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).
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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 |
Human Ai Collab
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No aggregation mechanism can simultaneously satisfy all desiderata of collective rationality (connection to Arrow's Impossibility Theorem); multi-agent deliberation navigates rather than resolves this constraint.
Theoretical argument connecting empirical multi-agent deliberation results to Arrow's Impossibility Theorem and observations that deliberation trades off competing desiderata rather than achieving all simultaneously.
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 design space articulates four configurations—No AI, Hidden AI, Translucent AI, and Visible AI—each trading off among accountability, autonomy, and coordination cost.
Conceptual taxonomy introduced in the paper (design artifact). No empirical evaluation or sample reported in the abstract; tradeoffs are argued theoretically.
CLARITI matches GPT-5's resolution rate on underspecified issues while generating 41% fewer questions.
Empirical evaluation comparing CLARITI and GPT-5 on a task set of underspecified software engineering issues; the result reported in the abstract indicates parity in resolution rate and a quantified reduction in questions (41%) but the abstract does not report sample size, test set composition, or statistical significance.
They can produce fluent outputs that resemble reflection, but lack temporal continuity, causal feedback, and anchoring in real-world interaction.
Descriptive claim made in the text contrasting surface-level fluency with missing properties; no empirical data or experiments provided.
A within-subject human study with 20 players and 600 games shows that our interventions significantly improve performance for low- and mid-skill players while matching expert-engine interventions for high-skill players.
Within-subject human experiment reported in the paper: N = 20 players, 600 games total; comparisons of performance under the proposed interventions versus expert-engine interventions.
This work establishes a foundation for understanding how generative AI systems not only augment cognitive performance but also reshape self-perception and perceived expertise.
Paper's stated contribution presenting theory and conceptual groundwork; no empirical validation provided in the abstract.
The LLM fallacy has implications for education, hiring, and AI literacy.
Implications and argumentation presented in the paper; these are prospective and conceptual rather than supported by empirical data in the abstract.
Removing safety layers made the system less useful: structured validation feedback guided the model to correct outcomes in fewer turns, while the unconstrained system hallucinated success.
Qualitative and quantitative comparisons from the deployed evaluation across the three conditions (observations about turn counts, validation-feedback loops, and model hallucinations in unconstrained condition over the 25 scenario trials).
AI plays a dual role as enhancer and eroder, simultaneously strengthening performance while eroding underlying expertise (the 'AI-as-Amplifier Paradox').
Framing claim presented in the paper's conceptual argument and grounded by the paper's stated year-long empirical study among cancer specialists (no numerical sample size reported in abstract).
Although some frontier models exceed human performance, model accuracy is still far below what would enable reliable experimental guidance.
Paper reports instances where top-performing (frontier) models outperform aggregate human expert accuracy on SciPredict, but concludes overall accuracies are insufficient for reliable experimental guidance.
The local labor market will follow a dual trajectory: low-skill, routine jobs face high automation risk while demand will rise for AI-collaborative, higher-skill roles.
Paper's analytical prediction based on distinguishing current job roles into routine/repetitive vs cognitive/non-routine and projecting likely impacts; no numeric forecasts or sample sizes provided in the excerpt.
Subjectivity persisted in AI-powered recruitment decisions; human judgment remained an important factor.
Theme 2 (subjectivity in AI-powered recruitment) from interviews indicating retained human subjectivity and judgement in recruitment processes (n = 22).
Sensitivity analyses indicate the observed positive belief changes likely reflect recovery from carry-over effects rather than genuine training-induced shifts.
Authors' sensitivity analyses discussed in the paper that examined alternative explanations (e.g., carry-over effects) and concluded the belief-change result is likely due to recovery from such effects.
We ran two large preregistered experiments (N=17,950 responses from 14,779 people) using conversational AI models to persuade participants on a range of attitudinal and behavioural outcomes, including signing real petitions and donating money to charity.
Statement in paper reporting two preregistered experiments, sample sizes (17,950 responses; 14,779 people), use of conversational AI models, and target outcomes including petition signing and charitable donations.
Bounded agents act as an amplifying but not necessary extension to the foundation-model stack for changing work coordination.
Conceptual argument within the paper distinguishing bounded agents from the core stack; no empirical comparison or measurement 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.
Although the concurrent paradigm performs worse than the sequential paradigm in terms of immediate task performance, it is more effective in promoting users' emotional trust.
Comparison between concurrent and sequential AI-assisted decision-making paradigms in the RCT (N=120); authors report concurrent < sequential for immediate task performance, but concurrent > sequential for emotional trust.
