Evidence (4114 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Innovation
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The data show a temporary increase in the pace of new technology creation beginning in the 1970s, accelerating in the 1980s, and tapering off in the 2000s.
Time series of identified new technologies from text-based measures (patent text/job posting linkage) covering 1976–2007 (as in Kalyani et al., 2025) used to measure arrival rates by cohort.
The pace of technology creation is a key driver of the skill premium: a rapid pace of technology creation leads to a sustained increase in the skill premium (because skilled workers learn to use new technologies faster).
Theoretical model developed in the paper in which new technologies arrive exogenously and skilled workers have a comparative advantage in learning new technologies; supported by calibration using novel text-based data (patent text and job postings) and CPS wage data.
Autor et al. (2024) show that the majority of current employment is in job specialties that did not exist in 1940, with new task creation driven by augmentation-type innovations.
Citation reported in the paper summarizing Autor et al. (2024); no sample size provided in excerpt.
Firms may not sufficiently account for non-monetary aspects of technological progress (well-being, safety, quality of work); a planner would include such considerations in steering technological progress.
Normative conclusion based on theoretical analysis comparing firm objective functions (profits) vs social planner objectives (including non-monetary utility).
The planner can raise social welfare by focusing technological progress on making goods cheaper that are disproportionately consumed by relatively poorer agents, thereby raising their real income.
Extension of the baseline model to multiple goods showing distributional gains via composition of price changes (real income channel).
When capital and labor are gross complements, a planner concerned with workers' welfare would favor capital-augmenting innovations to raise wages.
Analytical result from the model analyzing factor-augmenting technological progress and complementarity between capital and labor.
A planner with sufficient welfare weight on workers will impose positive robot taxes, with the tax rate increasing in the planner's concern for workers' welfare.
Application of the baseline model to robot taxation; analytical derivation of optimal robot tax under planner preferences.
As labor's economic value diminishes, steering progress focuses increasingly on enhancing human well-being (non-monetary aspects) rather than labor productivity.
Theoretical discussion and model results in the paper showing planner's shifting objective when labor is devalued.
The welfare benefits of steering technology are greater the less efficient social safety nets are.
Analytical result from the paper's theoretical model comparing a planner who can/cannot perform transfers and evaluating steering as second-best when redistribution is costly.
Despite the diminishing returns they predict, progress in practice has often continued through rapidly improving efficiency, visible for example in falling cost per token.
Observed industry/empirical trend cited in the paper (example: falling cost per token). No numerical samples or sample size given in the excerpt.
Scaling laws are largely empirical and observational, but they appear repeatedly across model families and increasingly across training-adjacent regimes.
Paper asserts repeated empirical appearance across model families and training-adjacent regimes; claim is descriptive/observational without sample size in the excerpt.
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).
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.
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.
AI methods improve risk management (managing risk) in sustainable finance.
Claim synthesized from literature reviewed on AI applications in climate risk analytics and risk modeling; no numerical sample details provided in the excerpt.
AI methods improve portfolio management (managing portfolio) in sustainable finance contexts.
Asserted by the review as part of the assessment of AI effectiveness for managing portfolios and risk in sustainable investing; no quantitative sample size or effect estimate reported in the excerpt.