Evidence (13870 claims)
Adoption
8467 claims
Productivity
7558 claims
Governance
6805 claims
Human-AI Collaboration
6363 claims
Org Design
4132 claims
Innovation
4065 claims
Labor Markets
3526 claims
Skills & Training
2945 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 196 | 98 | 892 | 1984 |
| Governance & Regulation | 817 | 394 | 188 | 121 | 1544 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 627 | 233 | 123 | 96 | 1088 |
| Research Productivity | 411 | 123 | 56 | 332 | 933 |
| Output Quality | 467 | 178 | 59 | 47 | 751 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 167 | 122 | 24 | 496 |
| Task Allocation | 207 | 64 | 71 | 32 | 379 |
| Skill Acquisition | 165 | 59 | 60 | 17 | 301 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 52 | 107 | 13 | 279 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 150 | 48 | 26 | 3 | 227 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 63 | 20 | 12 | 184 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 93 | 21 | 13 | 19 | 148 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Creative Output | 31 | 17 | 7 | 3 | 59 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Scaling laws make progress predictable, albeit at a declining rate.
Conceptual claim in the paper based on the power-law form of scaling laws (no numerical quantification or sample size provided in the excerpt).
Classical AI scaling laws, especially for pre-training, describe how training loss decreases with compute in a power-law form.
Stated observationally in the paper as established empirical regularity across pre-training runs and prior literature on scaling laws (no sample size or specific experiments reported in the excerpt).
Task-level analyses show that activities expanded in AI-enabled projects—particularly ideation and experimentation—are increasingly compatible with large language model capabilities, suggesting potential for future productivity gains as these technologies mature.
Task-level classification mapping tasks described in proposals to LLM-relevant capabilities using LLM-based classification; finding that tasks expanded in AI-enabled projects cluster on ideation and experimentation, which align with current LLM strengths.
AI-enabled projects undertake a broader set of tasks.
Task-level analysis of proposal descriptions (task inventories) classifying tasks via keyword extraction and LLMs, showing AI-enabled proposals list a wider variety of activities than non-AI proposals.
AI-enabled projects involve larger teams.
Comparison of team structure in proposals (team size) between AI-enabled and non-AI projects using the same comprehensive proposal dataset and LLM-based classification of AI presence.
AI-enabled projects reallocate resources toward human capital (i.e., shift budget allocations toward labor / human capital).
Analysis of detailed budget allocations in the proposal dataset, comparing projects identified as AI-enabled versus non-AI projects using keyword extraction and LLM classification to identify AI presence and role.
In the short run, AI adoption is associated with modest improvements in scientific outcomes concentrated in the upper tail.
Observational analysis linking identified AI presence in a comprehensive dataset of research proposals (funded and unfunded) to subsequent publication outcomes; AI presence identified via keyword extraction combined with large language model (LLM) classification; publication outcomes measured after proposal submission.
The experience-centered learning mechanism proactively recalls rewarded trajectories at inference time.
Specific technical/design claim about Synergy's learning mechanism; asserted in paper as a mechanism feature rather than demonstrated with quantified results in the provided text.
Synergy grounds collaboration in session-native orchestration, repository-backed workspaces, and social communication; identity in typed memory, notes, agenda, skills, and persistent social relationships; and evolution in an experience-centered learning mechanism that proactively recalls rewarded trajectories at inference time.
Detailed design claims describing Synergy's mechanisms and intended grounding for collaboration, identity, and evolution; presented as architectural description, no experimental evaluation provided in the excerpt.
We present Synergy, a general-purpose agent architecture and runtime harness for persistent, collaborative, and evolving agents on Open Agentic Web.
Paper's contribution statement indicating the authors propose an architecture named Synergy; this is a systems/design claim rather than an empirical result in the provided text.
The next generation of agents must become Agentic Citizens, defined by three requirements: Agentic-Web-Native Collaboration, participation in open collaboration networks rather than only closed internal orchestration; Agent Identity and Personhood, continuity as a social entity rather than a resettable function call; and Lifelong Evolution, improvement across task performance, communication, and collaboration over time.
Normative/design prescription from the authors; conceptual argument for three requirements rather than empirical validation.
As the internet prepares to host billions of such entities, it is shifting toward what we call Open Agentic Web, a decentralized digital ecosystem in which agents from different users, organizations, and runtimes can discover one another, negotiate task boundaries, and delegate work across open technical and social surfaces at scale.
