Evidence (11633 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 |
Model routing can mitigate the cost of agentic tool use, but existing routers are designed for chat completion rather than tool use.
Argument/positioning in the paper and literature discussion (no specific empirical test reported for existing routers in this statement).
The novel governance problem is not that AI creates new failure modes, but that AI changes their incidence, observability, and persuasive force enough to require different governance responses.
Normative/analytic claim in the paper; argumentation rather than empirical evidence.
The turning point of the inverted-U relationship occurs at 2.948 (AI measure).
Estimated quadratic model that yields a calculated turning point value of 2.948.
There is an inverted-U-shaped relationship between firm-level AI adoption and firm innovation.
Estimated fixed-effects models and U-tests on the 25,204 firm-year sample showing a non-linear (quadratic) AI–innovation coefficient pattern.
The study provides new empirical evidence that technological innovation (specifically generative AI) reshapes financial spillover networks and highlights the importance of considering both the level and structure of connectedness in assessing systemic risk.
Overall empirical results from the TVP-VAR analysis of connectedness across AI equities, cryptocurrencies, and traditional assets, and discussion of implications for systemic risk assessment.
The impact of AI on financial markets is better understood as a structural transformation of interconnectedness rather than a simple intensification of linkages.
Synthesis of empirical findings from the TVP-VAR showing changes in network structure and heterogeneous directional roles across asset groups, rather than a monotonic increase in aggregate connectedness.
The structure of spillovers undergoes significant changes over the sample period.
TVP-VAR estimated time-varying spillover/connectedness network showing changes in directional spillovers and network topology (paper states 'significant changes').
Introducing taxes on AI returns (τ_ai) and financial gains (τ_f) yields three distinct long-run regimes: low-tax (extreme inequality), moderate-tax (stable mixed economy), and high-tax (post-scarcity with universal basic income).
Model extension with tax parameters τ_ai and τ_f and analysis of steady states/long-run regimes; bifurcation analysis identifying regime types associated with ranges of (τ_ai, τ_f).
The finding that recurrence and neighborhood statistics are stronger predictors than complaint volume has direct implications for complaint routing given the demographic correlates of those features.
Interpretive implication drawn by the authors from the SHAP results; presented as a logical consequence rather than a separately tested empirical result in the excerpt.
Aesthetic and functional attributes load onto a single latent factor, suggesting users perceive quality as a unified construct rather than separable aesthetic and functional dimensions.
Factor analysis (or similar latent-variable analysis) on participant ratings of multiple attributes showing a single dominant factor combining aesthetic and functional attributes.
Successful AI implementation in logistics requires not only technological capability but also organizational readiness and effective data governance.
Conclusion drawn from the structured qualitative review of 31 scholarly sources synthesizing reported success factors and preconditions for AI adoption.
The rapid emergence of agentic AI tools raises new questions that the political science discipline must address.
Epilogue of the report raises agentic AI tools as a rapidly emerging phenomenon and lists questions for the discipline; based on expert judgment and forward-looking analysis rather than empirical measurement in the introduction/epilogue.
AI will affect political science research and teaching.
Report introduction explicitly notes the report investigates implications for political science research and teaching; based on the task force's review and analysis rather than a quantitative study.
AI will affect public opinion and the information ecosystem.
Introductory chapter enumerates public opinion and the information ecosystem as report topics; based on conceptual synthesis and literature review.
AI will affect the labor market.
Report introduction identifies the labor market as an area the task force examines; presented as a conceptual claim without primary-sample estimates in the introduction.
AI will affect international relations.
Introductory chapter lists international relations as a topic the report investigates; claim arises from conceptual analysis and synthesis by task force authors.
AI will affect national security.
Report introduction stating a section addressing national security implications; based on expert assessment and literature review rather than a specific empirical sample.
AI will affect public administration.
Report introduction describing a section focused on how AI will affect public administration; based on expert synthesis rather than reported empirical study.
AI will affect democracy (i.e., democratic processes and institutions).
Report introduction listing a section of the report devoted to democracy and AI; conceptual argumentation rather than reported empirical tests.
AI has the potential to reshape politics and political science, similar to how it is transforming other social phenomena and academic fields.
