Evidence (4189 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 |
Org Design
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These elements specify workflows that couple machine execution with human oversight, enabling enhanced self-organization of techno-socio-economic systems, which we call DePAI.
Theoretical workflow specification and argumentation in the paper; no reported experimental or observational validation.
We connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other.
Conceptual linkage and theoretical argumentation drawing on literature from DAO design and digital-democracy research; no empirical test or sample described.
We synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics.
Literature synthesis and conceptual review within the paper; no empirical sample or experimental method reported.
We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems.
Conceptual proposal and architectural synthesis presented in the paper (theory/design contribution). No empirical evaluation or sample reported.
Results provide operations managers with tech-backed playbooks for responsible resource use without compromising profit motives, enabling operational excellence while meeting environmental and social responsibilities.
Paper conclusion/implication statement asserting managerial applicability of findings; grounded in the study's reported results but presented as a recommendation/implication rather than a quantified finding.
Firms maintain competitive costs while implementing AI-IoT eco-networks.
Paper claims that waste and emissions reductions are achieved without compromising costs; specific cost metrics or statistical tests not provided in the abstract.
Firms embracing AI-IoT eco-networks trim carbon output by 20-35%.
Paper results reported as empirical findings; presumably measured via carbon footprint assessments and IoT/operational metrics across the case study firms and facilities.
Firms embracing AI-IoT eco-networks cut waste by 30-50%.
Paper results reported as empirical findings; based on mixed-methods case studies of 12 multinational companies and IoT data from 45 facilities (as stated in methods).
By placing networked IoT sensors in factories, trucks, storage sites, and upstream suppliers, real-time data were paired with machine-learning routines to schedule preventive maintenance, forecast orders, and guide blockchain tracking, routing adjustments, and automated decisions balancing green goals with everyday performance.
Paper description of system design and interventions: placement of sensors across supply chain nodes and pairing with ML routines for maintenance, forecasting, blockchain tracking, routing, and automated decisions.
Key research directions—joint capacity planning, multi-timescale control, a compute–power protocol stack, and market innovation—must be pursued to power the future of AI sustainably and reliably.
Prescriptive recommendations in the paper listing research and policy priorities; presented as required directions rather than empirically validated interventions in the excerpt.
The resulting entanglement of compute and power infrastructure requires a shift from implicit coexistence to explicit co-development between the historically decoupled data center and electric power industries.
Normative recommendation and synthesis in the paper arguing that current practices are insufficient and that new collaborative development is needed; based on conceptual reasoning rather than empirical evaluation in the excerpt.
For government policy, it is necessary to establish precise dynamic intervention and orderly exit mechanisms to effectively govern the computing power innovation ecosystem.
Policy implication drawn from the model's analysis of equilibria and regime transitions, and numerical experiments indicating path-dependent/regime-dependent outcomes under different regulatory strategies (method: theoretical model implications + simulation).
A leading computing power incumbent could strengthen its ecological niche and maintain its role as an industry cornerstone by opening its underlying interfaces and software stacks while remaining integrated.
Implication derived from the model's strategic equilibrium analysis and simulations regarding incumbents' strategies for preserving niche/market position (method: evolutionary game analysis + simulations).
Downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures.
Result of the theoretical model (tripartite evolutionary game) and numerical simulation experiments showing advantages to downstream innovators when pursuing vertical integration and co-optimization (method: theoretical model + simulation).
Synthesizing evidence, the paper identifies gaps and opportunities in current responsible AI research: (1) to engage with the diverse range of levers that influence organizations to abandon AI development, and (2) to better support appropriate engagement or disengagement with AI system development.
Synthesis and discussion section combining the taxonomy and empirical case analysis to produce research agenda and recommendations.
Decisions taken in earlier stages of development shape which systems are ultimately released, representing potential points for intervention to influence AI deployment outcomes.
Conceptual argument supported by the paper's taxonomy and case analyses showing pre-deployment factors that lead to abandonment.
Academic responsible AI communities often emphasize ethical risks as reasons to not develop AI.
Observation from the scoping review and literature synthesis comparing academic emphases with other sources.
The authors collected data on real-world cases of AI system abandonment via an AI incident database and a practitioner survey to evidence and compare factors that drive abandonment both prior to and following system deployment.
