Evidence (8807 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
Filter claims →
Productivity
8807 claims
Filtered →
Governance
7870 claims
Filter claims →
Human-AI Collaboration
7560 claims
Filter claims →
Org Design
4892 claims
Filter claims →
Innovation
4781 claims
Filter claims →
Labor Markets
4004 claims
Filter claims →
Skills & Training
3308 claims
Filter claims →
Inequality
2332 claims
Filter claims →
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 |
Productivity
Remove filter
Developers actively manage the collaboration, externalizing plans into persistent artifacts, and negotiating AI autonomy through context injection and behavioral constraints.
Observed behaviors in chat transcripts and committed artifacts showing developers creating persistent plans, injecting context, and specifying constraints to shape AI behavior.
Developers redistribute cognitive work to AI, delegating diagnosis, comprehension, and validation rather than engaging with code and outputs directly.
Content and interaction analyses of chat sessions showing developer prompts delegating diagnosis, comprehension, and validation tasks to the AI assistants (Cursor and GitHub Copilot) across the dataset.
Conversational programming operates as progressive specification, with developers iteratively refining outputs rather than specifying complete tasks upfront.
Qualitative/content analysis of the 74,998 messages across 11,579 sessions indicating patterns of iterative prompts and refinements rather than one-shot complete specifications.
The influence of human capital (number of specialists in scientific and technological fields) on value added varies across sectors.
Number of specialists in scientific and technological fields included as a covariate in MMQR; reported heterogeneous effects across sectors/quantiles in the results section.
The influence of R&D expenditure on value added varies across sectors.
R&D expenditure included as a core explanatory variable in panel MMQR estimations; authors report differing coefficient sizes/signs across sectors/quantiles.
These AI capability improvements would impact the economy and labor market as organizations adopt AI, which could have a substantially longer timeline.
Theoretical implication/interpretation by the authors (economic and labor market impact contingent on organizational adoption; timeline longer than capability improvements).
AI automation is a continuum between (i) crashing waves where AI capabilities surge abruptly over small sets of tasks, and (ii) rising tides where the increase in AI capabilities is more continuous and broad-based.
Conceptual framing proposed by the authors (theoretical proposition).
This paper proposes three archetypal AI technology types: AI for effort reduction, AI to increase observability, and mechanism-level incentive change AI.
Conceptual taxonomy introduced by the authors (theoretical classification presented in the paper).
Big Data-based FinTech can contribute to financial stability only when its implementation is strategically justified, ethically grounded and supported by effective regulation, robust data governance and investment in human capital.
Normative conclusion drawn from systemic and structural analysis of literature and synthesis of empirical studies; no empirical test provided within the paper.
The effectiveness of Big Data solutions varies across the financial sphere and depends critically on data quality, regulatory alignment and organisational readiness.
Derived from comparative analysis of sector-specific applications and synthesis of findings in the reviewed literature; no quantified cross-sector sample reported.
AI intensity and employment elasticity are linked by a U-shaped relationship.
Result reported by the paper based on the authors' empirical/econometric analysis of international datasets (OECD/ILO/World Bank).
The paper analyzes AI as a continuous process using data from the OECD, ILO, and the World Bank to study job displacement, creation, and reallocation.
Empirical analysis described in the paper using datasets from OECD, ILO, and World Bank; econometric approach implied.
AI is recognized as a primary change agent that influences various aspects of economies the world over, and thus it profoundly changes not only the number of jobs but also their quality.
Stated as a high-level conclusion in the paper's introduction/abstract; based on literature synthesis of studies from 2013-2025 and references to international sources (OECD, ILO, World Bank).
AI plays a dual role by enhancing productivity while intensifying energy use in the short run.
Synthesis of empirical findings in the paper: documented short-run increase in electricity growth (energy use) following AI adoption alongside statements/evidence that AI enhances productivity (exact productivity measures and estimates not provided in the summary).
The four-variable account (produced output, underlying understanding, calibration accuracy, self-assessed ability) better explains phenomena like overconfidence, over- and under-reliance on AI, 'crutch' effects, and weak transfer than the simpler claim that generative AI merely amplifies the Dunning–Kruger effect.
Argumentative synthesis in the paper comparing explanatory power of the proposed four-variable framework against the more general Dunning–Kruger metaphor; draws on examples and empirical patterns from the reviewed literature rather than a single empirical test.
A useful working model is 'AI-mediated metacognitive decoupling': LLM use widens the gap among produced output, underlying understanding, calibration accuracy, and self-assessed ability.
Conceptual synthesis and theoretical proposal grounded in reviewed empirical findings from multiple literatures (human–AI interaction, learning research, model evaluation); presented as the paper's working model rather than as a single empirical estimate.
There is a fundamental trade-off between operational stability and theoretical deliberation across multi-agent coordination frameworks.
