Evidence (16496 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
Filter claims →
Productivity
8807 claims
Filter claims →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filter claims →
Inequality
2332 claims
Filter claims →
Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Contemporary organizations operate within hybrid intelligence environments where human expertise and algorithmic systems collaboratively produce economic knowledge, prediction, and action.
Theoretical synthesis using posthumanist and socio-technical perspectives within the paper; no empirical measurement or sample provided.
This article develops the concept of algorithmic agency to explain how artificial intelligence participates in economic decision-making within modern business systems.
Author's conceptual contribution described in the paper (theoretical development), no empirical testing reported.
Emerging posthumanist scholarship suggests a deeper transformation in which economic agency itself becomes distributed across human and algorithmic actors.
Synthesis of posthumanist scholarship and theoretical literature cited in the paper; conceptual rather than empirical evidence.
Artificial intelligence is fundamentally reshaping contemporary economic systems as algorithmic infrastructures increasingly participate in interpreting information, generating predictions, and influencing organizational decision-making.
Conceptual argument in the paper drawing on posthumanist theory, socio-technical research, and digital economy scholarship; no empirical sample or quantitative data reported.
Each country's legal framework could influence the ultimate trajectory of the AI race.
Framed in the chapter as a concluding implication of the comparative analysis; presented as a reasoned projection rather than an empirically validated prediction in the provided text.
Data privacy, intellectual property (IP rights), and export restrictions are three critical aspects of the American and Chinese legal infrastructure that significantly impact AI innovation.
Author(s) state this as the organizing premise of the chapter; comparative legal analysis and normative argumentation rather than empirical measurement.
These results suggest the need for AI model development to prioritize scaffolding long-term competence alongside immediate task completion.
Authors' policy/research recommendation based on experimental findings showing short-term gains but longer-term harms.
These effects are observed across a variety of tasks, including mathematical reasoning and reading comprehension.
Trials included multiple task types (explicitly naming mathematical reasoning and reading comprehension); cross-task analysis reported.
Only a small subset of LLM retailers can consistently achieve capital appreciation, while many hover around the break-even point.
Empirical results from the 20-agent benchmark experiments reported in the paper, contrasting capital appreciation for winners vs break-even for many agents.
Benchmarking on 20 open- and closed-source LLM agents reveals significant performance disparities and a winner-take-most phenomenon.
Empirical evaluation described in the paper using 20 LLM agents (open- and closed-source); results reported show uneven performance distribution.
Chinese Marxism's dialectical approach—rooted in the yin‑yang principle—constitutes an alternative epistemology that fundamentally differs from Western either/or logic, and this epistemology underpins the semi‑core's policy and strategic stance.
Philosophical and textual analysis of contemporary Chinese Marxist thought presented in the paper, interpreted in relation to Bauman's philosophical work; no empirical measurement reported, presented as conceptual/theoretical evidence.
Tool developers, users, and social scientists conceptualize 'context' differently, and these divergent conceptualizations reveal specific pitfalls inherent in computational approaches to context.
Analytic comparison across stakeholder perspectives derived from interviews and conceptual analysis in the paper (qualitative evidence; sample size unspecified).
AI adoption significantly reshaped task profiles for 73% of respondents, particularly affecting routine data processing, administrative tasks, and scheduling activities.
Survey data and secondary data analysis reported in this study (sample size not stated); self-reported change in task profiles with reported percentage (73%).
Providing issue-specific design guidance reduces design violations, but substantial non-compliance remains.
Intervention experiments in paper: agents were given issue-specific design guidance and resulting patch compliance measured; reported reduction in violations but remaining non-compliance.
Evolutionary dynamics in the model reflect not just current fitness but factors related to the long-run growth potential of descendant lineages.
Mathematical analysis of the proposed model showing lineage growth potential influences dynamics (theoretical derivations/proofs within the paper).
Policy implication: encouraging public sharing of AI-assisted solutions offsets the decline associated with private diversion (flow margin) but cannot repair participation-driven deterioration in conditional resolution; the latter requires directly maintaining contributor engagement.
Prescriptive conclusion from the theoretical model comparing interventions: public-sharing encouragement helps with flow-margin diversion but not with supply-side contributor thinning.
Diagnostic prediction: in a congested regime, observing a joint decline in posted volume and conditional resolution implies supply-side pool thinning is quantitatively present; by contrast, volume decline with stable or rising resolution indicates private diversion (flow margin) alone is the dominant force.
Analytical diagnostic derived from the model that links empirical patterns (volume and conditional resolution) to underlying mechanisms; no empirical validation given in the excerpt.
