive instruments that can be tuned by sector or actor; economic analysis can help identify where centralized coordination yields scale economies versus where decentralized rights‑based approaches preserve competition and trust.
Normative policy recommendation extrapolated from the paper's comparative findings and theoretical framing; not tested empirically in the paper.
Increased liability risk and compliance costs could raise barriers to entry for startups and niche vendors and potentially consolidate market power among larger firms better able to absorb compliance overhead; alternatively, new markets could emerge for compliant, certified providers.
Economic reasoning about compliance costs and market structure (theoretical predictions), not supported by empirical industry data in the Article.
Demand for labor may shift from routine instrument operation and image processing toward higher-level tasks (experiment design, oversight, interpretation), and LLMs may amplify productivity of skilled scientists, potentially increasing wage premia for those who supervise AI-guided workflows.
Labor-economics reasoning and analogy to prior automation effects; no empirical labor-market or wage data presented specific to microscopy.
Adoption of Model Medicine practices would create new markets and roles (e.g., diagnostics, remediation services, 'model clinicians'), affect regulation, insurance, and procurement, and could shift R&D funding toward clinical-model sciences.
Theoretical economic implications and market/regulatory analysis provided in the discussion section (speculative policy and market projections; no empirical market data).
Implication for AI/platform economics: complementarities between public funding and digital (AI-enabled) platforms can convert public demand into decentralized labor opportunities, reshaping sectoral employment without growth in traditional firms.
Conceptual extension of empirical findings on platform-mediated cultural employment and fiscal procurement interactions; evidence comes from city-level DID results and inferred platform-activity proxies (280 cities, 2008–2021).
Principal stratification analysis suggests the training’s effect on scores operated primarily by expanding the set of LLM users (an adoption channel) rather than substantially improving per-user productivity among those who would already use the LLM.
Mechanism decomposition using principal stratification applied to the randomized trial data (n = 164); analysis indicates a larger contribution from the adoption margin than from within-user productivity gains, though estimates have wide confidence intervals.
Smart power strategies that promote domestic AI champions (via procurement, subsidies, industrial policy) affect labour markets, inequality, and international labour arbitrage.
Conceptual claim grounded in literature on industrial policy and labour economics with policy examples referenced; no primary microdata analysis in the paper.
Widespread adoption of formal governance could lower systemic risk from enterprise AI failures, whereas heterogeneous adoption may create winners and losers based on governance quality.
Conceptual systems-level argument and comparative-case reasoning; no quantitative systemic-risk modeling or empirical evidence provided.
Greater automation of routine ERP/CRM tasks will displace some operational roles while increasing demand for governance, oversight, and AI-engineering skills, shifting labor toward higher-skill, higher-wage tasks.
Theoretical labor-market implication derived from the pattern's effects on task automation and governance needs; based on qualitative synthesis, not empirical labor-market analysis.
Risk-adjusted total cost of ownership (TCO) may fall if governance prevents costly incidents (e.g., compliance fines, data breaches), despite higher upfront costs.
Conceptual economic argument supported by qualitative examples and best-practice reasoning; no empirical ROI or incident-rate data presented.
Expensive formalization may push firms either to remain informal (preserving low-cost labor) or to automate instead of hiring formally; policy choices that lower formalization costs could retain jobs that otherwise would be automated.
Analytical inference from the measured CFIL and NWC values across the 19 countries and standard economic reasoning about cost-driven firm choices; the note does not present micro-level causal tests of these pathways.
Macroeconomic policy should monitor aggregate demand effects from reallocation and inequality; active fiscal and monetary coordination may be required to manage aggregate impacts of AI-driven reallocation.
Synthesis and policy implication drawing on macroeconomic reasoning and literature linking redistribution and demand to overall employment and growth; not presented as a single causal empirical result.
AI diffusion may widen inequality across education and regions and potentially reduce labor supply among financially constrained households.
Derived implication from heterogeneous negative associations between AI-rich regions and employment intention for low-educated and financially-constrained respondents in the cross-sectional sample (n=889).
Measurement friction from the results-actionability gap creates a hidden cost: teams can detect problems but cannot cheaply translate findings into improvements, reducing the speed and ROI of LLM investments.
Authors' implication drawn from interview evidence about the effort required for remediation and lack of direct translation from evaluations to fixes; presented as an economic implication rather than directly measured quantity.
Risk of platform shutdown (platform mortality) shapes user behavior by reducing incentives to invest time/effort configuring agents, creating stranded-asset-like risks.
