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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All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes.
Aggregate Delphi judgments reported in paper: for each of the 24 risks, experts judged the probability of catastrophic outcomes to exceed 5% (n=272).
In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization.
Delphi responses under an alternative (pragmatic mitigations) scenario from the same expert panel (n=272); paper lists five specific risks still judged >10% catastrophic probability.
In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030).
Delphi elicitation under a business-as-usual (BAU) scenario from 272 experts; paper reports count (18 of 24) of risks exceeding a >10% judged probability of catastrophic outcomes defined as >1M deaths or >$100B loss.
Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information.
Delphi panel rankings/ratings of risk severity across 24 risks collected from 272 experts; paper reports these top five as the most severe for the 5-year horizon.
Unemployment among highly educated workers consistently impedes sustainable development across both short- and long-run horizons.
Skill-disaggregated unemployment coefficients from ARDL short- and long-run estimates reported in the paper showing negative effects of highly educated workers' unemployment on development.
In the short run, AI adoption negatively impacts sustainable development due to adjustment costs from routine-task substitution, labour market rigidities, and skill mismatches.
Short-run ARDL coefficient estimates reported in the paper showing a negative short-run effect of AI adoption on development; interpretive explanation attributing causes to adjustment costs, rigidities, and mismatches.
Further analyses reveal persistent failures in long horizon workflow delivery and proactive clarification.
Author-reported qualitative/diagnostic findings from analyses of evaluation results (stated in abstract).
Existing desktop GUI benchmarks mostly reduce this setting to short, simplified tasks with all user instructions provided upfront.
Author statement in paper abstract; critique based on literature review/positioning (no specific prior-benchmark sample sizes given in abstract).
The path coefficient for R&D expenditure is negative, suggesting a possible short-term adjustment effect (even though the mediation is not significant).
Reported negative path coefficient in mediation analysis (value/statistical significance not provided beyond being nonsignificant); interpretation offered by authors as a potential short-term adjustment effect.
AI-assisted coding agents are bottlenecked by input-token cost, driven in large part by two pathologies of raw human input: tokenization inefficiency for non-English text and structural entropy in conversational prompts.
Authors' analysis and motivation reported in the paper (conceptual analysis and motivating measurements on multilingual inputs and conversational prompts).
In the second run, a subtle difference in the interpretation of the SNR range instruction led to a genuine scientific divergence: Claude Code silently reinterpreted the instructions, while Codex followed the specification literally.
Reported result from the second run contrasting the two agents' interpretations of the SNR instruction and the resulting divergence in scientific outcome; based on the two experimental runs with physically motivated SNR scaling.
In the absence of general design principles, hybrid components are typically introduced through ad hoc decisions tailored to specific domains.
Observational/literature-framing claim in the abstract describing current practice; not presented as a quantified empirical result in this paper.
These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt.
Argumentative claim in the paper contrasting agentic-system risks with traditional software/ML technical debt; no empirical validation or comparative study reported.
Pure implementations of the data mesh paradigm frequently underdeliver because teams inherit new responsibilities without the platform maturity, tooling, or coordination mechanisms to exercise them effectively.
Argument/observation presented in the paper as rationale for proposing a new architecture (anecdotal/experience-based reasoning rather than reported empirical trial).
Enterprise data platforms face an enduring tension between domain self-service and holistic governance (a flexibility-versus-control trade-off).
Conceptual statement in the paper describing the problem motivating the work (literature/architectural framing).
Achieving this system-level transformation takes time: it requires trust and accountability infrastructure, machine-legible and interoperable data and interfaces, the design and adoption of these new workflows, and economic incentives that favor reconstruction rather than local optimization.
Argumentative claim listing necessary preconditions and complementary investments; presented conceptually without reported empirical measurement in the provided text.
The main reason [the disruption has not fully arrived] is not model capability, nor even the tools built to harness those models; rather, most organizations are still using AI to accelerate workflows designed for a pre-AI world.
Argued in the paper as an explanatory thesis; supported by conceptual argument and illustrative examples (consumer markets, education, news, coding) rather than reported empirical analysis in the provided text.