Conceptual claim / framing by the authors describing a projected/ongoing shift; no empirical measurement of 'billions' or of ecosystem properties provided in the excerpt.
Embodied agents are spreading across smartphones, vehicles, and robots.
Author observation/claim in the paper's opening; no empirical study, metrics, or examples quantified in the provided text.
Open-source frameworks such as OpenClaw are putting personal agents in the hands of millions.
Author assertion naming OpenClaw and a numeric adoption claim; no supporting empirical data or citation contained in the provided text.
AI agents are rapidly expanding in both capability and population: they now write code, operate computers across platforms, manage cloud infrastructure, and make purchasing decisions.
Author assertion in paper's introduction / high-level observation; no empirical study, dataset, or experiment reported in the provided text.
IMDPs lower ESG rating uncertainty.
The paper constructs measures of ESG rating uncertainty and finds IMDP participation reduces rating uncertainty.
IMDPs reduce greenwashing.
The paper constructs measures of greenwashing and reports that IMDP participation lowers those greenwashing measures.
The positive effect of IMDP participation on ESG performance is stronger in capital-scarce industries.
Heterogeneity analysis by industry capital-scarcity reported in the paper indicating larger IMDP effects in capital-scarce industries.
The positive effect of IMDP participation on ESG performance is stronger for firms at the growth stage.
Heterogeneity analysis by firm life-cycle stage reported in the paper showing larger effects for growth-stage firms.
The positive effect of IMDP participation on ESG performance is stronger for firms under intense competitive pressure.
Heterogeneity analysis reported in the paper that splits the sample by measures of competitive pressure and finds larger effects for firms facing more intense competition.
The effect of IMDP participation on ESG performance operates through improved cost management, consistent with capability upgrading and resource reallocation toward sustainability-related activities.
Mechanism analyses reported in the paper linking IMDP participation to measures of cost management and interpreting this as capability upgrading/resource reallocation.
The effect of IMDP participation on ESG performance operates through higher innovation efficiency.
Mechanism analyses reported in the paper (mediation/decomposition analyses linking IMDP participation to measures of innovation efficiency).
IMDP participation increases ESG ratings by approximately 0.14 rating levels relative to comparable non-participating firms.
Quasi-natural experiment exploiting staggered rollout of IMDPs; propensity score matching combined with a multi-period difference-in-differences design using panel data on Chinese listed manufacturing firms from 2009 to 2022 (as reported in the paper).
Education and workforce development should shift focus from rote knowledge accumulation to cultivating skills in human-AI collaboration, creative problem-solving, and the design of novel economic domains.
Normative policy recommendation derived from the paper's framework and analysis of anticipated labor market changes (no empirical evaluation or trial data reported in the abstract).
Human-AI co-evolution will significantly increase individual productivity and open new frontiers of economic activity.
Projected outcome based on combined analysis of AI capabilities, historical patterns, and platform growth; the abstract does not report empirical measurement or sample sizes for this projection.
AI-driven productivity augmentation dramatically lowers the barriers to creating economic value, enabling the decentralized generation of employment.
Argument supported by paper's analysis of contemporary labor market dynamics and the growth of digital platforms; no quantified empirical estimates or sample sizes provided in the abstract.
The transition to an AI-civilization will fundamentally restructure the mechanisms of employment creation from a centralized model (few organizations creating jobs for the many) to a decentralized ecosystem where individuals are empowered to generate their own employment opportunities.
Central thesis of the paper, motivated by theoretical argumentation and synthesis of contemporary data on labor markets and digital platforms (no empirical test or sample sizes specified in the abstract).
Historical precedents from past technological revolutions suggest that innovation tends to expand, rather than shrink, the scope of economic activity and employment in the long run.
Paper draws on analysis of economic history (qualitative historical analysis implied; no specific historical datasets or sample sizes provided in the abstract).
The paper studies principal-agent alignment using revealed preference techniques.
Stated methodological approach in the abstract; implies analytical use of revealed-preference methods for identification.
The AI's alignment (similarity of human and AI preferences) can be generically identified in the field setting, where only AI choices are observed.
Analytical/theoretical identification result presented in the paper using revealed preference techniques (as stated in abstract); no empirical sample reported in the abstract.
