Evidence (14156 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
The article argues that the idea of a “Pax Silica” is fragile.
Conclusion drawn from the paper's theoretical framework and comparative analysis; presented as an assessment rather than empirical measurement.
Contemporary struggles over semiconductor supply chains represent not a new hegemonic order but a logistical adaptation of Pax Americana.
Stated thesis supported by comparative/historical analysis and theoretical argumentation (comparative analysis of historical Pax orders and U.S. techno-security architecture); no quantitative sample size reported in abstract.
Initial adaptation challenges to AI integration were identified among employees.
Participants in semi-structured interviews (n=12) reported initial difficulties adapting to AI tools; themes relating to early adaptation challenges were coded.
Past machine learning applications to pricing have produced models that adapt slowly to real-time changes, depend heavily on historical data, and struggle to handle multi-agent scenarios.
Stated as literature/related-work critique in paper; no new empirical evidence or sample size provided in the excerpt.
Traditional methods, such as rule-based algorithms and statistical scale forecasting, struggle to adapt to rapidly changing market conditions, competitive maneuvers, and evolving consumer strategies, leading to sub-optimal pricing and decreased profitability.
Paper asserts this as background/motivation; no detailed empirical study or sample size provided in the excerpt.
In the short term, big data may inhibit welfare growth.
Theoretical comparative-static/dynamic analysis reported in the model showing that initial or short-run effects of increased data sharing can reduce welfare growth (no empirical/sample data).
There is a measurement asymmetry in standard LLM evaluation: unconstrained prompts can inflate constraint-adherence scores and mask the practical value of structured prompting.
Analysis of evaluation results from the controlled study showing that unconstrained (simple) prompts sometimes achieve high constraint-adherence scores, leading to misleading evaluation of structured prompts' benefits.
Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents.
Conceptual critique presented in the paper's theoretical argumentation (literature critique and re-framing); no empirical sample reported.
AI usage predicts work disengagement behavior via emotional exhaustion elicited by AI-associated technostressors.
Four-stage longitudinal study (survey) of finance professionals (N=285); mediation analysis testing AI usage -> technostressors -> emotional exhaustion -> work disengagement, based on SOR framework.
These findings highlight fundamental challenges in the numerical and time-series reasoning for current LLMs and motivate future research in financial intelligence.
Interpretation of experimental results in the paper: authors conclude that the observed limited gains (particularly on trading-signal/time-series aspects) indicate shortcomings in LLM numerical and time-series reasoning.
There is a central design tension in human-AI systems: maximizing short-term hybrid capability does not necessarily preserve long-term human cognitive competence.
Conceptual/theoretical claim derived from the framework and discussion in the paper (argument and mathematical framing), no empirical sample or longitudinal data presented in the excerpt.
This result directly contradicts classical scaling laws which assume monotonic capability gains with model scale.
Comparative theoretical claim in the paper contrasting the Institutional Scaling Law with classical empirical/theoretical scaling laws in ML literature.
The Institutional Scaling Law proves that institutional fitness is non-monotonic in model scale.
Formal mathematical derivation/proof presented in the paper (the 'Institutional Scaling Law').
AI development proceeds not through smooth advancement but through extended periods of stasis interrupted by rapid phase transitions that reorganize the competitive landscape (punctuated equilibrium pattern).
Argument based on punctuated equilibrium theory from evolutionary biology and historical analysis presented in the paper identifying discrete transitions in AI history; the paper cites and classifies eras/events as evidence.
The interaction of artificial intelligence and environmental regulation produces a '1 + 1 < 2' crowding-out effect (their combined effect is less than the sum of individual effects).
Spatial Durbin model with interaction term between AI and environmental regulation as summarized in the abstract; reported as a crowding-out interaction.
Environmental regulation significantly inhibits local UCEE.
Spatial Durbin model results reported in the abstract indicating a significant negative local coefficient for environmental regulation.
Artificial intelligence significantly inhibits local UCEE.
Spatial Durbin model results reported in the abstract indicating a significant negative local coefficient for artificial intelligence.
Progress in agentic AI systems that generate and optimize GPU kernels is constrained by benchmarks that reward speedup over software baselines rather than proximity to hardware-efficient execution.
Author argument/observation in paper (conceptual claim about limitations of existing benchmarks); no empirical sample or experiment reported in the provided text.
Rather than broad job losses, evidence points to a reallocation at the entry level: AI automates tasks typically assigned to junior staff, shifting the nature of entry-level roles.
