Evidence (2066 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Inequality
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These errors are diffusely embedded across many papers but especially pronounced in fields with rapid AI uptake.
Cross-field comparison within the audited dataset showing higher rates of non-existent references in fields identified as having rapid AI adoption.
We provide a conservative estimate of 146,932 hallucinated citations in 2025 alone.
Quantitative extrapolation/estimation from the audit of references in the dataset, producing an annualized (2025) conservative count.
We find a sharp rise in non-existent references following widespread LLM adoption.
Temporal analysis of the audited references comparing prevalence of non-existent (hallucinated) citations before and after the period of widespread LLM adoption across the 111M-reference dataset.
The results imply an urgency of early intervention in AI-driven economies to avoid extreme inequality and loss of redistribution options.
Synthesis and policy discussion in the paper based on the finite-time singularity, super-exponential divergence of wealth ratios, and the policy-irreversibility result.
Under mild conditions, the system exhibits a finite-time singularity where AI capability, AI capital, and financial capital diverge.
Analytical dynamical-systems analysis and proofs in the paper demonstrating finite-time blow-up (singularity) of A (AI capability), K_a (AI capital), and K_f (financial capital) for parameter ranges satisfying the stated mild conditions.
Post-hoc SHAP attribution reveals that complaint recurrence and neighborhood-level statistics are stronger predictors of actionable violations than raw complaint volume.
Empirical claim based on post-hoc SHAP feature-attribution analysis applied to the paper's models; the excerpt reports a relative feature importance finding but provides no numeric effect sizes or sample counts.
We formalize each domain as a Markov Decision Process (MDP) in which equitable classification coverage is a first-class reward objective.
Methodological specification in the paper asserting each operational domain was modeled as an MDP with equity-aware reward structure. No further empirical details in the excerpt.
The proposed technique is designed to maximize throughput, minimize misclassification cost, and actively narrow historical equity gaps in service delivery.
Stated design objectives of the RL approach in the paper. No quantified outcomes or evaluation reported in the provided text.
Rather than replacing human classifiers, our agents act as intelligent intake routers that learn to assign incoming complaints to action categories: escalate, batch, defer, inspect now.
Descriptive claim of agent behavior and intended design; asserts agents perform routing decisions into four action categories. No empirical performance numbers provided in the excerpt.
We develop an equity-centered reinforcement learning (RL) framework that augments call classification capacity across six New York City Department of Buildings operational domains (boiler safety, crane and derrick oversight, heat and hot water, housing complaint triage, scaffold safety, and Natural Area District protection).
Methodological development described in the paper; claimed application domain spans six named DOB operational areas. No evaluation metrics or sample sizes provided in the excerpt.
Long-term disparities can vanish under simple investment policies that achieve a low Price of Fairness.
Theoretical analysis of a sequential selection model showing dynamics under 'investment' policies lead to convergence of group distributions and low PoF.
Under conditions of strong productivity growth, high-skill complementarity, low obsolescence, and broad ownership, automation raises output, capital, and consumption.
Comparative-static results from the heterogeneous-agent general-equilibrium model calibrated/analyzed under parameter configurations (strong productivity growth, high-skill complementarity, low obsolescence, broad ownership).
Automation raises productivity.
Analytical results from a theoretical framework: a static benchmark and a stationary heterogeneous-agent general equilibrium model in which firms choose automation from a profit function and final-good production is Cobb–Douglas.
Human labor retains premium value when human judgment, attention, accountability, authorship, or relational participation is not incidental to the output but constitutive of what is being purchased (the paper proposes 'constitutive human presence' as the relevant standard for evaluating hybrid human-AI work).
Conceptual definition and prescriptive standard introduced in the paper; no empirical validation or measurement reported in the excerpt.
Because these premiums depend on credible verification, AI governance should treat human-provenance verification systems as labor infrastructure rather than as luxury authenticity labels.
Normative/policy recommendation based on the paper's conceptual analysis; the excerpt contains argumentation but no empirical evaluation of governance interventions.