Introductory chapter of the APSA Presidential Task Force report; conceptual framing and literature synthesis by the task force authors (no primary empirical sample reported).
There are factor-share consequences from agent adoption (i.e., implications for the shares of income accruing to factors such as labor and capital).
Model-based discussion and comparative-static analysis in the paper deriving implications for factor shares as agents/compute capital alter production technology. The excerpt indicates qualitative/theoretical analysis rather than empirical measurement.
The CAW result generalizes through CES aggregation and, when tasks are separated into substitutable versus complementary, yields a directional inversion of skill-biased technical change.
Theoretical extension of the core model using CES (constant elasticity of substitution) aggregation and task decomposition in the paper; the claim arises from model generalization and comparative-static reasoning. No empirical validation provided in the excerpt.
Agents are not labor; they are a production technology that converts compute capital K_c into effective units of cognitive labor L_A.
Theoretical argument and definitional framing in the paper: the authors recast agents as a technology that transforms compute capital into effective cognitive labor units within an analytical model (textual/theoretical exposition). No empirical sample or experimental data reported in the excerpt.
We empirically validate these theoretical observations using both synthetic and real datasets.
Experimental evaluation reported in the paper applying proposed policies and measures to synthetic data and at least one real dataset (details not given in abstract).
Two minimal extension policies, each derived from the observation, close the regime along orthogonal axes: a sample-size-aware static rule (Periodic-with-floor) closes the granularity-failure case, while a history-conditioned suspicion-escalation policy closes the coverage-failure case for the naive Drift strategy — and neither closes both, exactly as the observation predicts.
Design and analysis of two auditor policies in the paper; theoretical argument from Observation 1 and supporting simulation results illustrating which failure modes each policy addresses.
A standard learning agent can obtain near-reference revenue per available room (RevPAR) while failing to learn market-like yield management: it sells too aggressively, undercuts, or collapses to modal price buckets.
Experiments in a two-hotel revenue-management simulator where Hotel A is trained against a fixed rule-based competitor (Hotel B); comparison of learned agent behavior to market-like yield management patterns observed in traces.
The trajectory of AI systems is shaped not only by model design, but by the dynamics of human-AI co-evolution.
Conclusion drawn from the minimal model, analytical regimes, and simulation experiments presented in the paper.
Our analysis identifies three regimes: co-evolutionary enhancement, fragile equilibrium, and degenerative convergence.
Model analysis (categorization of dynamical behaviors) presented in the paper.
This feedback can give rise to distinct dynamical regimes.
Analytical results derived from the minimal dynamical model described in the paper.
We introduce a minimal model with three variables -- human cognition, data quality, and model capability.
Model development in the paper (mathematical/minimal dynamical model); presented as a constructed model rather than empirical measurement.
Humans and language models form a coupled dynamical system linked by a feedback loop of usage, generation, and retraining.
Conceptual framing and theoretical proposal in the paper; model formulation rather than empirical data.
Prior work has studied cognitive offloading in humans and model collapse in recursive training, but these effects are typically considered in isolation.
Literature review / related-work statement in paper; references to prior research (qualitative, no sample size stated).
Large language models (LLMs) are reshaping how knowledge is produced, with increasing reliance on AI systems for generation, summarization, and reasoning.
Background/literature observation cited in paper (qualitative claim), no empirical sample or quantified data reported in text provided.
Routine automation primarily dismantles specialised physical skills, enhancing mobility only within homogeneous manual clusters.
Simulation results distinguishing effects of the routine-task automation exposure measure vs. AI exposure; analysis of which skill types are eroded and resulting changes in mobility within occupational clusters.
Modeling fiscal policy as a government problem (instead of an abstract planner) implies a tax changes the firm's automation first-order condition, raises revenue only on the remaining automation base, and requires specifying rebates and administrative losses.
Explicit governmental optimization and budget-accounting setup in the model: taxes enter firms' automation first-order conditions; revenue is computed on post-tax automation activity and rebates/administration are modeled.
The central analytic object is the derivative of household consumption demand and the collective wage bill with respect to automation.
Paper's stated modeling focus: comparative-static derivatives linking automation to household consumption demand and aggregate wages; used to characterize incidence and welfare effects.
