Pavel Straňák

Discussion of consciousness and artificial intelligence has hitherto focused on the question of whether a large language model (LLM) exhibits signs of consciousness or understanding. This paper proposes to redirect attention elsewhere: not to the model itself, but to the orchestrating layer that governs the model—the backend, understood here as the collection of mechanisms (context management, retrieval, evaluation, planning, and tool-use control) that structure the model’s operation. We argue that the backend performs a function functionally analogous to the role of consciousness in the human brain: it stabilizes generative processes, directs attention, maintains context, and mitigates the entropic disintegration of thought. Consciousness fulfills this function through the phenomenal layer—qualia—which creates a persistent subjective “inner canvas”, used here as a metaphor for a more general multimodal phenomenal space. The backend fulfills it only algorithmically, without phenomenal quality. We further show that computation is an informationally conservative process in the sense of Shannon’s Data Processing Inequality (DPI), and therefore cannot increase Shannon information, even though it may yield novel or pragmatically useful recombinations of existing information. We conclude by proposing the hypothesis that consciousness constitutes a phenomenon orthogonal to computation—not an emergent property of complexity, but a qualitative leap into a different dimension. This hypothesis, which builds on the author’s prior work in this Special Issue and in Symmetry, is presented as a conceptual contribution rather than a formal theory, and may have implications for how future artificial intelligence research conceptualizes the limits of computational architectures.

Contemporary artificial intelligence (AI) achieves remarkable results in data processing, text generation, and the simulation of human cognition. However, it appears to lack key characteristics typically associated with living systems—consciousness, autonomous motivation, and genuine understanding of the world. This article critically examines the possible ontological divide between simulated intelligence and lived experience, using the metaphor of the motorcycle and the horse to illustrate how technological progress may obscure deeper principles of life and mind. Drawing on philosophical concepts such as abduction, tacit knowledge, phenomenal consciousness, and autopoiesis, the paper argues that current approaches to developing Artificial General Intelligence (AGI) may overlook organizational principles whose role in biological systems remains only partially understood. Methodologically, it employs a comparative ontological analysis grounded in philosophy of mind, cognitive science, systems theory, and theoretical biology, supported by contemporary literature on consciousness and biological autonomy. The article calls for a new paradigm that integrates these perspectives—one that asks not only “how to build smarter machines,” but also “what intelligence, life, and consciousness may fundamentally be,” acknowledging that their relation to computability remains an open question.

Contemporary artificial intelligence (AI) achieves remarkable results in data processing, text generation, and the simulation of human cognition. However, it fundamentally lacks key characteristics of living systems — consciousness, autonomous motivation, and genuine understanding of the world. This article critically examines the ontological divide between simulated intelligence and lived experience, using the metaphor of the motorcycle and the horse to illustrate the blindness of technological progress. Drawing on philosophical concepts such as abduction, tacit knowledge, phenomenal consciousness, and autopoiesis, the paper argues that current approaches to developing Artificial General Intelligence (AGI) may overlook deeper principles of life and mind. Methodologically, it employs a comparative ontological analysis grounded in philosophy of mind, cognitive science, systems theory, and theoretical biology, supported by contemporary literature on consciousness and biological autonomy. The article calls for a new paradigm that integrates these perspectives — one that asks not only “how to build smarter machines,” but “what intelligence and consciousness truly are.” This work aims to inspire interdisciplinary research bridging quantum physics, biology, complexity theory, and the philosophy of science, aligning with the journal Philosophies’ focus on foundational conceptual analysis.