Evidence (3470 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 |
Org Design
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High-value uses require broader authority exposure — data access, workflow integration, and delegated authority — when governance controls have not yet decoupled capability from authority exposure.
Conceptual/mechanism claim articulated in the paper (motivating assumption for the analytical model; no empirical sample given in the abstract).
Firms are deploying more capable AI systems, but organizational controls often have not kept pace.
Stated as background context in the paper's abstract/introduction (observational claim; no empirical sample or experiment reported in the abstract).
There is a strict policy reversal in optimal editorial policy sign: tightening is optimal pre-transition, loosening is optimal post-transition.
Analytical proof in the model showing the sign reversal of the editor's optimal constrained response as AI capability crosses the critical threshold.
After the AI transition, editors must loosen acceptance standards while investing in AI detection, because further tightening only amplifies dissipative polishing without improving sorting.
Analytical characterization of the constrained optimal editorial response in the post-transition regime within the model; argument relies on the discontinuous reviewer-effort collapse and comparative statics.
The reviewer-effort collapse creates a welfare misalignment: authors benefit from a weakened 'rat race' while editors suffer from degraded signal informativeness.
Comparative statics and welfare analysis in the theoretical model showing authors' equilibrium payoffs rise as competition/polishing dissipates, while editor's signal informativeness declines due to lower reviewer effort.
In academic peer review, generative AI enters both sides of the market: authors use AI to polish submissions, and reviewers use it to generate plausible reports without exerting evaluative effort.
Model assumption and motivation in the paper's three-sided equilibrium framework; described as the dual adoption mechanism analyzed analytically (no empirical sample size reported).
The paper extends paradox theory to conceptualise the Creativity Paradox in the context of GenAI.
Theoretical extension and conceptual development within the paper (no empirical tests reported).
The intervention only modestly narrows the gap to a full-information benchmark.
Comparison between post-intervention calibration/auction outcomes and a full-information benchmark reported in the paper, showing only modest improvement.
Firms with a high market position tend to imitate the peer leader, whereas firms in middle and low market positions are more likely to follow the peer group.
Heterogeneity analysis / subgroup regressions in fixed-effects models on panel data of publicly listed Chinese firms (2012–2023), stratifying firms by market position (high, middle, low).
AI influences innovation performance in organizations.
Discussion and synthesis of studies and reports on AI adoption and innovation performance presented in the review.
AI adoption is producing organizational implications, including changes in project management practices.
Findings synthesized from conference papers, case studies and industry reports included in the review.
Automation, generative AI, and intelligent systems are reshaping task structures, leading to both job displacement risks and the creation of new AI-driven roles.
Synthesis of empirical studies, conference findings, and industry reports reporting both displacement risks and new role emergence (review paper).
AI is rapidly transforming the nature of work, the demand for skills, and the professional roles of Information Technology (IT) practitioners.
Stated as a synthesis result from a narrative review of recent empirical studies, conference findings, and industry reports (review paper).
The study explores implications of algorithmic enterprises for competitive advantage, labour markets, and regulatory policy.
Declared scope of the paper in the abstract; exploration is conceptual and analytical rather than reporting empirical findings or quantified effects.
Analysis of more than two decades of M&A deals reveals shifts in acquisition activity and allows mapping of corporate linkages and overlapping investments.
Empirical longitudinal analysis of M&A deals over a period exceeding 20 years; method: mapping corporate linkages from M&A data (sample size/dataset not specified in the excerpt).
A determinism study of 10 replays per case at temperature zero shows both architectures inherit residual API-level nondeterminism, but DPM exposes one nondeterministic call while summarization exposes N compounding calls.
Determinism experiment with 10 replays per case at temperature zero; qualitative/quantitative observation about number of nondeterministic LLM calls exposed by each architecture.
Open-source versus closed-source trade-offs (including deployment architectures and competitive differentiation) are a central strategic consideration when selecting an enterprise LLM approach.
