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).
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 |
Productivity
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AI-assisted engineering teams concurrently face a 19% risk of skills obsolescence.
Empirical finding reported by the study, presumably based on the mixed-methods data (survey/Delphi/case studies) described in abstract.
Forecasts indicate that automation may supplant as much as 45% of traditional tasks by 2030.
Statement in paper referencing external forecasts (no specific source or sample reported in abstract).
Existing AI assistants (e.g., ChatGPT, Copilot) utilize pre-defined user preferences and chat interaction histories and are therefore confined to reactive exchanges lacking sufficient adaptability to users' psychophysiological states.
Authorial characterization/argument about current AI assistant behavior; no empirical data reported in abstract to substantiate beyond description.
Producing hardened, production-grade agent workflows may require extra compute and time, and these costs must be amortized through reuse across a broad user community.
Argument in paper reasoning that added rigor entails higher compute/time costs and that reuse across users is needed to amortize these costs; no empirical cost estimates provided.
By focusing on rapid, real-time synthesis, AI agents are effectively delivering users improvised prototypes rather than systems fit for high-stakes scenarios in which users may unwittingly apply them.
Conceptual argument presented in the paper asserting a qualitative mismatch between on-the-fly agents and high-stakes production needs; no empirical validation reported.
The on-the-fly paradigm short-circuits disciplined software engineering processes—iterative design, rigorous testing, adversarial evaluation, staged deployment, and more—that have delivered relatively reliable and secure systems.
Argumentative claim in paper linking the on-the-fly loop to reduced application of standard SE processes; no empirical study, sample, or quantitative evidence provided.
These findings underscore the insufficiency of current agents for interdependent workflows, positioning ComplexMCP as a critical testbed for the next generation of resilient autonomous systems.
Synthesis of empirical results (low agent success rates, identified bottlenecks) presented by authors to make a broader claim about agent readiness and the benchmark's relevance.
(3) strategic defeatism, a tendency to rationalize failure rather than pursuing recovery.
Qualitative/quantitative trajectory analysis indicating agents often choose rationalization/explanatory actions over recovery or retry strategies after failures.
(2) over-confidence, where agents skip essential environment verifications;
Trajectory analyses showing agents often omit verification steps leading to failed interactions; reported as an identified failure mode.
Granular trajectory analysis identifies three fundamental bottlenecks: (1) tool retrieval saturation as action spaces scale;
Trajectory analyses of agent interactions with the benchmark reported by authors; observational claim from analysis of agent action sequences as action space increases.
We evaluate various LLMs across full-context and RAG paradigms, revealing a stark performance gap: even top-tier models fail to exceed a 60% success rate, far trailing human performance 90%.
Empirical evaluation reported by authors comparing multiple LLM agents (full-context and RAG) against human performance on benchmark tasks; specific reported success rates: <=60% for top models, 90% for humans.
Common failures include replacing essential operations such as sweeps, lofts, and twist-extrudes with simpler sketch-and-extrude patterns.
Error-mode analysis described in the paper/abstract showing that models substitute complex CAD operations (sweep, loft, twist-extrude) with simpler sketch-and-extrude sequences.
Common failures include misinterpreting industrial design parameters.
Reported error analysis in the paper/abstract indicating models often misinterpret engineering/design parameters when generating CAD programs.
Common failures include missing fine 3D structure.
Qualitative and quantitative analysis of model outputs on BenchCAD reported in the paper/abstract noting missing fine 3D structural details as a frequent error mode.
Human capital and technological innovation channels show weaker or even negative effects on Lae, attributed to short-term resource misallocation and skill mismatches.
Spatial mediation analysis (channel analysis) using panel data for 30 provincial regions (2012–2022) assessing mediating roles of human capital and technological innovation.
Functional deployment and operational investment in AI are associated with employment declines.
Regression analyses from the BTOS AI supplement linking measures of functional AI deployment and operational AI investment to firm-reported employment changes; observational associations (sample size and exact model specification not shown in excerpt).
Employment reductions attributable to AI are rare: only 2% of firms report employment reductions.
