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).
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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 |
Org Design
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Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i) synthesizing new features from standards or research papers into production code; (ii) conformance and interoperability testing; (iii) hardening against field anomalies and diverse deployment environments; (iv) data-driven optimization of network functionalities; (v) discovering and prototyping novel waveforms, functionalities, and capabilities for future standards; and (vi) securing the stack against vulnerabilities.
Author assertion in the paper (qualitative analysis / domain expertise). No empirical sample size or quantitative study reported in the abstract.
Stochastic Tax can remain positive even when Agentic Technical Debt is minimized.
Theoretical claim in the paper's model and discussion: even with minimized debt (stock), the model predicts a nonzero recurring operating burden from stochastic agents; illustrated via examples and an accounts-payable simulation.
Stochastic Tax is a recurring flow of operating burden that arises when stochastic agents are used in business workflows.
Definition provided in the paper as part of the conceptual framework describing Stochastic Tax as a flow (recurring operating burden) associated with stochastic agents in workflows.
Using GoEmotions-based affective prompting, we show that emotion substantially shifts negotiation outcomes, suggesting that emotion is a strategic action channel rather than a surface style.
Experiments using GoEmotions-based affective prompting applied to negotiation agents; reported shift in negotiation outcomes attributed to emotional framing. (Specific experimental details such as number of runs, seeds, or exact metrics are not provided in the excerpt.)
Organizations implementing AI without responsible transition mechanisms may worsen workforce anxiety, skill obsolescence, inequality, and trust erosion.
Paper's theoretical/conceptual assertion about risks of poorly-managed AI adoption; no empirical validation reported in the excerpt.
The International Monetary Fund estimates that nearly 40% of global employment is susceptible to AI, with exposure rising to 60% in advanced economies owing to cognitive task-oriented jobs.
Cited IMF estimate reported in the paper (reference to an IMF analysis; no sample size given in the excerpt).
Tenure negatively relates to AI use (OR = 0.846 per category).
Reported odds ratio from logistic regression for tenure categories predicting AI use; OR = 0.846 per tenure category.
The requirement that review + expected rework attention be lower than manual completion attention is substantially more stringent than the requirement that AI merely generate faster drafts.
Comparative analytical argument based on the model's derived stability conditions (theoretical/model-based reasoning; no empirical sample reported).
Under congestion, reviewers rationally raise the risk threshold for checking AI outputs, reducing scrutiny precisely when it would matter the most.
Analytical implication derived from the queueing model presented in the paper (theoretical/model-based inference; no empirical validation reported).
Mean-based metrics (e.g., tasks completed per worker-hour or mean handle time) can misrepresent AI's effects in workflows where tasks accumulate and compete for scarce human attention.
Argument and analysis presented in the paper; theoretical reasoning and illustrative queueing model (no empirical sample reported).
LLM-assisted discovery can increase report volume while maintainer-side validation, triage, funding, and release capacity may not scale—an effect that is acute in open source.
Claims supported by case material from Mozilla Firefox collaborations and Anthropic Mythos Preview public data, plus discussion of open-source maintainer constraints; no sample size given in the abstract.
The resulting bottleneck is not only finding more bugs; it is absorbing, validating, triaging, patching, and shipping a larger stream of reports.
Argument based on observed changes in report volume and workflow demands from public collaborations and market/program data referenced in the paper; exact empirical counts not provided in the abstract.
Regardless of apparent performance advances in AI technology, human and environmental factors of the organization may substantially attenuate — or even negate — the effective productivity benefits.
Conceptual argument in the paper; theoretical reasoning and literature synthesis (no primary empirical data reported in the abstract).
Adopting AI in organizational practice does not guarantee productivity gains, because human and environmental factors critically moderate the relationship between AI deployment and realized productivity improvements.
Position paper's conceptual argument presented in the abstract; no empirical sample or quantitative study reported.
AI evaluation methods (benchmarks, red teaming, leaderboards) cannot be easily applied to human workers or yield comparable metrics.