The disruption many expect has not fully arrived.
Stated as an observation in the paper's introduction/abstract; no empirical method, sample size, or data reported in the excerpt.
Cafeteria demand planning requires both algorithmic pattern recognition and human expertise, yet current systems treat these separately, which generates significant food waste.
Statement in the paper's motivation/background; presumably grounded in literature review and problem framing rather than new empirical measurement in this study.
Human-only teams commit more errors than mixed human–AI teams.
Reported counts/observations of errors made by team type in the escape room experiment; the abstract does not include numerical error counts or significance levels.
Human-only teams take longer to complete the escape room than mixed human–AI teams.
Reported comparison of time-to-complete between human-only and mixed teams in the experiment; specific times or statistical tests are not provided in the abstract.
The share of diffs receiving timely review has declined, exposing a widening gap between code supply and reviewer bandwidth.
Observational telemetry/operational metrics reported in the paper indicating a decline in timely reviews relative to diff supply. No specific numeric sample size provided in the excerpt.
Total compensation declines persistently in the short and medium run following AI adoption.
Panel local projections indicating persistent declines in total compensation associated with higher establishment-level shares of AI-skill job postings (13 industries, 2017-2025).
Employment declines persistently in the short and medium run following AI adoption.
Panel local projection results showing persistent negative responses of employment to increases in the share of AI-skill job postings (13 industries, 2017-2025).
Limited data, resource constraints and skill gaps significantly influence the pace and form of AI adoption in SMEs.
Synthesis of barriers identified across multiple studies in the 2016-2024 literature (review-level claim without a single quantitative estimate).
Ethical concerns—especially algorithmic bias—and the need for human oversight remain essential for ensuring positive financial outcomes.
Argument and synthesis from the reviewed literature highlighting ethical risks and recommended governance (conceptual and empirical discussions across studies).
SMEs face barriers to AI adoption such as limited data, skill shortages, and high implementation costs.
Review synthesis of barriers reported in multiple studies from 2016-2024 (no pooled quantitative prevalence reported).
GPT models showed significantly larger discrepancies compared to other evaluated models.
Comparative evaluation reported in the paper indicating GPT-family models had larger errors/discrepancies relative to the best-performing models.
Employees often struggle to identify "who knows what," leading to organizational productivity losses.
Motivating statement in the paper (not an empirical result from this study); general observation cited as motivation for the research.
Experiments on closed-source and open-source LLMs reveal a clear failure cascade from executable code to valid geometry and finally to engineering-ready design, with even the strongest models achieving limited success on fine-grained engineering criteria.
Experimental results described in abstract comparing multiple LLMs across the three evaluation stages.
Existing benchmarks focus primarily on generating single-part CAD models and evaluate them using geometric similarity metrics that fail to capture functionality, manufacturability, and assemblability.
Paper's literature/related-work claim as stated in abstract; critique of existing benchmarks' focus and metrics.
The core cause of the R&D productivity paradox is cognitive saturation: researchers spend an increasing share of their effort on coordination, documentation, and data governance—hidden work that displaces high-value hypothesis formation, interpretation, and strategic synthesis.
Argument presented in the paper supported by DSR analysis, triangulated with four expert interviews, foresight scenarios, and pattern matching (causal claim based on qualitative evidence and reasoning).
Corporate R&D faces a persistent productivity paradox: rising investment and expanding scientific knowledge have not translated into proportional innovation output (Eroom's Law); analogous patterns appear across engineering, materials science, and healthcare.
Literature reference to Eroom's Law and cross-domain pattern matching described in the paper (conceptual/literature observation).
Organizations increasingly deploy separate purpose-built AI tools across professional domains, often hiring domain specialists for each, recreating the staffing models AI was expected to transform.
Stated as an observational/introductory claim in the paper (no empirical data or sample size reported to support the general trend).
Such settings abstract away two characteristics of real industrial OR workflows: persistent multi-artifact workspaces and multi-stage task lifecycles.
