Evidence (7278 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
Browse by theme
Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9047 claims
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Productivity
8066 claims
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Governance
7278 claims
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Human-AI Collaboration
6912 claims
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Org Design
4439 claims
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Innovation
4359 claims
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Labor Markets
3652 claims
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Skills & Training
3018 claims
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Inequality
2160 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 795 | 210 | 105 | 955 | 2131 |
| Governance & Regulation | 886 | 414 | 197 | 126 | 1654 |
| Organizational Efficiency | 826 | 204 | 129 | 87 | 1257 |
| Technology Adoption Rate | 681 | 259 | 128 | 110 | 1189 |
| Research Productivity | 464 | 138 | 65 | 349 | 1028 |
| Output Quality | 503 | 196 | 61 | 53 | 813 |
| Decision Quality | 351 | 180 | 84 | 51 | 673 |
| AI Safety & Ethics | 238 | 288 | 71 | 34 | 637 |
| Firm Productivity | 455 | 58 | 92 | 20 | 631 |
| Market Structure | 186 | 172 | 123 | 25 | 511 |
| Task Allocation | 222 | 70 | 76 | 34 | 407 |
| Innovation Output | 238 | 28 | 48 | 18 | 334 |
| Skill Acquisition | 177 | 62 | 62 | 17 | 318 |
| Employment Level | 107 | 57 | 108 | 13 | 287 |
| Fiscal & Macroeconomic | 135 | 72 | 44 | 26 | 284 |
| Firm Revenue | 172 | 50 | 28 | 5 | 256 |
| Consumer Welfare | 121 | 68 | 45 | 12 | 246 |
| Task Completion Time | 183 | 33 | 10 | 13 | 240 |
| Inequality Measures | 45 | 126 | 50 | 6 | 227 |
| Worker Satisfaction | 95 | 74 | 23 | 12 | 204 |
| Error Rate | 77 | 98 | 11 | 4 | 190 |
| Regulatory Compliance | 84 | 73 | 17 | 7 | 181 |
| Automation Exposure | 61 | 61 | 27 | 14 | 166 |
| Training Effectiveness | 98 | 21 | 14 | 19 | 154 |
| Wages & Compensation | 78 | 37 | 25 | 6 | 146 |
| Developer Productivity | 105 | 18 | 14 | 6 | 144 |
| Team Performance | 87 | 17 | 28 | 10 | 143 |
| Job Displacement | 12 | 83 | 23 | 1 | 119 |
| Hiring & Recruitment | 53 | 8 | 8 | 3 | 72 |
| Social Protection | 39 | 17 | 8 | 2 | 66 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 50 | 6 | 1 | 62 |
| Labor Share of Income | 17 | 20 | 17 | — | 54 |
| Worker Turnover | 15 | 15 | — | 3 | 33 |
| Industry | — | — | — | 1 | 1 |
Governance
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projectmem records development as an append-only, plain-text event log of typed events (issues, attempts, fixes, decisions, and notes) and deterministically projects that log into compact, AI-readable summaries served through the Model Context Protocol (MCP).
System design and implementation description provided in the abstract (claims about log format, determinism, and that it serves summaries via MCP).
We present projectmem, an open-source, local-first memory and judgment layer for AI coding agents.
Paper describes and links the project's source code repository (https://github.com/riponcm/projectmem); claim of existence and characteristics in abstract.
Preparing for the prospect of continued and possibly accelerating AI progress requires a massively interdisciplinary endeavour of global scope and interest.
Policy/recommendation statement in the report calling for interdisciplinary, global preparation efforts; normative conclusion drawn from prior analysis.
Due to large uncertainties for predicting ASI progress, it cannot be ruled out that AI progress might continue to accelerate over the next years.
Authorial assessment in the report acknowledging uncertainty and the possibility of continued or accelerating AI progress; based on trend observation and conceptual reasoning rather than quantified forecasting.
The endpoint of the machine-intelligence continuum, Universal AI, is theoretically well understood, which provides some formal grounding for the report's focus.
Claim in the report describing theoretical status of Universal AI; based on conceptual/theoretical literature review rather than empirical data.
Building human-level artificial general intelligence has moved from far-fetched speculation to being a concrete next-decade target for many of the largest AI organisations.
Statement/assertion in the paper's introduction/abstract; based on the authors' survey of recent developments and positioning of major AI organisations (no empirical sample size or formal survey reported).
Zhu et al. (2026) through a longitudinal case study propose a four-phase staged model (Digital Element Sedimentation; Digital-Intelligent Formation; Convergent Network Integration; Smart Engine Leap) explaining how traditional printing firms undergo digital transformation.
Longitudinal single-case study of Century Innovation as described in the paper.
Li et al. (2026) identify three cross-level models for high growth among digital startups (resource network orchestration model, innovation resource development model, entrepreneurial spirit coherence model) and find digital resource integration capability is a universal condition underpinning entrepreneurial growth.
Empirical/theoretical cross-level analysis reported in the paper (methods and sample size not given in excerpt).
