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 |
The GPTs exposure scores have temporal, geographic, and ontological limitations that do not always travel with the scores as they are reused.
Authors' methodological critique discussing the limits named by Eloundou et al. (2023) and how those limits are often ignored when scores are repurposed.
AI serves as a financial risk factor for platform-based illustrators by increasing price pressures, enhancing market transparency, and increasing exposure to revenue volatility.
Author interpretation based on the statistical finding of a significant association between AI and income plus theoretical/accounting discussion; no additional quantified causal mechanism presented in the reported results.
Under individual selection, self-interested prompts dominate, causing populations to collapse into collective defection.
Simulation experiments with individual-level selection/transmission showing emergence and dominance of self-interested prompts and subsequent decline into collective defection.
As frontier training shifts toward individual rewards for verifiable tasks (e.g., mathematics and coding), this outcome-based focus may further undermine cooperation in multi-agent settings.
Argumentative/prognostic claim in the paper's motivation; not an empirical result from the study but framed as a risk informed by the literature and authors' reasoning.
Current approaches to instill prosociality in LLM agents often rely on humans specifying desired behaviors at the individual level, which does not guarantee cooperation within LLM populations.
Background statement in paper; conceptual critique of human-specified, individual-level reward/behavior specification as commonly used in LLM alignment and fine-tuning literature (no new empirical test reported in this study).
In the Sakha Republic (Yakutia), factors shaping last-mile costs and platform dependence include territorial scale, low population density, concentration of demand in Yakutsk, seasonal navigation and northern supply constraints.
Regional empirical analysis focused on the Sakha Republic (Yakutia) considering territorial scale, population density, demand concentration, seasonal navigation and supply chains as presented in the paper.
Common mechanisms through which food delivery platforms form market power include network effects, economies of scale and scope, data control, algorithmic management and ecosystem lock-in.
Comparative case analysis of major Chinese platforms (Meituan, Ele.me/Taobao Instant Commerce, JD Waimai), supported by statistical data review and academic literature on platform markets.
Traditional indicators of market share, price and commission do not sufficiently reflect the influence of platforms that control data, algorithms, access rules, ratings and couriers’ work practices.
Conceptual argument and comparative case analysis drawing on the study's qualitative review of platform governance (Meituan, Ele.me/Taobao Instant Commerce, JD Waimai), supplemented by literature and regulatory/legal acts analysis.
Lagged AI-related R&D activity is negatively associated with subsequent participation in education and training (one-year lag).
Lagged (one-year) structural panel models (two-way fixed effects) on 18 European countries, 2017–2024. One-year lag coefficients reported as −1.2310 (ages 18-74), −0.9392 (ages 45-54), and −0.8911 (ages 50-74).
Average lifelong-learning participation declines with age: 20.09% among adults aged 18-74, 14.82% among those aged 45-54, and 9.34% among those aged 50-74.
Descriptive statistics computed from the study panel of 18 European countries (2017–2024).
Critical post-work thought posits that not only certain jobs, but also jobs in general, are disappearing.
Statement summarizing the position of a body of theoretical work ('critical post-work thought') as described by the author; this is a characterization of a viewpoint rather than an empirical finding.
The majority of extant studies focus exclusively on the 'technical' aspect of new technologies replacing labour, thereby ignoring their social dimension and consequently falling into the trap of technology fetishism.
Claim about the literature based on the paper's review and critique of existing studies; no citation counts or systematic review methodology described in the excerpt.
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).
Neither the task design nor the retrieval approach of Finance Agent v2 addresses the distinct challenges of IPO due diligence.
Author argument comparing periodic reporting tasks to IPO due-diligence requirements, noting Finance Agent v2's task and retrieval design do not address IPO-specific complexities.
The Finance Agent v2 agentic harness relies on naive, unenriched chunk retrieval.
Author statement describing the retrieval approach used by Finance Agent v2 as naive chunk retrieval.
Policy asymmetries, digital literacy gaps, and regional inequalities deepen digital divides and impede inclusive development.
Policy analysis and comparative case studies documenting how policy differences, literacy, and regional disparities affect digital inclusion; China used as a focal example. No quantitative sample sizes or causal estimates given in summary.
Agriculture remains digitally marginalized due to infrastructural and institutional deficits.
Comparative case studies and sectoral data showing lower digital adoption in agriculture; qualitative policy analysis identifies infrastructure and institutional shortcomings. No sample size or quantified adoption metrics provided in summary.
Fertility is strongly countercyclical and almost perfectly negatively correlated with hours worked in the model, placing household time allocation at the center of the mechanism.
Model-simulated correlations and business-cycle dynamics showing fertility and hours worked time series and their correlation.
The longevity shock compresses asset returns and lowers the real interest rate, and generates hump-shaped, persistent dynamics.
Numerical impulse-response dynamics from the overlapping-generations model following a longevity shock; reported time paths for returns and the real interest rate.
