Evidence (269 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
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Brown AI (S3) is macro-neutral in GDP terms but imposes a consumption cost of 0.42% by 2030 as infrastructure investment crowds out household expenditure.
Model simulation of scenario S3 (Brown AI) in the 23-sector recursive dynamic CGE calibrated to Vietnam 2019 I-O table; S3 modelled as an exogenous IT hardware and services investment surge.
This shift raises fundamental questions for consumer theory, which has traditionally modeled humans as the primary decision-makers.
Conceptual argument presented in the paper framing the research problem and motivating the new theoretical framework; literature critique rather than empirical test.
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).
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.
The opportunities of AI in human good are real and vast; and the opportunities in human ill, in human society, in human institutions of government, and in the longer term in the environment in which humanity thrives are real and underestimated.
Author's evaluative judgment asserting both substantial benefits and substantial underestimated harms of AI (normative claim without empirical substantiation in the excerpt).
AI is altering nearly every aspect of human interaction—such as work and society.
Statement in the paper's abstract/intro; presented as a general observation in the paper (literature review/qualitative synthesis implied). No primary sample size or empirical estimate reported in the provided text.
Human anchors build trust through a broadly effective relational pathway (perceived intimacy), while AI anchors' functional advantage converts into trust only under specific motivational conditions (high utilitarian motivation).
Interpretation of moderated mediation results from randomized experiment (N = 439) showing intimacy-mediated trust for human anchors and responsiveness-mediated trust for AI anchors only under high utilitarian motivation.
Consumer trust in live-streaming commerce is a conditional, motivation-dependent process rather than a uniform preference for either anchor type.
Synthesis of experimental results showing differential mediation/moderation patterns by hedonic and utilitarian motivation in sample N = 439 (moderated mediation analyses).
Perceived responsiveness became a significant pathway favoring AI anchors only when utilitarian motivation was high; at low utilitarian motivation, this pathway reversed direction.
Conditional (moderated) mediation analyses from the experiment (N = 439) including utilitarian motivation as moderator; reported that responsiveness→trust path favored AI anchors at high utilitarian motivation and reversed at low utilitarian motivation.
Network externalities create an opportunity for win-win industrial policies, but the realisation of such mutually beneficial outcomes depends on market structure (product differentiation/substitutability) and the nature of innovation (product vs process).
Synthesis of model results across parameter regimes in the two-country strategic trade and R&D model showing conditional win-win equilibria; theoretical arguments (no empirical sample).
The welfare consequences of an industrial policy targeting a sector with network externalities are determined by the interaction between the strength of the externality, the type of R&D, and the degree of product differentiation between the home and the imported goods.
Analytical results from a two-country theoretical model with strategic trade and R&D investment; comparative-static analysis of equilibrium outcomes (no empirical sample).
At equilibrium prices in symmetric markets, consumer surplus is improved by cheaper search but may be decreased by more informative search, due to weakened inter-business competition.
Equilibrium price analysis within the theoretical model for symmetric firms; comparative statics showing how search cost and signal informativeness affect pricing, competition intensity, and consumer surplus. No empirical validation reported.
Consumption is affected by the multiplier effect and the transformation patterns of R&D.
Theoretical: model analysis links consumption dynamics to a multiplier effect and to how R&D transforms inputs/outputs (comparative statics/dynamics in the theoretical framework).
Individuals’ welfare is influenced by both the privacy cost of big data sharing and their consumption levels.
Theoretical: welfare in the model is specified as a function of consumption and a privacy cost term arising from big data sharing; result follows from analytic derivation within the model (no empirical/sample data).
Financial well-being is not an automatic byproduct of automated credit efficiency but an emergent outcome of architectural alignment among technology, borrower capability, and governance structures.
Theoretical conclusion drawn from empirical results showing mixed effects (positive on repayment and resilience, negative on stress) and significant moderation by human capability and institutional design.
Distinct AI features (recommendation engines, chatbots, and comparison tools) influence consumer outcomes when modeled as latent constructs.
Methodological claim: the study modeled three AI features as latent constructs and analyzed their relationships with dependent variables using SEM (quantitative questionnaire data).
Some patients value human contact for sensitive cases; automated interactions can feel impersonal.
Semi-structured interviews with patients/staff and open-ended survey responses documenting preferences for human interaction in sensitive/complex complaints.