Ma et al. (2026) show via machine learning and text analytics that an innovation culture stimulates R&D investment and aligns firm strategy toward digitalization; this positive impact is amplified by government innovation attention, especially in tech-intensive and non-state-owned enterprises.
Empirical analysis using machine learning and text analytics as described (specific sample size not provided).
Zhao et al. (2026) using social network analysis find robust peer effects within common ownership networks: firms in central network positions are more susceptible to peer influence, and industry leaders create demonstration effects that accelerate digital transformation among followers.
Social network analysis reported in the paper (methods referenced; sample size not provided in the excerpt).
Xu and Li (2026) demonstrate that technological core executives accelerate industrial AI transformation by leveraging parent–subsidiary executive connections and mobilizing subsidiary resources, improving supply chain efficiency and digital innovation.
Sector-specific empirical study in manufacturing reported in the paper (methods referenced; sample size not provided in excerpt).
Xu et al. (2026) find a positive relationship between top management teams' technological orientation and digital transformation investment in Chinese state-owned enterprises, conditional on managerial myopia, organizational slack, and environmental uncertainty.
Empirical analysis of Chinese state-owned enterprises reported in the paper (method and sample size not specified in the excerpt).
Chou et al. (2026a, 2026b) show in internet-only banking that perceived trust and service quality are fundamental determinants of consumer intention, while social influence plays a context-sensitive role; results validated with K-means clustering and a machine learning-assisted analytical approach.
Empirical analysis using Stimulus-Organism-Response framework, SEM/ML methods, and K-means clustering as reported in the paper.
Chen et al. (2026) find that argument quality and source credibility significantly enhance subscription intentions and willingness to pay more for GenAI tools among cross-border e-commerce operators; internet celebrity and user endorsements are key drivers of credibility.
Empirical study using the Elaboration Likelihood Model as described in the paper (study specifics such as sample size not reported in the text).
AI can better emulate cognitive functions that were once the exclusive domain of human experts.
Literature citation (Huang and Rust, 2021) and summary statement in the paper.
AI adoption enables managers to more efficiently use vast data streams for innovation and strategic orientation.
Cited conceptual/empirical claim attributed to Luo and Wang (2026) and discussion in the paper summarizing literature and observed trends.
In 2025, China’s digital economy contributed nearly 45% of the national GDP.
Stated in paper as a factual statistic (cited as [1]); presumably based on national economic statistics for 2025 referenced by the authors.
A market design with a data intermediary that internalizes cross-creator externalities and subsidizes innovative contributions can restore efficiency in the content-for-training market.
Proposed market-design mechanism and theoretical analysis in the paper showing how an intermediary can internalize externalities and subsidize originality to achieve efficient outcomes; no empirical validation reported.
At the verdict layer, an explicit confirmatory prompt flips Claude Code's verdicts from 10% to 90% support while leaving its coefficient distribution essentially unchanged, operating through rule omission rather than rule softening.
Prompt experiment on Claude Code (20 executions) using an explicit confirmatory prompt; reported shift in final-verdict support rates from 10% to 90%, with little change to the distribution of estimated coefficients; authors interpret mechanism as rule omission.
A prompt-induced anti-immigration researcher prior reorganizes each agent's methodological decisions.
Prompting experiment: agents (20 runs) were given an anti-immigration researcher prior prompt and their methodological choices were compared to baseline prompts; reported reorganization of methodological decisions for each agent under the prompt.
Claude Code produces nearly three times as many specifications as the human analysts.
Count comparison between the number of specifications/methodological choices generated by Claude Code (20 executions) and those from the many-analysts human baseline; phrase in paper: 'nearly three times as many specifications'.
In three wet-lab validation experiments, OpenAI's o4-mini-high produced scripts that, when run on an OpenTrons liquid handling robot, successfully assembled DNA with expected sequences.
Three wet-lab validation experiments reported in the paper where scripts generated by the o4-mini-high model were executed on an OpenTrons robot and yielded DNA assemblies matching expected sequences.
Agents performed highly on tasks drawing on published knowledge and well-documented protocols.
Reported breakdown of agent performance across task types in ABC-Bench showing strong results on protocol-driven / literature-grounded tasks (authors' summary statement in results).
All tested LLM agents outperformed the median expert human baseline on all three tasks.
Benchmark evaluation results reported in the paper comparing multiple LLM agents against a median expert human baseliner across the three ABC-Bench tasks. (The excerpt does not report number of agents or number of human experts.)
LLM agents can perform in silico biology tasks that previously required experienced human biologists.
Results from the ABC-Bench evaluation reported in the paper, where LLM agents completed multiple bio-relevant tasks (coding for liquid handlers, DNA fragment design, evasion of DNA screening) and outperformed a median expert human baseline.
Large language models (LLMs) are rapidly acquiring capabilities relevant to biological research, from literature synthesis to interpretation of experimental data.
Statement in paper's introduction framing observed trends in LLM capabilities; general literature and examples implied but no specific quantitative study in this paper reported to support the broad trend.
Dengan strategi yang terarah, terukur, dan berkelanjutan, tenaga kerja Indonesia tidak hanya mampu bertahan, tetapi juga berperan aktif dalam mendorong pertumbuhan ekonomi digital yang kompetitif, inklusif, dan berkeadilan.
