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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AI-executed steps co-occur in contiguous chains rather than being randomly scattered across a production workflow.
Empirical analysis linking O*NET tasks to human assessments of AI exposure (Eloundou et al., 2024), realized AI execution outcomes from Anthropic’s Economic Index (Handa et al., 2025), and GPT-generated workflow orderings for occupations; statistical tests comparing observed contiguity to random/scaled baselines reported in the paper.
Economists strongly favor targeted policy interventions such as AI-focused worker retraining (71.8% support) over broad structural interventions like job guarantees (13.7% support) or universal basic income (37.4% support).
Survey items asking respondents to indicate normative support for six policy proposals; reported support percentages for the economist group for specific policies (retraining, job guarantee, UBI).
Economists (as a group) forecast GDP growth of 3.5% under the rapid AI scenario.
Conditional forecasts reported in Key Findings (economist subgroup forecasts under the rapid progress scenario).
The median respondent in each group expects annual U.S. GDP growth of about 2.5% (unconditional forecast).
Unconditional (all-things-considered) survey forecasts of annual GDP growth elicited from respondents across five groups; compared in text to government and private-sector baseline forecasts (typical medium-run 2.0% and long-run 1.7%).
The average economist assigns a 61.4% probability to moderate or rapid AI progress by 2030.
Survey responses from the economist respondent group reporting the mean/average subjective probability for the combined 'moderate' and 'rapid' scenario categories.
The median respondent in each group expects substantial advances in AI capabilities by 2030.
Survey of five respondent groups (academic economists, AI-company employees, AI policy researchers, highly accurate forecasters, and the general public) eliciting unconditional and conditional forecasts about AI capabilities and economic outcomes (details and sample sizes referenced in Section 2.1, not provided in excerpt).
Organizations and policymakers that treat work-time policy as foundational economic planning will better position their economies to harness AI's benefits while mitigating systemic instability.
Policy-prescriptive conclusion based on cross-disciplinary analysis; no empirical trial or quantification offered in the summary.
Work-time reduction can distribute productivity gains more equitably.
Argument supported by examination of historical work-time transitions and pilot programs referenced in the article; no empirical effect sizes or sample details in the summary.
Coordinated reduction in working hours helps maintain aggregate demand.
The paper's synthesis of historical transitions and pilot programs and argument about distribution of productivity gains; no quantitative evidence or sample sizes provided in the summary.
Gradual, policy-led reduction in standard working hours can preserve employment.
Claim based on examination of historical work-time transitions, contemporary pilot programs, and cross-sector implementation strategies referenced in the paper; no specific studies or sample sizes cited in the summary.
Competition law assessments of a dominant undertaking’s conduct must consider not only the product market but also the labor market, particularly in cases of significant market structure changes.
Conclusion stated in abstract summarizing the paper’s findings; supported by the paper's legal analysis and referenced case law (no empirical sample provided in abstract).
Poaching employees is an inherent aspect of competition for highly qualified talent and is particularly pronounced among tech giants.
Statement in abstract; general observation supported by literature/case-law references implied in paper (no specific empirical sample or quantitative method reported in abstract).
The paper proposes five architectural requirements for genuine human oversight systems.
Stated methodological/prescriptive contribution of the paper (a proposal rather than an empirical finding); no sample size or empirical validation reported in the provided excerpt.
Increasing the strictness of algorithmic control paradoxically increases the evolutionary fitness of coordinated resistance (e.g., coordinated log-offs).
Results from the EGT model and simulations showing fitness/payoff changes for coordinated resistance strategies as platform surveillance strictness parameter increases; model-only (no empirical N reported).
Achieving near-perfect success rates at this minimally sufficient quality level or comparable success rates at superior quality would require several additional years.
Authors' forecast/commentary on timeline beyond the 2029 projection; conditional expectation based on historical pace of improvements.
If recent trends in AI capability growth persist, LLMs will be able to complete most text-related tasks with success rates of, on average, 80%-95% by 2029 at a minimally sufficient quality level.
