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).
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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 |
Productivity
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Pure behavioural teams (N=8) failed to scale beyond 74.1%.
Reported team performance metric for 'pure behavioural' teams with sample size N=8; maximum reported performance 74.1%.
Fast AI induced instant, blind compliance; human accuracy under deception collapsed to 50.2%.
Reported experimental result comparing Fast/Less-Accurate AI condition to baseline conditions; numeric accuracy reported as 50.2% for humans under deception.
There is an urgent question of how humans can effectively supervise and control an economy operated by AI agents when this system may expand beyond the capacity of traditional governance.
Framed as a central research/policy concern in the paper's abstract; conceptual argument rather than empirical finding.
The Agent Economy raises new regulatory challenges concerning data privacy, security, ethics, and the risk of job displacement.
Stated in paper abstract as identified risks; based on literature synthesis and comparative policy analysis approach (method described), but no empirical incidence metrics reported.
The requirement that review + expected rework attention be lower than manual completion attention is substantially more stringent than the requirement that AI merely generate faster drafts.
Comparative analytical argument based on the model's derived stability conditions (theoretical/model-based reasoning; no empirical sample reported).
Under congestion, reviewers rationally raise the risk threshold for checking AI outputs, reducing scrutiny precisely when it would matter the most.
Analytical implication derived from the queueing model presented in the paper (theoretical/model-based inference; no empirical validation reported).
Mean-based metrics (e.g., tasks completed per worker-hour or mean handle time) can misrepresent AI's effects in workflows where tasks accumulate and compete for scarce human attention.
Argument and analysis presented in the paper; theoretical reasoning and illustrative queueing model (no empirical sample reported).
Regardless of apparent performance advances in AI technology, human and environmental factors of the organization may substantially attenuate — or even negate — the effective productivity benefits.
Conceptual argument in the paper; theoretical reasoning and literature synthesis (no primary empirical data reported in the abstract).
Adopting AI in organizational practice does not guarantee productivity gains, because human and environmental factors critically moderate the relationship between AI deployment and realized productivity improvements.
Position paper's conceptual argument presented in the abstract; no empirical sample or quantitative study reported.
AI adoption presents workforce adaptation challenges.
Reported in the study's literature synthesis and thematic analysis of secondary sources (qualitative review). No sample size reported.
AI adoption raises ethical considerations.
Authors' thematic evaluation of secondary literature identifying ethical issues associated with human-AI collaboration (qualitative synthesis). No sample size reported.
AI adoption presents challenges related to skill gaps.
Thematic findings from peer-reviewed literature and secondary data (qualitative review). No sample size reported.
There is a 'speedup illusion' where people have accurate forecasts of independent completion times but significantly underestimate AI-assisted times.
Empirical pattern reported in the abstract: comparison of predicted vs. actual times shows accurate independent forecasts but underestimation of AI-assisted completion times (preregistered study, N = 1237).
As these systems scale, the bottleneck shifts away from raw model capability toward coordination.
Analytical/argumentative claim in the paper framing a shift in primary constraint; no empirical study or quantified benchmark reported.
More persuasive narratives may have had a detrimental effect on the ability to discriminate between a correct and incorrect AI prediction.
Exploratory analyses in the paper reporting reduced discrimination between correct and incorrect AI predictions when narratives were more persuasive.
More persuasive narratives may have had a detrimental effect on decision response times.
Exploratory analyses reported in the paper indicating persuasive narratives were associated with longer decision response times.
Higher benchmark performance does not reliably show that a system can carry out knowledge work in real-world deployment settings.
Argument based on review of current knowledge-work evaluation and benchmark design literature; paper motivates with conceptual analysis and references to empirical work showing mismatch between benchmark tasks and deployed work settings.
Current systems still struggle with evidence preservation, reproducibility, weak-direction rejection, provenance tracking, cross-domain robustness, and accountable scientific closure.
Survey-identified recurring failure modes and limitations reported in literature and system descriptions; qualitative synthesis.
Current systems remain fragmented, differing in autonomy, domain scope, execution environment, validation mechanism, and human oversight.