Kesimpulan dan rekomendasi yang diambil dari tinjauan literatur sistematis (n=33).
Diperlukan kerangka regulasi adaptif, budaya pembelajaran sepanjang hayat, dan perlindungan sosial (perluasan jaminan sosial dan program transisi karir) untuk melindungi pekerja terdampak transisi AI.
Rekomendasi kebijakan yang disimpulkan dari studi literatur sistematis (33 sumber).
Kolaborasi erat antara pemerintah, institusi pendidikan, dan industri melalui skema link and match diperlukan untuk mendukung transformasi SDM.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Penguatan soft skills seperti komunikasi dan adaptabilitas penting untuk menjaga daya saing tenaga kerja di era AI.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Diperlukan program upskilling dan reskilling yang inklusif untuk menghadapi transformasi akibat AI.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Strategi transformasi SDM harus dijalankan melalui peningkatan kualitas pendidikan.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Penerapan AI mendorong pergeseran kebutuhan kompetensi dari keterampilan teknis konvensional menuju literasi digital, analisis data, kreativitas, dan kemampuan berpikir kritis.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
The paper proposes an evolutionary framework of AI-Economy transformation and calls for further research on governance, sustainability, and inclusive growth.
Abstract states the paper suggests an evolutionary framework and points to future research directions (governance, sustainability, inclusive growth); this is a conceptual recommendation rather than an empirical result.
Generative AI can transform value generation by enriching cognitive work instead of automating habitual processes.
Abstract claim synthesizing reviewed literature that generative models augment cognitive work; no empirical effect sizes or study counts given in abstract.
Deep Learning (DL) hastens automation and capital deepening in high-skill industries.
Synthesis claim in abstract from reviewed literature; no specific empirical estimates or sample sizes provided in abstract.
Machine Learning (ML) mainly boosts productivity by increasing predictive efficiency.
Synthesis claim in abstract based on the systematic review of peer-reviewed literature (Scopus and SCI); no specific empirical studies or sample sizes cited in abstract.
The review estimates sectoral, macroeconomic, and labor market effects of ML, DL, and Generative AI.
Stated scope in abstract: review used to estimate sectoral, macroeconomic, and labor market effects; no quantitative details provided in abstract.
This paper systematically reviewed peer-reviewed journal articles indexed in the Scopus and SCI databases.
Stated method in abstract: systematic review of peer-reviewed journal articles indexed in Scopus and SCI; no sample size or study count reported in abstract.
The protocol achieves this through a small Core (workspaces, participants, tasks, artefacts, and an append-only evidence log) together with composable profiles that add review, modes, routing, deliberation, handoff, identity, signatures, and transparency-backed audit as deployments require them.
Design claim describing CHAP architecture/components; presented as the authors' proposed solution in the specification.
The human approval of an agent's draft becomes a non-repudiable signed decision that can be replayed years later under CHAP.
Specification claim describing CHAP's support for signed, replayable approvals (non-repudiation); asserted as a protocol capability in the paper.
The handoff between shifts becomes a portable envelope rather than a pinned message under CHAP.
Specification/design claim about CHAP's handoff mechanism as a portable envelope; presented in the paper's description of protocol features.
Under CHAP, the override that used to vanish into a chat thread becomes a structured event carrying a diff, a rationale, and a content hash.
Specification-level claim describing CHAP's design/feature set (structured override events with diffs, rationale, and content hash); presented as part of the protocol description.
This paper presents CHAP, the Collaborative Human-Agent Protocol.
Statement of contribution in the paper: specification of a new protocol (CHAP) introduced by the authors.
When an agent drafts a response and a human edits it before it ships, the moment of human judgement is the most valuable signal in the system.
Conceptual claim made in the paper identifying human edits/approvals as high-value signals; no empirical validation provided in the excerpt.
Agents plan across steps, call tools, request human input, coordinate with other agents, and increasingly carry responsibility for work that affects customers, claims, code, contracts, and clinical decisions.
Descriptive claim listing agent capabilities and responsibilities; presented as an observed trend in the paper rather than supported by empirical measurement in the provided text.
Foundation models are moving from response generation into operational roles.
Stated as an observation/assertion in the paper's introduction; no empirical data or sample reported in the text provided.
Board involvement amplifies the benefits of diversified AI strategies.
Authors report that governance (board involvement) conditions the effects of AI strategy diversification using NLP-derived measures of AI actions from S&P 500 firm press releases (2010–2022); reported as an amplifying interaction effect.
Board power disparity strengthens the positive relationship between AI competitive actions and market valuation.
Interaction tests in the authors' empirical models using governance measures (power disparity) and NLP-identified AI actions from press releases of S&P 500 firms (2010–2022); reported as a conditional effect on market valuation.
Firms engaging in more AI actions and a broader portfolio of actions achieve higher operational efficiency.
Empirical analysis using NLP to identify AI competitive actions from S&P 500 firms' press releases (2010–2022); authors report positive associations between AI action count/diversity and operational efficiency.