Evidence (7560 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
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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 |
Human Ai Collab
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A notable subset of studies identifies critical risks associated with LLM-assistants.
Synthesis across included studies noting reported risks (e.g., cognitive offloading, collaboration issues).
Pre-launch testing exposed failures that text-only benchmarks rarely measure, including fabricated trading rules, fee paralysis, numeric anchoring, cadence trading, and misread tokenomics.
Outcome of pre-launch test cases and observed failure modes during testing.
There is limited but suggestive early evidence of labor market disruption from AI/LLMs.
Paper summarizes emerging empirical research indicating early signs of disruption; the abstract characterizes the evidence as limited and suggestive without presenting numeric estimates or sample sizes.
Certain occupations face the greatest risk from AI-driven automation (the article examines which occupations are most at risk).
Paper claims to examine occupation-level risk using synthesized empirical studies; the abstract does not list which occupations or quantitative risk estimates.
There is a gap between theoretical automation potential and observed real-world implementation of AI/LLMs.
Synthesis of recent empirical studies that compare task-level exposure metrics with employment and usage data; no specific sample sizes or numeric estimates provided in the abstract.
Seed quality bounds what search can achieve: evolution can refine and extend an existing mechanism, but cannot compensate for a weak foundation.
Authors' experimental observations and analysis comparing outcomes starting from different seed designs (qualitative conclusion drawn from experimental runs).
Humans are more aggressive negotiators, accepting deals without a counteroffer only 56.3% of the time compared to 67.6% for LM-based agents.
Quantitative comparison reported in the user study (acceptance rates for humans vs LM-based agents).
Monthly operational cost of running the system is approximately USD 4,000.
Full-scale performance characterization reports monthly cost estimate of approximately USD 4,000.
The supply of AI-literate workers attenuates wage inequality effects.
Presented in the article as a distributional mechanism informed by synthesized theoretical and empirical findings; no concrete empirical methods or sample sizes are provided in the excerpt.
The framework addresses emerging tensions captured in the Creativity Paradox, whereby GenAI may weaken intrinsic motivation, conceptual risk-taking, and evaluative depth.
Theoretical extension of paradox theory and conceptual discussion of potential negative effects; presented as conceptual risks rather than empirically demonstrated outcomes.
Making AI usable can thus make procedures easier for future governments to learn and exploit.
Synthesis concluding claim based on the paper's formal model and argumentation (theoretical; no empirical testing reported).
The model shows why expansions in AI use may be difficult to unwind.
Analytical conclusion from the paper's formal model (theoretical argument without empirical sample).
The model explains why reforms that initially improve oversight can later increase that vulnerability.
Analytical/theoretical result from the paper's formal model (presented as an explanation; no empirical data).
The model shows when these systems become vulnerable to strategic use from within government.
Analytical result derived from the paper's formal theoretical model (no empirical validation reported).
The compliance layer can also create a stable approval boundary that political successors learn to navigate while preserving the appearance of lawful administration.
Stated conclusion/insight from the paper's formal argument and conceptual framing (theoretical, no empirical sample).
Manual tools like mind maps support structure creation but lack intelligent (AI) assistance.
Paper's comparison of manual tools versus AI-augmented tools (background/related-work discussion; no empirical evaluation reported for this claim).
Current LLM-based systems let users query information but do not let users shape how knowledge is organized.
Paper's analysis of existing tools and limitations (literature/feature comparison described in introduction; no new empirical test reported).
Knowledge workers face increasing challenges in synthesizing information from multiple documents into structured conceptual understanding.
Statement in paper's introduction/motivation; conceptual observation (no empirical data reported here).
In the absence of intervention, individually rational adoption of genAI will assuredly and profoundly reduce collective welfare.
Conclusion drawn from the paper's theoretical model (normative/predictive claim based on model dynamics; no empirical validation or sample reported in abstract).
