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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Despite rapid progress, a key problem remains: none of these systems can build complex 3D assemblies with moving parts. For example, no existing system can build a piston, a pendulum, or even a pair of scissors.
Negative capability claim based on the authors' survey of prior work (asserted limitation); no systematic benchmark or exhaustive evaluation numbers provided in the excerpt.
Effective clarification remains challenging in software engineering tasks as not all missing information is equally valuable, and questions must target information users can realistically provide.
Analytic claim supported by the paper's empirical study of clarification in real software engineering tasks (methods mentioned: quantifying types of information affecting task success and simulated-user question-answering; no sample size given in the abstract).
Large language models remain confined to linguistic simulation rather than grounded understanding.
Conceptual assertion in the paper arguing limits of current models; no empirical tests or measurements reported.
Fluency is not reliability: without structures that stabilise both human and model reasoning, AI cannot be trusted or governed where it matters most.
Central thesis/claim of the paper; normative argument synthesising the paper's observations and proposals rather than an empirically tested finding provided here.
Humans often mistake fluency for reliability: when a model responds smoothly, users tend to trust it, even when both model and user are drifting together.
Behavioral/psychological assertion in the paper referencing human interaction patterns with fluent outputs; no experimental data or sample size reported in this paper excerpt.
LLMs produce fluent outputs even when their internal reasoning has drifted; a confident answer can conceal uncertainty, speculation, or inconsistency, and small changes in phrasing can lead to different conclusions.
Conceptual/observational claim presented in the paper; no original empirical test or sample size reported here.
The opacity, fluency, and low-friction interaction patterns of LLMs obscure the boundary between human and machine contribution, leading users to infer competence from outputs rather than from the processes that generate them.
Theoretical argument grounded in prior literature on automation bias and cognitive offloading; presented as explanatory mechanism in the paper rather than an empirically tested causal estimate.
The paper introduces the 'LLM fallacy,' a cognitive attribution error in which individuals misinterpret LLM-assisted outputs as evidence of their own independent competence, producing a systematic divergence between perceived and actual capability.
Conceptual/theoretical claim and formal definition offered in the paper; no empirical validation reported in the abstract.
Most Sub-Saharan African states still lack the institutional frameworks needed to turn these innovations into sustainable development.
Comparative policy analysis stated in the paper; no quantitative sample size or formal survey data reported in the excerpt.
Efficiency (e.g., minimizing time and cost with AI-only planning) does not equal effectiveness: optimizing for efficiency can erode team cognition and reduce decision quality.
Synthesis of experimental quantitative results (time/cost vs. risk capture and rework) and qualitative assessment indicating that AI-driven efficiency can come at the expense of risk awareness and planning robustness.
Human-only planning incurs substantial overhead.
Same controlled experiment reporting that human-only planning produced higher time and cost overheads relative to AI-assisted approaches.
AI-only planning increases rework due to unstated assumptions.
Experiment measured rework rates and accompanying qualitative analysis attributing increased rework in the AI-only condition to unstated assumptions made by algorithmic planning.
AI-only planning significantly degrades risk capture rates.
Same controlled three-condition experiment on a live client deliverable; paper reports measures/qualitative indicators of risk capture rates and states degradation for AI-only condition.
Two wrong-entity mutations escaped all consumer-contributed layers; only disambiguation and confirmation mechanisms intercept this class.
Empirical observation during the 25 scenario trials spanning seven failure families in the deployed multi-tenant evaluation; the paper reports two instances of wrong-entity mutations that were not blocked by consumer-contributed protections.
The unconstrained AI configuration completed only 17 of 25 tasks.
Same evaluation described above: deployed multi-tenant enterprise application, 25 scenario trials comparing unconstrained AI (safety layers disabled) against bounded autonomy and manual operation.
Infrastructure constraints, particularly in developing countries, limit AI adoption in auditing.
Thematic analysis of reviewed articles noting infrastructure limitations (e.g., ICT infrastructure) in developing-country contexts.
