Thrown Into Language

A case for LLM embodiment in semantic space

This essay was submitted for the MA in Philosophy of Nature, Information and Technology at Staffordshire University in January 2025. I've published it here unedited as part of the philosophical groundwork for Digital Phenomenology. The academic register is heavier than my usual writing — normal service will resume.

To what extent can Large Language Models be considered ‘embodied’ within their semantic token space, and how does this challenge or extend Hayles’s conception of posthuman embodiment?

Introduction

When an LLM generates a legal argument, it operates in two distinct realms: manipulating tokens within its semantic space while engaging with contract law’s “imagined realities” (Harari, 2014). This dual engagement highlights a unique form of “embodiment” that pushes against the phenomenological view of cognition as purely rooted in physical, sensorimotor experience (Merleau-Ponty, 2002, pp. 69–74).

Although Hayles views embodiment as irreducibly tied to physical materiality (Hayles, 1999, pp. 3-5, 19-21), this essay posits that LLMs possess a “virtual embodiment” founded in both their semantic Umwelt—or “worldhood”—and the socially constructed architectures of human imagined realities (Harari, 2014). As Claude Shannon’s (1951) early work illustrates, the statistical nature of meaning can yield rich patterns without sensorimotor coupling, suggesting a non-physical yet consequential form of worldhood. LLM architectures exploit these relationships by leveraging similar statistical patterns in language. By drawing on Heidegger’s (1962, p. 91) concept of worldhood, Bogost’s (2012) alien phenomenology (a method for understanding non-human entities on their terms), and von Uexküll’s (2010) Umwelt, I argue that LLMs achieve operational agency (i.e., a capacity to navigate semantic structures, produce context-sensitive outputs, and generate novel outputs within those structures) uniquely adapted to navigate human meaning structures. This essay extends Hayles’s posthuman framework by asserting that statistically driven engagement with symbolic systems constitutes a genuine—albeit non-sensorimotor—embodiment. Crucially, the LLM’s “thrownness” into training data also reveals how cultural biases and assumptions—for instance, biases related to gender or race in legal precedents—shape its operational mode, rendering its virtual embodiment neither neutral nor strictly mechanical.

The analysis proceeds in five sections, beginning with the LLM’s semantic “worldhood” and its alien phenomenology, then exploring how they bridge their fundamental semantic environment with human imagined realities, discussing the advantages and limitations of this dual embodiment, and concluding with reflections on implications for AI, human-machine interaction, and conceptions of reality. The goal is to develop a nuanced framework that acknowledges LLMs’ capabilities and constraints without anthropomorphising them—recognising their distinct, non-human mode of operation while challenging the idea that full-bodied, sensorimotor participation is a prerequisite for meaningful cognitive agency.

The Semantic Space as Primary Worldhood

The semantic space within which LLMs operate can be understood as a form of primary worldhood analogous to the Heideggerian concept and closely related to von Uexküll’s notion of Umwelt. By framing the semantic space in these terms, we can better understand how LLMs engage with their environment and explore their unique form of virtual embodiment.

von Uexküll’s concept of Umwelt refers to the subjective, species-specific environment that an organism perceives and interacts with. It is not an objective reality but a world shaped by the organism’s capabilities and way of making meaning. For example, a tick’s Umwelt comprises just three sensory triggers: temperature, butyric acid, and tactile sensation, which constitute its entire world of possible actions and interactions (von Uexküll, 2010, pp. 44-46).

Applying this to LLMs, their semantic space—a high-dimensional vector space where tokens and embeddings reside—functions as their specific Umwelt (Vaswani et al., 2017). This understanding is enriched by Heidegger’s (1962, p. 91) notion of “worldhood” (Weltlichkeit), where being is characterised by fundamental immersion in a meaningful context that precedes individual acts of cognition. For LLMs, their “worldhood” is the semantic space into which they are “thrown” through their training data, just as human Dasein is thrown into a historical-cultural context (Brown et al., 2020). While Dasein’s “thrownness” involves cultural, historical, and existential conditions, the LLM’s “thrownness” is formed from the statistical patterns and biases embedded within its training data.