AI adoption outcomes depend on organizational routines, data arrangements, accountability structures, and public values.
Empirical and theoretical literature review and argument in the article drawing on scholarship in digital government and public-sector technology adoption.
Qualitative results underscored both perceived benefits in comprehension and challenges when interpretations of gaze behaviors were inaccurate.
Qualitative analysis of participant feedback from the study (n=36) reporting themes of improved comprehension and occasional problems when the assistant misinterpreted gaze.
The productivity decomposition classifies deployments into five regimes that separate beneficial adoption from harmful adoption and identifies which deployments are vulnerable to the augmentation trap.
Model-based taxonomy produced from the analytical decomposition (classification into five regimes described in the paper).
Small differences in managerial incentives can determine which skill path a worker takes (whether they realize full potential or deskill).
Comparative statics / theoretical sensitivity analysis in the dynamic model indicating tipping behavior based on managerial incentives.
Result 3: When AI productivity depends less on worker expertise, workers can permanently diverge in skill: experienced workers realize their full potential while less experienced workers deskill to zero.
Analytical result from the dynamic model showing path-dependent divergence in skill levels under particular parameterizations (lower dependence of AI on worker expertise).
Mathematics (SAFI: 73.2) and Programming (71.8) receive the highest automation feasibility scores; Active Listening (42.2) and Reading Comprehension (45.5) receive the lowest.
SAFI benchmark results reported for specific O*NET skills (numerical SAFI scores provided in the paper).
The rise of agentic AI development, where LLM-based agents autonomously read, write, navigate, and debug codebases, introduces a new primary consumer with fundamentally different constraints.
Conceptual claim argued in the paper; refers to the emergence of agentic LLM-based tools as new consumers of software artifacts rather than an empirical measurement; no sample size reported.
Analysis uncovers dramatic asymmetries: inhibition 17.6% vs. preference 75.0%.
Paper reports specific aggregated percentages for two types of implicit effects (inhibition and preference) observed in their analysis; methodology context implies these are results from the benchmark evaluation (300 items / 17 models).
The effects of generative AI depend not only on the technology itself, but also the behavioral strategies and incentive structures surrounding its use.
Synthesis and interpretation of RCT results showing interactions between incentive structure and AI-use patterns (no formal interaction coefficients or sample details provided in excerpt).
Through a pre-registered randomized control trial, we show that incentives mediate AI's homogenizing force in a creative writing task where participants can use AI interactively.
Pre-registered randomized controlled trial (experimental design) conducted on a creative writing task with interactive AI use (details such as sample size not provided in excerpt).
By conceptualizing the emergence of a posthuman economy, this study contributes to interdisciplinary debates on artificial intelligence, digital capitalism, and the transformation of economic organization.
Author-stated contribution of the paper based on conceptual/theoretical work; no empirical validation reported.
Contemporary organizations operate within hybrid intelligence environments where human expertise and algorithmic systems collaboratively produce economic knowledge, prediction, and action.
Theoretical synthesis using posthumanist and socio-technical perspectives within the paper; no empirical measurement or sample provided.
This article develops the concept of algorithmic agency to explain how artificial intelligence participates in economic decision-making within modern business systems.
Author's conceptual contribution described in the paper (theoretical development), no empirical testing reported.
Emerging posthumanist scholarship suggests a deeper transformation in which economic agency itself becomes distributed across human and algorithmic actors.
Synthesis of posthumanist scholarship and theoretical literature cited in the paper; conceptual rather than empirical evidence.
Artificial intelligence is fundamentally reshaping contemporary economic systems as algorithmic infrastructures increasingly participate in interpreting information, generating predictions, and influencing organizational decision-making.
Conceptual argument in the paper drawing on posthumanist theory, socio-technical research, and digital economy scholarship; no empirical sample or quantitative data reported.
These results suggest the need for AI model development to prioritize scaffolding long-term competence alongside immediate task completion.
Authors' policy/research recommendation based on experimental findings showing short-term gains but longer-term harms.
These effects are observed across a variety of tasks, including mathematical reasoning and reading comprehension.
Trials included multiple task types (explicitly naming mathematical reasoning and reading comprehension); cross-task analysis reported.
Chinese Marxism's dialectical approach—rooted in the yin‑yang principle—constitutes an alternative epistemology that fundamentally differs from Western either/or logic, and this epistemology underpins the semi‑core's policy and strategic stance.
Philosophical and textual analysis of contemporary Chinese Marxist thought presented in the paper, interpreted in relation to Bauman's philosophical work; no empirical measurement reported, presented as conceptual/theoretical evidence.
