Evidence (8807 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
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 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 | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Productivity
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Every additional mechanism we test (planner evolution, per-tool selection, cold-start initialization, skill extraction, and three credit assignment methods) degrades performance.
Findings from the nine-variant ablation study reported in the paper; comparison of variants that add each listed mechanism versus the memory+reflection combination.
There is a stark geopolitical divide between 'AI Core' nations and the Global South; the Global South faces acute risks of 'Digital Dependency' and eroded digital sovereignty.
Cross-study synthesis in the systematic review (2018-2026) identifying geopolitical patterns and risks; abstract does not quantify the number of studies or present empirical effect sizes.
The 'black box' nature of automated systems undermines the democratic social contract and principles of procedural justice, epitomised by the Australian 'Robo-debt' scandal.
Case study material and literature synthesized in the systematic review referencing the Australian Robo-debt case as an exemplar; abstract does not provide primary data or sample sizes.
Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness in volatile-demand, uncertain-supply industries.
Positioning/background statement in the paper motivating the integrated framework (literature-based claim).
The stakes are particularly high in spreadsheet environments, where process and artifact are inseparable: each decision the agent makes is recorded directly in cells that belong to and reflect on the user.
Conceptual / domain-specific argument made by the authors (no empirical sample attached to the claim).
AI agents can perform sophisticated, multi-step knowledge work autonomously from start to finish, yet this process remains effectively inaccessible during execution: by the time users receive the output, all underlying decisions have already been made without their involvement.
Author assertion / conceptual description in the paper (no empirical quantification provided for this general statement).
Advances in AI agent capabilities have outpaced users' ability to meaningfully oversee their execution.
Author assertion / literature-level observation presented in the paper (no empirical sample reported for this claim).
Selective forgetting remains underexplored compared to retention in LLM agent memory research.
Authors' literature survey / position statement in paper (assertion made in abstract).
Beyond technical barriers there are organizational ones: a persistent AI literacy gap, cultural heterogeneity, and governance structures that have not yet caught up with agentic capabilities.
Interview data (over 30) reporting organizational challenges including limited AI literacy, diverse cultural attitudes across organizations, and lagging governance relative to agentic AI capabilities.
Adoption is constrained less by model capability than by fragmented and machine-unfriendly data, stringent security and regulatory requirements, and limited API-accessible legacy toolchains.
Stakeholder interviews (over 30) reporting barriers to deployment; qualitative synthesis identifies data fragmentation, security/regulatory requirements, and legacy toolchain access as primary constraints.
Users push back against agent outputs -- through corrections, failure reports, and interruptions -- in 44% of all turns.
Turn-level coding of user behavior in the SWE-chat dataset: proportion of conversational turns containing correction/complaint/interrupt signals, computed across >63,000 user prompts and sessions.
Agent-written code introduces more security vulnerabilities than code authored by humans.
Comparative analysis of security vulnerabilities attributed to agent-authored code versus human-authored code within the SWE-chat dataset (method details not specified in excerpt).
Just 44% of all agent-produced code survives into user commits.
Empirical measurement of code provenance and survival within the SWE-chat dataset: proportion of agent-produced code that becomes part of subsequent user commits across sessions.
Despite rapidly improving capabilities, coding agents remain inefficient in natural settings.
Authors' summary claim supported by dataset-derived metrics such as agent code survival rate (44%) and user pushback (44% of turns); observational analysis of SWE-chat.
Regulated deployment imposes four load-bearing systems properties — deterministic replay, auditable rationale, multi-tenant isolation, statelessness for horizontal scale — and stateful architectures violate them by construction.
Conceptual/architectural argument presented in the paper (theoretical analysis), not an empirical measurement in the abstract.
The policy and research challenge posed by platform-mediated automation is not merely job quantity (technological unemployment) but institutional continuity — how societies reproduce practical competence when platforms optimize for efficiency rather than formation.
Normative and conceptual claim developed through literature synthesis (institutional economics, platform governance, workforce development); presented as an analytical reframing rather than an empirically tested hypothesis.
Entry-level roles have historically functioned as apprenticeships in which workers acquire tacit knowledge and critical judgment; if platforms curtail these formative occupational layers, organizations may lack future workers capable of exercising contextual reasoning required to manage complex systems.
