Evidence (4189 claims)
Adoption
8625 claims
Productivity
7686 claims
Governance
6917 claims
Human-AI Collaboration
6574 claims
Org Design
4189 claims
Innovation
4131 claims
Labor Markets
3588 claims
Skills & Training
2985 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 761 | 200 | 101 | 904 | 2020 |
| Governance & Regulation | 829 | 400 | 191 | 122 | 1566 |
| Organizational Efficiency | 784 | 193 | 125 | 84 | 1197 |
| Technology Adoption Rate | 637 | 236 | 124 | 97 | 1103 |
| Research Productivity | 431 | 131 | 58 | 340 | 972 |
| Output Quality | 481 | 183 | 59 | 47 | 770 |
| Decision Quality | 332 | 177 | 82 | 49 | 647 |
| Firm Productivity | 439 | 57 | 88 | 20 | 610 |
| AI Safety & Ethics | 218 | 279 | 66 | 33 | 602 |
| Market Structure | 181 | 170 | 123 | 24 | 503 |
| Task Allocation | 214 | 64 | 72 | 33 | 388 |
| Skill Acquisition | 174 | 62 | 62 | 17 | 315 |
| Innovation Output | 204 | 27 | 45 | 18 | 295 |
| Employment Level | 105 | 54 | 108 | 13 | 282 |
| Fiscal & Macroeconomic | 132 | 69 | 43 | 26 | 277 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 154 | 48 | 26 | 3 | 231 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 123 | 50 | 6 | 223 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 71 | 92 | 10 | 2 | 175 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 58 | 56 | 26 | 13 | 156 |
| Training Effectiveness | 96 | 21 | 14 | 19 | 152 |
| Wages & Compensation | 77 | 37 | 25 | 6 | 145 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 81 | 21 | 1 | 115 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 32 | 20 | 8 | 3 | 64 |
| Skill Obsolescence | 5 | 47 | 6 | 1 | 59 |
| Social Protection | 28 | 16 | 8 | 2 | 54 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Org Design
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Make is most compelling for commodity utilities and for differentiating custom applications in the AI era.
Paper's typology and normative recommendation derived from conceptual analysis (no empirical validation reported).
AI fundamentally transforms the governance properties of the Make option, shifting it from Williamson's pure hierarchy to a hybrid governance form that combines code ownership with external AI infrastructure dependency.
Conceptual argument combining transaction cost economics, resource-based view, and assessment of AI infrastructure characteristics (no empirical testing reported).
The 'SaaSocalypse' narrative predicts that AI will render large segments of the Software-as-a-Service market obsolete by enabling firms to build software in-house at a fraction of historical cost.
Statement summarizing an extant narrative in industry and literature (paper cites/describes this narrative; no empirical test in the paper).
Advances in generative artificial intelligence, particularly agentic coding systems capable of autonomous software development, are disrupting the economics of the make-or-buy decision for enterprise applications.
Paper's conceptual analysis combining transaction cost economics, resource-based view, and assessment of current AI capabilities (no empirical sample reported).
The framework produces a list of testable empirical questions that we leave as open problems.
Statement in the paper that it derives testable empirical questions from the theoretical framework; no empirical tests are executed in the paper itself.
The framework operationalizes aspects of earlier qualitative work on supervisory control (Sheridan, 1992), common ground (Clark & Brennan, 1991), and mixed-initiative interaction (Horvitz, 1999) within a single normative ratio.
Conceptual synthesis and mapping of prior qualitative literature into the new per-task leverage formalism presented in the paper; this is a theoretical linkage rather than empirical validation.
The per-task ceiling does not bind the windowed measure, though both remain bounded: L_task by per-task novelty, L_window by the stock of accumulated planning investment that pays out within the window.
Theoretical derivation/argument in the paper distinguishing bounds on per-task leverage (L_task) and windowed leverage (L_window) and identifying their respective limiting factors; no empirical evidence provided.
We extend this per-task analysis to a windowed leverage measure that accommodates recurring tasks, spawned subtasks, and amortized system-design investment.
Conceptual/theoretical extension in the paper defining a windowed leverage metric and describing how it accounts for recurring tasks, subtasks, and amortized design investments; no empirical tests reported.
The asymptotic behavior of leverage decomposes into two scaling axes (capability and memory) with a non-zero floor on the planning term set by irreducible task novelty bounded by human throughput.
Mathematical/theoretical asymptotic analysis within the paper; conceptual derivation linking capability and memory as scaling axes and asserting a lower bound on planning cost due to task novelty and human throughput.
Information density itself is directional and bounded by separate ceilings on human-to-agent and agent-to-human flow.
Theoretical argument/derivation in the paper establishing directional information-density and distinct upper bounds for each flow direction; no empirical validation reported.
