Artificial Intelligence as Monism: Ontological, Organisational, and Methodological Implications

• URL: http://arxiv.org/abs/2606.20704v1
• Date: Mon, 15 Jun 2026 16:43:23 GMT
• Title: Artificial Intelligence as Monism: Ontological, Organisational, and Methodological Implications
• Abstract summary: The paper contends that AI is not merely a collection of components but a single, indivisible essence reflecting the singularity it replicates.<n>Epistemologically, it positions AI as the central interpretive force across technological, organisational, and societal domains.<n>Ultimately, this paper calls for a paradigm shift in how AI is conceptualised, governed, and integrated.
• Score: 31.495834538807525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper argues that Artificial Intelligence should be understood as a form of monism: a unified substance that cannot be decomposed into separate elements such as data, algorithms, or technical architectures. Drawing from philosophical traditions of monism, dualism, and holism, the paper contends that AI is not merely a collection of components but a single, indivisible essence reflecting the phenomena it replicates. Treating AI as monism has deep implications across multiple dimensions. Epistemologically, it positions AI as the central interpretive force across technological, organisational, and societal domains, while raising ethical and existential concerns regarding singularity, the homogenisation of innovation, and the concentration of decision-making power. At the organisational level, a monistic approach challenges traditional siloed structures, advocating instead for transversal, problem-centric teams whose mandate derives from the integrity of the problem rather than from departmental hierarchy. In project management, it implies a unified vision and an integrated evaluation of complexity in which no single stakeholder perspective dominates the assessment of outcomes. In data and information management, it calls for architectures that reflect the irreducible unity of the phenomena being modelled. Ultimately, this paper calls for a paradigm shift in how AI is conceptualised, governed, and integrated, suggesting that only by embracing AI as monism can organisations achieve genuine agility and avoid the structural inefficiencies inherent to reductionist approaches.

Related papers

• Metis AI: The Overlooked Middle Zone Between AI-Native and World-Movers [5.202950948929751]
  We identify a class of tasks we call Metis AI, that are performed entirely on computers yet resist reliable AI automation.<n>These tasks are not computationally intractable; they are institutionally, socially, and normatively entangled in ways that defeat algorithmic approaches.<n>We propose five structural characteristics that define the Metis AI zone: consequential irreversibility, relational irreducibility, normative open texture, adversarial co-evolution, and accountability anchoring.
  arXiv  Detail & Related papers  (2026-05-14T05:47:07Z)
• Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution [80.98492754957466]
  Core trustworthy AI objectives, such as fairness, robustness, privacy, and explainability, are hard to achieve simultaneously.<n>This paper argues that causality is necessary to understand and balance trade-offs in performance and multiple objectives of trustworthy AI.
  arXiv  Detail & Related papers  (2026-05-04T14:26:28Z)
• AI Integrity: A New Paradigm for Verifiable AI Governance [3.7184769644515896]
  AI systems increasingly shape high-stakes decisions in healthcare, law, defense, and education.<n>Existing governance paradigms -- AI Ethics, AI Safety, and AI Alignment -- share a common limitation: they evaluate outcomes rather than verifying the reasoning process itself.<n>This paper introduces AI Integrity, a concept defined as a state in which the Authority Stack of an AI system is protected from corruption, contamination, manipulation, and bias.
  arXiv  Detail & Related papers  (2026-04-13T06:45:30Z)
• Large Language Models as a Semantic Interface and Ethical Mediator in Neuro-Digital Ecosystems: Conceptual Foundations and a Regulatory Imperative [83.99688944263843]
  This article introduces and substantiates the concept of Neuro-Linguistic Integration (NLI)<n>It is a novel paradigm for human-technology interaction where Large Language Models act as a key semantic interface between raw neural data and their social application.<n>We argue for the development of a second-order neuroethics,' focused not merely on neural data protection but on the ethics of AI-mediated semantic interpretation itself.
  arXiv  Detail & Related papers  (2026-03-18T07:34:17Z)
• A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms [49.66022971508878]
  We argue that reasoning should be elevated from a modular component to the system's cognitive core.<n>We derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning.<n>We identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control.
  arXiv  Detail & Related papers  (2026-03-11T07:40:53Z)
• Modularity is the Bedrock of Natural and Artificial Intelligence [51.60091394435895]
  modularity has been shown to be critical for supporting the efficient learning and strong generalization abilities.<n>Despite its role in natural intelligence and its demonstrated benefits across a range of seemingly disparate AI subfields, modularity remains relatively underappreciated in mainstream AI research.<n>In particular, we examine what computational advantages modularity provides, how it has emerged as a solution across several AI research areas, and how modularity can help bridge the gap between natural and artificial intelligence.
  arXiv  Detail & Related papers  (2026-02-21T21:47:09Z)
• The Vibe-Automation of Automation: A Proactive Education Framework for Computer Science in the Age of Generative AI [0.7252027234425333]
  generative artificial intelligence (GenAI) represents a qualitative shift in computer science.<n>GenAI operates by navigating contextual, semantic, and coherence rather than optimizing predefined objective metrics.<n>The paper proposes a conceptual framework structured across three analytical levels and three domains of action.
  arXiv  Detail & Related papers  (2026-02-09T06:02:04Z)
• Embodied AI: From LLMs to World Models [65.68972714346909]
  Embodied Artificial Intelligence (AI) is an intelligent system paradigm for achieving Artificial General Intelligence (AGI)<n>Recent breakthroughs in Large Language Models (LLMs) and World Models (WMs) have drawn significant attention for embodied AI.
  arXiv  Detail & Related papers  (2025-09-24T11:37:48Z)
• A Theory of Information, Variation, and Artificial Intelligence [0.0]
  A growing body of empirical work suggests that the widespread adoption of generative AI produces a significant homogenizing effect on information, creativity, and cultural production.<n>This paper argues that the very homogenization that flattens knowledge within specialized domains simultaneously renders that knowledge into consistent modules that can be recombined across them.<n>The paper concludes by outlining the cognitive and institutional scaffolds required to resolve this tension, arguing they are the decisive variable that determine whether generative AI becomes an instrument of innovation or homogenization.
  arXiv  Detail & Related papers  (2025-08-20T16:21:13Z)
• Explainability Through Systematicity: The Hard Systematicity Challenge for Artificial Intelligence [0.0]
  This paper argues that explainability is only one facet of a broader ideal that shapes our expectations towards artificial intelligence (AI)<n>I offer a conceptual framework for thinking about "the systematicity of thought" that distinguishes four senses of the phrase.<n>To determine whether we have reason to hold AI models to this ideal of systematicity, I then argue, we must look to the rationales for systematization.
  arXiv  Detail & Related papers  (2025-07-29T19:50:21Z)
• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.