Evidence (4131 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 |
Innovation
Remove filter
The emergence of AI agents—systems where large language models serve as the primary reasoning engine, dynamically generating and discarding code as an instrumental resource—constitutes a fundamental restructuring of the software paradigm rather than an incremental improvement.
Argument based on first-principles analysis of complexity scaling and conceptual comparison between traditional software and agentic systems (theoretical analysis presented in the paper).
ALE is intended not merely as another leaderboard, but as an instrument for closing the gap between benchmark success and GDP-relevant impact.
Author-stated intent and high-level goal of the benchmark.
ALE is designed as a living benchmark: its task pool grows continuously as new workflows and industries are onboarded.
Design and maintenance policy described by the authors.
ALE was developed in collaboration with 250+ industry experts.
Author statement specifying collaborator count.
This paper introduces Agents' Last Exam (ALE), a benchmark designed to evaluate AI agents on long-horizon, economically valuable, real-world tasks with verifiable outcomes.
Description of benchmark introduced by the authors (design claim).
Recent AI systems have achieved strong results on a wide range of benchmarks.
Statement in paper (background/context); refers to existing benchmark results in the literature (no specific benchmarks or datasets named in this excerpt).
Understanding the evolution of LLM-augmented search is critical for organizations seeking to maintain brand relevance in an AI-augmented information landscape.
Prescriptive concluding claim in paper; based on the authors' synthesis of observed trends and conceptual analysis rather than empirical validation in the provided excerpt.
Public examples referenced include the reported PocketOS and Replit agentic database-deletion incidents and Moffatt v. Air Canada as an adjudicated output/reliance case.
The paper cites specific public incidents and a legal case as examples supporting its discussion.
The paper makes three contributions: it defines the AI-specific reconstruction problem, operationalizes that problem through CER, and specifies claim-grade evidence for AI reconstruction.
Author-stated contributions in the paper; descriptive of the paper's goals and deliverables.
The paper introduces CER, a use-case-level diagnostic for AI residual risk transfer: C (control boundary) asks whether the system had an enforceable operating envelope; E (evidence reconstruction) asks whether the system state and causal chain can be reconstructed from retained artifacts; R (insurance response) asks whether the reconstructed loss is insured (coverage available and placed, and proof needed to support claim recovery).
Framework introduction and operationalization described in the paper; presented as the paper's primary methodological contribution.
The paper addresses losses in which the insured's AI system is in the causal chain, including externally triggered failures such as prompt injection, retrieval-augmented generation (RAG) poisoning, malicious tool output, credential misuse, and data poisoning.
Scope statement in the paper listing specific failure modes; descriptive rather than empirical.
The relevant question for such losses is not only what loss occurred, but what the system was allowed to do, what it actually did, and whether that reconstructed loss can support insurance claim recovery.
Conceptual framing provided in the paper; presented as the diagnostic/analytic focus rather than backed by empirical data in the excerpt.
AI losses that arise through an insured organization's generative or agentic AI system require state reconstruction, not merely event reconstruction, because the relevant state changes as the system reasons, retrieves, calls tools, and acts.
Argument presented in the paper as a conceptual/theoretical claim about the nature of AI-system-caused losses; no empirical sample or quantitative study reported in the excerpt.
The future of agentic-AI insurance lies not in a single monoline product but in a layered ecosystem of complementary coverages supported by improved governance, transparency, telemetry, and regulatory clarity.
Analytic conclusion/recommendation based on the paper's risk taxonomy, actuarial framework, and parallels to cyber insurance; forward-looking synthesis rather than empirical causal evidence.
A coordinated insurance architecture integrating cyber, technology errors and omissions, product liability, performance-warranty, and affirmative AI-liability coverages with explicit allocation mechanisms and dedicated AI aggregates is proposed.
Design proposal in the paper detailing a layered insurance architecture combining multiple coverages and allocation mechanisms; conceptual design not empirically tested.
The paper proposes an actuarial framework based on exposure assessment, scenario analysis, dependency mapping, and accumulation-risk management, drawing parallels to the evolution of cyber insurance.
