Evidence (4892 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 |
Org Design
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Current research on AI-supported conflict techniques has focused predominantly on Devil's Advocate (DA) and has neglected Dialectical Inquiry (DI).
Literature review / gap statement in the paper pointing to relative emphasis on DA in prior research and lack of work on DI.
The framework reframes the education–employer gap as a structural failure in the pathway and outlines implications for universities, employers, accreditors, and policymakers.
Conceptual claim and implications drawn by the author(s) in the paper (stated in the abstract).
The architecture of the undergraduate degree is structurally incapable of replacing the informal post-degree apprenticeship system through curricular revision alone.
Argument presented in the paper, supported by the systematic review of eighteen peer-reviewed studies and labor-market analyses cited in the abstract.
The informal post-degree apprenticeship system that historically completed graduate formation no longer reliably exists.
Claim based on the paper's systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
Higher education has misdiagnosed the resulting challenge as curriculum misalignment—a content problem assumed to be solvable through revised syllabi, AI electives, and marginal expansions of experiential learning.
Argument presented in the paper, supported by the paper's systematic review of eighteen peer-reviewed studies and labor-market analyses (as described in the abstract).
Artificial intelligence and automation are restructuring early-career knowledge-work roles by compressing the entry-level functions through which graduates historically built portfolios, developed professional judgment, and earned professional credibility.
Statement supported in the paper by a systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
Manual processing of these documents is time-consuming, inconsistent across reviewers, and unscalable.
Author claim / background motivation; no quantitative time or consistency metrics reported in the statement.
Actuaries rely primarily on structured numerical data for reserving and ratemaking, while valuable predictive information in unstructured text including medical records, adjuster notes, and call transcripts remains largely unused.
Author statement/observation in paper introduction; no empirical data or sample size provided to support prevalence claim.
By framing AI risk exclusively in cybersecurity terms, the Order constructs an AI-risk universe in which provenance, labor, education, culture, meaning, and the commons are rendered 'not testable' within the policy regime.
Argumentative/theoretical claim backed by textual analysis and the counted absence of relevant terms in the EO.
The Executive Order frames AI risk overwhelmingly through cybersecurity language.
Textual analysis of the EO; supported by the paper's verified word-count analysis showing high frequency of security/cyber terms relative to other domains.
An incentive sweep reveals Goodhart-style drift where measured performance becomes anti-correlated with true outcomes.
Simulation results in Medi-Sim showing that optimizing measured metrics leads to a decrease (anti-correlation) in true outcomes (Goodhart effect).
Existing healthcare AI benchmarks hold this [strategic provider] response fixed and so cannot evaluate mechanisms by the equilibrium they produce.
Author statement/argument in the paper about limitations of existing benchmarks (conceptual claim; not an empirical experiment).
Research on platform governance remains fragmented and lacks an integrative perspective.
Conclusion drawn from the systematic literature review (644 publications) indicating fragmentation in the scholarly literature.
Participants in platform ecosystems cannot be governed through traditional command-and-control mechanisms.
Conceptual claim supported by the literature synthesized in the systematic literature review (644 publications).
Research on AI-enabled decision-making and upper echelons theory (UET) has largely evolved in parallel (i.e., the two literatures are not well integrated).
Concept-centric literature review mapping management and IS literatures and identifying lack of integration (no quantitative meta-analysis or sample size reported).
Traditional review perspectives organized by method, data type, or application domain understate a deeper shift toward human–AI hybrid decision systems.
Critical assessment within the integrative conceptual review contrasting existing review axes with the proposed decision-system perspective (no empirical sample size).
A budget-neutral anti-gaming design reduces consumer harm by 0.025 relative to computable static rules.
ABM/RL simulation comparison reported in the paper (design variants evaluated across scenario/sweep runs and the firm-period panel).
A budget-neutral anti-gaming design reduces conduct boundary mass by 0.032 relative to computable static rules.