The denominator decomposes into three channels through which a conserved per-task information requirement must flow, each with its own time-cost scalar (specify the task, resolve mid-run interrupts, and review the result).
Analytic decomposition within the paper's theoretical framework; conceptual argument rather than empirical measurement.
We propose a per-task leverage ratio for human-agent collaboration: human work displaced by an agent, divided by the human time required to specify the task, resolve mid-run interrupts, and review the result.
Theoretical/conceptual proposal and formal definition provided in the paper; no empirical sample or experimental data reported.
Grounding recommendations in validated research offers leaders a framework for navigating AI's labor implications responsibly.
Paper asserts that its synthesis and recommendations provide a practical framework for leaders; no empirical validation of the framework is reported in the abstract.
Evidence-based organizational responses (transparent workforce planning, skills investment, redesigned roles, adaptive governance, and long-term capability-building) can mitigate harm and prepare organizations for workplace transformation.
Paper proposes these organizational responses grounded in the synthesized empirical literature; this is a recommendation rather than an empirically tested intervention in the paper abstract.
There is an absence of a comprehensive national strategy in Israel for AI in employment, and the paper calls for the development of a forward-looking regulatory framework that balances innovation with protection of fundamental rights (dignity, equality, privacy), transparency, human oversight, and fairness.
Normative policy recommendation based on the paper's regulatory analysis; not an empirical finding and no policy-design experiments are reported in the excerpt.
The AI-driven transformation is accompanied by an increasing emphasis on reskilling and continuous learning, reflecting a shift from workforce replacement to reconfiguration of modes of employment.
Reported observation in the paper about workforce development trends; no quantitative measures of reskilling uptake or program counts are provided in the excerpt.
Israeli legal scholarship reflects broad interdisciplinary engagement with AI across labor law, intellectual property, privacy, constitutional law, and additional fields; the study advances theoretical models, including reconceptualizations of accountability, creativity, and the role of AI as a legal actor.
Literature review/academic survey and theoretical contributions reported in the paper; specific counts of publications or analytical methods not provided in the excerpt.
Israel is a leading “AI Nation,” characterized by exceptionally high levels of technological integration across both the private and public sectors.
Statement in paper based on the author's characterisation of national-level technological integration; specific empirical measures or sample size not provided in the excerpt.
Governance maturity is therefore not merely a constraint on AI adoption; it is a condition that shapes whether capability improvements translate into productive deployment.
Synthesis/conclusion drawn from the analytical model showing governance affects the mapping from capability to productive deployment.
Governance investment that reduces breach-loss magnitude shrinks the paradox region itself.
Analytical model result showing how changes in governance (modeled as reductions in breach-loss magnitude) affect the parameter region where the deployment paradox occurs.
AI agents are now running real transactions, workflows, and sub-agent chains across organizational boundaries without continuous human supervision.
Statement in abstract describing observed industry trend; paper reports a structured survey of industry trends, emerging standards, and technical literature as its method for situating this observation.
Addressing AI in evaluative systems requires treating monitoring (AI detection) and loosened selectivity as complementary design instruments.
Policy implication derived from model results and constrained optimization of editorial policy in the post-transition regime; argued in the paper's conclusion.
Taken together, these insights provide theoretical clarity and practical guidance for responsible GenAI integration into creative work.
Authors' stated contribution and practical recommendations derived from the conceptual framework; no empirical evaluation of guidance effectiveness provided.
The study reinterprets process-oriented creativity theories through structural parallels with GenAI.
Conceptual reanalysis and theoretical reinterpretation based on literature synthesis (paper's theoretical contribution).
The authors propose a role-based integration model that aligns GenAI capabilities with key creative functions: idea generation, synthesis, strategic framing, and facilitation.
Presentation of a novel conceptual model / framework in the paper (theoretical design); no empirical validation or measured outcomes reported.
The paper repositions GenAI as a cognitive collaborator rather than merely a productivity tool.
Argumentative / conceptual claim supported by the proposed theoretical reframing and role-based model in the paper; no empirical testing reported.
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).
The study revisits foundational creativity theories to develop a framework for integrating GenAI into creative workflows.
Paper describes a conceptual review and theoretical synthesis of foundational creativity theories leading to a proposed integration framework; methodological (theoretical / conceptual) contribution rather than empirical validation.
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.
That compliance layer can improve oversight by making departures from law easier to detect.
Claim supported by the paper's analytical argumentation (no empirical evidence reported).
For probabilistic AI to be incorporated into public administration it must be embedded in a compliance layer that makes decisions reviewable, repeatable, and legally defensible.
Stated as a normative/architectural claim in the paper; supported by conceptual argument rather than empirical testing.
Governments are increasingly interested in using AI to make administrative decisions cheaper, more scalable, and more consistent.
Stated as background motivation in the paper (no empirical data or sample size reported).