India’s reported post-harvest loss is relatively low (3.2%) despite poor food-security outcomes (Global Hunger Index rank 111/125).
Reported statistics cited in the paper (FAO/Kaggle for post-harvest loss; Global Hunger Index ranking referenced).
Asymmetric consumer protections and unclear liability frameworks for online content allow platforms to extract value from users and avoid meaningful responsibility.
Paper's legal critique of consumer protection asymmetries and liability regimes across EU jurisdictions; based on doctrinal analysis rather than empirical measurement.
Brown AI’s infrastructure investment crowds out household expenditure, causing the reported consumption cost.
Mechanism described in the paper: modelled exogenous IT investment surge (S3) reallocates resources toward investment and away from household consumption in the CGE results.
AI development and deployment can shift costs onto others, including environmental pressures on local communities.
Author assertion listing environmental pressures as an externality of AI development and deployment; no empirical data or sample provided in excerpt.
The divergence between welfare-adjusted prosperity (GAGI) and headline GDP widens sharply after 2022, temporally coincident with the after-effects of COVID and the acceleration of generative-AI deployment, though this evidence alone does not demonstrate causation.
Temporal pattern observed in the authors' G7 2010–2026 empirical series (associational observation; authors explicitly note lack of causal identification).
Applying GAGI to the G7 economies over 2010-2026 shows that welfare-adjusted prosperity has diverged persistently and increasingly from headline GDP growth.
Empirical analysis performed on G7 countries over 2010–2026 (sample: 7 economies; time series comparison of GAGI vs. GDP per capita).
GDP per capita is blind to two first-order determinants of lived prosperity: income/wealth distribution and inflation impact.
Conceptual/definitional argument presented by the authors in the paper (no empirical test reported).
A 'critical transmission path' can occur in which AI-induced productivity gains are weakly transmitted to households and may generate absorption tension.
Conceptual framework / theoretical argument in the review (no empirical sample reported).
Productivity gains from AI do not automatically translate into broadly distributed welfare or into output fully absorbed by market demand.
Conceptual review / theoretical argument and literature synthesis presented in the paper (no empirical sample reported).
AI-enabled fraud is already mounting against African mobile-money systems, the part of the digital economy the continent leads.
Paper reports observed increases in AI-enabled fraud targeting African mobile-money systems (trend observation / incident reports referenced).
In the consumption phase, high costs lead to service stratification, making it difficult for technological dividends to benefit the general public.
Theoretical/qualitative argument about cost barriers and unequal access to AI-enabled services; no empirical evidence or sample sizes reported.
Urbanization reduces environmental carrying capacity (LCF), with reductions more pronounced under higher ecological pressure.
Panel ARDL estimates for the G-7 (1990–2019) finding a statistically significant negative relationship between urbanization (World Bank WDI data) and LCF; authors emphasize stronger negative effects under greater ecological pressure.
Globalization reduces environmental carrying capacity (LCF), particularly at higher levels of ecological pressure.
Panel ARDL estimates on the G-7 panel (1990–2019) report a statistically significant negative effect of the KOF globalization index on LCF, with stronger negative effects at higher ecological pressure (authors note the impact is pronounced 'particularly at higher levels of ecological pressure').
Emerging evidence suggests that general disclosures of AI involvement may unintentionally undermine consumer trust and reduce purchase intentions.
Paper cites 'emerging evidence' as motivating prior work; no specific studies, sample sizes, or effect estimates given in the provided text.
Liu et al. (2026a, 2026b) find experimentally that the severity of AI service failure in hotel contactless services significantly decreases customers' forgiveness willingness, but high levels of brand attachment mitigate this negative effect.
Experimental studies in hotel contactless service contexts (details and sample sizes not provided in the text).
The emergence of AI-generated summaries and answer-driven search experiences is shifting consumer discovery from link-based navigation to synthesized, context-aware responses.
Stated observation in the paper; argued via conceptual reasoning about AI-generated summaries and answer-driven interfaces rather than reported empirical metrics or sample-based experiments in the excerpt.
A budget-neutral anti-gaming design reduces consumer harm by 0.025 relative to computable static rules.
ABM/RL simulation comparison reported in the paper (design variants evaluated across scenario/sweep runs and the firm-period panel).
