Evidence (3231 claims)
Adoption
7395 claims
Productivity
6507 claims
Governance
5921 claims
Human-AI Collaboration
5192 claims
Org Design
3497 claims
Innovation
3492 claims
Labor Markets
3231 claims
Skills & Training
2608 claims
Inequality
1842 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 609 | 159 | 77 | 738 | 1617 |
| Governance & Regulation | 671 | 334 | 160 | 99 | 1285 |
| Organizational Efficiency | 626 | 147 | 105 | 70 | 955 |
| Technology Adoption Rate | 502 | 176 | 98 | 78 | 861 |
| Research Productivity | 349 | 109 | 48 | 322 | 838 |
| Output Quality | 391 | 121 | 45 | 40 | 597 |
| Firm Productivity | 385 | 46 | 85 | 17 | 539 |
| Decision Quality | 277 | 145 | 63 | 34 | 526 |
| AI Safety & Ethics | 189 | 244 | 59 | 30 | 526 |
| Market Structure | 152 | 154 | 109 | 20 | 440 |
| Task Allocation | 158 | 50 | 56 | 26 | 295 |
| Innovation Output | 178 | 23 | 38 | 17 | 257 |
| Skill Acquisition | 137 | 52 | 50 | 13 | 252 |
| Fiscal & Macroeconomic | 120 | 64 | 38 | 23 | 252 |
| Employment Level | 93 | 46 | 96 | 12 | 249 |
| Firm Revenue | 130 | 43 | 26 | 3 | 202 |
| Consumer Welfare | 99 | 51 | 40 | 11 | 201 |
| Inequality Measures | 36 | 106 | 40 | 6 | 188 |
| Task Completion Time | 134 | 18 | 6 | 5 | 163 |
| Worker Satisfaction | 79 | 54 | 16 | 11 | 160 |
| Error Rate | 64 | 79 | 8 | 1 | 152 |
| Regulatory Compliance | 69 | 66 | 14 | 3 | 152 |
| Training Effectiveness | 82 | 16 | 13 | 18 | 131 |
| Wages & Compensation | 70 | 25 | 22 | 6 | 123 |
| Team Performance | 74 | 16 | 21 | 9 | 121 |
| Automation Exposure | 41 | 48 | 19 | 9 | 120 |
| Job Displacement | 11 | 71 | 16 | 1 | 99 |
| Developer Productivity | 71 | 14 | 9 | 3 | 98 |
| Hiring & Recruitment | 49 | 7 | 8 | 3 | 67 |
| Social Protection | 26 | 14 | 8 | 2 | 50 |
| Creative Output | 26 | 14 | 6 | 2 | 49 |
| Skill Obsolescence | 5 | 37 | 5 | 1 | 48 |
| Labor Share of Income | 12 | 13 | 12 | — | 37 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Labor Markets
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There is a 15%–22% wage premium for workers demonstrating AI-augmentation capabilities.
Reported range across synthesized empirical studies documenting wage differences associated with demonstrated AI-augmentation capabilities.
The study draws policy implications for EU Cohesion programming and Sustainable Development Goals 4, 8, 9, 10, and 17.
Paper explicitly states policy implications and links to specific SDGs in its conclusions.
External technology partnerships, targeted education, and economic incentives operate as enablers [of AI adoption], all mediated by social and human capital availability.
Thematic analysis of interview data identifying these factors as enabling AI adoption, with mediation by social/human capital.
The socially optimal adoption speed and retraining capacity are complements: stronger institutions (larger retraining capacity) raise the optimal adoption speed.
Comparative-static result from the social-planner optimization in the dynamic model showing positive cross-partial effect between retraining capacity and optimal adoption speed.
Faster adoption produces a larger discouraged stock.
Analytical comparative-static result from the dynamic model linking adoption speed to the size of the discouraged (permanently exited) worker stock.
Faster AI adoption compresses the displacement window without reducing total displacement.
Analytical result from a dynamic theoretical model in which displaced routine workers enter a retraining pipeline with finite capacity (model derivation and comparative statics). No empirical sample reported.
Alternatives to one-size-fits-all chatbots—such as pluralistic system design, task-specific tools, and institutional safeguards—would better mitigate social and economic harm.
Prescriptive recommendations based on the paper's analysis; not supported by empirical trials or quantified evaluations within the paper.
A majority seems optimistic about [AI's] overall impact.
Paper reports a majority-level positive attitude in surveys about AI's overall impact (no survey details or sample sizes provided in the excerpt).
