Evidence (4781 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).
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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 |
Innovation
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Longevity produces a short-run welfare loss that recedes as capital deepening raises wages, since households initially compress consumption and fertility to finance a longer retirement.
Model-derived welfare time path following a longevity shock showing initial welfare decline and subsequent recovery as aggregate capital deepens and wages rise; mechanism traced to household saving and fertility responses in simulations.
Robustness checks across the capital share, shock persistence, and the utility specification show that only an empirically implausible labor–AI elasticity reverses the wage and fertility signs.
Sensitivity/robustness analysis of model results by varying parameters (capital share, shock persistence, utility functional form) and the labor–AI elasticity, reporting conditions under which sign flips occur.
A forecast-error variance decomposition attributes most aggregate volatility to the longevity shock, while the AI shock dominates the variance of the return to AI capital.
Model-based forecast-error variance decomposition implemented on the simulated stochastic model to apportion variance of aggregate variables and the return to AI capital across shocks.
The two shocks move fertility in opposite directions: the AI shock raises fertility modestly through an income effect, while the longevity shock lowers fertility by strengthening life-cycle saving motives and increasing the cost of childrearing.
Endogenous-fertility overlapping-generations model with counterfactual simulations for AI and longevity shocks; comparative statics and simulation results regarding fertility responses and their mechanisms.
The AI shock reallocates investment from physical to AI capital.
Model simulation showing changes in investment allocation across capital types following the AI technology shock.
While localized speculation and valuation excesses may exist in AI markets, the underlying economic foundations of the AI cycle differ substantially from those that characterized the collapse of the internet bubble.
Comparative evaluation using financial market data, historical analyses of the dot-com collapse, and contemporary literature cited in the paper (qualitative comparative review).
AI-driven technological progress generates localized efficiency improvements while diffusing only weakly across the broader economy.
Synthesis of empirical results: localized positive associations between intangible capital and sectoral productivity versus weak/insignificant associations between AI patent intensity and aggregate TFP (analysis based on OECD Productivity, OECD STAN, INTAN-Invest, OECD Patents, FUAs; panel and robust regressions and descriptive work).
The effect of AI development on firms' labor educational structure is substantially larger in high-technology industries: the effect in high-technology industries is approximately 2.5 times as large as that in non-high-technology industries.
Industry heterogeneity analysis reported in the paper comparing coefficients for high-technology vs. non-high-technology industry subsamples using firm-level data (Chinese A-share firms, 2014–2024); reported ratio ≈ 2.5.
The substitution (for low-educated labor) and complementarity (with high-educated labor) effects of AI on firms' labor educational structure exhibit significant regional heterogeneity: the substitution effect is stronger in developed regions, while the complementarity effect is more pronounced in less developed regions.
Subgroup/heterogeneity analysis across regions using the firm-level panel (Chinese A-share firms, 2014–2024); reported differences in coefficients by regional development level.
Firms' technological innovation capability significantly mediates the effect of AI development on labor educational structure: by enhancing technological innovation capability, AI reduces demand for low-educated labor and increases demand for high-educated labor.
Mediation/causal pathway analysis reported in the study using firm-level data and mediation regressions on Chinese A-share listed firms (2014–2024); the paper reports that technological innovation capability is a significant mediating variable linking AI development to changes in labor education composition.
The empirical tests reported in the study use a sample of agricultural enterprises.
Paper text explicitly frames findings and implications for agricultural enterprises and states empirical tests were conducted on agri-business firms.
The relationship between AI use levels and corporate carbon emission intensity exhibits a significant inverted U-shaped curve: at early stages AI adoption may increase emissions, but beyond a critical point further AI use significantly reduces emissions.
Empirical two-way fixed effects (TWFE) analysis on provincial panel data from China, with robustness checks; the paper reports a statistically significant inverted U-shaped relationship.
Quantile regression estimates reveal pronounced asymmetry across the biofuel production distribution: the AI effect is substantially stronger among low-production countries (Q10–Q25 elasticities: 0.58–0.61) and statistically insignificant among high-production countries.
Quantile regression analysis reported in the paper with elasticity estimates for Q10–Q25 and significance tests across quantiles.
The pattern of timing and magnitudes for publication volume and VC investment is theoretically consistent with a multi-stage technology diffusion process, implying two complementary pathways: a research output channel and a commercial adoption channel.
Interpretation based on differential lags and elasticities (2‑year lag for publications vs 1‑year for VC) and theoretical framing in discussion.
Panel autoregressive distributed lag estimates reveal strong support for the load capacity curve (LCC) hypothesis, indicating a nonlinear income–environment relationship.
Panel ARDL econometric analysis on G-7 countries over 1990–2019 (authors report use of LCC framework and panel ARDL estimation).
A third possibility — the collective and self-organized stewardship of AI-relevant resources by communities (commons-governed approaches) — remains comparatively under-theorized in scholarship even as it proliferates in practice (e.g., data trusts, cooperatives, federated learning consortia, public compute initiatives, open-weight collaborations, community data sovereignty regimes).