Empirical results from controlled benchmarks comparing agent architectures under fixed computational time budgets, as reported in the paper (no numeric sample size or statistical details provided in the abstract).
As technological progress devalues labor, the welfare benefits of steering are at first increased but, beyond a critical threshold, decline and optimal policy shifts toward greater redistribution.
Theoretical model extension analyzing planner's optimal choice as labor's economic value changes; the paper states a non-monotonic relationship with a critical threshold.
Using pre-existing exposure as an instrument for ChatGPT adoption in a long-difference IV design, ChatGPT adoption causes households to spend more time on digital leisure activities while leaving total time spent on productive online activities unchanged.
IV long-difference empirical design: instrumenting household adoption with pre-ChatGPT exposure (2021 browsing); outcome measured as changes in categorized browsing durations (LLM-based classification into 'leisure' vs 'productive' sites); controls include demographic-by-region fixed effects and browsing composition controls.
Once efficiency is made explicit, the main practical question becomes how many efficiency doublings are required to keep scaling productive despite diminishing returns.
Framing/forecasting claim in the paper presenting an operational research question (conceptual; no empirical sample in excerpt).
The practical burden of scaling depends on how efficiently real resources are converted into that (logical) compute.
Argument in the paper linking conceptual 'logical compute' to real-world conversion efficiency (qualitative claim; no empirical sample in excerpt).
The compute variable is best understood as logical compute, an implementation-agnostic notion of model-side work.
Conceptual argument presented in the paper reframing 'compute' as an abstract, implementation-agnostic quantity (no empirical sample provided).
These patterns are consistent with a reorganization of the scientific production process rather than immediate efficiency gains, in line with theories of general-purpose technologies.
Interpretation linking observed changes in budget allocation, team size, and task breadth (from the proposal dataset and task-level analyses) to theoretical predictions about general-purpose technologies (GPTs); empirical findings show organizational change rather than large average short-run productivity gains.
This paper offers a forward-looking framework that emphasizes the decentralizing potential of AI on labor markets, moving beyond the traditional displacement-versus-creation dichotomy.
Paper's stated contribution; based on conceptual framework and synthesis of historical and contemporary analyses (no empirical validation presented in the abstract).
The emergence of artificial intelligence and robotics is catalyzing a profound transformation in the nature of human labor.
Stated as a central premise in the paper's abstract; supported by the paper's synthesis of economic history, contemporary labor market data, and analysis of digital platform growth (no specific datasets or sample sizes reported in the abstract).
The resulting AI safety profile is asymmetric: AI is bottlenecked on frontier research (novel tasks) but unbottlenecked on exploiting existing knowledge.
Theoretical implication of the novelty-bottleneck model distinguishing novel (human-judgment) vs. routine (covered by agent prior) components of tasks.
Wall-clock time can be reduced to O(√E) through team parallelism, but total human effort remains O(E).
Model-derived result showing parallelism across humans can speed wall-clock completion time while aggregate human effort does not drop asymptotically.
Better agents improve the coefficient on human effort but not the exponent (i.e., they reduce the constant factor but do not change the asymptotic scaling class).
Analytic result from the stylized model under the paper's assumptions about task decomposition and novelty fraction ν.
India's systematic investment plan (SIP) flows provide a high-frequency observable for the model's endogenous participation rate and constitute the natural empirical laboratory for the displacement–participation mechanism.
Empirical suggestion in the paper proposing SIP flows as an observable proxy for the modelled participation rate and recommending India as a lab to test the displacement–participation channel (no empirical test reported in the excerpt).
Three analytical results characterise non-linear financial fragility, regime-contingent risk premium divergence, and the general equilibrium alignment squeeze.
Stated analytical results in the paper derived from the theoretical model describing three named phenomena (non-linear fragility, regime-contingent divergence, alignment squeeze).
Whether AI is equity-bullish or equity-bearish depends on which channel dominates—a condition that differs sharply between deep financial markets, where the ARP is the dominant driver of elevated risk premia (Regime D), and shallow markets, where participation compression dominates (Regime E).
Model regime analysis in the paper distinguishing Regime D (deep markets, ARP-dominated) and Regime E (shallow markets, participation-compression-dominated) and stating comparative dominance determines net bullish/bearish outcome.
The equilibrium equity risk premium decomposes into three additively separable terms corresponding to these three channels (Proposition 1).
Formal proposition (Proposition 1) in the paper deriving an additive decomposition of the equilibrium ERP into the productivity, participation compression, and alignment risk terms.
We develop a heterogeneous-agent framework in which AI-driven labour displacement affects the equity risk premium (ERP) through three co-equal channels.
Stated model contribution in the paper: a theoretical heterogeneous-agent framework that posits three channels linking AI-driven labour displacement to the ERP (productivity, participation compression, alignment risk).
The top four models are statistically indistinguishable (mean score 0.147–0.153) while a clear tier gap separates them from the remaining four models (mean score <= 0.113).