Empirical data collection described in the paper: use of an AI incident database and a practitioner survey; summary does not report sample sizes or survey response counts.
Through thematic analysis of reviewed sources, the paper develops a taxonomy of six categories of factors contributing to AI abandonment: ethical concerns, stakeholder feedback, development lifecycle challenges, organizational dynamics, resource constraints, and legal/regulatory concerns.
Qualitative thematic analysis of the scoping review materials, resulting taxonomy enumerated in the paper; number of documents/sources not stated in the summary provided.
The authors performed a scoping review of academic literature, civil society resources, and grey literature (including journalism and industry reports) to identify factors influencing AI abandonment.
Methods statement in the paper describing a systematic scoping review of multiple source types; no numeric sample size reported in the summary.
The paper reframes AI safety as layered control, authorization, and externally reviewable limits, linking alignment, security engineering, organizational economics, and institutional design.
Synthesis and prescriptive claim based on the paper's theoretical analysis and proposed framework; supported by conceptual integration rather than empirical testing.
The main result is a boundary stabilization theorem showing that safety need not require proving that advanced systems are always correct; instead it requires institutional and technical designs that prevent irreversible power from being released by a single high-efficiency node.
Formal/theoretical claim presented as the paper's primary theorem (a 'boundary stabilization theorem') demonstrated within the paper's formal model.
The frontier for AI-augmented science is not acceleration; it is the redesign of the certifying infrastructure around these new scarcities.
Prescriptive conclusion in the paper arguing priority of institutional redesign over mere speed gains; presented without empirical testing in the excerpt.
Competent-looking judgment, including selecting, ranking, attributing, and certifying, is now produced at scale at marginal cost approaching zero, inverting the dominant economics-of-AI reading that treats judgment as the scarce complement to cheap prediction.
Argumentative/theoretical claim in the paper; no empirical sample, experiment, or quantitative data reported in the excerpt (implicit basis: observation of scalable AI outputs).
HAAS can serve as a pre-deployment workbench for comparing and inspecting human–AI allocation policies before organisational commitment.
Claim about intended use and demonstration of HAAS as an implemented tool; based on the framework implementation and benchmark experiments reported. No deployment-scale evaluation or sample sizes provided in the excerpt.
In manufacturing, stronger governance can improve operational performance and reduce fatigue simultaneously — a workload-buffering effect.
Domain-specific empirical result reported for the manufacturing benchmark in the paper, comparing operational performance and fatigue under different governance strengths. No numeric sample size or effect sizes provided in the excerpt.
Task–agent fit is represented through five auditable cognitive dimensions and a five-mode autonomy spectrum (from human-only to fully autonomous) embedded in a reproducible benchmark spanning software engineering and manufacturing.
Design and benchmark description within the paper; specification of five cognitive dimensions and a five-mode autonomy spectrum and a reproducible benchmark across two domains. No numeric sample size provided.
HAAS combines a rule-based expert system that enforces governance constraints before any learning occurs, and a contextual-bandit learner that selects among feasible collaboration modes from outcome feedback.
Descriptive claim about the implemented HAAS framework as presented in the paper; method description of system architecture (rule-based expert system + contextual-bandit learner). No sample size reported.
The field's near-term research agenda should explicitly include collecting and using triadic data.
Normative recommendation in the paper; presented as the authors' advised research priority rather than empirically justified within the excerpt.
This data is the empirical key to four open questions in agent training.
Argumentative claim in the paper asserting centrality of triadic data to addressing unspecified four open research questions; no empirical demonstration included in the excerpt.
This triadic data is capturable in 12-18 months with methods already mature in adjacent fields.
Claim in the paper based on authors' assessment of methodological maturity in adjacent fields; no empirical project timeline or pilot data is provided in the excerpt.
Any such corpus -- triadic or otherwise -- must justify its quality to a fine-tuning researcher through a four-tier evidence framework: mechanical verification, statistical corpus characterization, probe experiments, and pre-registered blind evaluation.
Methodological proposal in the paper outlining a four-tier evidence framework; presented as normative guidance rather than validated by application to a corpus in the excerpt.