Habit formation around genAI use can couple otherwise separate domains, so that adoption in low-stakes tasks spills over into high-value tasks and amplifies welfare losses.
Theoretical/model-based claim showing coupling across domains via habit formation (model extension; no empirical sample reported in abstract).
The introduction of genAI—while initially beneficial at the individual level—will reduce social welfare for the most important types of tasks.
Model-derived result: theoretical analysis indicates social-welfare reductions in high-value tasks despite individual gains (no empirical sample reported in abstract).
Generative models are vulnerable to model collapse: when trained on data generated by earlier versions of themselves, their outputs can lose diversity and accuracy.
Theoretical claim / conceptual claim presented in the paper (no empirical sample size given in abstract); refers to degradation of model outputs when trained on self-generated data.
Industrial robots are widely used in manufacturing, yet most manipulation still depends on fixed waypoint scripts that are brittle to environmental changes.
Background statement in the paper's introduction; general literature/field observation (no new primary data reported for this claim in the abstract).
Each new task domain requires painstaking, expert-driven harness engineering: designing the prompts, tools, orchestration logic, and evaluation criteria that make a foundation model effective.
Author assertion in the paper's introduction/abstract describing the state of practice; no empirical method, dataset, or sample size reported in the excerpt.
Ungoverned coupling between humans and AI can produce fragility, lock-in, polarization, and domination basins.
Theoretical/modeling analysis showing destabilizing dynamics and multiple basins of attraction when governance regularization is absent or weak; no empirical sample.
Classical robot ethics framed around obedience (e.g. Asimov's laws) is too narrow for contemporary AI systems.
Literature synthesis and conceptual argument drawing on developments in adaptive, generative, embodied, and embedded AI; no empirical sample reported.
Current evaluation proxies are insufficient for predicting downstream human impact.
Empirical results in the paper showing decoupling between standard quantitative proxies (e.g., sparsity, faithfulness) and human outcomes (clarity, decision utility, confidence) across datasets and analyst reviews.
A highlighting policy that is optimal for sophisticated agents can perform arbitrarily poorly when deployed to naive agents.
Constructive worst-case examples and theoretical bounds in the paper demonstrating arbitrarily large performance degradation when applying sophisticated-optimal policies to naive agents.
Optimizing highlighting for sophisticated agents can be computationally intractable, even in simple discrete and binary settings.
Theoretical complexity results and proofs in the paper showing hardness of the optimization problem under the sophisticated-agent model; no sample/calibration required (formal/algorithmic analysis).
Ethical concerns—such as transparency, explainability, psychological effects, and responsible AI governance—are critical factors influencing employability outcomes.
Review synthesis highlighting ethical issues from empirical and industry literature as influential on employability outcomes.
There are significant AI adoption challenges in education and industry that affect employability and role transformation.
Synthesized evidence from industry reports and empirical studies discussed in the review highlighting barriers to adoption in education and industry.
From the perspectives of 'personal subordination' and 'economic subordination', AIGC deeply and implicitly controls the labor process through mechanisms such as dynamic path planning, blurring the boundaries of determination.
Analytical/legal argument in the paper linking conceptual standards of subordination to specific algorithmic mechanisms (e.g., dynamic path planning); supported by mechanistic discussion but no reported empirical measurement or sample.
AIGC constantly challenges traditional standards for determining labor relations.
Paper's analytic claim based on conceptual/legal argument that algorithmic features of AIGC complicate application of existing labor-relation tests; no quantitative validation or sample size provided.
The transformation toward algorithmic enterprises raises critical concerns regarding agency, accountability, data monopolization, and algorithmic bias.
Presented as a principal concern in the paper's conceptual discussion and interdisciplinary critique; based on analysis of governance and ethical literature rather than new empirical evidence in the abstract.
Algorithmic management and monitoring have reduced employees’ autonomy and perceived work meaningfulness, contributing to 'AI anxiety' characterised by concerns about job loss, skill obsolescence, and diminished control.