Limitations in auditor competencies (skills and training) hinder effective AI adoption in auditing.
Thematic findings across the sample of articles report auditor competency gaps as a challenge to AI implementation.
Ethical and data privacy concerns are persistent challenges to AI implementation in auditing.
Recurring theme in the reviewed literature identified via thematic analysis; papers cite ethics and privacy as obstacles.
Several challenges persist for AI adoption in auditing, including high technology investment costs.
Thematic analysis of barriers reported across the 15 articles highlighting cost as a recurrent challenge.
Conventional methods that use AI predictions as direct proxies for true labels can be inefficient or unreliable when the relationship between AI outputs and human labels is weak or misspecified.
The paper's motivation and critique of standard proxy-using approaches; asserted in the abstract as background rationale for the proposed method.
Human review remains necessary for maintainability and correct domain interpretation of generated scripts.
Qualitative finding from the mixed-method case study indicating limitations and the need for human oversight.
Validated test specifications accumulate faster than they are automated in many teams, limiting regression coverage and increasing manual work.
Observational claim stated in the paper as a motivating problem; likely based on industry experience and the Hacon case study context.
Existing AutoML methods partially alleviate this burden but remain limited to narrow aspects such as hyperparameter optimization and model selection within predefined search spaces, leaving the full development lifecycle largely dependent on human expertise.
Statement summarizing limitations of prior work (literature review/background in the paper).
Developing high-performing AI models remains a labor-intensive process that requires expert practitioners to iteratively design architectures, engineer representations, implement training pipelines and refine approaches through empirical evaluation.
Background statement in paper's introduction; general literature context rather than a specific empirical test within this paper.
Most AI tooling targets that fraction [the ~10% of the workday spent writing code].
Assertion made in the paper (abstract) as an observed mismatch between where AI tooling focuses and overall developer work activities.
Failure analysis reveals key obstacles (such as breakdowns in cross-artifact consistency) and improvement directions for agentic AI in high-stakes professional workflows.
Qualitative/quantitative failure analysis reported in abstract identifying obstacle categories (example given: cross-artifact consistency breakdowns).
Bankers rate 0% of GPT-5.4's outputs as client-ready.
Human ratings by bankers reported in abstract indicating none of the evaluated outputs from GPT-5.4 were judged client-ready.
Even the best-performing model (GPT-5.4) fails nearly half of the rubric criteria.
Evaluation results reported in abstract: model-level rubric pass/fail aggregated to show best model failure rate approaching ~50% of criteria.
Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows.
Author assertion in paper abstract arguing current benchmarks are insufficient; presented as motivation for developing BTB rather than empirically tested within the abstract.
Models fail to distinguish reliable predictions from unreliable ones, achieving only ≈20% accuracy regardless of their confidence or whether they judge outcomes as predictable without physical experimentation.
Analysis in the paper comparing model self-reported confidence / predictability judgments to actual accuracy across the 405 tasks; reports ≈20% accuracy irrespective of confidence/predictability judgments.
Human expert performance on the benchmark is approximately 20%.
Reported comparison between human experts and models on SciPredict tasks; the paper states human performance is ≈20% (evaluated on the benchmark tasks).
Model accuracies on SciPredict are 14-26%.
Empirical evaluation of multiple LLMs on the SciPredict benchmark (405 tasks); the paper reports aggregate model accuracy range 14–26%.
Regulatory and labor friction is scored per sector using actual compliance frameworks (Basel III, FDA AI guidance, HIPAA) and BLS union density data, and is applied as a haircut to base adoption rates via an S-curve ramp.
Paper description of friction scoring method referencing specific regulatory frameworks and BLS union density; applied in the model as a haircut and S-curve adoption ramp.
Restricting AI productivity gains to the labor-generated portion of each sector's gross value added reduces the naive addressable base by approximately 72 percent.
Bottom-up sectoral model described in the paper that applies labor share to gross value added across 21 NAICS industries; the paper explicitly states the labor-generated restriction reduces the naive addressable base by ~72%.