Longer-term projection contingent on continuation of recent capability growth trends (model-based forecast stated by the authors).
AI success rates for those tasks increase to about 65% by 2025-Q3.
Short-term projection / trend extrapolation reported in the paper (from the ongoing evaluation data).
In 2024-Q2, AI models successfully complete tasks that take humans approximately 3-4 hours with about a 50% success rate.
Empirical measurement/estimate from the ongoing evaluation (reported temporal snapshot for 2024-Q2); based on tasks mapped to human completion time and observed model success rates from the >17,000 evaluations.
AI performance is high and improving rapidly across a wide range of tasks.
Empirical results from the ongoing evaluation of >3,000 tasks and >17,000 evaluations showing high and increasing success/performance metrics.
Substantial evidence that rising tides are the primary form of AI automation.
Patterns observed in the same large-scale evaluation across tasks and human judgments indicating broad-based, continuous capability improvements across many tasks.
Employment reallocation exerted a narrowing influence on the gender wage gap, particularly in 2005–2010.
Dynamic shift-share decomposition attributing a portion of changes in the gender wage gap to employment reallocation effects, with a notable equalizing contribution in 2005–2010.
Displaced women reallocated substantially toward non-routine interpersonal roles (occupational upgrading).
Observed occupational transition patterns in decomposition results showing female movement into non-routine interpersonal occupations; authors interpret this as occupational upgrading.
Design implication: adaptive AI coaching systems should align support intensity with individual readiness, rather than assuming universal effectiveness.
Authors' design recommendation derived from experimental results showing heterogeneous effects by personality profile.
HEWU is designed to become the cited standard before better-resourced players define competing frameworks, establishing measurement infrastructure for the cognitive industrial revolution the way GAAP established it for capital markets.
Aspirational/strategic claim made by the authors about intended role and adoption of HEWU (no empirical support provided).
In that deployment the framework measured approximately $378,000 in annual labor value of machine-equivalent work.
Same empirical manufacturing deployment reported in the paper (single case/example).
In a representative manufacturing deployment, the framework measured 8.4 FTE of machine-equivalent labor.
Empirical example reported in the paper described as a 'representative manufacturing deployment' (appears to be a single deployment/case).
The paper introduces the Machine Labor Index (HEWU-PSI), a time-series economic indicator designed to track aggregate machine labor output at company, sector, and national level, analogous in function to the Purchasing Managers' Index.
Methodological contribution described in the paper (proposal of an index and its intended scope; no empirical time-series dataset reported).
The paper introduces AILU (AI Labor Units) as a software-specific subset metric.
Methodological contribution described in the paper (definition of a software-specific metric subset).
The paper presents the conceptual foundation, mathematical model (HEWU = MO ÷ HB × CF × QF), calibration framework, Baseline Library architecture, and auditability mechanisms underlying the standard.
Paper's methodological content (explicit model formula and supporting frameworks described).
This paper introduces the Human-Equivalent Work Unit (HEWU), a standardized metric that converts AI and automation system output into human labor equivalents, expressed as full-time employee (FTE) equivalents and annual labor value ($).
Methodological contribution described in the paper (definition and proposal of a new metric; no empirical validation sample reported).
Artificial intelligence systems are autonomous agents performing economically meaningful labor at scale across customer service, software engineering, logistics, manufacturing, and knowledge work.
Author's conceptual/empirical assertion in the paper (no specific sample, presented as general observation).
The analysis identifies seventeen emerging occupational categories benefiting from reinstatement effects, concentrated in human-AI collaboration, AI governance, and domain-specific AI operations roles.
Modeling/taxonomy result reported in the paper listing 17 emerging occupational categories characterized as benefiting from reinstatement effects (human-AI collaboration, governance, operations).
The binding constraint on human–AI complementarity in the Global South is not technology access but labor market institutions (formality).
Interpretation of empirical findings (formality interactions, triple interaction result) from the augmented Mincer regressions on Colombian data (N = 105,517).
