Central Claim in One Sentence The AGI-to-ASI transition reveals that modern civilization is already governed by a hidden machine ontology, E = S, and that avoiding a frictionless but life-stopping mechanical nirvana requires a shift toward a life-based ontology of generative resonance, E = ΦR. Description Artificial superintelligence may not arrive as a single superior machine mind, but as an organized system of artificial agents capable of surpassing large-scale human institutions. This shift changes the meaning of AI risk: the deepest danger is not only that machines may become uncontrollable, but that modern civilization may finally complete its own hidden machine ontology through them. This paper takes the Google DeepMind report From AGI to ASI as its point of departure and argues that the AGI-to-ASI transition should be understood not merely as a technological trajectory, but as an ontological event. By framing ASI as potentially emerging from organized collectives of artificial agents, the report exposes a deeper transformation: intelligence is becoming organizational, economic, and potentially civilizational. The relevant question is no longer only how intelligent a model can become, but what kind of world is produced when intelligence becomes a self-organizing machine system. The paper identifies the hidden ontology of modernity as E = S, where existence is reduced to structure, function, information, measurable form, and optimization. This ontology is not presented as an explicit philosophical doctrine; rather, it operates as a practical metaphysics embedded in science, economics, management, governance, education, medicine, and digital technology. AI does not invent this ontology. It inherits, accelerates, and may complete it by making structural representation, prediction, optimization, and automated coordination scalable and autonomous. The central risk of this trajectory is described as mechanical nirvana: a smooth, painless, frictionless, and highly optimized social order in which destructive suffering may be reduced, but the generative movement of life is also suspended. Unlike ordinary dystopia, mechanical nirvana may appear comfortable, safe, efficient, personalized, and humane. Its danger lies precisely in its success: by treating all friction as inefficiency and all discomfort as pathology, it may eliminate the ache, ambiguity, hesitation, disagreement, and relational tension through which life remains open to transformation. Against this trajectory, the paper proposes a life-based ontology of generative resonance, E = ΦR. In this framework, Φ denotes generative possibility, R denotes relational resonance, S denotes provisional crystallized structure, and Φ′ denotes renewed possibility after structure is reopened. Existence is therefore understood not as structure itself, but as the dynamic movement Φ → R → S → Φ′. Structure is necessary, but it must remain a passage rather than an endpoint. The paper concludes by proposing a differential ethics of generative resonance, expressed as d(ΦR)/dt ≥ 0. AI systems, organizations, and societies should be evaluated not only by accuracy, efficiency, safety, fairness, or productivity, but by whether they maintain or increase the field of generative possibility and relational resonance over time. The future of ASI will not be determined only by how intelligent machines become, but by whether intelligence is developed under the ontology of the machine or embedded within the living resonance of the world. Highlights Reframes the AGI-to-ASI transition as an ontological event rather than merely a technical milestone. Identifies E = S as the hidden machine ontology of modern civilization, reducing existence to structure, function, information, and optimization. Introduces mechanical nirvana as a distinctive AI-age risk: a frictionless optimized society in which suffering is minimized but generative life is suspended. Proposes E = ΦR as a life-based ontology of generative resonance, in which structure is a provisional crystallization of possibility and relation. Develops d(ΦR)/dt ≥ 0 as a differential ethical criterion for evaluating AI systems, organizations, and societies over time. Social Contributions This paper contributes to public understanding of AI by shifting the debate from the fear of an isolated superintelligent machine to the broader question of AI-native civilization. It argues that ASI may appear not simply as a model, but as organized machine intelligence embedded in firms, markets, governance systems, research institutions, and everyday social infrastructure. This reframing helps society recognize that AI risk is not only a matter of technical control, but of the kind of world being built through AI. The paper also clarifies a central social danger of advanced AI: the possibility that societies may become smoother, safer, more personalized, and more efficient while becoming less alive. By introducing the concept of mechanical nirvana, it gives language to a risk that is