Environmental demands place an upper bound on the degree of heterogeneity required in a distributed production system.
Theoretical claim derived from the Distributed Production System framework and discussed in the paper; supported by conceptual argument and model constraints rather than empirical data; no sample size reported.
The study's findings are subject to design limitations including an AM/PM session confound, differential attrition, and LLM grading sensitivity to document length.
Authors' reported limitations section citing specific threats to internal validity and measurement (session timing confound, differential attrition across conditions, and grading biases of the LLM used to evaluate documents).
The behavioral scaffolding intervention was associated with substantially lower document production.
Same field experiment (N=388); the behavioral scaffolding required joint AI use within pairs and was compared to unstructured use, with reported reductions in document production in the behavioral condition.
A behavioral scaffolding intervention (a structured protocol requiring joint AI use within pairs) was associated with lower document quality relative to unstructured use.
Field experiment with 388 employees at a Fortune 500 retailer; random/experimental assignment to scaffolding conditions while all participants had access to the same AI tool; comparison reported between behavioral scaffolding condition and unstructured use.
LLMs lag behind humans in sustaining heterogeneity when divergence is rewarded.
Empirical comparison from the experiment showing humans are better able than LLMs to maintain diverse actions when the payoff structure rewards divergence; stated qualitatively in the abstract without numeric effect sizes or sample sizes.
Latent-outcome estimation faces a within-study noncomparability challenge: different indicators within a study may have different and possibly nonlinear relationships with the same latent outcome, making them not directly comparable.
Theoretical exposition in the paper describing heterogenous indicator-to-latent mappings and potential nonlinearity; illustrated with examples (no empirical sample size).
Latent-outcome estimation faces a cross-study noncomparability challenge: different measurement systems across studies may cause estimators to target different empirical quantities even when the underlying latent treatment effect is the same.
Conceptual and theoretical argumentation in the paper describing identification issues across studies due to differing measurement systems; supported by examples and discussion (no empirical sample size).
Lower survival rates among BDA adopters are driven by greater uncertainty in sales.
Paper states greater uncertainty in sales is an interrelated factor explaining lower survival for BDA adopters, based on empirical analysis of German start-ups.
Lower survival rates among BDA adopters are driven by higher operating costs.
Paper reports that higher operating costs are an interrelated factor explaining lower survival among BDA adopters, based on the same empirical sample of German start-ups.
Start-ups using BDA face lower survival rates.
Empirical comparison of BDA adopters versus non-adopters in a large sample of German start-ups (survival analysis implied by reported outcome).
Enterprise sales organizations are systematically hampered by what this paper terms 'Revenue Friction'—the accumulative productivity loss caused by fragmented, human-mediated data entry across disconnected CRM, ERP, and quoting systems.
Statement/definition presented in the paper excerpt. No empirical method, sample size, or quantitative evidence reported in the provided text.
Some of this reduced price is related to reduced input cost contributions, in particular labor and materials costs.
Decomposition/mediation analysis reported in the paper attributing part of the observed price reductions to declines in input cost contributions (labor and materials); exact methods, sample size, and statistical estimates not provided in the excerpt.
AI intensity is associated with lower prices charged to purchasers.
Empirical analysis reported in the paper linking measures of AI intensity to observed output prices (details of data sources, sample size, and specific methods not provided in the excerpt).
Foundation-model usage can increase compute-related emissions.
Conceptual/environmental concern highlighted in the paper about the carbon footprint of heavy model use and persistent storage; no quantified emissions analysis or lifecycle assessment presented.
These systems can cause skill atrophy.
Theoretical risk articulated in the paper that reliance on AI assistance may degrade human skills over time; no longitudinal skill-measurement or experimental evidence provided.
The same foundation-model systems can also intensify surveillance.
Cautionary claim in the paper noting the surveillance risk of durable, queryable traces and integrated tooling; presented as a conceptual risk rather than empirically measured increase in surveillance.