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 |
Qualitative results underscored both perceived benefits in comprehension and challenges when interpretations of gaze behaviors were inaccurate.
Qualitative analysis of participant feedback from the study (n=36) reporting themes of improved comprehension and occasional problems when the assistant misinterpreted gaze.
The productivity decomposition classifies deployments into five regimes that separate beneficial adoption from harmful adoption and identifies which deployments are vulnerable to the augmentation trap.
Model-based taxonomy produced from the analytical decomposition (classification into five regimes described in the paper).
Small differences in managerial incentives can determine which skill path a worker takes (whether they realize full potential or deskill).
Comparative statics / theoretical sensitivity analysis in the dynamic model indicating tipping behavior based on managerial incentives.
Result 3: When AI productivity depends less on worker expertise, workers can permanently diverge in skill: experienced workers realize their full potential while less experienced workers deskill to zero.
Analytical result from the dynamic model showing path-dependent divergence in skill levels under particular parameterizations (lower dependence of AI on worker expertise).
India exhibits a distinctive polarisation pattern: a shrinking middle-skill workforce alongside a persistently large low-skill labour segment.
Descriptive analysis of secondary data and official reports from 2020–2024 comparing occupational and skill distributions in India.
Mathematics (SAFI: 73.2) and Programming (71.8) receive the highest automation feasibility scores; Active Listening (42.2) and Reading Comprehension (45.5) receive the lowest.
SAFI benchmark results reported for specific O*NET skills (numerical SAFI scores provided in the paper).
The rise of agentic AI development, where LLM-based agents autonomously read, write, navigate, and debug codebases, introduces a new primary consumer with fundamentally different constraints.
Conceptual claim argued in the paper; refers to the emergence of agentic LLM-based tools as new consumers of software artifacts rather than an empirical measurement; no sample size reported.
Analysis uncovers dramatic asymmetries: inhibition 17.6% vs. preference 75.0%.
Paper reports specific aggregated percentages for two types of implicit effects (inhibition and preference) observed in their analysis; methodology context implies these are results from the benchmark evaluation (300 items / 17 models).
Model behaviors vary strongly with levels of reasoning and with users' inferred socio-economic status.
Reported findings from evaluations that varied model reasoning prompts/levels and user socio-economic status signals; paper states behavior differences across these dimensions. Abstract does not give sample sizes or exact quantitative differences.
The rapid deployment of multi-agentic AI systems is reshaping the foundations of copyright law and creative markets.
Theoretical and conceptual argumentation presented in the paper; no empirical sample or quantitative analysis reported.
The effects of generative AI depend not only on the technology itself, but also the behavioral strategies and incentive structures surrounding its use.
Synthesis and interpretation of RCT results showing interactions between incentive structure and AI-use patterns (no formal interaction coefficients or sample details provided in excerpt).
Through a pre-registered randomized control trial, we show that incentives mediate AI's homogenizing force in a creative writing task where participants can use AI interactively.
Pre-registered randomized controlled trial (experimental design) conducted on a creative writing task with interactive AI use (details such as sample size not provided in excerpt).
By conceptualizing the emergence of a posthuman economy, this study contributes to interdisciplinary debates on artificial intelligence, digital capitalism, and the transformation of economic organization.
Author-stated contribution of the paper based on conceptual/theoretical work; no empirical validation reported.
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.