Conceptual critique in the paper contrasting standard AI evaluation methods with human evaluation (no empirical comparisons provided).
Common criteria used to assess people (e.g., education, experience, references) cannot feasibly scale to AI systems.
Argumentative claim in the paper contrasting human hiring/evaluation practices with AI system assessment (conceptual; no empirical validation provided).
Human and machine workers may 'compete' for a given task, reproducing aspects of adversarial games.
Theoretical/assertional claim in the paper (conceptual discussion; no empirical data provided).
The increased use of algorithms in allocation decisions creates a Reverse Turing Test dynamic wherein the machine is now the judge.
Conceptual framing and argument presented in the paper (theoretical description; no empirical test reported).
AI adoption presents workforce adaptation challenges.
Reported in the study's literature synthesis and thematic analysis of secondary sources (qualitative review). No sample size reported.
AI adoption raises ethical considerations.
Authors' thematic evaluation of secondary literature identifying ethical issues associated with human-AI collaboration (qualitative synthesis). No sample size reported.
AI adoption presents challenges related to skill gaps.
Thematic findings from peer-reviewed literature and secondary data (qualitative review). No sample size reported.
As these systems scale, the bottleneck shifts away from raw model capability toward coordination.
Analytical/argumentative claim in the paper framing a shift in primary constraint; no empirical study or quantified benchmark reported.
AI systems intended to simulate companionship or emotional responsiveness raise risks such as emotional manipulation, addictive interaction patterns, and potential impact of prolonged AI interaction on users’ mental well-being, particularly for vulnerable users.
Asserted risk statement in policy recommendations; no empirical study, prevalence data, or sample provided in the text.
Rule debt is a governance burden that accrues when organizational decision rules migrate from formal information systems into ungoverned agentic execution environments.
Conceptual construct introduced and defined in the paper; supported by illustrative examples, no empirical measurement reported.
AI-enabled capabilities whose outputs require evidence, review, signoff, or assignable responsibility may retain integrated accountability boundaries even when their technical interfaces become modular.
Theoretical claim supported by conceptual analysis and domain illustrations; no empirical sample or formal measurement reported.
A complementary Oaxaca–Blinder decomposition shows that shifts in occupational composition account for about 90% of the exposure change attributable to observable job characteristics.
Oaxaca–Blinder decomposition reported in the paper attributing ~90% of exposure change (among the portion explained by observable job characteristics) to occupational composition shifts.
Within-job redesign accounts for 39.5% of the aggregate decline in generative-AI exposure and becomes increasingly important over time.
Same decomposition as above reported in the paper (result: within-job redesign = 39.5% of aggregate decline; authors note its increasing importance).
Hiring reallocation explains the largest share of the aggregate decline in generative-AI exposure, accounting for 52% on average.
Decomposition of changes in aggregate exposure into two margins (reallocation across jobs and within-job redesign) reported in the paper (result: hiring reallocation = 52% of aggregate decline).
Other changes are more nuanced and put the typical career growth opportunities, like receiving feedback from professional networks and promoting leadership and mentorship, at risk.
Qualitative reports from interview participants (n=24) expressing concerns that AI-driven changes may reduce feedback, leadership development, and mentoring opportunities.
Notable challenges to AI implementation include concerns about algorithmic bias, privacy, transparency, job displacement, organizational culture, and issues related to ethical and legal oversight.
Synthesis of reported challenges across the 29 empirical studies included in the scoping review.
This transition proceeds without tools to forecast how individual employees will respond psychologically and behaviorally.
Asserted by the authors as a gap/need; no empirical inventory or systematic review presented in the excerpt to substantiate completeness of tool absence.
Workforce transformations are difficult to forecast and costly to mismanage.
Stated as a general assertion in the paper's introduction; no empirical data, sample, or formal analysis reported in the excerpt.
Cost and lack of applicable use case are the most cited barriers to AI adoption, followed by expertise.
