Evidence (4892 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).
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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 |
Org Design
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A conceptual model of the AI productivity paradox is proposed to explain underlying causes of efficiency loss and formalize the role of micro-mechanisms in slowing macroeconomic growth.
Theoretical model development drawing on empirical BLS trend analysis and micro-level case evidence; presented as an explanatory framework in the paper.
Key micro-mechanisms underlying the labor productivity paradox under AI are: task expansion, blurring of boundaries between work and non-work time, intensification of multitasking, and accumulation of 'AI debt' by organizations.
Identification and systematization based on theoretical development and analysis of corporate cases and empirical reports.
The productivity gap is attributable to organizational and behavioral factors.
Theoretical analysis, generalization, and synthesis of corporate cases and empirical reports linking observed micro-behaviors to macro-level productivity outcomes.
There is a gap between anticipated macroeconomic efficiency gains (aggregate labor productivity) and observed micro-level outcomes following AI adoption.
Comparison of aggregate productivity trends (BLS series/AAPC calculations) with micro-level evidence drawn from corporate case studies and empirical reports documenting localized impacts of AI.
Free overrides also cut sales by 1.19%.
Randomized field experiment comparing free-overrides arm to control; effect reported as 1.19% reduction in sales.
Free overrides reduce inventory by 1.95%.
Randomized field experiment comparing free-overrides arm to control; effect reported as 1.95% reduction in inventory.
Aligning AI systems with human teams remains a major challenge to realizing AI's full potential in organizations.
Authors' statement in abstract framing the motivation for the study; supported by literature cited in full paper (abstract asserts this as a core challenge).
Unconstrained agents introduce security risks, erode codebase scalability, and make human review increasingly costly.
Authors' argumentative claim supported by the controlled experiment showing lower recall (more missed backdoors) in unconstrained condition and discussion of costs and scalability.
Computational theorising, synthetic task simulations, real LLM agent traces, and robustness analyses show that human-imitation forms often underperform when they add lossy handoffs, correlated deliberation, and verification burdens.
Empirical and simulation-based methods listed in the paper (computational theorising, synthetic task simulations, analysis of real LLM agent traces, robustness checks). The excerpt does not report sample sizes, numeric effect sizes, or statistical tests.
GAI adoption produces a suppression (negative) effect on SCR via increased upstream supplier concentration, indicating a trade-off between flexibility gains and coordination stability losses.
Mechanism analysis of the panel data showing upstream supplier concentration operates as a suppressor (negative mediator) in the relationship between GAI adoption and SCR.
Public discourse still focuses heavily on job losses while paying less attention to the opportunities that AI creates.
Author's observation/argument in the paper (qualitative commentary comparing public discourse emphasis).
The report identifies 'AI washing,' a practice in which companies mention AI as justification for what are really financially motivated layoffs.
Identification/term introduced in the paper based on examples or synthesis of corporate reporting and layoff cases (as described).
Roughly 92 million jobs might face displacement by 2030.
Projection synthesized from cited external reports (WEF/PwC/MGI/Gartner/IMF) as reported in the paper.
The identified gap is the price of non-credible oversight communication.
Theoretical argument and formal analysis linking the existence of the slab (gap) to non-credible communication/signaling constraints in the oversight interface; the paper interprets the gap as arising from inability to credibly communicate oversight-relevant information.
There is a slab (region) of avoidable harm: cases where the AI privately knows the proposed action is harmful and shutdown would help, yet a myopic human, trusting her prior, declines to oversee.
Analytic characterization of the gap between the team-optimal policy and the myopic human rule in the one-shot model; the paper identifies parameter-region (the 'slab') where the myopic rule fails to oversee while oversight would reduce harm.
The relational value of workplace AI companions remains underexplored.
Claim motivated by the authors' systematic interdisciplinary literature review (paper states relational value is underexplored); no numeric count of studies provided in excerpt.
The paper reframes humans not as passive users, but as core system components whose competencies, limitations, and adaptive capacities constrain the performance envelope of optimized AI systems.
Framing/interpretive claim derived from the paper's perspective and literature synthesis (conceptual; no empirical support provided in text).
Organizational structures, bias susceptibility, retraining constraints, and interface design co-determine system stability, error propagation, and optimization ceilings.
Conceptual claim based on synthesis of literature across organizational adoption and ML lifecycle management (no empirical tests or sample sizes reported).