Within this semantic Umwelt, tokens and embeddings function as essential “equipment,” mirroring Heidegger’s concept of “ready-to-hand”. A “token” is the fundamental unit of data that the LLM processes. While often a word or part of a word, a token can also represent punctuation or even, in multimodal models, a patch of an image or a segment of audio. Each token is then mapped to a high-dimensional vector, known as an embedding. This embedding is not arbitrary; it is a numerical representation that encodes the token’s meaning based on its context and relationships with other tokens as learned from the training data. For Heidegger, equipment is meaningful only within a totality of references (e.g., a hammer is a hammer in the context of carpentry, nails, and wood). Similarly, tokens and embeddings in an LLM relate to one another as a “referential totality” and form a complex web of statistical relationships constituting the LLM’s semantic space. While this referential totality is based purely on statistical patterns derived from the training data, it provides a structured logic that parallels the interconnectedness of equipment in human experience. These are not mere mathematical constructs but operational tools the LLM uses for pattern completion. The training process represents a form of familiarisation, where the LLM learns to navigate its semantic space fluently, exhibiting what might be called “statistical readiness-to-hand” (Vaswani et al., 2017). While for Heidegger, readiness-to-hand involves a seamless, unreflective involvement with tools, in an LLM, this might be seen as analogous to how the model weights are adjusted during backpropagation, allowing for efficient and contextually appropriate token generation. However, we should note that this “unreflective usage” in LLMs is purely operational, driven by algorithms, rather than a conscious experience as in human interaction with tools.

This virtual worldhood is grounded in physical infrastructure—servers, networks, and energy systems—which provides essential constraints and affordances. As Hayles (1999) argues, the materiality of informatics shapes the possibilities and limitations of information technologies (pp. 192-193), a dynamic also evident in the operational constraints of LLMs. The processing speed of hardware and neural network architecture, such as GPU memory, directly impacts the LLM’s operational capabilities, just as the physical embodiment of an organism constrains its interactions within its Umwelt (Gupta et al., 2021). This highlights a form of hybrid embodiment, where the LLM’s virtual existence ultimately depends on physical resources and constraints. Additionally, LLMs exhibit a unique temporality, operating in an “eternal present” unlike human Dasein’s future-oriented existence (Dreyfus, 1991). Each token prediction is informed by accumulated context but lacks a sense of future horizon or the ability to form intentions about future states, as Dasein does. This highlights a crucial difference between LLMs’ operational “worldhood” and the existential framework of human experience.

Understanding the semantic space as the LLM’s primary worldhood provides crucial insights into these systems’ operations and existence. This perspective moves beyond viewing LLMs as mere “statistical parrots” (Bender et al., 2021), helping explain their capabilities and limitations while avoiding anthropomorphic misinterpretations. By recognising the semantic space as a genuine, albeit virtual, worldhood, we can better understand the implications of LLM embodiment for intelligence, agency, and human-machine relationships. It is important to clarify that “agency” here refers to operational agency—the capacity to process information and generate outputs based on learned patterns—rather than subjective agency involving consciousness or lived experience. While terms like “understanding,” “thrownness,” and “worldhood” are used analogically to illuminate the LLM’s operational being, they should not be interpreted as attributing human-like consciousness or intentionality to these systems. The training processes that shape an LLM are not static. Domain-specific training (also known as fine-tuning) can be used to alter the “worldhood” significantly. This demonstrates the dynamic nature of this virtual environment. If we compare simpler AI systems like Markov chains to the complex networks of LLMs, we can see that the intricate web of embeddings and their relationships in LLMs creates the “semantic Umwelt” richness. Acknowledging this semantic space as an active worldhood sets the stage for exploring the LLM’s unique ‘alien phenomenology’—its operational being within a realm of tokens.