The essay introduces the concept of a 'vouching gap' to describe a growing divide between students who graduate with credible advocates willing to stake their reputations on their behalf and those who do not.
Conceptual contribution defined in the essay and motivated by social capital theory and mentoring research; no empirical quantification or sample provided.
Automation of student work and candidate screening will widen existing inequalities between students.
Theoretical claim in the essay linking AI-driven automation to differential outcomes across students, motivated by social capital and mentoring literature; no empirical data or sample reported.
This automation threatens to hollow out the value of a university degree.
Argument presented in the essay, grounded in social capital theory and mentoring research; no empirical test or sample size reported.
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.
The demand premium enjoyed by workers with strong human capital declines in more AI-exposed categories.
Heterogeneity analysis within the Upwork dataset: workers characterized by stronger human-capital signals (via profile embeddings) show a reduced demand premium in job categories more exposed to AI following ChatGPT; identified using difference-in-differences around ChatGPT release. (Sample size not reported in abstract.)
In more AI-exposed job categories, the importance of human capital information in predicting labor demand declines.
Empirical analysis of Upwork platform data using high-dimensional text embeddings to represent worker profiles; the paper computes the predictive importance of human-capital-related profile information and uses a difference-in-differences design around the release of ChatGPT to estimate changes by AI exposure of job categories. (Sample size not reported in abstract.)
The most significant challenge for the AI ecosystem is not creating demand but the capacity of supporting infrastructure to scale alongside rapidly growing computational requirements.
Theoretical scaling arguments (scaling laws) and empirical/secondary-source discussion in the article, drawing on energy and infrastructure literature (IEA, Fed, Brookings) and observed compute demand trends.
Energy availability, grid expansion, and infrastructure financing constitute the principal unresolved risks and may represent the primary bottleneck to future AI growth.
Argument based on International Energy Agency reports, analyses of energy and infrastructure constraints cited in the article, and supporting literature on scaling/computational requirements (review and secondary data).
One of the most consequential layers of American law remains largely absent from existing machine-readable corpora: local ordinances.
Paper's literature/field observation asserting gap in existing corpora (no systematic comparison details provided in abstract).
Algorithmic management introduces significant challenges related to fairness, transparency, and worker dignity.
Synthesis of qualitative interview findings (16 gig workers and 21 stakeholders) interpreted through a social justice framework.
Algorithmic systems are not structured to reward additional labour with proportionate pay.
Worker and stakeholder interviews (N=37) reporting that increased labour/intensity does not yield proportionate compensation under platform algorithms.
Algorithmic systems produce inequitable outcomes for gig workers.
Interview data (16 workers, 21 stakeholders) reporting examples and perceptions of unequal treatment and distributional harms arising from algorithmic rules.
Algorithmic systems are opaque by design (lack transparency in allocation, monitoring, and evaluation).
Qualitative evidence from interviews with 16 gig workers and 21 stakeholders describing opaque/black-box practices of algorithmic management.
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.
A 'critical transmission path' can occur in which AI-induced productivity gains are weakly transmitted to households and may generate absorption tension.
Conceptual framework / theoretical argument in the review (no empirical sample reported).
Productivity gains from AI do not automatically translate into broadly distributed welfare or into output fully absorbed by market demand.
Conceptual review / theoretical argument and literature synthesis presented in the paper (no empirical sample reported).
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).
Automation AI raises program closures and reduces new program openings.
Chapter 3: program-supply analysis (program closures and openings) using U.S. higher-education program data 2010–2022 with IV identification (lagged CS research intensity); reported associations for automation AI exposure.
Automation AI is associated with a greater likelihood of not pursuing postgraduate studies and with higher rates of field-switching after graduation.
Chapter 3: individual-level analyses of post-graduation decisions (postgraduate enrollment and field-switching) using U.S. data 2010–2022 and IV with lagged CS research intensity.
In those European countries, demand for Social skills declines in AI-exposed occupations.
Chapter 2: same 75 million job postings dataset, multilingual skill extraction, and IV approach with lagged CS research intensity to identify effects on skill demand between 2018–2023.
Automation AI harms low-skilled workers.
Chapter 1: heterogeneous effects across skill groups estimated using occupational exposure measures and IV approach (lagged CS research intensity); results reported by skill group (low-skilled vs high-skilled).
Automation AI depresses wages in the U.S.
Chapter 1: same occupational exposure measures and IV strategy (lagged computer-science research intensity) applied to U.S. wage data, 2015–2022.
Early detection of disruptive technologies is difficult because disruptive impact is uncertain and often becomes visible only years after invention.
Conceptual background statement in the paper; literature-motivated assertion (no empirical sample or experiment reported for this claim).
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.
Mainstream multi-agent hierarchical decision architectures often rely on coarse-grained instructions that underspecify analytical procedures, leading to degraded inference quality and reduced transparency.
Framing/critique stated in the paper about prior approaches; no empirical comparison statistics provided in the excerpt to quantify the extent of degradation.