Reported mean performance scores across 8 models and statement of statistical indistinguishability for the top four vs lower-tier four; numerical means provided.
Behavioral factors — specifically trust calibration, cognitive load, and affective reactions — shape the transition of corporate AI initiatives from pilot deployments to scalable, sustained use.
Synthesis of human-AI interaction literature integrated with adoption frameworks (TAM and TOE); conceptual linkage rather than new empirical testing in this paper.
AI accelerates value-chain maturation while creating distinct risks — including professional responsibility tensions and potential system-level externalities.
Conceptual argument and risk analysis in the Article (theoretical reasoning and synthesis of management/ethics literature). No empirical causal estimate reported in the excerpt.
The legal profession is at a crossroads, caught between intensifying fears of AI-driven displacement and a generational opportunity for transformation.
Author's synthesis and framing in the Article (conceptual assessment; literature/contextual synthesis). No empirical sample or experiment reported in the excerpt.
This advantage is contingent upon robust AI governance, ethical frameworks, and the transition from 'pilot-lite' projects to integrated, data-driven 'AI-first' business models.
Conditional claim in the paper linking success to governance, ethics, and organizational integration; appears to be normative/analytical rather than empirical in the abstract.
Machine-readable metrics and open scholarly infrastructure are reshaping scholarly profiles and incentives.
Conceptual and historical discussion referring to platforms and metrics (e.g., arXiv, Google Scholar, ORCID) as mechanisms changing incentives; no new empirical estimates provided.
That interconnected ecosystem is fundamentally restructuring who can do science (access), how fast discoveries propagate, and what counts as a valid scientific contribution.
Argumentative claim linking infrastructural and tool changes to changes in access, dissemination speed, and norms of contribution. The paper presents examples and narrative but no systematic empirical evaluation or sample.
The most consequential development is not any single tool but the emergence of an interconnected ecosystem—AI agents, preprint platforms, open source codebases, and citation infrastructure—that forms a feedback loop.
Synthesis/argument based on multiple examples (LLM agents, preprint servers like arXiv, open-source code repositories, citation indices). No quantitative measurement or causal identification reported.
The central tension in AI for science is between automation (building systems that replace human researchers) and augmentation (tools that amplify human creativity and judgement).
Analytical claim based on the paper's review of historical examples and conceptual discussion; no primary data or experimental design reported.
Science has repeatedly delegated its bottlenecks to machines—first inference, then search, then measurement, then the full workflow—and each delegation solves one problem while exposing a harder one underneath.
Interpretive historical argument drawing on examples across AI-for-science milestones (e.g., DENDRAL, search and inference systems, measurement automation, and contemporary end-to-end workflows). No quantitative sample or experimental method reported.
Testing revealed AI excels at computational tasks but consistently misses nuanced factors like new construction rent premiums and infrastructure proximity impacts, validating the framework's hybrid structure as essential for professional-grade underwriting.
Findings from the controlled ChatGPT-4 test on the single 150-unit scenario: qualitative and comparative observations showing AI handled computations well but failed to capture specific local-market nuances, leading authors to endorse a hybrid human-AI framework.
Phase Two requires human-led professional validation to correct AI limitations, apply local market knowledge, and integrate risk factors.
Framework description supported by observations from the controlled test where human review was used to correct AI outputs and apply local knowledge (e.g., adjusting for nuanced market factors).
Traffic performance is sensitive to the distribution of safe time gaps and the proportion of RL vehicles.
Simulation results comparing Fundamental Diagrams across scenarios with different distributions of safe time gaps and shares of RL-controlled vehicles. Number of simulation runs or replicates not stated in the claim text.
AUROC_2 and M-ratio produce fully inverted model rankings, demonstrating these metrics answer fundamentally different evaluation questions.
Metric comparison across models showing that AUROC_2-based ranking and M-ratio-based ranking are fully inverted in the reported results on the evaluated dataset.
Temperature manipulation shifts Type-2 criterion while meta-d' remains stable for two of four models, dissociating confidence policy from metacognitive capacity.
Experimental manipulation (temperature changes) applied to models; reported result that Type-2 criterion shifted with temperature while meta-d' was stable for two models (out of four) in the 224,000-trial dataset.
Metacognitive efficiency is domain-specific, with different models showing different weakest domains, invisible to aggregate metrics.
Domain-level analyses reported in the paper showing per-domain M-ratio results and identification of different weakest domains per model, contrasted with aggregate metric behavior.
Metacognitive efficiency varies substantially across models even when Type-1 sensitivity is similar — Mistral achieves the highest d' but the lowest M-ratio.
Empirical comparison of Type-1 sensitivity (d') and metacognitive efficiency (M-ratio) across the four evaluated LLMs on the 224,000 QA trials; explicit statement that Mistral had highest d' but lowest M-ratio.