ABM/RL simulation comparison reported in the paper (design variants evaluated across scenario/sweep runs and the firm-period panel).
Ordinary adaptive updates lower consumer harm (0.202 to 0.194).
ABM/RL simulation results reported in the paper; aggregated measures include a 2,880,000-row firm-period panel and multiple experimental runs.
AI reconfigures comparative advantage and reduces efficient scale.
Theoretical claim presented as a core conclusion of the paper's Cognitive Economic Geography framework, supported by argumentation and the paper's investment-pattern analysis (2018-2024).
Artificial Intelligence (via generative design, autonomous logistics, and predictive analytics) is methodically undermining agglomeration economies that have traditionally focused on advanced manufacturing in coastal and global megaregions.
Paper's analytical claim supported by empirical investigation of capital investment (2018-2024) in specified facility types (EV batteries, semiconductor fabs, additive manufacturing) and theoretical discussion of AI capabilities.
This phenomenon is the self-undermining property of unilateral optimization.
Terminology/label introduced by the authors to describe the preceding conceptual phenomenon; no empirical validation provided in the excerpt.
Deploying AI systems induces endogenous non-stationarity, resulting in a train-test-deploy gap where historical distributions diverge from the deployment context.
Conceptual claim offered in the paper about deployment feedback effects; presented as an argument rather than supported by reported empirical measurement.
Superintelligence, an extremely capable task solver, born out of such a solipsistic approach to AI design, is unlikely to be cooperative.
Theoretical/argumentative claim in the paper linking design assumptions to likely cooperative behavior; no empirical evidence or formal model reported in the excerpt.
The dominant paradigm in AI research focuses on developing powerful agents that treat the world as an exogenous and stationary source of feedback.
Paper's critique/characterization of current research paradigms; presented as an observed trend without empirical backing.
These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt.
Argumentative claim in the paper contrasting agentic-system risks with traditional software/ML technical debt; no empirical validation or comparative study reported.
Pure implementations of the data mesh paradigm frequently underdeliver because teams inherit new responsibilities without the platform maturity, tooling, or coordination mechanisms to exercise them effectively.
Argument/observation presented in the paper as rationale for proposing a new architecture (anecdotal/experience-based reasoning rather than reported empirical trial).
Enterprise data platforms face an enduring tension between domain self-service and holistic governance (a flexibility-versus-control trade-off).
Conceptual statement in the paper describing the problem motivating the work (literature/architectural framing).
Post-merger IS integration often threatens the human-centered and IT-embedded knowledge of acquired firms.
Statement based on literature and the authors' framing; supported by observations in the paper's case discussion about two acquisitions (qualitative, case-based).
Cloud orchestrators follow efficiency-oriented logics of integration and standardization with limited openness.
Claim presented as a finding from the paper's comparative taxonomy and qualitative analysis of platform business models; method appears to be conceptual/qualitative comparison rather than a reported quantitative sample (no sample size in abstract).
Achieving this system-level transformation takes time: it requires trust and accountability infrastructure, machine-legible and interoperable data and interfaces, the design and adoption of these new workflows, and economic incentives that favor reconstruction rather than local optimization.
Argumentative claim listing necessary preconditions and complementary investments; presented conceptually without reported empirical measurement in the provided text.
The main reason [the disruption has not fully arrived] is not model capability, nor even the tools built to harness those models; rather, most organizations are still using AI to accelerate workflows designed for a pre-AI world.
Argued in the paper as an explanatory thesis; supported by conceptual argument and illustrative examples (consumer markets, education, news, coding) rather than reported empirical analysis in the provided text.
The disruption many expect has not fully arrived.
Stated as an observation in the paper's introduction/abstract; no empirical method, sample size, or data reported in the excerpt.
Human-only teams commit more errors than mixed human–AI teams.
Reported counts/observations of errors made by team type in the escape room experiment; the abstract does not include numerical error counts or significance levels.
Human-only teams take longer to complete the escape room than mixed human–AI teams.