Comparative literature review noting fewer theoretical treatments of commons approaches alongside cited examples of practical manifestations (lists of existing initiatives and models).
Technological containment policies may unintentionally accelerate open innovation ecosystems as a competitive response, with implications for global leadership in both academic and commercial artificial intelligence.
Synthesis and inferential claim in the paper drawing on the temporal association of containment measures, policy shifts, developer behavior, diffusion patterns, and patent/research evidence described earlier in the paper.
Across compression sweeps, real factor archives, and LLM-SRBench tasks, hybrid gains concentrate in weakly represented but target-bearing directions and vanish as the hypothesis space approaches full rank.
Empirical claim based on experiments over compression sweeps, analyses of real factor archives (A-share factor discovery), and LLM-SRBench tasks; no numerical sample sizes or effect magnitudes provided in the abstract.
The transition is in trivia count, not rate; the gap 1-α is the unrecorded mass.
Analytic argument/proof in the model showing that whether trivia allowance is finite or infinite (count) determines the phase transition in achievable coverage, and identifying 1-α as the portion of valuable mass not recorded by the literature core.
Sharp dichotomy on the tight family: generators emitting finitely many trivia achieve optimal coverage α/2, while any infinite trivia allowance, even at vanishing rate, jumps the optimum to 1-α/2 (both tight, for cores presented as the candidate intersection), and one generator attains both ends.
Mathematical theorem(s) in the paper establishing tight upper/lower bounds on coverage for the 'tight family' under two regimes (finite trivia vs infinite trivia), expressed as functions of the core density parameter α.
With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.
Extended model incorporating endogenous capital accumulation: analytical solution/characterization showing unbounded (explosive) growth in aggregate variables while real wages remain stagnant in the long run (model derivation).
Along the transition path of automation, data simultaneously augments the productivity of already-automated tasks and expands the automation frontier (dual role).
Analytical results from the dynamic model showing two mechanisms: (i) data increases productivity of tasks already automated; and (ii) data enables automation of additional tasks (model derivations).
This study identifies critical gaps in current Nvidia-centric roadmaps and proposes a competing reference architecture.
Paper's comparative analysis of existing (described as Nvidia-centric) roadmaps and presentation of an alternative reference architecture; no empirical validation or case-study evaluation reported.
Environment engineering can amplify productive behaviors (e.g., open-ended exploration, systematic artifact management, inter-agent collaboration) while suppressing harmful behaviors (e.g., reward hacking and high-friction human oversight).
Framing and argument in the paper describing expected effects of environment design (conceptual; no quantification provided in the excerpt).
The image of a single transformative step change caused by the introduction of human-level AGI may be inaccurate; a more apt prospect is a series of transformative societal changes caused by AI-enabled progress and breakthroughs across many areas of science and technology.
Interpretive claim in the report arguing for a multi-step, multifaceted impact scenario rather than a single-step discontinuity; based on conceptual synthesis of possible pathways and impacts.
There exist frictions and bottlenecks along these AGI→ASI pathways, and whether their impacts are negligible or substantial is an open set of concrete research questions.
Report analysis identifying potential frictions and bottlenecks and posing open research questions; conceptual analysis without quantified empirical measures.
The comparative evaluation shows differences in economic inclusiveness between ML, DL, and Generative AI.
Abstract states differences in economic inclusiveness found in the review; no quantitative inclusiveness metrics or sample sizes provided in abstract.
The comparative evaluation shows differences in explainability among ML, DL, and Generative AI.
Abstract notes comparative differences in explainability as part of review findings; no empirical measures of explainability included in abstract.
The comparative evaluation shows differences in patterns of substituting labor across ML, DL, and Generative AI.
Abstract states comparative differences in labor-substitution patterns based on the systematic review of literature; no empirical counts or sizes in abstract.
The comparative evaluation shows differences in scale of impact across ML, DL, and Generative AI.
Abstract reports a comparative evaluation highlighting scale differences across AI phases; no quantitative scale measures given in abstract.
Generative AI brings innovative disruption with profound effects on the structure of employment, knowledge-based ecosystems, and high-skill industries.
Synthesis claim in abstract based on reviewed peer‑reviewed literature; no specific studies, sample sizes, or quantitative effects reported in abstract.
For all the hype, today's scientific AI still represents a collaborator whose imagination, outputs and judgment benefit from human grounding.
Synthesis of study findings: limited diversity in non-reasoning models, field-specific failures, weak agreement of automated evaluators with experts, and modest gains from augmentations, all supporting the conclusion that human grounding improves AI outputs and judgment.
Reasoning models roam a wider hypothesis space, yet no model class spontaneously proposes null hypotheses — a move humans make more freely.
Model-output analysis comparing 'reasoning' vs 'non-reasoning' classes on hypothesis-space breadth and presence/absence of null hypotheses; human responses used as comparison.