Institutional economics and workforce development literature cited in the paper; conceptual synthesis without original empirical measurement reported.
Platform-mediated automation risks hollowing out labor structures from both directions: eroding repetitive, junior roles from below and automating supervisory coordination functions from above.
Theoretical argument synthesizing institutional economics and platform literature; articulated as a conceptual risk rather than demonstrated with original empirical data.
Algorithmic systems are displacing routine tasks across both low-wage entry-level work and middle-management functions.
Stated in paper's argumentation; supported by a literature-based review drawing on platform governance literature and recent research on AI-enhanced automation (no original empirical sample or quantitative study reported).
The observed negative OPM effect is consistent with short-term 'J-curve' transition costs (process redesign and capability buildup) during early AI adoption.
Interpretation of empirical patterns (short-term decline in OPM concurrent with no ROA change) offered by the authors as an explanatory mechanism; not presented as separately estimated or experimentally tested.
AI adoption had a significantly negative impact on the operating profit margin (OPM).
Causal analysis of KOSDAQ-listed companies (2018–2025) with AI-adoption timing identified via multi-step, contextually validated text analysis of DART business reports; endogeneity addressed using two-way fixed effects (TWFE) and Propensity Score Matching (PSM).
An alternative specification that makes different choices about the timing of the pervasiveness of AI yields less robust results, though it also suggests that AI is labor saving.
Reported sensitivity analysis / alternative empirical specification in the paper; authors state the alternative yields less robust results but still indicates labor-saving effects.
Our baseline model finds evidence that AI is input saving.
Outcome reported from the baseline empirical specification indicating reductions in inputs associated with AI (authors' baseline model results).
The infrastructure for cross-user agent collaboration is entirely absent, let alone the governance mechanisms needed to secure it.
Authoritative claim in paper framing the research gap; presented as observational/argumentative (no empirical audit reported).
Current AI agent frameworks have made remarkable progress in automating individual tasks, yet all existing systems serve a single user.
Statement in paper's introduction/positioning; conceptual survey-style claim (no empirical study or systematic benchmark reported).
Standard benchmarks often fail to isolate an agent's core ability to parse queries and orchestrate computations.
Paper asserts that existing/standard benchmarks do not adequately isolate parsing and computation-orchestration abilities, motivating the new benchmark.
As multimodal AI achieves human-parity understanding of speech and gesture, [the keyboard's] necessity dissolves.
Theoretical claim supported by multidisciplinary review (history, neuroscience, technology, organizational studies); no quantified empirical test reported.
General-purpose LLMs pose misinformation risks for development and policy experts, lacking epistemic humility for verifiable outputs.
Conceptual/argumentative claim stated in the paper's motivation; no empirical test reported in the abstract.
There was a nonsignificant absolute retest performance reduction in the AI condition and a larger retest performance decrement in the AI condition (i.e., retention decreased more after using Copilot).
Comparison of retest (one-week) performance across conditions reported in results; authors report a nonsignificant reduction and larger decrement for the AI/Copilot condition (n=22).
Current operational approaches typically involve scattered testing tools, resulting in partial coverage and errors that surface only after deployment.
Authors' characterization of industry practice and limitations (assertion in paper; no empirical sample size reported in abstract).
Network change validation remains a critical yet predominantly manual, time-consuming, and error-prone process in modern network operations.
Statement in paper framing the problem; based on authors' characterization of current operational practice (no empirical sample size reported in abstract).
The paper identifies governance challenges such as accountability gaps, digital sovereignty risks, ethical pluralism, and strategic weaponization arising from embedding AI in diplomatic practice.
Conceptual and normative analysis section of the paper outlining risks and governance challenges; illustrated by examples and argumentation.
Thin training coverage fosters anxiety about substitution and slows diffusion of AI tools.
Reported associations from surveys of mid-level managers and technical staff, interviews, and document analysis across cases; thematic coding identified links between limited training, worker anxiety, and slower diffusion. (Sample size not reported.)
Upstream textile SMEs frequently exhibit constrained supply chain resilience owing to persistent information latency and structural dependence on downstream orders.
