Evidence (4114 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 |
Innovation
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AI-related equities initially act as net transmitters of shocks.
Directional spillover measures from the TVP-VAR showing AI equity group had positive net directional connectedness early in the sample.
The theoretical superiority of SignSGD accurately predicts its faster convergence during the pretraining of a 124M parameter GPT-2 model.
Empirical experiment reported in the paper: pretraining runs of a 124M-parameter GPT-2 model comparing SignSGD (or Muon) vs baseline SGD/variants; details (number of runs, datasets, seeds) are not provided in the abstract.
Extending the sign operator to matrices preserves the optimal scaling with dimensionality: we provide an equivalent optimal lower bound for the Muon optimizer in the matrix domain.
Theoretical extension of the analysis to matrix-valued problems and derivation of a matching optimal lower bound for the Muon optimizer, demonstrating preserved scaling.
SignSGD effectively reduces the complexity by a factor of d under sparse noise, where d is the problem dimension (comparison of SignSGD upper bound with SGD lower bound shows a factor-d improvement).
Theoretical comparison between the derived upper bound for SignSGD and the derived lower bound for SGD within the paper, under the separable/sparse noise model and specified smoothness assumptions.
Under this distinct problem geometry (l1-stationarity, l_infty-smoothness, separable noise), we derive matched upper and lower bounds for SignSGD and explicitly characterize the problem class in which SignSGD provably dominates SGD.
Theoretical derivation of both upper bounds (for SignSGD) and matching lower bounds (for the problem class) presented in the paper; proofs establishing tightness.
By analyzing sign-based optimizers under l1-norm stationarity, l_infty-smoothness, and a separable noise model, we can better capture the coordinate-wise nature of signed updates and overcome the barrier that prevents sign-based methods from outperforming SGD in standard settings.
Theoretical analysis in the paper introducing these alternative geometric/assumption settings (l1-stationarity, l_infty-smoothness, separable noise) and deriving results under these assumptions.
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.
Participants rated outputs along multiple dimensions and submitted bids for access using a Becker-DeGroot-Marschak (BDM) mechanism.
Methodological description: multi-dimensional rating scales and BDM bidding procedure reported in the paper.
The study used an online experiment in which participants evaluated responses from four anonymized models across academic, professional, and personal contexts.
Study design description in the paper: four anonymized models, multiple contexts (academic, professional, personal), online experimental protocol.
Participants systematically distinguish between outputs and exhibit consistent preferences over stylistic features.
Participant ratings of outputs along multiple dimensions across responses from four anonymized models in academic, professional, and personal contexts; consistent patterns in stylistic feature preferences reported.
The driving effect of digital technology integration on low-carbon transformation is more prominent for firms located in central and western regions.
Regional heterogeneity analysis reported in the paper shows stronger effects for firms in central and western regions of China within the panel sample (2009–2021).
The driving effect of digital technology integration on low-carbon transformation is more prominent in non-state-owned enterprises.
Heterogeneity analysis in the paper finds larger estimated effects for non-state-owned firms compared with state-owned firms, using the same panel sample and methods.
The driving effect of digital technology integration on low-carbon transformation is more prominent in large-scale firms.
Heterogeneity analysis reported in the paper shows stronger estimated effects for larger firms in the panel sample (energy-intensive A-share listed firms, 2009–2021).
Digital technology integration boosts low-carbon transformation mainly by reducing operating costs.
Mechanism analysis in the paper identifies reduced operating costs as a key channel through which digital technology integration promotes low-carbon transformation; based on panel regressions and mediation-style tests using the same sample.
Digital technology integration boosts low-carbon transformation mainly by enhancing corporate R&D innovation capacity.
Mechanism analysis in the paper finds that an increase in R&D innovation capacity is a primary transmission channel linking digital technology integration to low-carbon transformation; based on the same panel and empirical methods.
The positive effect of digital technology integration on low-carbon transformation remains valid after a series of robustness tests.
Authors report that the main finding holds after conducting multiple robustness checks (details not provided in the summary); same panel sample and measurement approach as main analysis.
Digital technology integration significantly promotes the low-carbon transformation of energy-intensive enterprises.
