Why Large Language Models will Never be Embodied: A Categorical, Not Engineering, Impossibility

Abstract

Abstract A persistent ambition in artificial intelligence research holds that large language models (LLMs) will eventually become embodied—through attachment to robots, augmentation with multimodal sensors, integration into vision–language–action architectures, or sheer scale until grounding emerges from training data. This ambition rests on a misunderstanding of embodiment. Embodiment is a constitutive condition of being a self-producing, self-maintaining living system whose continued existence is genuinely at stake in its couplings with the environment. Adding sensors, actuators, or physical instantiation to a computational substrate yields engineered coupling, which is something else entirely. We identify five conditions that any genuinely embodied system must satisfy—autopoietic closure, precariousness, constructive self-determination, intrinsic temporality, and mereological closure—and show that no computational artifact can satisfy any of them, let alone all five. The barrier is categorical incompatibility: the same property that makes a system computational (heteronomous design and maintenance through external specification) is the property that precludes it from being embodied. Coupling artifacts to environments through transducers achieves engineered interaction. Having a body and being a body are different conditions. We close by examining why the embodiment-equivalence claim persists despite its categorical impossibility, and by arguing that the confusion sustains two harms—overselling AI systems for tasks they cannot perform, and misdirecting public concern toward speculative futures while the actual harms of deployed systems accumulate without being addressed. Clarifying the categorical distinction is a precondition for taking the genuine stakes of AI deployment seriously rather than as a philosophical aside.

The authors' abstract, as published at the source. Minds and Machines, 2026 · DOI ↗