Evidence (70 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 |
Approach motivation (BAS Drive) moderates whether interactive partnership benefits originality.
Moderation analysis reported from the pilot (N = 62) showing interaction between BAS Drive (a measured personality/motivation scale) and the effect of interactive partnership on originality.
Reasoning models roam a wider hypothesis space, yet no model class spontaneously proposes null hypotheses — a move humans make more freely.
Model-output analysis comparing 'reasoning' vs 'non-reasoning' classes on hypothesis-space breadth and presence/absence of null hypotheses; human responses used as comparison.
AI advances science through structurally distinct creative pathways rather than a single mechanism; the creative pathway depends on how AI is incorporated into the research process.
Interpretation synthesized from observed heterogeneity in creativity outcomes across classified AI research modes (Tool-oriented vs Adaptation-oriented) in the >1M publication analysis.
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).
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.
AI usage has dual effects on employees: it can both enhance innovative behavior and predict disengagement, as revealed by a dual-path (SOR-based) model.
Interpretation/synthesis from the four-stage longitudinal study of 285 finance professionals using a dual-path model based on SOR theory (combining the mediation and moderation results).
Self-reported cognitive outsourcing predicts lower originality specifically in human-human dyads.
Correlation / regression result from the in-person pilot (N = 62) reporting that self-reported cognitive outsourcing is associated with lower originality in human-human dyads but not in other conditions.
More innovative creators are especially harmed under the strong-IP regime — a phenomenon the paper terms the "originality penalty."
Analytical result derived from the static game model in the paper highlighting differential effects by creator innovativeness; theoretical characterization labeled "originality penalty."
A regime of strong intellectual property rights, modeled as a static Stackelberg game, also fails to provide adequate creative incentives (it underpowers creative incentives).
Theoretical analysis using a static Stackelberg-game model developed in the paper; analytical results show reduced creator incentives under this regime.
Non-reasoning LLMs collapse into a narrow 'hivemind' of similar ideas.
Comparative analysis of idea outputs from different LLM classes showing reduced diversity/similarity concentration for non-reasoning models (as described in results).
GenAI usage significantly decreased creativity-relevant skills.
Experiment with 82 participants reported in the paper; authors report a statistically significant decrease in measures of creativity-relevant skills for participants using GenAI.
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.
Generated ideas often degrade after implementation.
Paper statement about the gap between idea generation and implemented results reported in the Creation-phase analysis; no quantified follow-up study reported in the excerpt.
Using LLMs led to fewer creative moments observed in participants (p=0.002).
Within-subject comparison between LLM-assisted and unassisted conditions with reported p-value p=0.002. Study sample N=20.
Patent text similarity analysis confirms a 'homogenization trap' (AI-associated increases in patent-text similarity).
Text-similarity analysis of patent documents reported in the paper showing increased patent similarity associated with AI use.
Across short stories, marketing slogans, and alternative-uses tasks, three frontier LLMs fall below parity across crowding kernels.
Empirical experiments reported in the paper evaluating three frontier large language models on three task domains (short stories, marketing slogans, alternative-uses) and finding ρ < 1 (below parity) across crowding kernels. The abstract specifies three models but does not report the number of generated samples per model or other sample-size details.
This creates an evaluation blind spot, as AI can improve individual outputs while increasing population-level crowding.
Theoretical/ conceptual claim in the paper arguing that improvements at the individual-output level can still increase similarity (crowding) at the population level; no empirical numbers given in the abstract.
Creative AI systems are typically evaluated at the level of individual utility, yet creative outputs are consumed in populations: an idea loses value when many others produce similar ones.
Conceptual argument presented in the paper's introduction motivating a population-level perspective on creative outputs (no empirical sample size reported).
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.
Early evidence suggests generative AI increases productivity but does so at the cost of collective diversity, potentially narrowing the set of ideas and perspectives produced.
Statement refers to prior literature/early studies (no specific study, sample size, or method reported in the excerpt).