The economic consequences of generative AI in financial markets depend critically on institutional context (regulatory and governance capacity).
Synthesis of heterogeneous treatment effects and interaction results across markets with varying governance/regulatory quality in the cross-market panel analysis.
AI is best understood as a real technological revolution with localized bubble dynamics rather than as either a pure speculative mania or a bubble-free productivity miracle (central conclusion).
Synthesis of the paper's review and diagnostic findings combining asset-pricing theory, empirical evidence on fundamentals, and bubble-detection diagnostics.
Current evidence shows both genuine fundamentals and bubble-like fragilities in AI valuations.
Synthesis of reviewed empirical findings in the paper: realized revenue growth, enterprise adoption, productivity evidence (supporting fundamentals) and faster capex vs monetization, concentrated private-market valuations, and narrative-driven investor behavior (supporting fragilities).
National AI development can be interpreted as a controlled balance between information injection and entropy dissipation.
Theoretical mapping using HCLM; paper presents this dynamical framing and definitions of the two processes; no empirical sample.
These examples show an important shift in the governance of wealth chains – the creation of new forms of infrastructural power through which algorithmic models may become central nodes in tax governance.
Synthesis/interpretive conclusion in the abstract that the illustrative examples imply a governance shift and new infrastructural power; presented as interpretive argument rather than empirically demonstrated in the abstract.
This signals a transformation of the assumed information asymmetries between suppliers, clients, and regulators that sits at the heart of the Global Wealth Chains framework.
Conceptual claim in the abstract linking technological change to shifts in information asymmetries within the Global Wealth Chains framework; presented as interpretive argument rather than supported by reported empirical data in the abstract.
A key development is a move away from deliberate opacity for secrecy purposes into systems that search for the optimal exploitation of legal affordances.
Analytic/interpretive claim made in the abstract about a shift in practices; presented as an argument based on the authors' reflection and examples rather than empirical measurement in the abstract.
Analysis of recent benchmark evidence including SWE-bench Verified, EvoClaw, and LangChain's multi-agent coordination studies demonstrates both the transformative potential of the agentic paradigm and its current limitations.
Empirical/benchmark analysis referencing SWE-bench Verified, EvoClaw, and LangChain multi-agent studies as sources of evidence; the paper analyzes these benchmarks qualitatively or comparatively (specific sample sizes and quantitative effect sizes not stated in the abstract).
By redefining discoverability metrics and authority signals, LLM-integrated search ecosystems are reshaping digital marketing economics.
Argumentative claim in the paper linking shifts in discoverability and authority to broader digital marketing economic effects; presented as conceptual synthesis without quantitative evidence in the excerpt.
Visibility in LLM-integrated search is shifting from click-through optimization to 'Answer Inclusion Optimization' (AIO), where visibility depends on whether content is selected, synthesized, and cited within AI-generated responses rather than on SERP ranking alone.
Conceptual proposition and terminology introduced by the authors (AIO); presented as a reframing of visibility metrics rather than backed by quantified experiments in the excerpt.
The rapid integration of large language models (LLMs) into search engines and conversational AI platforms is fundamentally transforming the landscape of search engine optimization (SEO).
Statement in paper's introduction asserting observed integration of LLMs into search engines and conversational platforms; based on conceptual analysis and literature synthesis (no empirical sample or quantified measurement provided).
AI functions as a conditional capability amplifier, expanding agency while producing uneven inclusion shaped by disparities in connectivity, skills, and infrastructure.
Analytical synthesis and illustrative empirical evidence from interviews showing differential effects tied to connectivity, skills, and infrastructure.
AI-mediated financial decisions are reflexive: they reshape organizational workflows, prices, liquidity, credit allocation, and the future data on which subsequent decisions rely.
Conceptual argument supported by literature across finance and related fields (review-level synthesis; no single empirical sample size reported).
Human–AI complementarity in finance is conditional rather than automatic, depending on task structure, private information, feedback quality, incentives, explanation design, and governance.
Synthesis of literature from finance, management, HCI, and AI showing moderating factors for complementarity (conceptual integration; no unified empirical sample size reported).
AI advances science through structurally distinct creative pathways rather than a single mechanism; the creative pathway depends on how AI is incorporated into the research process.
Interpretation synthesized from observed heterogeneity in creativity outcomes across classified AI research modes (Tool-oriented vs Adaptation-oriented) in the >1M publication analysis.
As a representative of new quality productive forces, brain–computer interface (BCI) technology raises high expectations but also acute concerns about brain‑privacy protection.
Statement in paper's introduction/abstract; conceptual observation based on literature and contextual analysis (no empirical study reported).
Adaptive governance conditions how AI-driven capabilities translate into sustainability and risk outcomes.
Comparative analysis across the three jurisdictions (China, US, UK, 2022–2025) integrating quantitative indicators and qualitative documentary evidence, with the abstract highlighting the 'conditioning role of adaptive governance'.