Automation reallocates income and ownership claims.
Theoretical model with heterogeneous households who hold capital/equity claims; equilibrium determines wages and returns and shows changes in income and ownership shares when automation increases.
Institutional expertise (such as that created or possessed by universities and corporations) is viewed as in need of liberation or reform so it can be incorporated into the latest artificial intelligence systems.
Analysis of public communications from five annotation organizations and their CEOs indicating calls or framing that institutional knowledge should be freed/restructured to be integrated into AI systems.
Demand for expert-annotated data on the part of leading AI labs has created an expert gig economy with the potential to reshape white collar work and society's understanding of expertise.
Qualitative analysis of public communications (social media feeds and podcast appearances) from five industry data annotation organizations and their CEOs; sample of five organizations and their public-facing leaders.
Human anchors build trust through a broadly effective relational pathway (perceived intimacy), while AI anchors' functional advantage converts into trust only under specific motivational conditions (high utilitarian motivation).
Interpretation of moderated mediation results from randomized experiment (N = 439) showing intimacy-mediated trust for human anchors and responsiveness-mediated trust for AI anchors only under high utilitarian motivation.
Consumer trust in live-streaming commerce is a conditional, motivation-dependent process rather than a uniform preference for either anchor type.
Synthesis of experimental results showing differential mediation/moderation patterns by hedonic and utilitarian motivation in sample N = 439 (moderated mediation analyses).
Perceived responsiveness became a significant pathway favoring AI anchors only when utilitarian motivation was high; at low utilitarian motivation, this pathway reversed direction.
Conditional (moderated) mediation analyses from the experiment (N = 439) including utilitarian motivation as moderator; reported that responsiveness→trust path favored AI anchors at high utilitarian motivation and reversed at low utilitarian motivation.
Across studies, causal modeling reveals that cognitive alignment systematically drives attentional coordination in successful collaboration, while mismatches between effort and attention characterize unproductive regulation.
Synthesis of causal inference results from the three studies using time-series measures (JME, JVA) and episode-based analyses across the pooled dataset (182 dyads total).
There is substantial heterogeneity in the productivity effects across settings.
Meta-analytic heterogeneity assessment reported in the paper (subgroup/moderator analyses indicate variability by context). The paper states 'substantial heterogeneity across settings.'
The strategic interplay between antitrust regulation and vertical integration materially influences the evolutionary transitions of the computing power ecosystem.
Core focus of the paper's tripartite evolutionary game model which explicitly models government regulators, incumbents, and downstream innovators and analyzes resulting equilibria and transitions (method: theoretical evolutionary game + analytical derivation).
The evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics, potentially transitioning from an initial 'natural monopoly and passive dependence' state through intermediary states (e.g., 'comfort zone trap' or 'regulatory stalemate') toward a mature configuration of 'co-opetition and endogenous growth.'
Derived from the paper's tripartite evolutionary game model and analytical derivation of evolutionarily stable strategies, with supporting numerical simulations exploring parametric sensitivities (method: theoretical evolutionary game + numerical simulation).
The computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems driven by the rapid proliferation of generative artificial intelligence.
Conceptual claim presented in the paper's introduction/motivation; supported by the paper's theoretical framing and literature-based motivation rather than empirical data (method: narrative/theoretical framing).
Program outcomes are moderated by a person's prior occupational skill set, their area of work, and features of the local economy.
Heterogeneity analyses across subgroups defined by prior occupational skill composition, industry/area of work, and local labor-market conditions in the WIOA administrative data (2017-2023) show variation in outcomes.
These findings challenge the notion of a universal technological dividend from AI (i.e., AI does not automatically deliver uniform productivity gains across firms).
Overall interpretation/synthesis of heterogeneous empirical results from the panel and cluster analyses showing variation in productivity effects across firm types.
AI adoption yields asymmetric productivity gains depending on firms' resource constraints and competitive environments (i.e., heterogeneity rather than a homogeneous effect).
Heterogeneity analysis using multidimensional clustering (firm size, age, market competitiveness, digital infrastructure) applied to the panel dataset; reported differential effects across clusters.