Evidence (8570 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Adoption
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Humans often mistake fluency for reliability: when a model responds smoothly, users tend to trust it, even when both model and user are drifting together.
Behavioral/psychological assertion in the paper referencing human interaction patterns with fluent outputs; no experimental data or sample size reported in this paper excerpt.
LLMs produce fluent outputs even when their internal reasoning has drifted; a confident answer can conceal uncertainty, speculation, or inconsistency, and small changes in phrasing can lead to different conclusions.
Conceptual/observational claim presented in the paper; no original empirical test or sample size reported here.
Aligning the generative policy with nuanced user preference signals is a challenge for generative recommendation.
Paper lists this as one of three scaling challenges motivating the proposed methods (problem statement about preference alignment).
Encoding long user behavior sequences with multi-token item representations based on semantic IDs is prohibitively costly (a scaling challenge).
Paper lists this as one of three scaling challenges for deploying GR at industrial scale (problem statement about computational/cost burden).
Within a single request, identical model inputs may produce inconsistent outputs due to the pagination request mechanism (a challenge for GR/NTP recommendation at industrial scale).
Paper lists this as one of three scaling challenges for generative retrieval in large-scale industrial systems (problem statement).
This mismatch makes it difficult to predict post-deployment success and obscures competitive effects such as early-adoption advantages and market dominance.
Argument in paper linking limitations of current evaluation methods to inability to predict deployment outcomes; conceptual claim without empirical demonstration in the abstract.
Evaluation is still largely conducted on static benchmarks with accuracy-focused measures that assume systems operate in isolation.
Statement in paper critiquing prevailing evaluation practice; presented as a general observation without cited systematic review or quantitative evidence in the abstract.
Infrastructure constraints, particularly in developing countries, limit AI adoption in auditing.
Thematic analysis of reviewed articles noting infrastructure limitations (e.g., ICT infrastructure) in developing-country contexts.
Limitations in auditor competencies (skills and training) hinder effective AI adoption in auditing.
Thematic findings across the sample of articles report auditor competency gaps as a challenge to AI implementation.
Ethical and data privacy concerns are persistent challenges to AI implementation in auditing.
Recurring theme in the reviewed literature identified via thematic analysis; papers cite ethics and privacy as obstacles.
Several challenges persist for AI adoption in auditing, including high technology investment costs.
Thematic analysis of barriers reported across the 15 articles highlighting cost as a recurrent challenge.
The study proposes an integrative conceptual model and research propositions highlighting cross-functional challenges in governance, organizational capabilities, socio-technical alignment, and responsible implementation.
Statement in abstract that the authors developed a conceptual model and research propositions based on their review and identified cross‑functional challenges.
Most AI tooling targets that fraction [the ~10% of the workday spent writing code].
Assertion made in the paper (abstract) as an observed mismatch between where AI tooling focuses and overall developer work activities.
Low-skill roles in packaging, sorting, and basic assembly face a high risk of automation.
Paper's findings/prediction derived from task-level classification (routine/repetitive tasks) applied to jobs in Nagpur's medium enterprises; no reported sample size or quantified risk metrics in the excerpt.
Regulatory and labor friction is scored per sector using actual compliance frameworks (Basel III, FDA AI guidance, HIPAA) and BLS union density data, and is applied as a haircut to base adoption rates via an S-curve ramp.
Paper description of friction scoring method referencing specific regulatory frameworks and BLS union density; applied in the model as a haircut and S-curve adoption ramp.
Restricting AI productivity gains to the labor-generated portion of each sector's gross value added reduces the naive addressable base by approximately 72 percent.
Bottom-up sectoral model described in the paper that applies labor share to gross value added across 21 NAICS industries; the paper explicitly states the labor-generated restriction reduces the naive addressable base by ~72%.
Lower survival rates among BDA adopters are driven by greater uncertainty in sales.
Paper states greater uncertainty in sales is an interrelated factor explaining lower survival for BDA adopters, based on empirical analysis of German start-ups.
Lower survival rates among BDA adopters are driven by higher operating costs.
Paper reports that higher operating costs are an interrelated factor explaining lower survival among BDA adopters, based on the same empirical sample of German start-ups.
Start-ups using BDA face lower survival rates.
Empirical comparison of BDA adopters versus non-adopters in a large sample of German start-ups (survival analysis implied by reported outcome).
Some of this reduced price is related to reduced input cost contributions, in particular labor and materials costs.
Decomposition/mediation analysis reported in the paper attributing part of the observed price reductions to declines in input cost contributions (labor and materials); exact methods, sample size, and statistical estimates not provided in the excerpt.
AI intensity is associated with lower prices charged to purchasers.
Empirical analysis reported in the paper linking measures of AI intensity to observed output prices (details of data sources, sample size, and specific methods not provided in the excerpt).
A foreign state actor threat model for enterprise identity governance establishing that Silk Typhoon, Salt Typhoon, Volt Typhoon, and North Korean AI-enhanced identity fraud operations have already operationalized AI identity vulnerabilities as active attack vectors.
Paper claims to provide a threat model and asserts these named actors have operationalized AI identity vulnerabilities; stated grounding implied to be threat intelligence and incident analysis, though not detailed in the excerpt.
Nation-state actors including Silk Typhoon and Salt Typhoon have operationalized ungoverned machine credentials as primary espionage vectors against critical infrastructure.
Asserted in paper and described as grounded in threat intelligence; no specific threats, incidents, or data described in the excerpt.
A single ungoverned automated agent produced $5.4-10 billion in losses in the 2024 CrowdStrike outage.
Statement in paper attributing a $5.4-10B loss to an ungoverned automated agent during the 2024 CrowdStrike outage; no citation or method shown in excerpt.
No integrated framework exists to govern machine identities (AI agents, service accounts, API tokens, automated workflows).