AI-saturated markets are likely to create Veblen-good premiums, termed human-provenance premiums, for verified human presence (i.e., consumers will pay price premiums for verified human-produced outputs).
Theoretical claim drawing on economic reasoning about Veblen goods and market preferences; paper presents argumentation rather than reported empirical estimation in the excerpt.
This compression reallocates demand for human labor toward work valued for its visible human character (performative humanity), including relational presence, aesthetic provenance, and accountability.
Theoretical/conceptual reasoning and typology proposed in the paper (no empirical sample or measurement reported in the excerpt).
AI development may widen income disparities across industries.
Further cross-industry analysis reported in the paper indicating that AI-related development is associated with greater inter-industry income dispersion.
The effect of AI development on the firm-level skill premium is more pronounced in firms operating in industries with lower market concentration.
Heterogeneity analysis by industry market concentration (industry-level concentration measures used to stratify firms).
The effect of AI development on the firm-level skill premium is more pronounced in firms with higher levels of digitalization.
Heterogeneity analysis using measures of firm digitalization to split the sample and compare effects.
The effect of AI development on the firm-level skill premium is more pronounced in non-state-owned firms.
Heterogeneity analysis / subgroup regressions reported in the paper comparing ownership types (state-owned vs non-state-owned firms).
The main findings remain robust after addressing endogeneity using an instrumental variable approach and conducting a series of robustness checks (alternative constructions/measures, AI pilot zone policy shock tests, alternative sample restrictions).
Reported IV analysis and multiple robustness checks in the paper (alternative dependent variable constructions, alternative AI measures, policy shock tests, sample restrictions).
AI increases the firm-level skill premium by facilitating technological upgrading.
Mechanism analysis showing AI development correlates with indicators of technological upgrading or innovation within firms.
AI increases the firm-level skill premium by promoting capital deepening.
Mechanism analysis in the paper indicating AI development is associated with higher capital intensity / capital deepening at the firm level.
AI increases the firm-level skill premium by improving firm productivity.
Mechanism analysis showing positive association between AI development and measures of firm productivity in regression analyses.
AI development significantly increases the firm-level skill premium.
Econometric analysis on Chinese listed firms using the constructed firm-level AI development measure; baseline regressions reported, with endogeneity addressed using an instrumental variable (IV) approach.
Policy recommendations: invest in digital infrastructure, human capital development, and inclusive technology diffusion strategies to ensure more equitable distribution of AI-driven economic value.
Policy implications drawn from study findings (heterogeneous effects and mediation by structural conditions).
The magnitude of AI's growth effects varies across economic contexts: developed economies experience substantially stronger growth impacts (approximately 0.33) than emerging economies (approximately 0.15).
Heterogeneity analysis / subgroup comparisons (developed vs emerging economies) using the panel data regressions and/or quantile regressions on the 2015–2024 dataset; exact sample sizes per subgroup not reported.
AI adoption has a comparatively weaker direct effect on economic growth (direct effect β = 0.09).
Mediation/structural decomposition from the paper showing direct (non-mediated) coefficient from AI adoption to growth.
Agentic AI influences economic growth primarily through a productivity channel (mediated effect β = 0.35, p < 0.01).
Mediation analysis (panel data) estimating indirect effect of AI adoption on GDP growth via measured productivity channel; data sources: World Bank and OECD indicators, 2015–2024.
AI adoption significantly improves firm-level productivity (β = 0.18, p < 0.01).
Fixed-effects panel regression using an AI Adoption Index as predictor on firm-level productivity; data drawn from World Bank (World Development Indicators and Enterprise Surveys) and OECD AI indicators for 2015–2024 (sample size not reported in text).
Agentic AI has strong potential to boost productivity and growth.
Statement in paper motivated by literature review and the study's empirical results linking AI adoption to productivity and growth.
There is an urgency to implement measures to promote digital inclusion, equitable AI development, investment in education, and international cooperation to spread the benefits of AI more widely and equitably.