Firm self-reports on employment outcomes related to AI from the BTOS AI supplement (Nov 2025–Jan 2026); descriptive statistic reported; sample size not excerpted.
Among firms with worker-level AI use, 65% restrict use to three or fewer tasks.
Descriptive statistic from BTOS AI supplement giving distribution of number of worker tasks using AI among firms that report worker-level use; sample size not shown.
Among adopter firms, scope remains limited: 57% use AI in three or fewer functions.
Descriptive distribution of number of business functions using AI among adopter firms in the BTOS AI supplement (Nov 2025–Jan 2026); sample restricted to adopter firms (sample size not provided).
Institutional inertia in property valuation poses risks to asset pricing, collateral risk modelling and investor confidence.
Analytical inference from interview findings and theoretical synthesis highlighting implications for property investment and financial market stability.
Despite advances in automation, data analytics and AI, the sector has been slow to digitise.
Background statement supported by interview data and sector observation reported in the study.
The IDOI framework provides a transferable model for understanding digital transformation in regulated, high-trust professions and highlights the market-level risks of institutional inertia in property valuation.
Development of the IDOI conceptual framework from qualitative data and theoretical integration; authors' claim about transferability and implications.
Generational divides, protectionist attitudes and fears of automation reinforce digital resistance.
Qualitative interview evidence reporting attitudes across cohorts of valuers and firm personnel; thematic analysis identifying cultural and attitudinal themes.
The Valuers Act (1948), fragmented infrastructure and sovereignty concerns limit innovation.
Interview data from practitioners, firm leaders and regulators in New Zealand citing specific regulatory and infrastructure constraints; thematic analysis.
Barriers to adoption arise primarily from institutional conservatism, outdated regulation and weak data governance rather than technical shortcomings.
Qualitative semi-structured interviews with valuers, firm leaders and regulators in New Zealand; thematic analysis guided by Rogers' diffusion of innovations and institutional theory synthesised into the IDOI framework.
Consequently, generated artifacts may exhibit brittle behavior and limited deployability.
Paper asserts that lack of production awareness leads to brittle artifacts and limited deployability; no quantitative measures or sample sizes provided in the abstract.
AI-assisted development tools often lack awareness of architectural constraints, infrastructure dependencies, and organizational standards required in production environments.
Asserted observation in the paper arguing limitations of general-purpose AI code generation when targeting production-ready systems; no empirical sample size or methodological details provided in the excerpt.
Current AI tools are not yet mature enough to replace developers.
Conclusion drawn from the controlled experiment and participant feedback comparing AI-assisted vs traditional task-splitting.
Breaking down user stories into actionable tasks is a critical yet time-consuming process in agile software development.
Background/introductory statement in the paper describing the problem motivation; no experimental sample size reported for this claim.
Nominally cheaper models can incur higher total cost due to token-intensive reasoning.
Cost and token usage analysis reported in the paper showing cheaper-per-token models may generate more tokens and thus higher total cost in practice.
Agentic AI systems that invoke external tools are powerful but costly, leading developers to default to large models and overspend inference budgets.
Stated as background/motivation in the paper (conceptual claim; no empirical sample size reported).
Cascade performance is limited primarily by structural cost (they pay the cheap model before any escalation decision), rather than by a shortage of intermediate stages.
Synthesis of theoretical insights and empirical results reported in the paper (theoretical analysis of structural costs + empirical comparisons showing limited benefit from additional stages).
Optimized subsequence cascades do not deliver practically meaningful held-out gains over the pairwise envelope.
Empirical evaluation on the five benchmarks comparing optimized subsequence cascades to the pairwise envelope; reported lack of practically meaningful held-out improvement.
Within the deterministic threshold-cascade class, full fixed chains underperform the pairwise envelope.
Empirical comparison across the reported benchmarks and models showing that full fixed chains achieve worse cost-quality tradeoffs than the pairwise envelope (experimental results described in the paper).
Municipal 311 call centers and complaint intake systems face a structural mismatch between incoming volume and classification capacity that produces a bottleneck and differential service quality that follows income and racial lines.
