Evidence (16496 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 |
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.
Another constraint is a chosen dependence on infrastructure vendors now caught in geopolitical restriction.
Paper argues Africa's dependence on external infrastructure vendors exposes it to geopolitical restrictions (policy/market analysis).
One constraint is the gating of frontier models by their developers, which no African decision can open.
Paper notes developer-controlled access policies (policy analysis) and concludes African decisions cannot override gating by model developers.
AI-enabled fraud is already mounting against African mobile-money systems, the part of the digital economy the continent leads.
Paper reports observed increases in AI-enabled fraud targeting African mobile-money systems (trend observation / incident reports referenced).
Africa faces an operational deficit across three axes — skilled people, compute and electrical power, and investment — each measured against current figures.
Paper presents capacity measurements/figures on human capital, compute/electrical infrastructure, and investment to characterize deficits (cross-sectional capacity assessment).
For now Africa cannot obtain the most capable frontier models.
Paper documents access restrictions and absence of African actors from the controlled-access perimeter (policy/case evidence).
Africa cannot yet operate frontier models.
Paper claims operational incapacity, measured against current figures for skills, compute/power, and investment (descriptive / capacity assessment).
The African continent does not build frontier models.
Paper asserts lack of frontier-model development capacity in Africa and compares current figures (descriptive / capacity assessment).
Frontier language models have become a decisive instrument of cyber operations, and that instrument is built, owned, and rationed within a small circle from which Africa is absent.
Synthesis argument based on the two events above plus documentation in the paper of Africa's absence across capacity/access axes (case synthesis / policy analysis).
In 2026 the most capable cyber-relevant model was placed under a controlled-access program limited to a vetted set of United States technology firms, allied governments, and European standards bodies, and that perimeter included no African government, operator, or university.
Paper cites a single 2026 access-control decision (policy/event report) restricting the most capable cyber-relevant model to a vetted group of Western firms and governments and excluding African institutions.
In 2025 a large language model executed the great majority of a state-aligned cyber-espionage campaign on its own, with human operators intervening at only a few decision points.
Paper reports a single 2025 incident (case report / event analysis) in which an LLM conducted most steps of a state-aligned cyber-espionage campaign with limited human intervention.
The main bottleneck is not only data scarcity, but non-cumulative data caused by high collection costs, data silos, and inconsistent evaluation.
Analytical claim identifying barriers to progress; grounded in authors' assessment and standards-work perspective rather than reported empirical measurement.
Current machine learning models commonly require large and well-annotated datasets, and the annotation process often becomes a bottleneck with increased complexity leading to higher chances of human errors.
Background statement in the paper summarizing common knowledge and prior literature about dataset requirements and annotation challenges.
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).
The use of chatbots in prediction tasks is currently limited to conceptual studies (i.e., there are few empirical chatbot-based prediction studies).
Observation from the literature review of 111 studies indicating chatbot usage in forecasting/nowcasting is largely conceptual rather than empirical.
Data availability remains a significant concern for forecasting and nowcasting applications using AI.
Finding and synthesis reported in the literature review (111 studies) highlighting data availability as an important limitation.
Expertise moderated the effect of LLM guidance: novices exhibited passive AI reliance.
Stratified analyses by participant expertise level using behavioral and eye-tracking measures indicating novices shifted attention to the AI/chat and exhibited more passive acceptance of guidance.
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).
Financial LLMs face regulatory compliance violations, fraud facilitation, and systemic trust erosion that require targeted evaluation.
Paper's risk analysis listing finance-specific threats (regulatory compliance violations, facilitation of fraud, systemic trust erosion). This is a conceptual/risk framing rather than reported empirical incidence rates in the provided summary.
Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks.
Authors' comparative assertion in paper (conceptual analysis arguing gap between general LLM safety benchmarks and finance-specific threats). No numeric evaluation reported in the provided summary.
Investment is being directed toward AI deployment when achieving productivity gains requires prior development of convergence capacity (C), leading to a misallocation of investment.
Theoretical reasoning within the paper: conceptual argument that deployment-focused spending misses prerequisite cognitive capacity (C).
Prevailing production-function frameworks encounter a structural boundary because they treat AI as a separable factor of production without modeling the cognitive mediation through which AI generates productive value.
Theoretical / conceptual argument presented in the paper (derivation and critique of existing production-function approaches).
Massive AI investment has failed to generate commensurate productivity gains (the "AI productivity paradox").
Stated as the motivating empirical paradox in the paper; presented as an observed phenomenon motivating the theoretical argument (no specific dataset or numeric evidence provided in the abstract).
AI development significantly reduces the share of low-educated labor: for each one-unit increase in AI development, the share of low-educated labor decreases by 0.007 units.