Asserted in paper as a gap in existing governance frameworks; no empirical test or survey reported in the excerpt.
Automated agents, service accounts, API tokens, and automated workflows now outnumber human identities in enterprise environments by ratios exceeding 80 to 1.
Statement in paper (asserted prevalence); no sample size or data source provided in the excerpt.
Foundation-model usage can increase compute-related emissions.
Conceptual/environmental concern highlighted in the paper about the carbon footprint of heavy model use and persistent storage; no quantified emissions analysis or lifecycle assessment presented.
These systems can cause skill atrophy.
Theoretical risk articulated in the paper that reliance on AI assistance may degrade human skills over time; no longitudinal skill-measurement or experimental evidence provided.
The same foundation-model systems can also intensify surveillance.
Cautionary claim in the paper noting the surveillance risk of durable, queryable traces and integrated tooling; presented as a conceptual risk rather than empirically measured increase in surveillance.
Digital–intelligent integration generates positive spatial spillovers, reducing carbon intensity in neighboring provinces.
Spatial Durbin model results reported on the 30-province panel indicating significant negative effects on neighboring provinces' carbon intensity (spatial spillover effects).
Industrial structure upgrading and green technology innovation were identified as mediating pathways through which digital–intelligent integration reduces carbon intensity.
Mediation analysis reported in the paper showing these two mechanisms mediate the effect (mediation models applied to the provincial panel).
The negative association between digital–intelligent integration and carbon intensity is robust to endogeneity concerns and alternative model specifications.
Robustness checks and endogeneity treatments (as reported): alternative specifications and methods addressing endogeneity (details not provided in the summary).
Digital–intelligent integration is significantly associated with lower carbon intensity.
Fixed-effects regression estimates reported on the 2014–2023 provincial panel (30 provinces); significance described in the paper; models control for covariates.
Coder employment has continued to grow in recent years, though much more slowly than it did pre-2022.
Time-series comparison of coder employment levels/growth rates from CPS before and after 2022.
The deceleration in coder employment is not attributable to coders' exposure to slowing industries, implying an occupation-specific shock around the introduction of ChatGPT.
Regression/controlled analysis using a novel industry-level control variable for industry shocks to separate industry-level from occupation-specific effects.
Aggregate employment of coders has decelerated sharply since the introduction of ChatGPT.
Empirical analysis linking O*NET to CPS employment data showing a sharp slowdown in coder employment growth coinciding with ChatGPT's introduction.
Job insecurity emerges as a critical mediating factor influencing employee attitudes and behavioural responses to generative AI, including upskilling intentions and resistance to technological change.
Review-level synthesis identifying job insecurity reported in included studies as mediating relationships between AI adoption and employee attitudes/behaviours (e.g., upskilling, resistance).
Employees express concerns about role displacement (job loss or role changes) associated with generative AI adoption.
Reported across multiple studies included in the review; the review summarises these concerns as part of mixed employee perceptions.
These positive perceptions coexist with employee concerns about skill obsolescence related to generative AI.
Synthesis of studies included in the review documenting worker concerns about skills becoming obsolete due to AI-driven changes.
Persistent challenges to AI implementation include resistance to change, data quality limitations, and concerns regarding transparency and algorithmic bias.
Recurring barriers identified across the 27 included studies, summarized in the review's findings.
The explanatory interface suppresses the natural development of both cognitive trust and emotional trust.
Longitudinal/within-experiment measures of cognitive and emotional trust reported in the RCT; authors state that explanatory interface suppressed the natural development of these trust dimensions in the 120-participant experiment.
The explanatory interface exerts a negative effect on learned trust.
Randomized controlled experiment measuring learned trust; authors report a negative (statistically significant) effect of explanatory interface on learned trust in their sample of 120 pre-service teachers.
The improvement in task performance due to the explanatory interface is confined to the task execution stage and does not transfer to subsequent independent tasks.
Experimental measurement of immediate (during-assisted) task performance and subsequent independent task performance; authors report improvement only during task execution and no transfer effect to later independent tasks in their RCT with 120 participants.
This combination (rapid but uneven capability advance and lagging knowledge about harms/safeguards) creates a difficult policy condition: governments must decide under uncertainty across multiple plausible technological trajectories through 2030.
Reasoned argument in the article synthesizing foresight scenarios and the literature on uncertainty in AI progress (references to documents like OECD foresight and the International AI Safety Report 2026).
Knowledge about harms, safeguards, and effective interventions remains partial and lagged relative to capability advances.
Analytic claim in the article, supported by cited reports and literature that document gaps in understanding of harms and safeguards.
Income inequality, measured by the Gini index, rises moderately in every scenario we examine due to the polarising effect of job losses and wage and capital income increases on the income distribution.
Calculation of Gini index across multiple simulated scenarios using the SWITCH-linked distributional analysis; reported in the report.
The largest average losses are experienced by middle and higher income households, for whom job displacement outweighs any wage or capital income gains. Lower income households also lose, but by much less.
Distributional results from microsimulation (SWITCH) applying scenarioled job displacement, wage and capital effects across income groups; reported in the report.
When these effects are combined, we find an average decline in household disposable income as a result of AI adoption.
Combined scenario simulations incorporating job displacement, wage effects and capital income effects linked to the Irish tax-benefit system using SWITCH; result reported in the report's main findings.
These wage gains are not large enough to counterbalance the average fall in income due to job displacement.
Combined simulation results (displacement + wage effects) using scenario assumptions and microsimulation (SWITCH), reported in the report's distributional analysis.
Those most likely to experience this disruption are found in higher income households, where the share of workers transitioning into unemployment is substantially larger than in lower income families.
Microsimulation (SWITCH) linking simulated job displacement scenarios to household income groups; results reported in the report.