Background/contextual claim stated in paper (motivation for study); no specific quantitative test reported in abstract.
The pharmaceutical R&D process is persistently challenged by high financial costs, protracted timelines, and remarkably low success rates.
Background statement in the review synthesizing prior literature and field knowledge; no original empirical data or sample sizes reported in the provided text.
Existing evaluations of large language models remain limited to judgmental tasks in simple formats, such as binary or multiple-choice questions, and do not capture forecasting over continuous quantities.
Literature/benchmark critique asserted in the paper (argument that current benchmarks focus on simple judgmental formats and miss continuous numerical forecasting capabilities).
Calibration degrades sharply at extreme magnitudes, revealing systematic overconfidence across all evaluated models.
Empirical observations from QuantSightBench evaluation showing model calibration performance as a function of magnitude (paper statement noting sharp degradation and overconfidence at extremes).
The top performers Gemini 3.1 Pro (79.1%), Grok 4 (76.4%), and GPT-5.4 (75.3%) all fall at least 10 percentage points short of the 90% coverage target.
Reported empirical coverage percentages from evaluation on QuantSightBench for the listed models (paper provides these percentage values).
None of the 11 evaluated frontier and open-weight models achieves the 90% coverage target.
Empirical evaluation on the newly introduced QuantSightBench benchmark across 11 frontier and open-weight models; models were assessed on empirical coverage of prediction intervals versus a 90% target (paper statement).
The study identified significant implementation challenges including algorithmic bias, digital divide concerns, data privacy risks, and low technology readiness among HR teams in Tier 2 cities.
Synthesis of qualitative case study findings from 4 organizations plus survey responses (N=150) reporting barriers and risks encountered during adoption.
Current LLMs are unreliable delegates: they introduce sparse but severe errors that silently corrupt documents, compounding over long interaction.
Qualitative and quantitative analysis of errors observed across the DELEGATE-52 experiments (19 LLMs) showing sparse, high-severity, and silently introduced errors that accumulate over long workflows.
Degradation severity is exacerbated by document size, length of interaction, or presence of distractor files.
Additional experiments and analyses varying document size, interaction length, and presence of distractor files reported in the paper showing increased degradation under these conditions.
Agentic tool use does not improve performance on DELEGATE-52.
Additional experiments reported in the paper that compare plain LLM delegation vs. agentic tool-using configurations on DELEGATE-52 and find no performance improvement from agentic tool use.
Even frontier models (Gemini 3.1 Pro, Claude 4.6 Opus, GPT 5.4) corrupt an average of 25% of document content by the end of long workflows.
Reported results from the experiment evaluating 19 LLMs on DELEGATE-52; these named models are highlighted and an average corruption fraction (25%) is reported at the end of long workflows.
Our large-scale experiment with 19 LLMs reveals that current models degrade documents during delegation.
Large-scale experiment reported in the paper evaluating 19 LLMs on DELEGATE-52 long delegated workflows; observed document degradation across models.
Underreliance on AI might deprive software developers of potential gains in productivity and quality.
Stated in the paper and motivated by themes from twenty-two developer interviews indicating missed benefits when developers underuse LLM tools.
Overreliance on AI may lead to long-term negative consequences (e.g., atrophy of critical thinking skills).
Paper explicitly states this risk and grounds the discussion in findings from twenty-two developer interviews (qualitative evidence and participant-reported concerns).
Small and medium-sized practices face challenges of skill gaps and resource constraints that hinder adoption of technology and data analytics.
Consistent findings across included studies highlighting barriers in small and medium-sized practices (SMPs).
AI adoption is reinforcing existing structural disparities within the BRICS bloc, creating a two‑tier productivity hierarchy (China & India vs. Brazil, Russia & South Africa).
Observed divergence in TFP trajectories and differing links between AI indicators and TC/EC across the five BRICS economies; comparative analysis shows stronger frontier-shifting effects in China and India and weaker or negative effects in the other three economies.
Brazil, Russia, and South Africa experience stagnation or decline in both efficiency and technological advancement over 2005–2023.
Malmquist TFP decomposition (EC and TC) for each BRICS economy showing flat or negative trends in EC and TC for Brazil, Russia, and South Africa during 2005–2023.