In the limiting case of full automation, the model predicts that optimal recombination distance collapses to zero, suggesting that fully AI-driven research would undermine the very knowledge creation that it seeks to accelerate.
Limiting-case analytical result of the model: as the share of AI-automated tasks approaches 1 (full automation), the derived optimal recombination distance converges to zero.
Excessive reliance on AI may reduce the originality of research and lead to duplication of research efforts.
Model implication: as the share of tasks automated by AI increases, the paper shows analytically that originality can decline and firms may duplicate research efforts (due to homogenization of methods or search), reducing novel knowledge creation.
LLM design agents can fixate on existing paradigms and fail to explore alternatives when solving design challenges, potentially leading to suboptimal solutions (a pathology analogous to human designers).
Literature/background claim and authors' characterization of observed agent behavior; motivated the proposed metacognitive interventions. No numerical sample size reported.
Significantly more heavy LLM users reported that the writing was less creative and not in their voice.
Self-reported measures from participants in the human user study comparing heavy LLM users to others; no sample size or exact statistics provided in the excerpt.
We show that ρ ≥ 1 is the no-excess-crowding parity condition and connect Δ to an adoption game with exposure-dependent redundancy costs.
Theoretical result derived in the paper linking the human-relative diversity ratio ρ to a parity condition and relating the excess-crowding coefficient Δ to an adoption-game model with exposure-dependent redundancy costs.
Under matched time limits, originality with a GPT-4 partner is statistically equivalent to that with a human partner.
Result from the in-person pilot (N = 62) comparing originality scores between participants partnered with GPT-4 versus human partners under matched time limits; reported as statistical equivalence in the paper.
Self-evaluated creative performance remained unchanged when using GenAI.
Same experiment with 82 participants; authors report no significant difference in self-evaluated creative performance between GenAI users and controls.
LLM-generated solutions contain roughly the same number of ideas as participant-generated solutions.
Comparative analysis of idea counts within solutions reported in the paper; phrased as 'roughly the same number of ideas' (no numeric effect size provided in the abstract).
Prior exposure to highly creative ideas improves later performance, suggesting a 'seeding' intervention.
Experimental observation from the pilot (N = 62) that participants exposed to highly creative ideas showed improved subsequent performance; interpreted as evidence for a seeding effect.
The paper proposes a Multi-Dimensional Creativity Assessment Framework as an alternative to current GPA-based evaluation.
Methodological contribution stated in the paper; framework is proposed and validated against GPA-based prediction.
The Creativity Assessment Framework significantly outperforms GPA-based prediction.
Validation reported in the paper comparing the new Creativity Assessment Framework against GPA-based predictive models; described as 'significantly outperforming' GPA-based prediction.
GenAI supports idea generation, business case analysis, scenario planning, data interpretation, and professional communication, thereby transforming knowledge production and management learning.
Review of examples and arguments in the literature (conceptual synthesis within the review article); no primary empirical sample size reported.
Adaptation-oriented AI research (modifying AI models for domain-specific problems) is associated with relatively higher object-based creativity.
Subgroup/heterogeneity analysis in the OpenAlex dataset classifying AI publications by research mode (Adaptation-oriented) and comparing object novelty outcomes across modes.
Tool-oriented AI research (applying existing AI models to domain tasks) is associated with the largest gains in recombinant-based creativity.
Subgroup/heterogeneity analysis in the OpenAlex dataset classifying AI publications by research mode (Tool-oriented) and comparing recombinant novelty outcomes across modes.
AI publications have a 5.5 to 10.2 percentage point higher likelihood to rank in the top creativity decile.
Reported quantitative effect from the paper comparing top-decile creativity probabilities between AI and non-AI publications in the OpenAlex sample.
AI publications are significantly more likely to achieve top-decile creativity relative to non-AI publications.
