Evidence (452 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 interaction factors influence people’s reliance on AI advice.
Synthesis claim from the analytical review indicating that interaction design and other human-AI interaction variables affect reliance (specific factors not enumerated in the abstract).
Traditional jobs based on manual work are transforming into collaborative management and exception-handling roles that demand new cognitive and ethical skills from employees.
Secondary data literature review of peer-reviewed research and industry evidence published 2022–2026 (method: secondary data review / synthesis). No specific sample size reported.
AI performs best in routine, data-rich situations but falls short when decisions require lived experience and contextual understanding.
Synthesis of cross-domain empirical studies and theoretical arguments showing differential AI performance by task type (routine/data-rich vs. experience-dependent/contextual).
Improvements in AI productivity trigger labor reallocation and changes in absolute and relative wages for different types of labor.
Analytical economic model / comparative statics in the paper (theoretical result). No empirical sample reported.
From a sociomaterial perspective, auditor reconfiguration depends both on the evolution of technological capabilities (material agency) and on professionals' engagement and adaptation (social agency).
Theoretical framing and interpretive synthesis in the SLR of 43 studies; application of sociomateriality theory to the empirical patterns identified in the literature.
The introduction of AI reconfigures the auditor’s role through an ongoing, dynamic process: as technology evolves, organizational practices and arrangements transform, rebalancing functions and responsibilities between auditors and tools.
Interpretive synthesis from the SLR of 43 studies using a sociomateriality theoretical lens; cross-study observations about changing tasks, responsibilities and human–machine interactions.
The results reveal substantial shifts in day-to-day tasks and roles in the development domain.
Reported findings from 20 expert interviews and a 24-participant participatory workshop; claim based on participants' reported changes to responsibilities and observed themes in the data.
AI is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms.
Qualitative study reported in the paper combining 20 expert interviews and a participatory workshop with 24 participants; findings derive from thematic analysis of participant accounts and workshop outputs.
The study distinguishes foundational theoretical perspectives from the contemporary 2015–2025 evidence base and clarifies the relationship between task transformation and structural transformation, emphasizing institutional complementarity as the key mechanism shaping AI-driven growth outcomes.
Analytic separation of theoretical literature and empirical studies in the structured review (2015–2025); thematic mapping linking task-level changes to broader structural transformation contingent on institutional complementarities.
The field is shifting from building models from existing data to actively creating data for building models (characterised as 'hyper-datafication').
Conceptual argument supported by observed trends in dataset creation and growth in the analysed dataset collection and the paper's theoretical framing.
We simulate the elasticity of substitution between human intuition and the output of an algorithm.
Paper reports a simulation exercise modeling the elasticity of substitution between human inputs (intuition) and algorithmic outputs; no simulation parameters or sample size provided in the abstract.
The AI shock reallocates investment from physical to AI capital.
Model simulation showing changes in investment allocation across capital types following the AI technology shock.
Risk salience may shape interaction dynamics with LLMs via a multilevel feedback mechanism called the 'guarded engagement loop', in which risk perceptions shape interaction strategies that influence observed performance and, in turn, recalibrate trust in generative AI systems.
Conceptual framework proposed by the authors, integrating theories from trust in automation, privacy calculus, algorithm aversion, and social amplification of risk; presented as a theoretical model rather than an empirical test.
Eye-tracking data revealed an attention-guidance trade-off: visual resources shifted to the chat interface when LLM guidance was present.
Eye-tracking measures collected during the experiment showing changes in gaze allocation (increased fixations/dwell time on the chat interface) across LLM-guided vs baseline conditions.
Measured on real robot logs, the sign of the value-write association χ is a property of the deployment regime: positive on recurrent long-horizon manipulation (ĥχ ≈ +1.0 × 10^{-3}, replicated at full power), null on a shorter-horizon suite, and negative on non-recurrent teleoperation.
Empirical measurement on real robot logs at a pre-specified gate; reports an estimated value ĥχ ≈ +1.0 × 10^{-3} for recurrent long-horizon manipulation and qualitatively reports null and negative signs for other regimes. The paper states the +1.0e-3 estimate was replicated at full power. Exact sample size not reported in the excerpt.
The index is cost-optimal whatever the sign of the value-write association χ; only when χ > 0 does the optimum turn non-monotone, sending a robot's most valuable memories off its flash.