The canonical instantiation of triadic data is two complementary products: long-horizon expert trajectories captured under stimulated-recall protocols, and simulated cross-functional companies -- instrumented teams of senior engineers, product managers, designers, and data scientists working through ambiguous deliverables on shared infrastructure.
Prescriptive specification in the paper proposing two concrete dataset types as canonical instantiations; presented as design/recommendation rather than empirically tested.
The substrate for the next generation of software-engineering (SWE) agents is neither larger GitHub scrapes nor more solo-agent trajectories nor -- sufficient by itself -- open human-AI dialogue logs; it is triadic data: synchronized capture of the human-human conversations where engineering context is formed, the human-AI sessions where that context is partially consumed, and the multi-week cross-functional work that surrounds both.
Argument and conceptual proposal in the paper; no empirical validation or comparative experiments are provided in the excerpt.
SCDPs are a useful framework for policy simulation for the digital economy, mechanism design for information systems, and digital twin modeling of cyberinfrastructure.
Paper posits these applications as prospective uses of the framework (argumentative/speculative; no empirical evaluation reported in abstract).
SCDPs are capable of modeling variable discounting, a tool used widely in social scientific modeling.
Paper states the capability as part of SCDP definition and examples (theoretical claim).
An SCDP can endogenously model the memory-formation process and is thus useful for modeling resource‑rational agents in dynamic settings.
Paper asserts SCDP can represent memory-formation endogenously and discusses application to resource-rational agents (theoretical modeling capability).
SCDPs are strictly more expressive than POMDPs because they do not assume rational belief formation.
Comparative expressiveness claim stated in the paper; supported by theoretical argument or formal separation result (paper text states the claim explicitly).
SCDPs inherit the composition properties of SCDMs (i.e., SCDPs benefit from SCDM composability).
Logical consequence argued in the paper from SCDP being constructed from SCDMs; likely supported by formal argumentation in the text.
A Structural Causal Decision Process (SCDP) is defined as a recurring SCDM with a discount variable.
Formal definition introduced in the paper (theoretical definition).
SCDMs have a well-defined and computationally useful property of composability.
Paper states and demonstrates ("We show") composability property — presumably via formal proofs or constructive arguments in the text (theoretical proofs/exposition).
SCDMs can have open root variables for which no probability distribution or structural equation is given.
Model definitions in the paper explicitly allow open root variables (theoretical description).
In SCDMs, agent decisions can be constrained by their causal antecedents (i.e., decisions can be constrained by their causal parents).
Model specification and definitions in the paper describing constraints on decisions as part of SCDM structure (theoretical construction).
Structural Causal Decision Models (SCDMs) expand on Structural Causal Influence Models by explicitly representing the causal relationships between model variables and the payoffs of agent decisions.
Formal model development and comparison to existing SCIMs provided in the paper (theoretical definitions and arguments).
We present two new classes of causal models of decision-making agents: Structural Causal Decision Models (SCDMs) and Structural Causal Decision Processes (SCDPs).
Paper introduces formal definitions for two model classes and describes their properties in the text (theoretical exposition).
The paper introduces a Specification Governance Model (SGM), grounded in Transaction Cost Economics, and provides a practical governance decision guide.
Conceptual/modeling contribution described in the paper: SGM grounded in TCE with an applied decision guide (theoretical plus prescriptive).
The paper proposes the AI-Augmented Methodology Taxonomy (AAMT), classifying six methodologies under three AI integration tiers.
Conceptual contribution: taxonomy introduced and described in the paper (six methodologies, three tiers).
Telemetry across 10,000+ developers shows a 98% increase in pull requests.
Observational telemetry data aggregated across >10,000 developers reported in the paper; metric reported is percent increase in pull request count.
Controlled studies report 20-56% productivity gains on well-scoped tasks.
Aggregate of multiple controlled experimental studies cited in the paper (2022–2026); reported as observed productivity improvements on well-scoped tasks in those studies. Specific study-level sample sizes not reported in the claim text.
Practical properties for Bayesian control that fit modern agentic AI systems and human-AI collaboration can be articulated, and calibrated beliefs plus utility-aware policies can improve agentic AI orchestration (illustrated via concrete examples and design patterns)
Paper provides articulated properties, examples, and design patterns but no empirical validation; claims of improvement are illustrated conceptually.