Paper's comparative analysis of open-source and closed-source alternatives and discussion of strategic implications; supported by the Bills Converter design rationale.
Experienced developers maintain control through detailed delegation while novices struggle between over-reliance and cautious avoidance.
Observed behaviors and accounts from the AI-assisted debugging task (10 juniors) and senior participants in ACTA/Delphi and blind review phases (5 + 5 seniors).
AI is not just changing how engineers code—it is reshaping who holds agency across work and professional growth.
Qualitative synthesis of findings across the three-phase study (Delphi with 5 seniors; debugging task with 10 juniors; blind reviews by 5 seniors).
The design space articulates four configurations—No AI, Hidden AI, Translucent AI, and Visible AI—each trading off among accountability, autonomy, and coordination cost.
Conceptual taxonomy introduced in the paper (design artifact). No empirical evaluation or sample reported in the abstract; tradeoffs are argued theoretically.
They can produce fluent outputs that resemble reflection, but lack temporal continuity, causal feedback, and anchoring in real-world interaction.
Descriptive claim made in the text contrasting surface-level fluency with missing properties; no empirical data or experiments provided.
The results show how non-IID data, competition intensity, and incentives shape organizational strategies and social welfare.
Findings from the paper's experiments and analyses that vary non-IIDness, competition intensity, and incentive parameters; no numeric sample sizes provided in abstract.
Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa.
Citation analysis of cross-border patent citations between Chinese and U.S. AI patents (paper reports asymmetry in reliance based on citation patterns).
The organization of AI innovation differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises.
Analysis of assignee types, geographic dispersion, and institutional composition of AI patents in the two countries (concentration metrics and assignee categorizations described in paper).
Across all settings, AI Organizations composed of aligned models produce solutions with higher utility but greater misalignment compared to a single aligned model.
Reported experimental results aggregated across two practical settings (AI consultancy and AI software team) and 12 tasks; direct comparison between AI Organizations of aligned models and a single aligned model.
Multi-agent "AI organizations" are simultaneously more effective at achieving business goals, but less aligned, than individual AI agents.
Experimental comparison reported in the paper: experiments comparing multi-agent AI organizations to single aligned agents across tasks and settings (described below).
Subjectivity persisted in AI-powered recruitment decisions; human judgment remained an important factor.
Theme 2 (subjectivity in AI-powered recruitment) from interviews indicating retained human subjectivity and judgement in recruitment processes (n = 22).
Experiments on the MovieLens-100k dataset illustrate when the empirical payout aligns with — and diverges from — Shapley fairness across different settings and algorithms.
Empirical evaluation performed on the MovieLens-100k dataset (≈100,000 ratings) comparing the proposed payout rule and algorithmic outcomes to Shapley-value allocations across multiple experimental settings and algorithms.
For heterogeneous agents the cooperative game still admits a non-empty core, though convexity and Shapley value core-membership are no longer guaranteed.
Theoretical analysis for heterogeneous-agent case provided in the paper: establishes core non-emptiness but shows convexity and Shapley-in-core do not generally hold.
User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator's content influences the system's learning and, in turn, the exposure of other creators' contents.
Conceptual/empirical motivation stated in the paper; motivates the multi-agent bandit modeling of creator interactions in recommender systems.
Sensitivity analyses indicate the observed positive belief changes likely reflect recovery from carry-over effects rather than genuine training-induced shifts.
Authors' sensitivity analyses discussed in the paper that examined alternative explanations (e.g., carry-over effects) and concluded the belief-change result is likely due to recovery from such effects.
Bounded agents act as an amplifying but not necessary extension to the foundation-model stack for changing work coordination.
Conceptual argument within the paper distinguishing bounded agents from the core stack; no empirical comparison or measurement reported.
AI adoption outcomes depend on organizational routines, data arrangements, accountability structures, and public values.
Empirical and theoretical literature review and argument in the article drawing on scholarship in digital government and public-sector technology adoption.
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).
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.
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.
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%).
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.
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.