Observational statistical analysis comparing AI-labeled vs non-AI publications across novelty and impact measures using the >1M OpenAlex dataset (novelty measured as recombinant and object novelty; impact measured as 3-year and 10-year citation impact).
AI-supported learning environments were linked to greater creativity, experimentation and technological improvement.
Survey responses (N=348) using established measurement scales; authors report associations between AIDLC measures and subcomponents of innovation (creativity, experimentation, technological improvement).
Employees' knowledge integration capability plays a critical complementary mediating role in the relationships between GenAI usage patterns (exploitative and exploratory) and creativity.
Mediation analysis conducted on three-wave lagged survey data from 381 matched employees in knowledge-intensive firms in China; knowledge integration capability measured and tested as mediator between GenAI usage patterns and creativity outcomes.
Exploratory GenAI use is more strongly positively associated with radical creativity than incremental creativity.
Three-wave lagged survey design; 381 valid matched employees from knowledge-intensive firms in China; statistical analysis comparing associations of exploratory GenAI use with radical vs. incremental creativity (mediation and moderation models reported in paper).
Exploitative GenAI use is more strongly positively associated with incremental creativity than radical creativity.
Three-wave lagged survey design; 381 valid matched employees from knowledge-intensive firms in China; statistical analysis comparing associations of exploitative GenAI use with incremental vs. radical creativity (mediation and moderation models reported in paper).
By modeling ideas as congestible resources, we show that source-level crowding is identifiable from within-distribution comparisons, yielding an excess-crowding coefficient Δ and a human-relative diversity ratio ρ.
Modeling/theoretical analysis in the paper that introduces metrics (Δ and ρ) and claims identifiability of source-level crowding using within-distribution comparisons (no empirical quantities given in the abstract).
We introduce a human-relative framework for benchmarking AI-induced human diversity collapse without requiring human-AI interaction data, providing an ex ante protocol to estimate crowding risk from model-only generations and matched unaided human baselines.
Methodological contribution described in the paper: a framework/protocol for estimating crowding using only model generations and matched unaided human baselines (no numeric sample sizes reported in the abstract).
There are structural parallels between GenAI architectures and human cognition—such as heuristic search, divergent thinking, and iterative refinement.
Conceptual mapping and theoretical comparison between GenAI architecture characteristics and cognitive/creativity constructs presented in the paper (literature synthesis / theoretical argument).
Generative Artificial Intelligence (GenAI) is reshaping organisational creativity by emulating cognitive processes traditionally associated with human innovation.
Paper's theoretical argument and literature-grounded conceptual claims (conceptual analysis / literature review); no empirical sample or quantitative data reported.
Participants rewarded for originality relative to peers produce collectively more diverse writing than those rewarded for quality alone.
Randomized assignment to incentive conditions (originality reward vs. quality reward) in the pre-registered RCT on a creative writing task (no sample size or numerical effect provided in excerpt).
Developing diverse AI teams addresses critics' concerns that current models are constrained by past data and lack the creative insight required for innovation.
Argumentative claim drawing on conceptual critique of current models and the proposed remedy of diverse AI teams; supported by referenced disciplinary literatures but no empirical validation provided in the excerpt.
A Metacognitive Co-Regulation Agent (in CRDAL) assists the Design Agent in metacognition to mitigate design fixation, thereby improving system performance for engineering design tasks.
Mechanistic claim supported by the paper's experimental results on the battery pack design problem showing CRDAL outperforming SRL and RWL; detailed measures of fixation reduction not provided in the excerpt.
The CRDAL system navigated through the latent design space more effectively than both SRL and RWL.
Empirical analysis on the battery pack design task comparing latent-space trajectories/exploration between CRDAL, SRL, and RWL; details on how 'more effectively' was quantified and sample size are not provided in the excerpt.
Cyborg workflows produce enhanced creative output via iterative human–AI refinement.
Qualitative claim supported by case studies and examples presented in the paper (no quantitative creativity metrics or sample sizes reported in the excerpt).