Survey question(s) on barriers to adoption in the Census Bureau survey in which respondents reported reasons for not adopting AI; ranking provided in the paper (cost, lack of use case, then expertise).
Intensity-weighted adoption is far lower than the 22.8 percent headline rate.
Survey-derived intensity-weighted measure of AI adoption constructed from the same Census Bureau survey (no numeric value reported in the excerpt).
Only 22.8 percent of plants report any AI use as of 2021.
Direct descriptive estimate from the Census Bureau survey of manufacturing establishments; year reported as 2021.
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.
The most significant barriers to AI adoption reported by entrepreneurs are human-centred—talent scarcity, organisational resistance, and change management—rather than technology or cost alone.
Theme 'Barriers and the Adoption Journey' from thematic analysis of interviews (n=16); interviewees repeatedly cited human-centred barriers (talent scarcity, resistance, change management) over purely technical/cost barriers.
Studies finding true synergy are scarce.
Authors' literature synthesis / meta-analytic overview claiming that few studies report combined human-AI performance exceeding both parties alone (no numerical count provided).
Genuine human-AI synergy—combined performance that exceeds what either party achieves alone—is uncommon.
Authors' synthesis of the literature and meta-analytic findings referenced in the paper indicating scarcity of studies showing combined performance > either alone (no specific counts or sample sizes given in the excerpt).
Agentic systems show persistent failures in repository setup, dependency handling, permission gating, and hardware verification.
Issue-resolution benchmarks and hardware/RTL verification research synthesized in the paper (specific failure rates or sample sizes not provided in abstract).
Controlled studies report slowdowns in mature open-source work when using agentic/code-generation systems.
Controlled studies and trials cited in the paper (no sample sizes given in abstract).
Using a frontier model's system prompt to supply the procedure exposes proprietary procedures to third-party providers.
Author statement describing privacy/proprietary risk as a cost of the system-prompt approach (qualitative claim).
Using a frontier model's system prompt to supply the procedure requires a frontier model for every conversation.
Author statement describing operational/cost trade-offs associated with the system-prompt approach (qualitative claim).
Using a frontier model's system prompt to supply the procedure has costs: it consumes the context window.
Author statement referencing trade-offs identified alongside the Dennis et al. result; cost described qualitatively (context window consumption).
Emerging evidence indicates that algorithms often inherit and amplify the historical biases present in training data.
Literature claim in paper referencing 'emerging evidence' and empirical studies (2024–2026) — specific studies, methods, and sample sizes not included in excerpt.
Seventy-four percent of task misalignments could be attributed to developers who tended to overfocus on efficiency and speed, especially for systems performing tasks in people-facing occupations such as those in the human resources sector.
Result from comparing traits causing incidents to developers' stated preferences (sample of 197 developers) and computing the proportion of misalignments where developer-desired traits matched the traits causing incidents; noted sectoral concentration in people-facing occupations (e.g., HR).
In most cases, workers wanted systems that are precise, insightful, or personal, but instead received systems that are basic, simple, or general.
Qualitative/quantitative comparison of preferred traits (from 202 workers) versus traits observed in AI systems in incident reports (LLM-coded); reported dominant preference traits versus dominant delivered traits.
As many as 83% of workplace incidents stem from worker-AI misalignments.
Result from comparing LLM-extracted traits of AI systems (from 1,524 incident reports) to the traits preferred by workers (sample of 202); counted incidents where traits did not match worker preferences and reported proportion.
So far, we lack a sound conceptual basis for categorizing and comparing these arrangements across organizations.
Statement of a gap in the literature based on the authors' literature review (no quantitative measure of literature coverage provided in abstract).
Twin agents dissolve that boundary, raising a class of trust calibration challenge these frameworks were not designed to handle.
Argument and design observations from the authors' ongoing project presented in the paper; conceptual claim explaining why existing frameworks may be insufficient for twin agents.