Survey of existing systems and categorization across the listed dimensions; descriptive synthesis rather than an empirical meta-analysis.
Even SOTA coding agents (Codex with GPT-5.4 and Claude Code with Opus 4.6) succeed on only 2/7 distributed key-value-store specifications.
Empirical evaluation reported in the paper comparing two SOTA coding agents on a suite of 7 distributed key-value-store specifications; success counted as meeting the specification.
Large retrieval models based on Small Language Models (SLMs) such as Qwen3-Embedding-4B/8B set strong upper bounds on public benchmarks but their deployment in high-throughput, latency-sensitive environments remains impractical.
Statement about model performance on public benchmarks (upper bounds) and practical deployment constraints (throughput and latency), asserted by authors; no numerical deployment analysis provided in excerpt.
Evaluating state-of-the-art kernel agents on FastKernels, the strongest agent achieves only 0.94× aggregate speedup over production baselines, with weaker agents at 0.78× and 0.53×.
Empirical evaluation of multiple state-of-the-art kernel-generation agents on the FastKernels benchmark; aggregate speedup factors reported in abstract. The number of benchmark tasks is likely the FastKernels task set (46), though the abstract does not explicitly state the evaluation sample size for this measurement.
Existing benchmarks are poorly aligned with production inference frameworks: they evaluate kernels on a single GPU with synthetic inputs, ignore the surrounding compilation stack, and reward replicating known optimizations rather than discovering new ones.
Stated as motivating observation in the paper (conceptual/empirical critique of existing benchmark design and incentives). No numerical sample size given in the abstract.
Integrations of AI that neglect human factors are associated with increased anxiety, burnout, and disengagement among users.
Aggregate findings from the systematic review reporting associations in the literature between non-human-centered AI integration and negative psychological/work outcomes.
Notable challenges to AI implementation include concerns about algorithmic bias, privacy, transparency, job displacement, organizational culture, and issues related to ethical and legal oversight.
Synthesis of reported challenges across the 29 empirical studies included in the scoping review.
Fragmented, uncoordinated approaches in the absence of national strategy constitute a structural barrier to technological development in Georgia.
Method: logical inference and country assessment presented in the paper documenting fragmentation across policy and institutional actors; qualitative evidence rather than quantitative causal estimation.
In Georgia, the total absence of a national AI strategy and legal definition produces fragmented approaches, creating a structural barrier to technological development.
Method: country-level assessment of policy and legal framework for AI in Georgia; descriptive analysis identifying lack of a national strategy and definition. (No sample size reported.)
Zero-shot evaluation shows the best positive-query mask success rate at IoU@0.75 remains below 0.17.
Empirical evaluation reported in the paper: zero-shot tests across 26 model configurations with reported mask success rate at IoU@0.75.
Zero-shot evaluation of 26 model configurations spanning closed-source MLLMs, open-source VLMs, and specialized grounding systems reveals persistent gaps: the best multi-target Set-F1 reaches only 0.35.
Empirical evaluation reported in the paper: zero-shot tests across 26 model configurations with reported Set-F1 metric.
Reliable evaluation of agricultural visual grounding remains challenging because agricultural targets are often small, repetitive, occluded, or irregularly shaped, and instructions may refer to one, many, or no objects in an image.
Problem characterization / motivation described in the paper (qualitative reasoning about dataset and task properties).
Technical bottlenecks (cross-border data compliance, algorithm interpretability) and ethical challenges (algorithmic bias, privacy infringement, cultural conflicts) are intertwined impediments to intelligent international marketing.
Synthesis of challenges identified across the reviewed literature (systematic review and content analysis, 2010–2025) as reported in the paper.
Traditional international marketing theories, constrained by static assumptions and linear logic, struggle to explain intelligent contexts.
Conclusion from the paper's systematic review and content analysis of core literature (2010–2025); no quantitative test or sample size reported in the summary.
Cost and lack of applicable use case are the most cited barriers to AI adoption, followed by expertise.
Survey question(s) on barriers to adoption in the Census Bureau survey in which respondents reported reasons for not adopting AI; ranking provided in the paper (cost, lack of use case, then expertise).