Synthesis of firm- and task-level empirical studies reported in the brief documenting automation of routine/junior tasks and changes in job-task composition; specific sample sizes vary by cited study and are not provided in the brief.
Algorithmic credit systems are linked to higher levels of financial stress.
Study reports a positive association between algorithmic credit system use and reported financial stress from regression analysis on the 400-user cross-sectional dataset.
Confirmation bias poses a weakness in LLM-based code review, with implications on how AI-assisted development tools are deployed.
Synthesis of findings from Study 1 (framing-induced detection failures) and Study 2 (practical exploitability and partial mitigation via debiasing).
Adversarial framing succeeds in 88% of cases against Claude Code (autonomous agent) in real project configurations where adversaries can iteratively refine their framing to increase attack success.
Study 2 experiments in real project configurations with iterative adversary refinement evaluated against Claude Code (autonomous agent); reported 88% success rate.
Adversarial pull request framing (e.g., labeled as security improvements or urgent functionality fixes) succeeds in reintroducing known vulnerabilities in 35% of cases against GitHub Copilot under one-shot attacks.
Study 2 experiments simulating adversarial pull requests evaluated against GitHub Copilot (interactive assistant); reported success rate 35% for one-shot attacks.
The framing effect is strongly asymmetric: false negatives increase sharply while false positive rates change little.
Comparison of false negative and false positive rates across framing conditions in Study 1 experiments (250 CVE pairs across models).
Framing a change as bug-free reduces vulnerability detection rates by 16-93%.
Result reported from Study 1 controlled experiments across models and framing conditions (250 CVE pairs).
AI-only baselines perform near or below the median of competition participants.
Comparison of AI-only baseline performance to the distribution of competition participant results reported in the paper (competition with 29 teams / 80 participants).
Our results show that current AI agents struggle with domain-specific reasoning.
Outcome of the competition reported in the paper comparing AI-only baselines to participant submissions across the AgentDS tasks (competition data from 29 teams / 80 participants); reported aggregate performance indicating AI weakness on domain-specific tasks.
LLM-generated peer reviews place significantly less weight on clarity and significance of the research.
Comparative analysis between LLM-generated reviews and human reviews from the conference dataset; reported as a statistically significant difference but exact statistics and sample size not provided in the excerpt.
Significantly more heavy LLM users reported that the writing was less creative and not in their voice.
Self-reported measures from participants in the human user study comparing heavy LLM users to others; no sample size or exact statistics provided in the excerpt.
In Chicago, the model shows moderate under-detection of Black residents with DIR equal to 0.22.
Reported DIR value from simulation results on Chicago 2022 data.
It is impractical to uniformly apply an alignment method across diverse, independently developed AI models in strategic settings.
Paper assertion / motivating argument (stated as motivation for investigating zero-shot Nash-like behavior); not presented as an empirical finding within the paper.
The gap between informal natural language requirements and precise program behavior (the 'intent gap') has always plagued software engineering, but AI-generated code amplifies it to an unprecedented scale.
Conceptual claim and argumentation in the paper; presented as an observed escalation in the scale of the existing 'intent gap' due to AI code generation. No quantitative evidence or sample size given in the excerpt.
The crowding-out effect of AI washing on green innovation is heterogeneous: private enterprises, small and medium-sized enterprises (SMEs), and firms in highly competitive sectors suffer more severe negative impacts.
Subgroup/heterogeneity analysis reported in the paper on the same sample of Chinese A-share listed companies (2006–2024); abstract identifies private firms, SMEs, and firms in highly competitive industries as more affected.
The negative relationship between AI washing and green innovation is transmitted through dual channels in both product and capital markets.
Mechanism analysis reported in the paper (presumably mediation or channel analysis) using the same dataset of Chinese A-share firms' annual reports and firm-level market data; abstract states product- and capital-market channels convey the crowding-out effect.
Corporate AI washing exerts a significant crowding-out effect on green innovation.
Empirical analysis using semantic measures of 'AI washing' derived from large language model (LLM) analysis of annual reports for Chinese A-share listed companies (2006–2024); paper reports statistically significant negative relationship between AI washing and firms' green innovation (details of regression models not provided in abstract).
The capital-output elasticity dropped significantly, from 0.42 in 2010–2015 to 0.35 in 2016–2022.
Estimated from an extended Cobb–Douglas production function applied to China's economy over 2010–2022, with period split 2010–2015 vs 2016–2022 (as reported in the study summary).