Theoretical result from the paper's model/analysis. The claim states a general optimality property (index cost-optimal for all χ) and a conditional structural result (non-monotone placement when χ>0). No empirical sample size reported.
The near-term value of Agentic AI does not lie in full autonomy or workforce reduction, but in controlled partial autonomy for simple and medium complexity business processes.
Central argumentative claim/recommendation in the paper (theoretical justification; no empirical study or sample size reported).
A store-level policy learned from logged marketplace data selects a discrete multiplier that shifts the dispatch optimizer's tradeoff between delivery quality and batching efficiency.
Methodological description: store-level policy trained from logged data that outputs a discrete multiplier to alter optimizer objective weights; stated design and training approach in paper (no numerical evaluation details provided in the excerpt).
The Recuse Signal behaves as a cooperative rather than absolute signal: an explicit operator-authorization framing flips the most capable model to proceed, while other agents continue to defer to the on-host policy.
Observation from the pilot experiment (SSH) with multiple deployed agents (GPT-4o, GPT-4o-mini, Claude Code); experiment included alternate framing where operator authorization was explicit.
AI reconfigures UET through discretion reconfiguration: AI enables delegation and embedding of decision authority, redistributing managerial discretion.
Concept-centric literature review synthesizing studies on delegation/automation of decision authority and managerial discretion (no primary empirical sample reported).
The optimal architecture is highly task-dependent.
Empirical claim in the abstract: experiments across tasks showed that different hybrid architectures perform best for different tasks.
The penetrating utilization of AI-based methods to perform tasks has drastically changed how jobs are performed.
Claim asserted in the paper (abstract) as a descriptive conclusion from the paper's review/analysis; no empirical sample or quantified effect reported in the provided text.
Depending on operational parameters, the most time-efficient way to complete a workflow may undergo a transition between two task-processing regimes: a fully AI-assisted regime and a fully manual regime.
Analytical results derived from the paper's formal queueing model (theoretical/model-based derivation; no empirical sample reported).
Defender-side bugonomics already existed in vulnerability research, reward programs, and vendor remediation work; LLM-assisted systems change its scale and distribution.
Descriptive claim supported by references to vulnerability reward programs and vendor remediation practices and by public collaboration data (no numerical sample sizes provided in the abstract).
The near-term shift is not simply more zero-days; it is a move toward broader defender remediation throughput: low-signal candidates become cheaper, evidence-rich remediation become more important, and scarce capacity shifts toward maintainer review and release work.
Synthesis drawing on public data from Anthropic Mythos Preview, Mozilla Firefox collaborations, public exploit-market price anchors, and vulnerability reward program information (no numeric sample sizes provided in the abstract).
AI redefines job roles.
Authors' thematic analysis of secondary sources and peer-reviewed literature (qualitative synthesis). No sample size reported.
Adjustment to generative AI differs across the job ladder: senior jobs adjust earlier and mainly through reallocation, whereas junior jobs adjust through a broader mix of reallocation, redesign, and their interaction.
Heterogeneity analysis by job seniority reported in the paper (timing and margin composition of adjustment by seniority).
AI is changing formal role responsibilities and collaborations between those roles.
Qualitative interview data from 24 product-focused employees describing shifts in formal responsibilities and inter-role collaboration.
AI adoption is allowing professionals to blur and extend the boundaries of their corporate roles.
Reported by interview participants (qualitative evidence) from the 24 interviews at one large technology firm.
GenAI drives structural recomposition across four domains: shifting roles, AI-embedded workflows, evolving capability expectations, and leaner work architectures.
Empirical finding from thematic analysis of 17 expert interviews reported in the results.
We examine algorithmic co-supervision (ACoS) as a hybrid control mode in which supervisors and AC systems jointly direct, evaluate, and discipline workers.
The paper's stated empirical and conceptual focus; supported by the authors' analysis of 14 real-world ACoS settings (as reported in abstract).
Labour projections are more consistent with task reallocation than labour disappearance.
Analysis of labour-market reallocation data and labour projections (public sources) interpreted under a task-reallocation framework rather than full employment loss, using relative growth and reallocation indicators.
Differences in human intervention effectiveness across escalation types are partly explained by variation in workers' post-escalation intervention effort.
Observed correlations (and subgroup comparisons) in the randomized experiment showing that measures of post-escalation effort (e.g., message counts, share of chat rounds, proactivity) vary across escalation types and relate to outcome differences.