Ordinary adaptive updates lower consumer harm (0.202 to 0.194).
ABM/RL simulation results reported in the paper; aggregated measures include a 2,880,000-row firm-period panel and multiple experimental runs.
All 24 risks were judged as being more than 5% likely to cause catastrophic outcomes.
Aggregate Delphi judgments reported in paper: for each of the 24 risks, experts judged the probability of catastrophic outcomes to exceed 5% (n=272).
In a scenario where pragmatic mitigations are implemented, experts still judged five risks as having a more than 10% probability of catastrophic outcomes: dangerous capabilities, weapons & cyberattacks, environmental harm, inequality & unemployment, and power centralization.
Delphi responses under an alternative (pragmatic mitigations) scenario from the same expert panel (n=272); paper lists five specific risks still judged >10% catastrophic probability.
In a business-as-usual scenario, experts judged 18 of 24 risks as having a more than 10% probability of catastrophic outcomes (e.g., more than 1 million deaths or more than USD 100B in financial loss) in the next 5 years (2025-2030).
Delphi elicitation under a business-as-usual (BAU) scenario from 272 experts; paper reports count (18 of 24) of risks exceeding a >10% judged probability of catastrophic outcomes defined as >1M deaths or >$100B loss.
Experts estimated the five most severe harms in the next 5 years were likely to come from dangerous capabilities, competitive dynamics, weapons & cyberattacks (including CBRNE), power centralization, and false information.
Delphi panel rankings/ratings of risk severity across 24 risks collected from 272 experts; paper reports these top five as the most severe for the 5-year horizon.
These systems have access to reams of sensitive user data.
Stated as a factual consequence of the described integration (conceptual observation in the paper); no empirical measurement or dataset cited in the excerpt.
Results across 15 experimental runs reveal that elderly female occupants consistently experience the lowest satisfaction in initial rounds.
Empirical experiment results reported in abstract: 15 experimental runs; observed satisfaction distribution across demographic profiles with elderly females lowest initially.
Content filtering (blocking searches for Gaza War and Tulsa race massacre).
Documented cases of content filtering cited/synthesized in the paper (specific blocked search topics reported).
Generative AI fundamentally changes advertising: rather than placing products into discrete slots, it enables interventions on the generative process itself, which induce commercial influence through less observable channels.
Conceptual argument backed by analysis of how generative models produce outputs and how interventions can operate on latent variables of generation; illustrated via taxonomy in the paper rather than quantified empirical tests.
Empirical research shows that ads woven directly into large language model (LLM) outputs often go undetected by users.
Reference to prior empirical studies (unspecified in the excerpt) showing user failure to detect embedded ads in LLM outputs; presented as an empirical finding rather than new experimental data in this paper.
Severe penalties in underfunded Eastern systems, mediated by financial distress, drive families toward resource exhaustion.
Cross-country comparisons in SHARE-derived analyses showing larger financial penalties in underfunded Eastern European systems, with mediation analysis implicating financial distress and resultant resource exhaustion.
Financial distress acts as a profound multiplier of the burdens associated with palliative care.
Interaction/moderation analyses in SHARE-derived synthetic data showing that pre-existing financial distress amplifies financial and caregiving burdens under PC.
Socio-demographics heavily modulate exposure: lacking a spousal net inflates the burden.
Subgroup/moderation analyses in SHARE-derived data comparing households with and without spousal support, showing higher burdens when no spouse is present.
Non-cancer trajectories drive massive structural penalties that escalate at the distribution's tail, mechanically compounded by physical dependency.
Stratified analyses by disease trajectory (non-cancer vs cancer) using SHARE data (2016-2021) and quantile models showing larger penalties for non-cancer cases, especially in tail quantiles; physical dependency identified as a compounding factor.
Quantile treatment models expose a 'broken shield' for vulnerable households and severe tail events (PC protection fails or reverses at distributional tails).
Application of quantile treatment effect models to synthesized SHARE-derived digital twins (2016-2021), explicitly examining distributional/tail effects.
Skewed ad delivery of public-service ads can prevent certain groups of individuals from accessing information about resources on the basis of their demographic identity.
Argument/implication drawn from observed demographic skew in ad delivery and its relevance to public-service outreach; no specific empirical sample size reported in the excerpt.