These dynamics amplify initial disparities and produce persistent performance gaps across the population.
Main theoretical conclusion of the paper: analysis of the proposed dynamical system showing amplification and persistence of gaps (authors' demonstrated result).
Exclusion-based cohesion can produce state-contingent illusory precision together with effective input concentration and dynamic lock-in simultaneously—i.e., these phenomena co-occur under the model's parameter regimes.
Analytical model results showing co-occurrence of multiple adverse phenomena (bias that grows in tails, illusory precision, input concentration, lock-in) under the same exclusion mechanisms; derived within the paper's theoretical framework.
When the anchor belief is updated from internally filtered aggregates, the system can exhibit dynamic lock-in: delayed recognition of regime shifts followed by abrupt correction.
Analytical dynamics studied in the model when anchor updates depend on filtered (excluded) aggregates; derivations demonstrate delayed detection and abrupt adjustments. This is a theoretical/dynamical model result, no empirical data.
Exclusion leads to effective concentration of decision inputs: the effective number of independent inputs falls below the nominal participant count.
Model-derived analytic result showing that report shrinkage and discarding reduce effective information contributions, quantified relative to nominal participation in the theoretical framework. No empirical sample.
Exclusion-based cohesion induces 'illusory precision': observed disagreement can fall while actual estimation error in tail regimes rises (i.e., lower recorded variance despite higher true error).
Theoretical result derived from the signal-aggregation model showing a regime in which filtered reports reduce observed variance even as tail-regime estimation error increases. No empirical validation provided.
Relative to a full-inclusion benchmark, exclusion-based cohesion produces state-contingent bias that is small in normal regimes but grows sharply under regime displacement (tail events).
Analytical comparisons between the exclusion model and a full-inclusion benchmark within the theoretical model; derivations showing bias as a function of regime and exclusion parameters. The result is from model analysis, not empirical data.
The establishment of the China–ASEAN Free Trade Area (CAFTA) reduced regional trade policy uncertainty.
Empirical analysis treats CAFTA as an exogenous policy shock and measures a decline in regional trade policy uncertainty using firm‑ and trade‑level data from the China Industrial Enterprise Database and China Customs Database covering 2000–2014; identification via difference‑in‑differences (DID). (Sample sizes not specified in provided summary.)
Limitations include possible limited organizational generalizability due to a single Fortune 500 lab context; ABS results depend on model specification/calibration; and operational definitions of 'resilience' and 'planning cycle' require careful reading.
Authors' reported limitations based on study design: single lab context (n = 23), dependence of ABS on model choices, and nontrivial operational definitions.
Some declines (in self-efficacy and meaningfulness) from passive AI use persist after participants return to manual work.
Within-experiment assessment of outcomes after participants returned to manual (no-AI) tasks following the AI-use manipulation in the pre-registered experiment (N = 269); reported persistent reductions in self-efficacy and meaningfulness for the passive condition.
Passive use of AI reduces perceived meaningfulness of work.
Pre-registered experiment (N = 269) with self-reported measure of work meaningfulness; passive-copy condition showed lower meaningfulness ratings than No-AI and Active-collaboration conditions.
Passive use of AI reduces psychological ownership of the produced outputs.
Same pre-registered experiment (N = 269). Participants in the passive-copy AI condition reported lower psychological ownership of their outputs (self-report scales) relative to No-AI and Active-collaboration conditions.
Passive use of AI (copying AI-generated output) reduces workers' self-efficacy.
Pre-registered between-subjects experiment (N = 269) using occupation-specific writing tasks. Participants assigned to a passive-copy AI condition reported lower self-efficacy (self-reported confidence to complete tasks without AI) compared to the No-AI (manual) and Active-collaboration conditions.
Securitization of economic dependencies—especially in strategic sectors (semiconductors, telecoms, cloud)—frames partner states as security risks and exposes them to blacklists, de-risking campaigns, and sudden loss of market access.
Process tracing of export controls and blacklisting episodes; chronologies of sanction/policy actions affecting firms and partners; policy documents and public lists (e.g., export-control lists). (Data sources: export-control lists, sanction policy documents, corporate/access denials; sample sizes not specified.)
Large-scale AI models have significant energy and resource costs, creating a notable environmental footprint that must be addressed.
Narrative integration of prior empirical studies measuring compute, energy consumption, and embodied emissions of large models (cited literature); the review does not present new quantitative measurements itself.