Human interfaces define throughput limits in areas such as prompt engineering, data-stream curation, adjudication of model outputs, and the orchestration of hybrid automation workflows including robotics, scraping, and digitization.
Theoretical assertion supported by the paper's systems-oriented analysis and literature synthesis (no empirical measurement or sample size provided).
Despite accelerating advances in AI capabilities, human capital remains the enduring and dominant system constraint.
Argument and synthesis of emerging research across human-AI interaction, ML lifecycle management, organizational adoption, and adult learning theory (conceptual synthesis; no empirical sample size reported).
Experts in the study assign a 14% probability to 'rapid-progress' scenarios characterized by substantial GDP growth, declining labor force participation, and accelerating wealth inequality.
Result from the 2025 forecasting study of experts (69 economists + 52 AI experts), reporting a probability estimate (14%) for a named scenario with specified macroeconomic and labor-market features.
Barriers limiting full AI adoption in auditing include resistance to change, algorithm aversion, heuristics and biases, lack of transparency, expertise and training gaps, and technological complexity.
Systematic Literature Review (SLR) of 43 studies synthesizing reported inhibitors and challenges to AI uptake in auditing from empirical and conceptual papers.
GenAI adoption carries risks including overreliance on models, misalignment between model outputs and human needs, and uneven performance across tasks and contexts.
Reported adverse effects and risks identified in the reviewed literature (task-level experiments and applied studies summarized by the paper).
Prior research has largely focused on established firms, with limited attention to startups.
Literature review / gap statement in the paper asserting an imbalance in existing research coverage (no quantitative meta-analysis reported).
Existing frameworks address AI-assisted development maturity or the productivity-reliability tension but offer no mechanism for calibrating human oversight intensity to regulatory impact.
Comparative framework analysis and literature review reported in the paper (claims about gaps in existing frameworks).
The adoption of agentic AI coding systems -- where autonomous agents generate, review, test, and deploy code with minimal human intervention -- creates a governance challenge in regulated industries.
Argumentation in the paper framing the problem; conceptual analysis of agentic AI capabilities and regulatory constraints (literature/contextual reasoning rather than empirical data).
Manual preparation of engineering designs for thousands of wells constitutes an enormous administrative burden and is prone to inconsistencies.
Introductory/background statement in the paper describing the pre-existing manual workflow burden; no numerical study reported for this specific statement.
Adoption remains fragmented and rarely aligned with transfer workstreams.
Findings reported from the same set of 12 semi-structured expert interviews and inductive qualitative analysis.
HCAI reduces AI-related ethical risks in firms by aligning AI design and implementation with stakeholders' diverse expectations.
Theoretical/conceptual argument integrating situated AI theory with socio-technical systems theory presented in the paper; authors posit HCAI as a strategy that lowers ethical risks through stakeholder alignment.
Executive shareholding strengthens the risk-reducing effect of HCAI on firm idiosyncratic risk.
Empirical moderation analysis using the multi-source panel dataset of Chinese listed firms (2015–2023); authors report that higher executive shareholding amplifies the negative association between HCAI and IR.
Digitalisation strengthens the risk-reducing effect of HCAI on firm idiosyncratic risk.
Empirical moderation analysis on the same multi-source panel of Chinese listed firms (2015–2023); authors report a positive moderating effect of digitalisation on the HCAI–IR relationship (i.e., greater digitalisation amplifies HCAI's ability to reduce IR).
Human-centric AI (HCAI) is associated with lower firm idiosyncratic risk (IR).
Empirical analysis using a multi-source panel dataset of Chinese listed firms from 2015 to 2023; authors report a negative association between HCAI and firm-level idiosyncratic stock volatility (IR).
Sentiment framing is unstable: whether a brand is framed positively or negatively flips about 6.7 times more often than whether it is mentioned at all.
Comparison of occurrence variability versus sentiment-flip frequency measured in the Ranqo dataset of AI responses; paper reports sentiment flips occur ~6.7× more often than mention-presence flips.
Adding relevant collaborators can lower performance when teams lack structure to coordinate their contributions.
Empirical comparisons across experimental sessions in the Collaborative Gym / DiscoveryBench setup; result reported across the study (1,482 sessions).
A wide range of empirical evidence shows that humans avoid complexity, delegate judgement, and prefer simplified social worlds.