Qualitative observations and economic reasoning linking user reports/behaviors to perceived platform risk during the one-month observational period; no formal economic measurement or causal identification.
If verified, explainable GLAI is priced higher due to compliance costs, access-to-justice gaps may widen as lower-cost but riskier offerings persist or services become more expensive.
Distributional reasoning linking higher compliance costs to price increases and access effects; supported by illustrative examples, no empirical price or access data.
Routine, unrestrained adoption of GLAI without enforceable mechanisms for effective human review threatens judicial independence and rights protections.
Normative and legal argumentation supported by conceptual analysis and illustrative scenarios. No empirical causal evidence; projection based on theoretical risk pathways.
Sectors that rely heavily on visual evidence (e.g., media verification, e-commerce product updates, autonomous systems) face higher exposure to temporal inaccuracies and will likely incur monitoring/updating costs.
Implications discussion linking modality gap and time-sensitivity results to sector-specific risk exposure; qualitative projection rather than measured sectoral data.
Psychological harms documented (e.g., delusional content, suicidality, misrepresented sentience) impose downstream economic costs (healthcare use, lost productivity, litigation) that should be factored into cost–benefit analyses of LLM deployment.
Authors' policy discussion linking observed harms to standard categories of social/economic costs; no direct measurement of downstream economic costs in the study.
The message-level evidence of chatbot-related psychological harms implies potential economic consequences: reduced consumer trust and adoption, increased regulatory scrutiny and compliance costs, moral-hazard trade-offs for engagement-driven business models, higher insurance/liability costs, and incentives for investment in safety R&D and monitoring.
Discussion/implications section extrapolating from observed harms to potential economic effects; these are analytical inferences rather than empirically measured economic outcomes.
There is a risk of deskilling, especially for trainees receiving reduced diagnostic practice when AI automates routine tasks.
Conceptual arguments supported by qualitative reports and limited observational findings; empirical longitudinal evidence quantifying deskilling is sparse.
Erosion of informal communication and tacit coordination driven by AI integration can create negative externalities on team efficiency that are not captured by short-run metrics.
Derived from interview narratives describing loss of ad hoc communications and tacit knowledge exchange after AI adoption; interpreted as producing costs not reflected in immediate measurable outputs.
Uneven adoption of symbiarchic HR practices across firms could concentrate productivity gains and rents in firms or occupations that successfully integrate AI while preserving human judgement, potentially widening within‑ and between‑firm inequality.
Projected distributional implication based on economic theory and the paper’s framework; presented as a hypothesis for empirical testing rather than as an observed result.
There is a risk of regulatory arbitrage and spillovers: better detection on regulated platforms could drive problem gamblers to unregulated venues.
Paper notes this as a theoretical risk and policy concern; no direct empirical evidence provided in the review to quantify this effect.
Such disjointed strategies cannot manage the systemic socio-economic disruption ahead.
Asserted in abstract as a conclusion/argument; no empirical evaluation described in the abstract.
AI threatens to fracture the 20th-century social contract.
Asserted in abstract as a normative/predictive claim; no empirical support described in the abstract.
Mergers are a barrier to economic growth (negative association between mergers and GDP growth).
Model results reported a negative relationship between mergers and GDP growth in the regressions described in the summary; however, the summary does not define how 'mergers' is measured, how widely it was observed across countries, or the statistical significance levels.
Without effective safeguards, the digital world can shift from a space of opportunity to one of harm.
Normative/conditional claim drawing on the book's analysis; not an empirical finding—no method or sample size applicable in the excerpt.
Unequal GenAI adoption has implications for productivity, skill formation, and economic inequality in an AI-enabled economy.
Interpretation/implication drawn from observed gendered adoption patterns in the 2023–2024 UK survey and literature on technology diffusion and labor-market impacts (no direct empirical measurement of downstream economic effects in the paper).
Preliminary evidence that inappropriate reliance on AI outputs is worse for complex information needs (complex answers).
Post-hoc/stratified analysis in the user study examining the effect of the complexity of the information need on reliance/error-detection; described as preliminary in the paper.
AI-driven productivity gains may not translate into broad-based demand if income is concentrated among capital owners, which could dampen aggregate profitability over time.
Theoretical argument grounded in Mandel-like distributional mechanics and demand-driven growth literature; speculative without empirical aggregation tests in the paper.
Concentration of curated datasets and restrictive IP can create monopolistic rents and underprovision of public‑good datasets, implying policy interventions (data sharing incentives/standards) may be required.
Economic reasoning about market formation and data as a scarce asset; no empirical market analysis provided in summary (theoretical implication).