There is a robust inverted U-shaped relationship between robotics manufacturing development and urban carbon emissions.
Panel data analysis using 277 Chinese prefecture-level cities from 2008 to 2019; econometric analysis reported in the paper finds an inverted U-shaped association and robustness checks are claimed.
AI adoption across firms is heterogeneous, varying across sectors such as finance, technology, and manufacturing.
Survey of 150 leading Nigerian firms across finance, tech, and manufacturing showing variation in AI integration; supported by qualitative interviews and policy analysis.
The rapid, heterogeneous integration of Artificial Intelligence (AI) technologies is profoundly reshaping the dynamics of work across the Nigerian business sector, generating both significant economic opportunities and acute labor market challenges.
Mixed-methods study combining a quantitative survey of 150 leading Nigerian firms across finance, tech, and manufacturing and qualitative analysis of government policy and workforce interviews.
As technological progress devalues labor, the welfare benefits of steering initially increase but, beyond a critical threshold, decline and optimal policy shifts toward greater redistribution.
Analytical result from the paper's theoretical model that compares planner's optimal technology choice under varying degrees of labor devaluation and redistribution costs.
For the short-run optimization problem of AI deployment given fixed job responsibilities and worker skill levels, the firm’s optimal strategy for an m-step job can be computed in time O(m^2) using dynamic programming; the long-run joint optimization including task assignment to workers can also be solved in polynomial time up to an arbitrarily small error term.
Algorithmic results and complexity analysis derived in the theoretical sections and appendices of the paper (dynamic programming construction and polynomial-time solution statements).
Appending a neighboring step to an existing AI chain adds no additional human verification burden (verification is a fixed cost at the chain level), which can make appending steps to a chain optimal even if manual execution is individually preferable for the appended step.
Theoretical model setup and formal argument showing verification is incurred only at the last augmented step of a chain; illustrative examples (data scientist workflow) and comparative-cost reasoning in the paper.
AI chaining can overturn standard comparative advantage logic in assignment: when multiple adjacent steps are executed as an AI chain, a step may be assigned to AI (as part of the chain) even if manual human execution would be preferred for that step in isolation.
Theoretical model of production as an ordered sequence of steps with firms endogenously bundling contiguous steps into tasks and jobs; formal comparative-static arguments and illustrative examples in the paper showing how fixed verification costs per chain change marginal assignment incentives.
Automation leads economic growth to accelerate, but the acceleration is remarkably slow because of the prominence of 'weak links' (an elasticity of substitution among tasks substantially less than one); even when most tasks are automated by rapidly-improving capital, output is constrained by the tasks performed by slowly-improving labor.
Theoretical mechanism from the task-based model (σ < 1 weak-links structure) combined with calibrated simulations that incorporate historical accounting results.
The general public supports both targeted programs and broader interventions (including job guarantees and UBI), contrasting with economists' preferences.
Survey comparisons across groups contrasting normative policy support (textual summary in Key Findings; exact public-group percentages not provided in excerpt).
Unconditional forecasts are relatively close to historical trends, but under the rapid scenario the range of plausible outcomes expands (greater uncertainty).
Comparison of unconditional (all-things-considered) survey forecasts to conditional rapid-scenario forecasts; dispersion metrics referenced qualitatively in Key Findings (detailed variance numbers not provided in excerpt).
The effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations.
Analytical comparative-statics in the theoretical model: varying the fraction of R&D tasks performable by AI yields a non-monotonic relationship between AI task-share and optimal recombination distance, with a threshold determined by human-AI complementarity.
Higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals.
Comparative-static result from the analytical model: the paper derives a condition comparing the direct facilitation effect of AI on accessing distant knowledge and the indirect effect from increased competition; when the former dominates, equilibrium recombination distance increases with AI productivity.
Both rapid model improvement and benchmark quality issues contributed to underestimating agent capabilities.
Synthesis of results: improved LLM performance plus audit findings showing benchmark errors together explain the prior underestimation; based on the re-evaluation and audit described in the paper.
Poaching by a dominant undertaking can, under certain conditions, constitute exclusionary abuse and structural abuse in both product and labor markets (drawing on Section 2 Sherman Act 'predatory hiring' scholarship and case law).
Paper's analytical claim based on comparative legal scholarship and case law (described in abstract); no empirical sample/experiment specified in abstract.
Models performed well on commonly discussed topics but struggled with specialized health data.
Task-level performance comparison across topics in the elicited population statistics: better accuracy on commonly discussed topics, poorer performance on specialized health data tasks.