Normative/recommendation in the paper based on its analysis of global disparities and risks; no policy evaluation or impact estimates provided in the excerpt.
High-income regions are pioneers in the implementation of AI.
Assertion in the paper based on cross‑regional comparison of AI implementation (no specific metrics, methods, or sample size provided in the excerpt).
High-income regions (North America, Europe, parts of the Asia-Pacific region) have virtually complete access to the Internet.
Statement in the paper based on a global comparative analysis of internet access across regions; the excerpt does not report specific data sources, methods, or sample size.
Based on these insights, we offer design recommendations for generative AI-powered learning tools for freelancers.
Paper contribution section — authors present design recommendations derived from study findings (not an empirical claim about an evaluated intervention).
Freelancers increasingly rely on generative AI to structure learning and support exploratory skill acquisition.
Reported finding from the paper's mixed-methods study (survey + semi-structured interviews with freelance knowledge workers).
The review ends with policy recommendations to address barriers and to facilitate increased public–private partnership (PPP) aimed at increasing health access in India.
Statement in the paper summarizing its policy recommendations; based on authors' synthesis of reviewed literature and conclusions.
AI, Blockchain, and 5G have great potential for transforming healthcare in India.
Forward-looking claim in the review summarizing technological potential as reported in the literature; presented as potential rather than demonstrated effect (no empirical effect sizes given).
These technologies can optimize workforce output in constrained healthcare contexts.
Review assertion synthesizing qualitative and quantitative literature describing impacts on workforce productivity/output; no specific sample size reported in excerpt.
These technologies can increase clinical effectiveness.
Claimed potential in the review based on prior studies (synthesis of evidence; no single quantified trial/sample provided in excerpt).
These technologies can be used to enhance operational effectiveness in healthcare organisations operating under severe constraints.
Review claims and discussion of use-cases/ways technologies may improve operations; based on synthesis of qualitative and quantitative studies (no single trial/sample reported).
Healthcare technology is considered a key organizational-efficiency enhancer, particularly in traditional [healthcare] settings addressing escalating health needs.
Synthesis statement from the review summarizing prior papers that view technology as improving organizational efficiency in traditional settings; method = literature review (qualitative/quantitative studies synthesis).
India has a vast population, meaning a vast market for healthcare technology adoption.
Statement in paper's introduction/abstract asserting India's large population makes it a large market; based on literature review/contextual framing (no primary sample size reported).
The capacity to create, maintain, and control digital agents becomes a new axis of international inequality, potentially devaluing the demographic dividend of developing countries and revising the logic of comparative advantages.
Geoeconomic theoretical analysis in the paper; no cross-country empirical analysis demonstrating changed comparative advantages presented.
The institutional architecture of modern societies (pension systems, taxation models, etc.) is built on assumptions that are systematically undermined by the rise of an agentic economy, necessitating a revision of fiscal and social models, including discrete taxation of algorithmic employment.
Normative and theoretical analysis linking institutional assumptions to agentic economy dynamics; no empirical policy evaluation or fiscal simulation results reported.
The agent energy profile (AEP) is introduced as a measure of annual energy consumption per unit of cFTE, allowing energy-based comparisons between algorithmic and human cognitive labour.
Methodological/conceptual proposal in the paper; no empirical measurements or energy accounting dataset provided.
The paper proposes a quantitative identification of algorithmic agents via the category of cognitive full-time equivalent (cFTE), enabling comparison of algorithmic and human productivity within a unified analytical framework.
Methodological proposal (definition and proposed use of cFTE) presented in the paper; no empirical validation or implementation sample reported.
The ontological status of technology is transforming from a productivity-enhancing tool to an autonomous participant in economic processes, forming a hybrid factor of production that combines characteristics of both capital and labour.
Theoretical analysis and conceptual framing in the paper; no empirical factor decomposition or production-function estimation provided.
Institutionalising digital agent registration could transform 'shadow demographics' into formal 'algorithmic demographics'.
Policy/theoretical proposition in the paper (institutionalisation as a mechanism); no empirical pilot or legal implementation evidence reported.