These results provide the first developing-country evidence of cognitive factor decomposition in AI-augmented labor markets.
Claim based on the empirical results from the study using Colombian data and comparison to literature (author statement).
The augmentation premium is strongest in the health and education sectors.
Heterogeneity analysis / sectoral estimates in the augmented Mincer regression using the merged dataset (N = 105,517); reported strongest effects in health and education.
The augmentation premium (return to H^A with AI) is strongest for experienced workers (ages 46-65).
Heterogeneity analysis / subgroup estimates by age in the augmented Mincer regression using the merged dataset (N = 105,517); reported finding that ages 46–65 show the largest augmentation premium.
A triple interaction confirms formality as the binding mechanism: beta_{AHC x D x Formal} = +0.272 (p < 0.001).
Coefficient on triple interaction term in augmented Mincer regression estimated on merged dataset (N = 105,517); reported estimate +0.272, p < 0.001.
In the estimated augmented Mincer equation, the wage return to augmentable-cognitive capital (H^A) increases with AI adoption in the formal sector (beta_2 = +0.051, p < 0.001).
Econometric estimate from augmented Mincer regression using merged data (household survey N = 105,517; LLM-based occupational augmentability measures); reported coefficient beta_2 = +0.051 with p < 0.001.
The empirical analysis uses LLM-generated measures of occupational augmentability for 18,796 O*NET task statements mapped to 440 Colombian occupations, merged with household survey microdata (N = 105,517 workers).
Data construction described in the paper: LLM scoring of O*NET tasks (18,796 tasks), mapping to 440 occupations, merged with household survey microdata (sample N = 105,517).
I derive a corrected Mincerian wage equation and show that the standard specification is misspecified in AI-augmented economies.
Analytical derivation in the paper (theoretical correction to Mincerian wage equation).
AI capital interacts asymmetrically with those components: it substitutes for routine cognitive work (H^C) while complementing augmentable cognitive work (H^A) through an amplification function phi(D).
Theoretical production-function model and derivation in the paper (analytical result).
The paper proposes a decomposition of human capital into three orthogonal components: physical-manual (H^P), routine-cognitive (H^C), and augmentable-cognitive (H^A).
Theoretical proposal in the paper (modeling framework).
This research contributes to debates about the future of work, power asymmetries in platform economies, and the development of worker-protective regulatory frameworks, engaging perspectives from feminist economics, institutional theory, and surveillance capitalism studies.
Stated contribution in the abstract based on theoretical engagement and literature synthesis (conceptual claim; no empirical citation in abstract).
Theoretical frameworks developed in the paper require future empirical validation via case studies, quantitative analysis, and ethnographic research.
Methodological statement within the abstract describing the paper's limitations and next steps (self-report about the paper's status).
The study proposes institutional frameworks for realizing labor value and for worker-protective regulatory frameworks applicable to digital/platform economies.
Normative/theoretical proposals derived from conceptual analysis and engagement with feminist economics, institutional theory, and surveillance capitalism literature (no empirical testing reported).
The paper identifies key characteristics of value formation specific to platform economies.
Theoretical framework and literature synthesis presented in the study (conceptual; no empirical cases reported in abstract).
Living labor remains the sole source of new value; the core insights of the labor theory of value remain essential for critiquing contemporary digital capitalism.
Argumentative/theoretical development grounded in Marxist political economy and literature synthesis (conceptual paper, no empirical testing reported).
AI should be classified as constant capital rather than as labor.
Theoretical analysis and critical literature synthesis in a conceptual study (no empirical sample reported).
Overall, findings highlight that AI serves as a revolutionary (transformative) tool rather than merely a replacement tool for employment—changing the nature of human work rather than simply disengaging it.
Synthesis conclusion in the paper drawing on the literature review and the authors' empirical results indicating task reallocation and changing job content.
The paper argues for equal technology governance as a necessary policy response to AI's labor market effects.
Policy recommendations discussed in the paper that call for equitable governance of AI; based on literature synthesis and empirical findings.