Intensity-weighted adoption is far lower than the 22.8 percent headline rate.
Survey-derived intensity-weighted measure of AI adoption constructed from the same Census Bureau survey (no numeric value reported in the excerpt).
Only 22.8 percent of plants report any AI use as of 2021.
Direct descriptive estimate from the Census Bureau survey of manufacturing establishments; year reported as 2021.
ID-centric ranking models fail to generalize in livestreaming recommendation due to the short-lived nature of live rooms and poorly learned item IDs.
Authors' assertion linking the cold-start item ID problem to poor generalization of ID-centric rankers (motivating claim). No specific experimental metrics or sample sizes cited in the excerpt.
A live room typically broadcasts for only tens of minutes, so its item ID remains poorly learned in a persistent cold-start state.
Authors' observational/operational claim about livestream characteristics stated in the paper (motivating problem statement). No sample size or quantitative backing provided in the excerpt.
Static benchmarks capture only part of how large language models behave in practice.
Argument supported by the paper's experimental design comparing static evaluations with a timed multi-phase Risk environment that includes repeated planning/execution loops and real-system constraints.
In deployed settings, the effects of AI systems on human agency, creativity, and institutional well-being emerge over time, shaped by repeated interaction, reuse, and integration into real-world workflows, and these dynamics are rarely visible through pre-deployment evaluation or isolated prompt–response analysis.
Argumentative observation based on conceptual reasoning; no empirical data or sample size reported.
The most significant barriers to AI adoption reported by entrepreneurs are human-centred—talent scarcity, organisational resistance, and change management—rather than technology or cost alone.
Theme 'Barriers and the Adoption Journey' from thematic analysis of interviews (n=16); interviewees repeatedly cited human-centred barriers (talent scarcity, resistance, change management) over purely technical/cost barriers.
Raw interaction logs are inherently noisy, contain trial-and-error and low information density, and are inefficient for direct model training.
Author assertion describing properties of raw interaction logs; no empirical quantification provided in the excerpt.
Static 'human data' is expensive to scale and bounded by the knowledge of its creators.
Author claim/argument in the paper's introduction; no empirical sample or quantitative test reported in the provided text.
People exhibit self-estimate miscalibration: on average they believe they are using AI less than they actually are.
Same three pre-registered user studies (combined N = 2691) comparing participants' self-reported AI use against observed/recorded AI use during tasks.
Low-information AI neither improves immediate performance nor preserves performance after AI assistance is removed, and is linked to weaker learning overall.
Within-study comparison of low-information AI assistance versus other conditions in the controlled logical reasoning task; immediate and post-AI performance measured (sample size not reported in abstract).
Greater AI usage is associated with weaker skill development: heavy AI users underperform relative to comparable peers, whereas light AI users perform similarly to matched users who do not use AI.
Controlled experiment using a logical reasoning task with on-demand AI assistance; comparison between heavy users, light users, and matched non-users reported in the study (sample size not stated in abstract).
Regulatory uncertainty and the absence of explicit legislation on digital data and artificial intelligence may leave the economic potential of these technologies unexplored while increasing market concentration, inequality, and the risk of personal information misuse.
Argued implications from the paper's theoretical model and comparative legal discussion; no empirical testing or quantified analysis provided.
Studies finding true synergy are scarce.
Authors' literature synthesis / meta-analytic overview claiming that few studies report combined human-AI performance exceeding both parties alone (no numerical count provided).
Genuine human-AI synergy—combined performance that exceeds what either party achieves alone—is uncommon.
Authors' synthesis of the literature and meta-analytic findings referenced in the paper indicating scarcity of studies showing combined performance > either alone (no specific counts or sample sizes given in the excerpt).
Agentic systems show persistent failures in repository setup, dependency handling, permission gating, and hardware verification.
Issue-resolution benchmarks and hardware/RTL verification research synthesized in the paper (specific failure rates or sample sizes not provided in abstract).
Controlled studies report slowdowns in mature open-source work when using agentic/code-generation systems.
Controlled studies and trials cited in the paper (no sample sizes given in abstract).