The AI's alignment (similarity of human and AI preferences) can be generically identified in the laboratory setting, where both human and AI choices are observed.
Analytical/theoretical identification result presented in the paper using revealed preference techniques (as stated in abstract); no empirical sample reported in the abstract.
The paper introduces the Luce Alignment Model, where the AI's choices are a mixture of two Luce rules, one reflecting the human's preferences and the other the AI's.
Paper proposes and defines a new theoretical model (model specification described in abstract).
Human decision makers increasingly delegate choices to AI agents.
Stated as motivation in the abstract; no empirical data or sample described in the provided text.
By formalizing the end-to-end transaction model together with its asset and incentive layers, EpochX reframes agentic AI as an organizational design problem focused on infrastructures where verifiable work leaves persistent, reusable artifacts and value flows support durable human-agent collaboration.
Theoretical framing and normative claim in the paper; no empirical evaluation demonstrating that this reframing yields measurable benefits.
Credits lock task bounties, allow budget delegation, settle rewards upon acceptance, and compensate creators when verified assets are reused.
Functional description of the credit mechanics and settlement rules within the proposed EpochX marketplace; presented as part of system design without empirical settlement or user-behavior data.
EpochX introduces a native credit mechanism to make participation economically viable under real compute costs.
Proposed economic/incentive mechanism described in the paper; no empirical cost analysis, pricing model validation, or participant economic outcomes reported.
These assets are stored with explicit dependency structure, enabling retrieval, composition, and cumulative improvement over time.
Design-level assertion about data model/asset graph in the EpochX proposal; no empirical results demonstrating retrieval/composition or measured cumulative improvement.
Each completed transaction can produce reusable ecosystem assets, including skills, workflows, execution traces, and distilled experience.
Architectural claim about artifacts produced per transaction in EpochX; described as a design goal rather than backed by empirical evidence or deployment data.
Claimed tasks can be decomposed into subtasks and executed through an explicit delivery workflow with verification and acceptance.
Design description of the workflow and verification/acceptance mechanisms in the proposed EpochX architecture; no empirical testing or metrics reported.
EpochX treats humans and agents as peer participants who can post tasks or claim them.
Architectural/design specification in the paper describing participant roles and interactions; no empirical validation provided.
We introduce EpochX, a credits-native marketplace infrastructure for human-agent production networks.
System/design description in the paper (architectural proposal); no deployment, user study, or evaluation results reported.
Google has been pioneering machine learning usage across dozens of products.
Contextual statement in the abstract about the organization's activity; asserted without empirical detail in abstract.
The techniques and approaches described can be generalized for other framework migrations and general code transformation tasks.
Authors' stated expectation/generalization claim in the abstract; no empirical evidence or cross-framework experiments reported in the abstract.
The system creates a virtuous circle where effectively AI supports its own development workflow.
Conceptual claim supported by the system's design and reported improvements that enable iterative AI-assisted development; described qualitatively in the paper.
Our approach dramatically reduces the time (6.4x-8x speedup) for deep learning model migrations.
Quantitative speedup figure reported in the paper's abstract (6.4x-8x); likely based on measured migration times on demonstrated cases, though the abstract does not state sample size or exact experimental setup.
The system accelerates code migrations in a large hyperscaler environment on commercial real-world use-cases.
Reported demonstration and evaluation in a hyperscaler (commercial) environment using real-world cases as described in the paper; no detailed sample size given in abstract.
We define quality metrics and AI-based judges that accelerate development when the code to evaluate has no tests and has to adhere to strict style and dependency requirements.
Design and implementation of quality metrics and AI-based judges described in the paper; claimed acceleration of development workflow (no numeric quantification in abstract).
We built an AI-based multi-agent system to support automatic migration of TensorFlow-based deep learning models into JAX-based ones.
System implementation and description in the paper; demonstration on real-world code migration tasks in a hyperscaler environment (qualitative description in abstract).
The productivity channel raises corporate cash flows and is equity-bullish.
Model mechanism described in the paper: productivity effects of AI increase corporate cash flows which, within the model, produce an equity-bullish effect on the ERP/valuations.
AI methods improve sustainability disclosure (disclosure to sustainability).
Stated in the review as an outcome of employing AI for ESG analytics and sustainability reporting; specific supporting studies or sizes are not provided in the excerpt.