Proposed actuarial approach described in the paper, invoking methods like scenario analysis and dependency mapping and analogizing to cyber insurance development; methodological proposal without empirical validation.
The paper develops a framework for understanding underwriting, pricing, reinsurance, and product-design implications for agentic-AI insurance.
Methodological contribution stated in the paper: proposed actuarial/underwriting framework (exposure assessment, scenario analysis, dependency mapping, accumulation-risk management); conceptual development rather than empirical validation.
Large-scale online experiments demonstrate consistent relative improvements in device cold-start engagement.
Reported results from large-scale online experiments in Tubi production (no numerical effect sizes or sample sizes provided in excerpt).
Large-scale online experiments demonstrate consistent relative improvements in impression acquisition.
Reported results from large-scale online experiments in Tubi production (no numerical effect sizes or sample sizes provided in excerpt).
Large-scale online experiments demonstrate consistent relative improvements in promotion speed.
Reported results from large-scale online experiments in Tubi production (no numerical effect sizes or sample sizes provided in excerpt).
Large-scale online experiments demonstrate consistent relative improvements in content cold-start engagement.
Reported results from large-scale online experiments in Tubi production (no numerical effect sizes or sample sizes provided in excerpt).
After training, the learned content encoder generates embeddings for both warm and newly ingested content, enabling implicit graph completion through retrieval of warm surrogate neighbors.
Functional claim based on model training and retrieval behavior described in paper (mechanistic claim; supported by described architecture and training procedure).
The RHS content tower does not use ID-based embeddings, content-side subgraphs, neighbor aggregation, or interaction-derived representations, forcing the content encoder to map intrinsic features into a collaborative-filtering-aware embedding space.
Design choice and intended representational effect described in paper (architectural constraints and claimed representational consequence).
The article proposes a Strategic Action Framework to support more inclusive and context-responsive AI ecosystems.
Policy recommendation/framework presented by the authors as a conclusion; not empirically evaluated within the study.
Empirical observations show that youth mobilize AI tools for translation, content creation, customer engagement, and micro-entrepreneurial activities, enabling partial and situational approximation of selected formal-sector practices.
Qualitative interview data from the 125 semi-structured interviews in three DRC cities, used as illustrative grounding for observed uses of AI by youth.
By bridging established knowledge with emerging governance challenges, this study advances a more comprehensive understanding of platform governance and outlines future research avenues related to technological change, dynamic capabilities, and ecosystem perception.
Authors' stated contribution based on their integrative framework and literature synthesis of 644 publications.
The paper proposes a research agenda that examines how emerging technologies, including algorithmic governance, generative AI, and agentic systems, are reshaping governance practices.
Paper's concluding/prospective section proposing future research directions; conceptual proposal rather than empirical test.
The identified governance mechanisms foster innovation in platform ecosystems.
Claim based on the paper's integrative synthesis of 644 publications indicating governance's role in fostering innovation.
The identified governance mechanisms ensure quality in platform ecosystems.
Argument and synthesis from the systematic literature review of 644 publications as presented in the paper's framework.
The identified governance mechanisms (incentives, control, boundary resources) enable platform owners to coordinate value creation.
Argument based on the integrative framework derived from the systematic literature review (644 publications).
There are three core types of governance mechanisms that enable platform owners to coordinate value creation, ensure quality, and foster innovation: incentives, control, and boundary resources.
Synthesis and classification resulting from the systematic literature review of 644 publications, producing an integrative framework that identifies the three mechanism types.
This study conducts a systematic literature review of 644 publications to synthesize the governance landscape and develop an integrative framework.
Methodological statement from the paper reporting the authors performed a systematic literature review analyzing 644 publications.
Platform owners orchestrate complementor participation through governance mechanisms.
Synthesis and conceptual argument based on the systematic literature review of 644 publications.
Digital platform ecosystems rely on loosely coupled complementors to jointly create value with platform owners.
Synthesis of prior literature via the paper's systematic literature review (644 publications); conceptual framing in the literature on platform ecosystems.