Qualitative studies, survey evidence, and theoretical literature reviewed that document impacts of algorithmic management on autonomy, meaningfulness, and worker anxiety (mixed-methods literature).
Automation has intensified income inequality between high-skilled and low-skilled workers.
Synthesis of empirical literature linking automation adoption to widening wage and income gaps across skill groups (literature review).
Displacement effects have extended from manufacturing into cognitive roles such as clerical work and customer service.
Review of empirical studies documenting automation/substitution effects in cognitive, clerical, and customer-service roles (literature synthesis).
Automation has put downward pressure on wages.
Cited empirical studies and wage analyses in the reviewed literature indicating wage suppression associated with automation adoption (literature review).
AI and robotics have led to contractions in low-skilled occupations.
Synthesis of empirical literature reporting occupational contractions in low-skilled jobs following automation adoption (literature review).
Extensive empirical evidence shows that AI and robotics can substitute for rule-based, codifiable routine tasks.
Review cites extensive empirical studies demonstrating substitution of rule-based, codifiable routine tasks by AI/robotics (literature synthesis).
Artificial intelligence and robotic technologies are fundamentally reshaping labour markets and pose multifaceted challenges to workers engaged in routine and low-skilled tasks.
Narrative review of domestic and international scholarly literature over the past decade (literature review / synthesis).
Structural barriers, workforce biases, and digital skill gaps affect women’s participation in AI-enabled sectors.
Claim derived from the paper's synthesis of literature (peer-reviewed studies, policy analyses, preprints) identifying common barriers; the abstract does not report quantitative meta-analysis or specific sample sizes.
Vibe coding (unstructured GenAI-driven coding) promises rapid prototyping but often suffers from architectural drift, limited traceability, and reduced maintainability.
Paper asserts this as a motivating observation and characterizes vibe coding's weaknesses; the abstract frames these as commonly observed problems motivating the Shift-Up approach (no sample size given in abstract).
In post-AGI economies the presupposition of agent autonomy becomes nontrivial because artificial systems may exhibit varying degrees of autonomy, functioning as tools, delegates, strategic market actors, manipulators of choice environments, or possible welfare subjects.
Theoretical argumentation and conceptual classification in the paper; no empirical data reported (modeling/motivating discussion).
Scalable AI tutoring for procedural skill learning requires structured knowledge representations, yet constructing these representations remains a labor-intensive bottleneck.
Background/claim made in the paper's introduction framing the problem; no specific quantitative evidence reported in the abstract.
Under-represented groups tend to be systematically under-observed because of historical exclusion and selective feedback, which exacerbates uncertainty for those groups.
Conceptual claim supported by illustrative examples (e.g., lending context) and simulations demonstrating selective feedback effects; literature citation likely included in paper.
Policies that ignore the unobserved (counterfactual) space can harm decision makers (via unrealized gains or losses) and subjects (via compounding exclusion and reduced access).
Theoretical argumentation and illustrative examples (e.g., loan denial counterfactuals) and modelled simulations showing downstream harms when ignoring unobserved outcomes.
Experiments on simulated data with varying bias show that unequal uncertainty and selective feedback produce disparities across groups.
Simulation experiments described in the paper manipulate bias and feedback patterns and report resulting group disparities (synthetic datasets; experiment details in methods/results sections).
The study is framed based on Job Demands-Resources (JD-R) theory, positing that HAI-C task complexity is a job demand and AI self-efficacy/humble leadership act as resources that can mitigate negative effects on engagement.
Introduction states JD-R theory as the theoretical basis and describes job demands (HAI-C task complexity) and job/personal resources (humble leadership, AI self-efficacy) in the hypothesized model.
HAI-C tech-learning anxiety reduces employees' work engagement (serves as the mediator between HAI-C task complexity and work engagement).
Mediation analysis via hierarchical regression and bootstrapping on the three-wave survey sample of 497 employees; reported in Results as the mediating mechanism.