A follow-up intervention where we add information about capabilities from prior experiments to the context improves calibration.
Follow-up experimental intervention reported in the paper: augmenting model context with prior capability information and measuring calibration change.
Markets are a promising way to coordinate AI agent activity for similar reasons to those used to justify markets more broadly.
Conceptual/theoretical argument presented in the paper (no empirical test reported in the excerpt).
The framework shifts manual harness engineering into automated harness engineering, and takes one step further — automating the design of the automation itself.
Conceptual claim about the scope/implication of the proposed framework stated in the paper; the excerpt contains no empirical measures, experiments, or sample sizes to verify the claim.
The Meta-Evolution Loop optimizes the evolution protocol Λ across diverse tasks, learning a protocol Λ^(best) that enables rapid harness convergence on any new task — so that adapting an agent to a novel domain requires no human harness engineering at all.
Strong methodological claim and intended outcome stated in the paper (formalization and algorithms promised); no empirical validation, benchmarks, or sample sizes given in the excerpt to substantiate the universality or 'no human' guarantee.
The Harness Evolution Loop optimizes a worker agent's harness H for a single task: a Worker Agent W_H executes the task, an Evaluator Agent V adversarially diagnoses failures and scores performance, and an Evolution Agent E modifies the harness based on the full history of prior attempts.
Description of the proposed algorithmic component/architecture in the paper (conceptual specification); no empirical results or sample size provided in the excerpt.
We present a two-level framework that automates this process.
Methodological claim: the paper proposes a two-level framework (Harness Evolution Loop and Meta-Evolution Loop) and states it in the text; no experimental validation or sample size reported in the excerpt.
Adopting the proposed co-evolutionary governance framing enables a charter of coexistence that permits bounded AI development while preserving human dignity, contestability, collective safety, and fair distribution of gains.
Normative claim extrapolated from the theoretical framework and ethical argumentation; no empirical or quantitative validation provided.
Human-AI coexistence should be designed as a co-evolutionary governance problem rather than as a one-shot obedience problem.
Normative argument supported by the theoretical model and interdisciplinary synthesis; prescriptive conclusion, not empirically tested.
Reciprocal complementarity between humans and AI can strengthen stable coexistence.
Model analysis showing how reciprocal complementarity affects stability properties of equilibria in the formalized dynamical system; theoretical result rather than empirical test.
The proposed coexistence model yields conditions for existence, uniqueness, and global asymptotic stability of equilibria.
Analytical/mathematical results from the formal model presented in the paper (proofs/derivations claimed); no empirical validation sample.
Human-AI coexistence can be formalized as a multiplex dynamical system across physical, psychological, and social layers with reciprocal supply-demand coupling, conflict penalties, developmental freedom, and governance regularization.
Formal modeling work presented in the paper (mathematical formulation of a multiplex dynamical system); no empirical sample.
A better framework for human-AI relations is 'conditional mutualism under governance': a co-evolutionary relationship where humans and AI develop, specialize, and coordinate while institutions ensure the relationship is reciprocal, reversible, psychologically safe, and socially legitimate.
Theoretical proposal and normative argument supported by interdisciplinary synthesis (computability, machine learning, HRI, ecological mutualism, governance); no empirical trials reported.
Contemporary AI systems are increasingly adaptive, generative, embodied, and embedded in physical, psychological, and social worlds.
Synthesis of recent work across ML, deep learning, transformers, generative/foundation models, world models, and embodied AI; descriptive claim, no empirical sample provided.
The presence of a Chief Information Officer (CIO) strengthens the influence of both the peer group and the peer leader on a focal firm’s AI adoption, with the influence of the peer leader being more pronounced when a CIO is present.
Subgroup/interaction analysis in fixed-effects regression models on panel data of publicly listed Chinese firms (2012–2023), comparing firms with and without a CIO.
Industry digital maturity enhances (strengthens) the impact of the peer group on a focal firm’s AI adoption.
Interaction/heterogeneity analysis in fixed-effects regression models on panel data of publicly listed Chinese firms (2012–2023), using an industry digital maturity moderator.
The influence of the peer group on a focal firm’s AI adoption is stronger than the influence of the peer leader.
Comparative estimates from fixed-effects regression models using panel data of publicly listed Chinese firms (2012–2023); tests comparing coefficients/magnitudes for peer group vs. peer leader effects.
The AI adoption level of the peer leader (the most advanced AI adopter among industry peers) positively influences the focal firm’s AI adoption level.
Panel dataset of publicly listed Chinese firms (2012–2023); fixed-effects regression models estimating effect of peer leader AI adoption on focal firm AI adoption.
The AI adoption levels of the peer group positively influence the focal firm’s AI adoption level.
Panel dataset of publicly listed Chinese firms (2012–2023); fixed-effects regression models estimating effect of peer group AI adoption on focal firm AI adoption.