The productivity-enhancing effect of fintech is stronger in regions with higher levels of economic development.
Heterogeneity/subsample analysis reported for regional economic development levels using the sample of Chinese A-share listed manufacturing firms (2015–2023); paper states fintech's effect on TFP is more pronounced in more economically developed regions (no subgroup sample sizes or quantitative estimates provided in the excerpt).
The productivity-enhancing effect of fintech is more pronounced in high-tech industries.
Heterogeneity/subsample analysis in the paper using the sample of Chinese A-share listed manufacturing firms (2015–2023); paper reports stronger fintech–TFP effects in high-tech industry subsample (no subgroup sample sizes or coefficients provided in the excerpt).
The positive effect of fintech on corporate total factor productivity operates primarily through the channels of supply chain finance and innovation effects.
Mediation/ mechanism analysis reported in the study using the same sample of Chinese A-share listed manufacturing firms (2015–2023); paper states supply chain finance and innovation as the primary channels (specific mediation estimates not provided in the excerpt).
Fintech development can significantly enhance corporate total factor productivity for Chinese A-share listed manufacturing firms.
Empirical analysis on a sample of Chinese A-share listed manufacturing enterprises covering 2015–2023; result described as statistically significant in the paper (specific estimation methods and sample size not provided in the excerpt).
Dijital platformlar insan deneyimini veriye dönüştürerek ekonomik değere tahvil eden yeni bir rejim (gözetim kapitalizmi) kurmuştur.
Teorik ve kavramsal analiz; çalışma Zuboff'un gözetim kapitalizmi yaklaşımına atıf yapmaktadır. No empirical sample or quantitative evidence reported.
The field can be organized around an integrated decision-system framework consisting of five connected constructs—delegation frontier, reliance wedge, decision-useful XAI, meaningful oversight, and reflexive AI loop—to support cumulative research on investment, trading, credit, asset management, risk, compliance, and financial regulation.
Proposal of a conceptual framework grounded in the paper’s integrative literature review (no empirical validation or sample size reported in the abstract).
The review integrates evidence on methods, data, scenarios, explainability, trust, governance, financial large language models (FinLLMs), and agentic finance.
Descriptive claim about the scope of this paper’s literature synthesis (the review itself; content-based rather than empirical).
The central question is moving from model performance to decision architecture: how authority, oversight, and accountability should be allocated across financial workflows.
Argument based on synthesis of prior literature across relevant fields (conceptual review; no single empirical study or sample size reported).
AI is moving from a predictive tool to a component of human–AI hybrid financial decision systems.
Integrative conceptual literature review synthesizing work across finance, management, human–computer interaction (HCI), and AI (no primary empirical sample reported).
The benchmark is publicly available at: https://github.com/ant-research/meta-agent-challenge.
Statement of public release and URL provided in the paper.
MAC provides a rigorous, open-source benchmark for autonomous AI research and development and offers an empirical proxy for evaluating recursive self-improvement.
Claim about the utility and intended purpose of the released benchmark; supported by the benchmark's design and experiments described in the paper.
The few meta-agents that do match human-engineered baselines are dominated by proprietary frontier models.
Experimental observations reported in the paper indicating that successful meta-agents rely on proprietary frontier models; details (counts, model names) not provided in abstract.
To ensure evaluation integrity, the framework is secured by multi-layer defenses against reward hacking.
Methodological claim in paper about security measures implemented in the benchmark.
In MAC a code agent (the meta-agent) is given a sandboxed environment, an evaluation API, and a time limitation to iteratively program an agent artifact that maximizes performance on a held-out test set across five domains.
Method description of the benchmark setup; specification includes 'held-out test set across five domains'.
We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development.
Paper contribution: description of a new evaluation framework (methodological introduction).
The findings imply that research evaluation and science policy should adopt assessment frameworks that distinguish between recombinant and conceptual forms of creativity and recognize that different modes of AI adoption produce different types of scientific contribution.
Policy/recommendation statement grounded in the paper's empirical findings on heterogeneous creativity effects by AI research mode.