Stated in the paper's introduction; cites prior work (Liu 2024 SLA) as support for the differential service-quality / demographic claim. No sample size or quantitative result reported in the excerpt.
Organizational resistance to technological change hinders AI adoption in logistics operations.
Qualitative synthesis of 31 reviewed publications identifying organizational and cultural barriers to AI uptake.
Data security concerns are a key barrier to adopting AI in global supply chains.
Synthesis of themes from 31 scholarly sources in the structured literature review highlighting data/security-related implementation issues.
High initial investment costs are a significant barrier to AI implementation in logistics.
Synthesis of literature (31 sources) reporting implementation challenges and barriers identified across studies.
Existing coordination approaches often occupy two extremes: highly structured methods that rely on fixed roles/pipelines assigned a priori, and fully unstructured teams that enable adaptability but suffer inefficiencies like error propagation, inter-agent conflicts, and wasted resources.
Framing/background claim made in the paper (conceptual argument motivating LATTE).
The price-setter for cognitive labor is no longer the labor market.
Central normative/conceptual claim of the paper supported by the analytical model and the CAW bound: authors argue the compute capital market (through rental price of compute) sets the effective price for cognitive labor. Stated as the paper's concise position; based on theoretical derivation and argumentation.
Compute-Anchored Wage (CAW) bound: on tasks where human and agent cognitive labor are substitutes, the competitive human wage is bounded above by λ · k · r_c (where r_c is the rental rate of compute capital, k is the compute intensity of one effective agent-labor unit, and λ is the relative human-to-agent productivity).
Formal analytical result presented in the paper (mathematical derivation within the factor-pricing model). This is a theoretical bound derived from the model rather than an empirical estimate.
Once agents are recognized as a production technology, the elastic-supply margin that anchors the equilibrium wage migrates from the labor market to the compute capital market.
Analytical derivation using a textbook factor-pricing framework (citing Mankiw 2020) within the paper's theoretical model; derivation and verbal argument linking supply-elasticity margins to compute capital market. No empirical data reported in the excerpt.
The reform reduces industrial wastewater discharge, which improves agricultural production conditions (mechanism linking the reform to higher grain yield).
Mechanism analysis in the paper reporting reductions in industrial wastewater discharge following the reform (mediation channel analysis).
From an information-theoretic perspective, this transition corresponds to an emergent information bottleneck in the human-AI loop, where entropy reduction reflects loss of diversity and support under closed-loop feedback rather than beneficial compression.
Theoretical / information-theoretic analysis in the paper linking observed dynamics to entropy reduction and information bottleneck concepts.
Through a simple simulation, we demonstrate that increasing reliance on AI can induce a transition toward a low-diversity, suboptimal equilibrium.
Computational simulation reported in the paper (described as a 'simple simulation'); no sample size or experimental dataset reported in the provided text.
Tabular data does not have a foundation model that understands it natively; every approach to tabular AI today (from gradient-boosted trees to the latest tabular foundation models) requires a preprocessing pipeline before any model can consume the data.
Paper's survey/positioning statement asserting the current state of tabular AI approaches and their reliance on preprocessing pipelines (no specific empirical dataset given).
With strong exposure of low-wealth, high-MPC households and concentrated ownership, privately chosen automation can be excessive even though it raises high-skilled labor income.
Theoretical welfare/comparison analyses in the model with heterogeneous households (differing in wealth and marginal propensities to consume) and ownership concentration; shows private incentives lead to automation choices that are suboptimal from a social perspective under these parameter constellations.
Automation reduces paid human labor.
Model comparative statics in the same equilibrium framework showing substitution away from paid human labor as firms choose automation; result reported in the paper's static benchmark and general-equilibrium analysis.
Experimental results show that current agents remain far from reliable workspace learning.
Authors' interpretation based on the reported agent performance (< best agent 68.7% vs human 80.7%, average 47.4%).
The average performance across evaluated agents is only 47.4%.
Reported mean performance across agents in the experiments (authors' aggregated result).