Reported comparison of time-to-complete between human-only and mixed teams in the experiment; specific times or statistical tests are not provided in the abstract.
The share of diffs receiving timely review has declined, exposing a widening gap between code supply and reviewer bandwidth.
Observational telemetry/operational metrics reported in the paper indicating a decline in timely reviews relative to diff supply. No specific numeric sample size provided in the excerpt.
GPT models showed significantly larger discrepancies compared to other evaluated models.
Comparative evaluation reported in the paper indicating GPT-family models had larger errors/discrepancies relative to the best-performing models.
Employees often struggle to identify "who knows what," leading to organizational productivity losses.
Motivating statement in the paper (not an empirical result from this study); general observation cited as motivation for the research.
Existing AI education, AI literacy, and human-AI collaboration frameworks remain centred on prompting, task execution, and productivity support and are poorly equipped to address this tacit layer of expert cognition.
Argumentative critique in the paper drawing on conceptual analysis and review of prevailing frameworks; no empirical evaluation or sample reported.
The core cause of the R&D productivity paradox is cognitive saturation: researchers spend an increasing share of their effort on coordination, documentation, and data governance—hidden work that displaces high-value hypothesis formation, interpretation, and strategic synthesis.
Argument presented in the paper supported by DSR analysis, triangulated with four expert interviews, foresight scenarios, and pattern matching (causal claim based on qualitative evidence and reasoning).
Corporate R&D faces a persistent productivity paradox: rising investment and expanding scientific knowledge have not translated into proportional innovation output (Eroom's Law); analogous patterns appear across engineering, materials science, and healthcare.
Literature reference to Eroom's Law and cross-domain pattern matching described in the paper (conceptual/literature observation).
Incumbent workforce management frameworks remain anchored to a purely human labor model, rendering AI agents invisible to capacity planning, performance attribution, and governance enforcement.
Stated in paper's problem motivation / literature review; presented as an observed gap motivating the design-science contribution. No sample size or empirical study described in the provided text.
Organizations increasingly deploy separate purpose-built AI tools across professional domains, often hiring domain specialists for each, recreating the staffing models AI was expected to transform.
Stated as an observational/introductory claim in the paper (no empirical data or sample size reported to support the general trend).
The greatest organizational risk of AI may not be technical failure but structural over-optimization (i.e., AI-driven erosion of adaptive openness).
Argumentative claim derived from the AI fragility theory presented in the paper; no empirical validation or quantified risk assessment included.
Artificial intelligence functions as a 'hyper-crystallization' engine—by classifying, predicting, standardizing and optimizing it accelerates structural crystallization and may erode local judgment and generative adaptability.
Conceptual theory labeled 'AI fragility theory' developed in the paper; supported by argumentative reasoning rather than empirical testing.
When digital systems are reified into internal structural optimization and control, transformation efforts can intensify organizational rigidity and failure to adapt.
Theoretical/analytic argument contrasting two modes of digital transformation; no empirical estimates or dataset provided.
Structurally heavy firms with substantial material and institutional resources frequently experienced paralysis or collapse during the pandemic.
Qualitative claim grounded in the author's reading of pandemic outcomes; the paper does not report systematic data or case counts.
During the COVID-19 pandemic, firms with the most optimized structures were not necessarily the most adaptive under radical uncertainty.
Argument based on the COVID-19 pandemic presented as an empirical 'stress test' in the paper; no empirical sample, data, or statistical analysis provided.
LLMs heavily rely on simulations for designing algorithms, which is notorious for breaking when transferred to real hardware.
Paper's claim grounded in known transferability issues between simulation and hardware; no experimental quantification provided in the abstract.
LLM pitfalls worsen on Radio Access Network (RAN) use cases: they hallucinate Application Programming Interfaces (APIs) and mis-read specifications, which kills interoperability of RAN components at the first mistake.
Author assertion / observed behavior reported in the paper (qualitative examples implied); no formal experiment or sample size provided in the abstract.