Asserted as empirical background; paper references a broad empirical literature but does not report primary data, sample sizes, or specific studies in the provided text.
Most organizations (59%) approach AI implementation through a technology-first lens, layering intelligent systems onto legacy processes rather than intentionally redesigning how humans and machines collaborate.
Reported descriptive statistic from Deloitte's 2026 Global Human Capital Trends survey of over 3,000 business leaders across 15 countries (paper cites 59% figure).
Only 14% of organizational leaders report proficiency in designing effective human-machine interactions.
Reported descriptive statistic from the same Deloitte 2026 Global Human Capital Trends survey of over 3,000 business leaders across 15 countries.
At the macro level, values-driven withdrawal from AI use has the potential to narrow the diversity of visible applications, amplifying risk-focused narratives and reinforcing perceptions of harm in public discourse.
Theoretical extension of the guarded engagement loop to societal/public discourse dynamics; based on synthesis of social amplification of risk literature rather than empirical measurement in the abstract.
These constrained (guarded) interactions can lower output quality and increase the likelihood of visible errors, which may further erode trust and reinforce cautious engagement.
Theoretical causal chain posited by the authors within their conceptual framework; supported by literature-based argumentation rather than reported empirical results in the abstract.
At the micro level, elevated risk salience related to privacy, safety, or ethical concerns may lead users to adopt guarded interaction strategies characterized by reduced contextual disclosure and limited iteration.
Theoretical proposition within the paper's guarded engagement loop framework, drawing on prior research in privacy calculus and algorithm aversion; no specific empirical data reported in the abstract.
Generative AI adoption is often framed primarily as a question of learning technical skills, and this perspective overlooks a defining feature of large language models (LLMs): their output quality depends heavily on how users engage with them.
Conceptual argument presented in the paper's introduction/abstract; literature synthesis framing adoption debates (no empirical sample or experimental method reported in the abstract).
AI augmentation breaks the accounting link between labor time and productive contribution, yet firms continue to evaluate talent through time-based overhead bundles.
Theoretical argument and conceptual framing presented in the paper (no empirical sample reported for this specific proposition).
The translation of AI's potential into operational capability within government audit contexts requires navigating complex technical, institutional, legal, and ethical challenges that differ substantially from private sector environments.
Paper's conceptual analysis and comparative argument (paper contrasts government audit contexts with private sector origins of many AI tools); no quantitative empirical evidence or sample size reported.
Excluding individual features based on their manipulability alone is generally suboptimal.
Theoretical analysis and formal study of strategic classification through feature selection and its interaction with ridge regularization presented in the paper (main finding stated in abstract).
These growing interconnections create new vulnerabilities that can spread across public service networks.
Systems-theory informed synthesis from the review of empirical literature; paper's integrative conceptual framework drawing on reviewed studies.
In multi-brand GEO competition there is a social dilemma: when all brands adopt the same optimization strategy, individual payoff falls from +0.802 to +0.007 in our payoff proxy, and non-participating brands receive zero recommendations in our tests.
Experimental multi-brand GEO competition scenario reported in the paper with reported payoff proxy values (+0.802 to +0.007) and an observation that non-participating brands received zero recommendations under the tested conditions.
Authority-style marketing language, including fabricated clinical-evidence claims, breaks the Conditional Monopoly at a Bias Surplus Value equal to +0.17 rating points, with each model responding differently.
Experimental manipulation of marketing language (authority-style / fabricated clinical claims) and measurement of the Bias Surplus Value (reported threshold +0.17 rating points); observed heterogeneous responses across the three tested LLMs.
The dominance of well-known brands disappears with less than a +0.1-star rating advantage for a competitor.
Experimental variation in competitor rating reported in the paper showing that providing a competitor a rating advantage smaller than +0.1 stars is sufficient to remove the incumbent's dominance.
GPU-accelerated deep learning exacerbates this problem, as nondeterministic floating-point reductions can produce drift in long backtests, challenging regulatory reproducibility and auditability expectations.
Argument in paper linking known GPU-nondeterminism (floating-point reduction nondeterminism) to practical issues in long financial backtests; no empirical backtest dataset size provided in the excerpt.
For thirty years, quantitative finance has paid a costly two-language tax: models researched in Python are rewritten in C++ for production, often introducing numerical discrepancies.
Statement in paper's introduction/abstract describing historical practice; no quantitative sample size or systematic study reported in the excerpt.