Panel data of A-share listed firms in energy-intensive industries (2009–2021); measure of corporate digital technology integration from text analysis (frequency of digital-technology-related words in annual reports); low-carbon transformation measured using the LTFP method; empirical regression tests reported in the paper.
Adopting a critical software studies perspective enables the authors to offer final recommendations for socio-technical development programmes that could plausibly move toward AGI-adjacent capability while meeting requirements for transparency, moderation, wellbeing and sustainable business models.
Stated conclusion/intent in the paper's introduction that the chosen perspective allows the production of concrete recommendations; presented as a programmatic claim rather than empirically demonstrated in the excerpt.
Targeted environmental policies (like the LCCP) can unintentionally catalyze technological upgrading in firms.
Interpretation/generalization of the empirical DiD findings showing LCCP increased smart manufacturing adoption despite LCCP's carbon-mitigation focus; presented as a theoretical and policy implication in the paper.
Firms facing tighter financial constraints show a stronger effect of the LCCP on adopting smart manufacturing technologies.
Heterogeneity analysis by firms' financial constraint status within the DiD empirical strategy (paper reports stronger treatment effect for financially constrained firms).
Traditional manufacturing firms experience a larger adoption response to the LCCP compared with other firms.
Industry-level heterogeneity analysis in the staggered DiD framework contrasting traditional manufacturing firms with other industries.
The LCCP's effect on promoting smart manufacturing adoption is stronger in cities with more advanced industrial structures.
Heterogeneity/subsample analysis within the DiD design comparing treatment effects across cities grouped by industrial structure maturity (as reported in the paper).
The positive effect of the LCCP on smart manufacturing adoption is more pronounced among firms located in cities with lower resource dependence.
Heterogeneity/subsample analysis within the staggered DiD framework comparing LCCP effects across cities with different levels of resource dependence (as reported in the paper).
The Low-Carbon City Pilot (LCCP) policy significantly promotes firms' adoption of smart manufacturing technologies.
Empirical analysis using a staggered difference-in-differences (DiD) approach on A-share-listed firms in China from 2007 to 2023; treated vs. control comparison of firms located in LCCP cities.
By modeling ideas as congestible resources, we show that source-level crowding is identifiable from within-distribution comparisons, yielding an excess-crowding coefficient Δ and a human-relative diversity ratio ρ.
Modeling/theoretical analysis in the paper that introduces metrics (Δ and ρ) and claims identifiability of source-level crowding using within-distribution comparisons (no empirical quantities given in the abstract).
We introduce a human-relative framework for benchmarking AI-induced human diversity collapse without requiring human-AI interaction data, providing an ex ante protocol to estimate crowding risk from model-only generations and matched unaided human baselines.
Methodological contribution described in the paper: a framework/protocol for estimating crowding using only model generations and matched unaided human baselines (no numeric sample sizes reported in the abstract).
The main finding (that the reform increases grain yield) is robust to multiple checks, including parallel trend tests, placebo tests, propensity score matching DID (PSM-DID), and exclusion of special samples.
Battery of robustness tests reported in the paper: parallel trend tests, placebo tests, PSM-DID estimation, and analyses excluding special samples.
The grain-yield-enhancing effect is stronger in areas with stronger environmental regulation intensity.
Heterogeneity analysis in the paper comparing regions by environmental regulation intensity.
The grain-yield-enhancing effect is stronger in regions with higher levels of digital economy development.
Heterogeneity analysis dividing sample by regional digital economy development level.
The grain-yield-enhancing effect of the water resource tax reform is more pronounced in non-major grain-producing areas.
Heterogeneity analysis reported in the study comparing effects across major vs. non-major grain-producing regions.
The reform enhances regional green innovation, which contributes to higher grain yield by strengthening water-use efficiency and agricultural productivity.
Mechanism analysis presented in the study showing increases in measures of regional green innovation after the tax reform.
The water resource tax reform significantly increases grain yield.
Quasi-natural experiment using the pilot 'fee-to-tax' reform; panel dataset of Chinese prefecture-level cities, 2013–2019; multi-period difference-in-differences (DID) estimation supplemented by double machine learning and multiple robustness tests.
On missing value reconstruction, Schema-1 achieves lower reconstruction error than all classical statistical methods and frontier large language models on mean performance across conditions.