Tool developers, users, and social scientists conceptualize 'context' differently, and these divergent conceptualizations reveal specific pitfalls inherent in computational approaches to context.
Analytic comparison across stakeholder perspectives derived from interviews and conceptual analysis in the paper (qualitative evidence; sample size unspecified).
AI adoption significantly reshaped task profiles for 73% of respondents, particularly affecting routine data processing, administrative tasks, and scheduling activities.
Survey data and secondary data analysis reported in this study (sample size not stated); self-reported change in task profiles with reported percentage (73%).
Providing issue-specific design guidance reduces design violations, but substantial non-compliance remains.
Intervention experiments in paper: agents were given issue-specific design guidance and resulting patch compliance measured; reported reduction in violations but remaining non-compliance.
Policy implication: encouraging public sharing of AI-assisted solutions offsets the decline associated with private diversion (flow margin) but cannot repair participation-driven deterioration in conditional resolution; the latter requires directly maintaining contributor engagement.
Prescriptive conclusion from the theoretical model comparing interventions: public-sharing encouragement helps with flow-margin diversion but not with supply-side contributor thinning.
Diagnostic prediction: in a congested regime, observing a joint decline in posted volume and conditional resolution implies supply-side pool thinning is quantitatively present; by contrast, volume decline with stable or rising resolution indicates private diversion (flow margin) alone is the dominant force.
Analytical diagnostic derived from the model that links empirical patterns (volume and conditional resolution) to underlying mechanisms; no empirical validation given in the excerpt.
AI adoption across firms is heterogeneous, varying across sectors such as finance, technology, and manufacturing.
Survey of 150 leading Nigerian firms across finance, tech, and manufacturing showing variation in AI integration; supported by qualitative interviews and policy analysis.
The rapid, heterogeneous integration of Artificial Intelligence (AI) technologies is profoundly reshaping the dynamics of work across the Nigerian business sector, generating both significant economic opportunities and acute labor market challenges.
Mixed-methods study combining a quantitative survey of 150 leading Nigerian firms across finance, tech, and manufacturing and qualitative analysis of government policy and workforce interviews.
For the short-run optimization problem of AI deployment given fixed job responsibilities and worker skill levels, the firm’s optimal strategy for an m-step job can be computed in time O(m^2) using dynamic programming; the long-run joint optimization including task assignment to workers can also be solved in polynomial time up to an arbitrarily small error term.
Algorithmic results and complexity analysis derived in the theoretical sections and appendices of the paper (dynamic programming construction and polynomial-time solution statements).
Appending a neighboring step to an existing AI chain adds no additional human verification burden (verification is a fixed cost at the chain level), which can make appending steps to a chain optimal even if manual execution is individually preferable for the appended step.
Theoretical model setup and formal argument showing verification is incurred only at the last augmented step of a chain; illustrative examples (data scientist workflow) and comparative-cost reasoning in the paper.
AI chaining can overturn standard comparative advantage logic in assignment: when multiple adjacent steps are executed as an AI chain, a step may be assigned to AI (as part of the chain) even if manual human execution would be preferred for that step in isolation.
Theoretical model of production as an ordered sequence of steps with firms endogenously bundling contiguous steps into tasks and jobs; formal comparative-static arguments and illustrative examples in the paper showing how fixed verification costs per chain change marginal assignment incentives.
The effect of increasing the share of AI-automated R&D tasks is non-monotonic: firms initially target more radical innovations, but beyond a threshold of human-AI complementarity, they shift the focus toward incremental innovations.
Analytical comparative-statics in the theoretical model: varying the fraction of R&D tasks performable by AI yields a non-monotonic relationship between AI task-share and optimal recombination distance, with a threshold determined by human-AI complementarity.
Higher AI productivity encourages more distant recombinations, if the direct facilitation effect is stronger than the indirect effect due to intensified competition from rivals.
Comparative-static result from the analytical model: the paper derives a condition comparing the direct facilitation effect of AI on accessing distant knowledge and the indirect effect from increased competition; when the former dominates, equilibrium recombination distance increases with AI productivity.
Models performed well on commonly discussed topics but struggled with specialized health data.
Task-level performance comparison across topics in the elicited population statistics: better accuracy on commonly discussed topics, poorer performance on specialized health data tasks.
In a preliminary experiment, giving models web search access degraded predictions for already-accurate models, while modestly improving predictions for weaker ones.
A preliminary comparative test where some models were given web search access and changes in predictive performance were observed: degradation for already-accurate models and modest improvement for weaker models.