In a preliminary experiment, giving models web search access degraded predictions for already-accurate models, while modestly improving predictions for weaker ones.
A preliminary comparative test where some models were given web search access and changes in predictive performance were observed: degradation for already-accurate models and modest improvement for weaker models.
Developers actively manage the collaboration, externalizing plans into persistent artifacts, and negotiating AI autonomy through context injection and behavioral constraints.
Observed behaviors in chat transcripts and committed artifacts showing developers creating persistent plans, injecting context, and specifying constraints to shape AI behavior.
Developers redistribute cognitive work to AI, delegating diagnosis, comprehension, and validation rather than engaging with code and outputs directly.
Content and interaction analyses of chat sessions showing developer prompts delegating diagnosis, comprehension, and validation tasks to the AI assistants (Cursor and GitHub Copilot) across the dataset.
Conversational programming operates as progressive specification, with developers iteratively refining outputs rather than specifying complete tasks upfront.
Qualitative/content analysis of the 74,998 messages across 11,579 sessions indicating patterns of iterative prompts and refinements rather than one-shot complete specifications.
The influence of human capital (number of specialists in scientific and technological fields) on value added varies across sectors.
Number of specialists in scientific and technological fields included as a covariate in MMQR; reported heterogeneous effects across sectors/quantiles in the results section.
The influence of R&D expenditure on value added varies across sectors.
R&D expenditure included as a core explanatory variable in panel MMQR estimations; authors report differing coefficient sizes/signs across sectors/quantiles.
An Evolutionary Game Theory (EGT) framework produces a 'Red Queen' co-evolutionary dynamic between platforms' algorithmic control and worker behavior in which neither side reaches a stable static equilibrium.
Analytical EGT model and numerical simulations of a population-level game between workers (choices: compliance vs. algorithmic gaming) and a platform varying surveillance strictness; model-based result (no empirical sample size).
Policy enforcement maintains a 52.8% success rate for legitimate requests.
Quantitative result reported from the paper's experiments (52.8% success rate for legitimate requests under policy enforcement).
These AI capability improvements would impact the economy and labor market as organizations adopt AI, which could have a substantially longer timeline.
Theoretical implication/interpretation by the authors (economic and labor market impact contingent on organizational adoption; timeline longer than capability improvements).
AI automation is a continuum between (i) crashing waves where AI capabilities surge abruptly over small sets of tasks, and (ii) rising tides where the increase in AI capabilities is more continuous and broad-based.
Conceptual framing proposed by the authors (theoretical proposition).
The inequality-reducing impact of AI is weaker when carbon inequality is measured by the Theil index, implying persistent structural divides between advanced and less developed regions.
Same provincial panel dataset (2003–2021) with the Theil index as the dependent variable; results show a weaker (and impliedly less robust) association between AI development and Theil-measured carbon inequality.
This paper proposes three archetypal AI technology types: AI for effort reduction, AI to increase observability, and mechanism-level incentive change AI.
Conceptual taxonomy introduced by the authors (theoretical classification presented in the paper).
The results generalize to other technologies that feature safety externalities and first-mover advantages.
Authors' argument and model generalization: the mechanisms identified (preemption, externality, policy responses) are argued to apply beyond frontier AI to other technologies with similar strategic features.
Pigouvian safety taxes partially correct the safety externality but cannot eliminate the preemption distortion on their own.
Model policy counterfactuals: introducing a tax on unsafe releases reduces the externality-driven distortion but leaves residual preemption incentives so the first-best is not fully attained by tax alone.
Residual within-task group dynamics dominate the magnitude of the gender wage gap, though task-based employment and wage channels are important for timing and direction of changes in gender inequality in the formal sector.
Decomposition analysis partitioning the gender wage gap into within-task residuals and task-based employment and wage components, with residuals accounting for the largest share of the gap but task channels explaining temporal shifts.
The analysis focuses on formal wage workers in Indonesia from 2001 to 2019.
Stated sample and timeframe in the study description; analyses use data on formal wage workers in Indonesia covering 2001–2019.
AI-driven conversational coaching is increasingly used to support workplace negotiation, yet prior work assumes uniform effectiveness across users.
Background claim in paper indicating prior literature trends and assumptions (stated in introduction/motivation).
Participants were clustered into three profiles -- resilient, overcontrolled, and undercontrolled -- based on the Big-Five personality traits and ARC typology.
Paper reports clustering analysis on participants using Big-Five trait measures and ARC typology; clustering result described as three profiles. Total sample reported as N=267.