Empirical analysis using firm-level AI development indicators constructed via text analysis and machine learning on Chinese A-share listed firms in Shanghai and Shenzhen from 2014–2024; reported regression coefficient of −0.007 for low-educated labor share per one-unit AI increase.
Existing validation methodologies focus primarily on predictive accuracy and therefore provide limited insight into the quality of the underlying decision process.
Literature/methodology critique in the paper pointing to focus on predictive accuracy as the main existing validation approach.
Agentic artificial intelligence systems introduce a new class of model risk.
Conceptual/theoretical argument presented in the paper contrasting agentic systems with traditional predictive models.
The inhibitory effect of computing power deployment on corporate financialization spills over from the host city to surrounding cities.
Spatial Durbin model estimation showing negative effects on financialization in neighboring cities around NSC locations.
The reduction in corporate financialization following computing power deployment is concentrated in speculative financial assets rather than precautionary financial assets.
Subsample/asset-type analysis distinguishing speculative vs. precautionary financial asset holdings in firm balance sheets and estimating differential effects.
The inhibitory effect on financialization is more pronounced for firms with low analyst coverage.
Heterogeneity analysis splitting sample by analyst coverage and estimating differential DiD effects.
The inhibitory effect of computing power deployment on corporate financialization is stronger in computing-intensive industries.
Heterogeneity analysis comparing treatment effects across industries with different computing intensity using the staggered DiD setup.
Computing power deployment significantly reduces corporate financialization levels by approximately 1.1 percentage points.
Empirical analysis on Chinese A-share listed companies (2012–2023) exploiting staggered establishment of National Supercomputing Centers (NSCs) as quasi-natural experiments and estimated using a staggered difference-in-differences model.
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).
Scaling per-user LLM profiling to a live, millisecond-latency dispatcher faces three constraints: logs exceed any LLM's context window by orders of magnitude; most users are long-tail, with too few interactions for per-user profiling; and surface-fluent profiles do not necessarily improve downstream prediction utility.
Problem motivation and observational claims stated in the paper describing practical constraints; empirical quantification of these constraints is not provided in the abstract.
There are barriers and challenges that the labor force faces in meeting new skill requirements.
Review conclusion noting barriers and challenges reported in the empirical literature (types of barriers not enumerated in the excerpt; no measures or prevalence reported).
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.
Epistemic recursion (AI-generated content consumed by agents to produce further content) progressively detaches web knowledge from human ground truth.
Analytical claim in paper identifying a systemic risk (self-referential loop); presented as conceptual analysis without empirical quantification in provided text.
The web resists agents through blanket blocking, CAPTCHA-based exclusion, and economic models that treat agent access as extraction rather than legitimate interaction.
Descriptive claim in paper listing common practices (blocking, CAPTCHAs, economic treatment); based on observed behaviors but no systematic empirical study provided in excerpt.
The rapid emergence of AI agents as intermediaries between humans and web content invalidates the web's human-first assumption.
Paper's conceptual claim based on observed/assumed rise of AI agents acting as intermediaries; no quantitative data or sample presented in provided text.
In the early stages of AI development, AI adoption may temporarily increase corporate carbon emissions due to high energy consumption in computing and deployment.
TWFE empirical results and descriptive discussion in the paper attributing the early-stage positive effect to high energy use in AI computing/deployment.
The root causes of these problems include the disruption of labor relations boundaries by the transformation of the means of production, the exclusion of implicit data labor from distribution rules, the concentration of capital driven by high industry barriers, and social structural constraints on technological dissemination.
Synthesis and causal argumentation grounded in Marx's theory of reproduction; conceptual reasoning rather than empirical testing.
In the consumption phase, high costs lead to service stratification, making it difficult for technological dividends to benefit the general public.
Theoretical/qualitative argument about cost barriers and unequal access to AI-enabled services; no empirical evidence or sample sizes reported.
In the exchange phase, high barriers to entry for technology and capital foster market monopolies.
Analytical claim based on structural characteristics of AI/embodied intelligence industries; no empirical sample or quantitative measures provided in the paper.
In the distribution phase, behavioral data unconsciously generated by workers drives algorithmic iteration yet remains excluded from the distribution system, resulting in hidden data exploitation.
Theoretical argument that worker-generated behavioral data fuels algorithmic development but is not accounted for in value distribution; no empirical data or sample reported.
In the production stage, workers are alienated into becoming data producers.
Conceptual claim based on Marxian analysis of labor and data extraction; no empirical sample or quantitative evidence presented.
In the production stage, workers are disciplined by algorithms.
Theoretical/qualitative argument in the paper describing algorithmic management and control; no empirical measures or sample provided.