Policy responses must therefore move beyond predicting job loss to supporting workers in navigating newly emerging, and often counterintuitive, mobility pathways.
Policy recommendation derived from the paper's simulation findings and theoretical interpretation that automation reorganises tasks/skills and creates new mobility pathways; presented in the abstract as an implication.
AI-driven automation sustains occupational roles through emerging complementarity rather than substitution.
The authors' simulated tracing of changes in shares of skills reallocated to machines (using AI exposure measure) and observed patterns interpreted as complementarity that help sustain roles; stated in abstract as a primary theoretical interpretation.
Despite substantial task erosion, most occupations retain residual skills that enable adaptation rather than extinction.
Simulation of task removals (332 tasks) across 736 occupations showing that occupations typically maintain remaining skill bundles sufficient for adaptation; reported as a structural finding in the abstract.
AI automation moderates a broader range of cognitive and social skills, creating new bridges across heterogeneous domains.
Simulation results using the AI-driven cognitive automation exposure measure applied to O*NET task data, showing erosion/moderation patterns across cognitive and social skills and resulting cross-domain connectivity in the occupational network.
Automation increases skill overlap between occupations, promoting structural integration within the occupational network.
Result from the authors' simulations based on O*NET task-to-occupation mappings and the two exposure measures (routine and AI-driven automation); simulated task removals and analysis of resulting skill overlap/occupational network structure.
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.
The United States' existing public active labor market programming (WIOA) can support baseline wage recovery for vulnerable populations.
Aggregate results from the WIOA records (2017-2023) indicate general wage recovery among participants, interpreted as baseline support for vulnerable populations.
Employer-led programs—most notably apprenticeships—are associated with the highest incidence of successful outcomes.
Comparative analysis of program types within the WIOA dataset (2017-2023) showing employer-led interventions (apprenticeships) have higher rates on the Retrainability Index / success metrics than other program types.
Successful WIOA outcomes are driven mostly by post-program wage gains (possibly due to 'catch-up' mean reversion) rather than by occupational changes.
Decomposition of the Retrainability Index on the WIOA dataset (2017-2023) shows that observed program 'success' corresponds primarily to wage recovery measures rather than large shifts in RTI/occupation; authors note mean reversion as a possible explanation.
The index diverges sharply from existing AI exposure measures for specific occupation groups: power plant operators, railroad conductors, and aircraft cargo handling supervisors score high on RL feasibility but low on general AI exposure.
Empirical comparison between the RL Feasibility Index and existing AI-exposure measures, with named occupation groups showing opposite rankings.
Using LLM annotators guided by a rubric developed with RL experts and validated against confirmed deployment cases, we score all 17,951 O*NET tasks for training feasibility and aggregate to the occupation level, producing an RL Feasibility Index.
Empirical method described in paper: LLM-based annotation process guided by expert-developed rubric; validation against confirmed deployment cases; explicit enumeration of 17,951 O*NET tasks scored and aggregated into an index.
We examine this for every occupation in the US economy.
Statement of study scope in the paper (methodological claim about coverage).
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 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.
The concept of 'shadow demographics' describes a growing algorithmic population that expands in parallel with the stagnation or decline of the human population.
Conceptual definition and theorised dynamics in the paper; no empirical counts or longitudinal measurements of algorithmic population provided.
The expanding role of digital agents in production and market processes creates the preconditions for a gradual decoupling of demographic dynamics from economic growth.
Argumentative/theoretical exposition in the paper; no empirical panel or cross-country time-series evidence reported in the text provided.
AI-based digital agents can be interpreted as functional equivalents of economic actors.
Theoretical and conceptual argument presented in the paper (conceptual interpretation; no empirical sample or quantitative validation reported).
The authors conclude that these findings have implications for responsible and perceptible genAI use in hiring contexts.
Authors' conclusions/recommendations based on the interview findings and analysis.
Participants reported only marginal efficiency gains from genAI despite a seemingly seismic shift in how recruiting happens.
Self-reports from 22 interviewed recruiting professionals indicating small/marginal efficiency improvements.
Individual recruiters also felt compelled to adopt genAI because of the personal need to boost productivity.
Qualitative interview responses (n=22) reporting individual-level productivity motivations for using genAI.
Recruiters often felt compelled to adopt genAI to combat applicant use of AI.
Interview data from 22 recruiting professionals reporting adoption motivations tied to applicants' AI use.
When generative AI (genAI) systems are used in high-stakes decision-making, its recommended role is to aid, rather than replace, human decision-making.
Normative statement presented in the paper (literature/theoretical recommendation), no empirical data reported to support this recommendation within the study.