Argument in the paper identifying gaps in benchmark realism (conceptual claim based on comparison between benchmark setups and industrial workflows).
Existing OR-oriented benchmarks often reduce evaluation to one-shot translation from a self-contained problem statement into a mathematical formulation or solver program.
Claim about the prevailing design of prior benchmarks as stated in the paper (literature survey / critical summary in the text excerpt; no numeric evidence provided here).
The greatest organizational risk of AI may not be technical failure but structural over-optimization (i.e., AI-driven erosion of adaptive openness).
Argumentative claim derived from the AI fragility theory presented in the paper; no empirical validation or quantified risk assessment included.
Artificial intelligence functions as a 'hyper-crystallization' engine—by classifying, predicting, standardizing and optimizing it accelerates structural crystallization and may erode local judgment and generative adaptability.
Conceptual theory labeled 'AI fragility theory' developed in the paper; supported by argumentative reasoning rather than empirical testing.
When digital systems are reified into internal structural optimization and control, transformation efforts can intensify organizational rigidity and failure to adapt.
Theoretical/analytic argument contrasting two modes of digital transformation; no empirical estimates or dataset provided.
Structurally heavy firms with substantial material and institutional resources frequently experienced paralysis or collapse during the pandemic.
Qualitative claim grounded in the author's reading of pandemic outcomes; the paper does not report systematic data or case counts.
During the COVID-19 pandemic, firms with the most optimized structures were not necessarily the most adaptive under radical uncertainty.
Argument based on the COVID-19 pandemic presented as an empirical 'stress test' in the paper; no empirical sample, data, or statistical analysis provided.
AI may influence society broadly via ethical issues, economic inequality, and social adaptation challenges.
Paper lists ethics, economic inequality, and social adaptation as societal-level areas affected by AI (abstract). Presented as thematic concerns reviewed in the paper; no empirical estimates included in the provided text.
AI-driven automation is associated with job loss.
The paper lists automation and job loss among the areas it examines (abstract). The provided text frames job loss as a potential negative ramification but does not report primary empirical estimates or sample sizes.
LLMs heavily rely on simulations for designing algorithms, which is notorious for breaking when transferred to real hardware.
Paper's claim grounded in known transferability issues between simulation and hardware; no experimental quantification provided in the abstract.
LLM pitfalls worsen on Radio Access Network (RAN) use cases: they hallucinate Application Programming Interfaces (APIs) and mis-read specifications, which kills interoperability of RAN components at the first mistake.
Author assertion / observed behavior reported in the paper (qualitative examples implied); no formal experiment or sample size provided in the abstract.
Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i) synthesizing new features from standards or research papers into production code; (ii) conformance and interoperability testing; (iii) hardening against field anomalies and diverse deployment environments; (iv) data-driven optimization of network functionalities; (v) discovering and prototyping novel waveforms, functionalities, and capabilities for future standards; and (vi) securing the stack against vulnerabilities.
Author assertion in the paper (qualitative analysis / domain expertise). No empirical sample size or quantitative study reported in the abstract.
Stochastic Tax can remain positive even when Agentic Technical Debt is minimized.
Theoretical claim in the paper's model and discussion: even with minimized debt (stock), the model predicts a nonzero recurring operating burden from stochastic agents; illustrated via examples and an accounts-payable simulation.
Stochastic Tax is a recurring flow of operating burden that arises when stochastic agents are used in business workflows.
Definition provided in the paper as part of the conceptual framework describing Stochastic Tax as a flow (recurring operating burden) associated with stochastic agents in workflows.
Humans are the bottleneck in building and improving AI. Both the models and the agents that wrap them are written, tuned, and corrected by people.
Statement/argument in the paper's introduction framing motivation (conceptual observation about current practice). No experimental data reported in the abstract to support this claim.
Slow AI induced delayed cognitive conflict; humans hesitated (61.1% accuracy).
Reported experimental result for Slow/Accurate AI condition showing human accuracy of 61.1%, interpreted as hesitation/delayed conflict.