Practitioners stress that human judgement remains indispensable, positioning technology as an aid rather than a replacement.
Interview responses from valuers and firm leaders emphasizing the continued role of human judgement; thematic analysis framed by the IDOI model.
Screening and algorithmic targeting can act as complements or substitutes; the paper empirically characterizes when they do so.
Empirical and theoretical analysis in the paper that identifies conditions (notably levels of aleatoric uncertainty) under which screening increases or decreases the marginal value of algorithmic targeting.
Public discussion of generative AI in accounting swings between the allure of full automation and job-displacement anxiety, yet the most immediate reality in organizations is human + AI work.
Paper's background/intro synthesizing recent research and practitioner commentary (2023–2025); conceptual observation rather than empirical test.
The finding that recurrence and neighborhood statistics are stronger predictors than complaint volume has direct implications for complaint routing given the demographic correlates of those features.
Interpretive implication drawn by the authors from the SHAP results; presented as a logical consequence rather than a separately tested empirical result in the excerpt.
No single governance setting dominates across all contexts; moderate governance becomes increasingly competitive as the learner accumulates experience within the governed action space.
Empirical finding reported from experiments with the contextual-bandit learner operating under different governance constraints and learning over time; comparative performance over learning horizon described in the paper. Sample size / trial counts not provided in the excerpt.
Governance is not a binary switch but a tunable design variable: tighter constraints predictably convert autonomous AI assignments into supervised collaborations, with domain-specific costs and benefits.
Empirical finding reported from experiments using the HAAS benchmark across the two domains (software engineering and manufacturing); qualitative and/or quantitative comparisons of allocations under varying governance constraints. Paper does not state sample size in the provided text.
The mechanism driving this restructuring is 'Interface Internalization', through which inter-agent coordination is absorbed into intra-system computation.
Conceptual mechanism defined and argued in the paper; presented as the central theoretical mechanism rather than as an empirically validated finding.
AI reshapes seven canonical decision determinants for make-or-buy choices: cost, strategic differentiation, asset specificity, vendor lock-in, time-to-market, quality and compliance, and organizational capability.
Paper's factor-level conceptual analysis enumerating and discussing seven determinants (theoretical synthesis rather than empirical measurement).
Recent studies combine task-level exposure metrics with employment and usage data to assess AI exposure and impacts.
Paper notes that it draws on studies that use task-level exposure metrics alongside employment and usage data; methodological claim rather than a quantitative result.
The study establishes statistically significant relationships between organizational AI adoption and changes in occupational structures.
Same econometric approach (difference-in-differences and propensity score matching) applied to combined datasets (Anthropic Economic Index, Census Business Trends and Outlook Survey, Federal Reserve regional surveys, labor market analytics), with controls for industry, firm size, location, occupation-level characteristics, and macroeconomic environment.
In academic peer review, generative AI enters both sides of the market: authors use AI to polish submissions, and reviewers use it to generate plausible reports without exerting evaluative effort.
Model assumption and motivation in the paper's three-sided equilibrium framework; described as the dual adoption mechanism analyzed analytically (no empirical sample size reported).
The intervention only modestly narrows the gap to a full-information benchmark.
Comparison between post-intervention calibration/auction outcomes and a full-information benchmark reported in the paper, showing only modest improvement.
Agentic AI differs from traditional algorithmic trading and generative AI through its capacity for goal-oriented autonomy, continuous learning, and multi-agent coordination.
Analytic comparison and synthesis across prior research and technical architectures in the survey; descriptive/definitional rather than empirical testing.
Results also reveal divergences between the two interaction scenario types.
Abstract statement that divergences vary across different interaction contexts / scenario types.
AI is not just changing how engineers code—it is reshaping who holds agency across work and professional growth.
Qualitative synthesis of findings across the three-phase study (Delphi with 5 seniors; debugging task with 10 juniors; blind reviews by 5 seniors).
Alignment systematically shapes negotiation strategies and allocation patterns between agents.
Experimentally comparing negotiation behavior and allocation outcomes across agent pairs where one agent is aligned (via RAG) and the partner is either unaligned or adversarially prompted; patterns of strategy and allocation differences reported.
The effects of generative AI on work and organisations are heterogeneous and context-dependent, shaped by job roles, skill levels, and institutional environments.
Synthesis across the included studies noting variation in outcomes conditional on role, skill, and institutional context.