Empirical missing-value imputation experiments comparing Schema-1 to classical statistical methods and large language models, reporting lower mean reconstruction error across tested conditions (specific methods, conditions, and metrics not provided in the abstract).
Schema-1 outperforms gradient-boosted ensembles, AutoML stacks, and the tabular foundation models we evaluate on established row-level prediction benchmarks.
Empirical evaluation on established row-level prediction benchmarks comparing Schema-1 to gradient-boosted ensembles, AutoML stacks, and evaluated tabular foundation models (benchmarks and numerical results not detailed in the abstract).
Schema-1 is the first DLM: a 140M parameter model trained on more than 2.3M synthetic and real-world tabular datasets.
Model specification reported in the paper (explicit parameter count and training dataset count).
A Data Language Model (DLM) understands tables the way a language model understands sentences: natively, without serialization or preprocessing, directly from raw cell values.
Model design and description presented in the paper (Schema-1 is given as an instance of a DLM); claimed capability based on architecture and training approach.
DePAI offers a path to scalable, resilient self-organization that integrates physical infrastructure, AI, and community ownership under transparent rules, on-chain incentives, and permissionless participation, aiming to preserve human autonomy.
Normative/conceptual claim and argument based on the proposed architecture and incentive design; presented without empirical evaluation.
These elements specify workflows that couple machine execution with human oversight, enabling enhanced self-organization of techno-socio-economic systems, which we call DePAI.
Theoretical workflow specification and argumentation in the paper; no reported experimental or observational validation.
We connect DAO design with digital-democracy research on deliberation and voting, showing how each can advance the other.
Conceptual linkage and theoretical argumentation drawing on literature from DAO design and digital-democracy research; no empirical test or sample described.
We synthesize foundations in blockchains, decentralized autonomous organizations (DAOs), and cryptoeconomics.
Literature synthesis and conceptual review within the paper; no empirical sample or experimental method reported.
We propose DAO-enabled decentralized physical AI (DePAI), a democratic architecture for coordinating humans and autonomous machines in the operation and governance of physical-digital systems.
Conceptual proposal and architectural synthesis presented in the paper (theory/design contribution). No empirical evaluation or sample reported.
Insight-driven engagement drives profit while advancing environmental goals; firms must incorporate data-driven analysis into their sustainability plans to gain actionable insights and develop customer strategies that boost profits while enhancing ecological responsibility.
Authors' conclusion based on the study results showing increased loyalty and reduced ecological footprint after implementation of AI+IoT insights; specific supporting data limited to descriptive outcomes provided in the chapter.
Nigerian companies can utilize artificial intelligence in conjunction with Internet of Things tools to sift through complex streams of customer data in real-time and align their green actions with what shoppers perceive as environmentally friendly.
Chapter demonstrates this using a deployed approach combining AI and IoT; methods described include real-time data capture from retail kiosks, apps, home sensors, and wearables.
Businesses reduced their ecological footprint through tailored green messages, smarter product suggestions, and targeted eco-marketing aligned with shoppers' values.
Article statement that environmental footprint reductions occurred after implementation; environmental impact data were collected via IoT sensors measuring energy consumption, waste generation, and carbon footprint metrics over twelve months.
Businesses implementing these insights demonstrate a 25 to 40% increase in loyalty.
Article statement reporting observed outcomes after businesses implemented the AI+IoT-driven insights; no sample size or statistical test reported in the text provided.
Results provide operations managers with tech-backed playbooks for responsible resource use without compromising profit motives, enabling operational excellence while meeting environmental and social responsibilities.
Paper conclusion/implication statement asserting managerial applicability of findings; grounded in the study's reported results but presented as a recommendation/implication rather than a quantified finding.
Firms maintain competitive costs while implementing AI-IoT eco-networks.
Paper claims that waste and emissions reductions are achieved without compromising costs; specific cost metrics or statistical tests not provided in the abstract.
Firms embracing AI-IoT eco-networks trim carbon output by 20-35%.
Paper results reported as empirical findings; presumably measured via carbon footprint assessments and IoT/operational metrics across the case study firms and facilities.
Firms embracing AI-IoT eco-networks cut waste by 30-50%.
Paper results reported as empirical findings; based on mixed-methods case studies of 12 multinational companies and IoT data from 45 facilities (as stated in methods).