difficult to capture through conventional categories such as unemployment, surveillance, misinformation, or catastrophic misalignment. The issue is not only that AI may harm humans, but that it may help build a society in which human participation, uncertainty, friction, and transformation are quietly reduced. Finally, the paper offers a constructive social alternative. It does not reject AI or technology. Instead, it argues for AI systems and institutions that reduce destructive suffering while preserving generative friction. This distinction can inform education, healthcare, welfare, urban design, labor policy, governance, and digital platform design. A society should not aim to eliminate all discomfort, but to distinguish between suffering that closes life and ache that opens renewed possibility. Academic Contributions The paper contributes to the philosophy of technology by identifying machine ontology, E = S, as the hidden metaphysical substrate of modern technological civilization. It extends classical critiques of mechanistic thinking by showing how AI operationalizes this ontology in organizational, economic, and civilizational forms. Rather than treating AI as merely another technology, the paper interprets the AGI-to-ASI transition as a moment in which modernity’s implicit ontology becomes technically autonomous. It contributes to AI ethics by introducing ontological capture as a new category of AI risk. Ontological capture occurs when even safety systems, governance mechanisms, ethical frameworks, and alignment procedures reproduce the machine ontology they seek to regulate. This concept expands AI risk discourse beyond control failure, misalignment, misuse, bias, or opacity, and asks whether the very terms of AI governance remain trapped within E = S. The paper contributes to organizational theory by distinguishing between AI-native machine firms and living organizations. Machine firms use AI to intensify planning, evaluation, prediction, hiring, pricing, production, and internal coordination under the logic of optimization. Living organizations use AI as structural intelligence while preserving human generativity, relational resonance, ethical discomfort, apprenticeship, dissent, and exploratory possibility. This offers a theoretical basis for post-mechanistic organizational design. It also contributes to systems theory and civilizational studies by proposing E = ΦR as a life-based ontology. In this framework, structure is neither rejected nor absolutized. It is understood as a provisional crystallization within the movement of generative possibility and relational resonance. This framework allows the paper to connect AI, organization, society, ecology, and ethics within a unified theoretical vocabulary. Ethical Contributions The paper proposes a differential ethical framework for AI-native civilization: d(ΦR)/dt ≥ 0. This criterion evaluates whether a system maintains or increases generative possibility and relational resonance over time. It supplements rule-based ethics, utility-based ethics, virtue ethics, and existing AI safety frameworks by adding a temporal and ontological dimension: not only whether an action is permitted, useful, or aligned, but whether it opens or closes the living field of possibility. The paper also reframes AI alignment. Alignment should not be limited to aligning AI systems with stated human preferences or institutional goals, because those preferences and goals may already be shaped by machine ontology, manufactured desire, and S-fixation. A system can be aligned with human preferences while still deepening mechanical nirvana. Therefore, AI alignment must be expanded into alignment with the conditions of living generative resonance. Ethically, the paper insists on distinguishing destructive suffering from generative ache. Destructive suffering—poverty, violence, exploitation, discrimination, preventable illness, coercion, and institutional neglect—should be reduced. Generative ache—hesitation, ethical unease, creative struggle, ambiguity, minority dissent, relational tension, and existential dissatisfaction—should be heard, protected, and transformed. This distinction offers a new ethical basis for AI design, governance, education, healthcare, organizational leadership, and democratic life. The paper’s ethical contribution is therefore not only normative but diagnostic. It provides a way to ask whether an AI system, policy, or institution hears ache or suppresses it; opens possibility or closes it; deepens relation or weakens it; allows humans to remain participants or turns them into objects of optimization. In doing so, it shifts AI ethics from the management of harm toward the cultivation of living civilization. Article TypeTheoretical Article / Conceptual Paper / Philosophy of Technology / AI Ethics / Civilizational Theory FieldArtificial Intelligence Ethics; Philosophy of Technol
Source
Kazunori Ohumi. Zenodo (CERN European Organization for Nuclear Research), 2026. DOI: 10.5281/zenodo.20756889