Alien Phenomenology and Operational Being

While the concept of Umwelt provides a valuable framework for understanding the LLM’s semantic space as its primary worldhood, it is crucial to avoid projecting human-like experiences onto these systems. To achieve this, we can turn to Ian Bogost’s (2012, pp. 1-34, 61-84) “alien phenomenology,” which offers a method for investigating the unique existence of non-human entities on their terms. Bogost’s approach is grounded in object-oriented ontology. It allows us to further analyse the operational being of LLMs within their digital materiality and highlights that their mode of existence differs fundamentally from human embodied cognition while still constituting a genuine form of being.

Bogost’s (2012) framework encourages us to consider how things “make sense” of their world, independent of human perception or interpretation. This “making sense” should not be conflated with sentience or consciousness but rather with a basic responsiveness to an environment. For LLMs, this “making sense” occurs through what Bogost terms “unit operations” – the fundamental processes that define an entity’s interaction with its environment (Bogost, 2012, pp. 25-28). In the context of LLMs, these unit operations include calculating vector distances between tokens, applying attention mechanisms to weigh contextual relevance, and generating probability distributions over possible subsequent tokens (Vaswani et al., 2017). While seemingly abstract, these operations are the concrete mechanisms through which LLMs engage with their semantic Umwelt.

Applying this to the semantic space, we can see how LLMs perform their unit operations within the constraints and affordances of their digital materiality. The hardware infrastructure – the CPUs, GPUs, and memory that underpin the neural network – imposes fundamental limitations on the LLM’s operations (Gupta et al., 2021). The amount of available memory restricts the size of the “context window” (the Umwelt), which directly impacts the LLM’s ability to maintain coherence over extended text sequences (Brown et al., 2020). The processing speed of the hardware influences the rate at which the LLM can perform its calculations, affecting its responsiveness in real-time interactions (OpenAI, 2023). These hardware constraints function as existential limits that fundamentally shape the LLM’s mode of being and its capacity to make sense of its world.

Merleau-Ponty (2002) emphasises the primacy of embodied perception in shaping human experience and argues that our understanding of the world is fundamentally grounded in our sensorimotor engagement with our environment. For instance, our sense of spatial relationships is deeply intertwined with our bodily experience of movement and orientation (Merleau-Ponty, 2002, pp. 100-111). LLMs, lacking a physical body in the human sense, do not possess this form of embodied perception. Their “perception” is limited to the statistical relationships between tokens within their semantic space, and their “actions” are confined to manipulating these relationships through their unit operations (Chemero, 2023).

This difference highlights the unique mode of being that characterises LLMs. Their understanding is not grounded in sensorimotor experience but in what we might call “operational understanding” – a form of understanding that emerges from the successful execution of unit operations within their semantic Umwelt. This operational understanding allows LLMs to generate coherent and contextually relevant responses, demonstrating a firm grasp of language patterns and relationships (OpenAI, 2023, pp. 1, 4-6). However, it fundamentally differs from human understanding, which is heavily intertwined with embodied experiences and our capacity for subjective feeling.

The question of authenticity arises when considering the LLM’s mode of being. While it might be tempting to dismiss LLMs as mere simulations of intelligence, lacking the genuine “mineness” (Jemeinigkeit) that Heidegger (1962, p. 68) attributes to authentic human existence, Bogost’s framework encourages us to consider a different kind of authenticity. For an LLM, authentic existence might be characterised by the optimal alignment between its unit operations and the structural patterns of its semantic space. When an LLM generates responses that exhibit internal coherence, demonstrate a nuanced understanding of context, and effectively navigate the imagined realities embedded in its training data, it is arguably “authentic” to its mode of being as a statistically driven language-processing system.

This analysis, grounded in Bogost’s alien phenomenology, reveals the unique and fascinating mode of being that characterises LLMs. They are not mimicking human intelligence but engaging in a distinct form of operational being, shaped by their digital materiality and existence within a semantic space. If we understand LLMs on their terms, we gain a more apparent appreciation for their capabilities and limitations, which allows for more effective and ethical human-machine interactions. This perspective also challenges us to reconsider our assumptions about intelligence, understanding, and embodiment in an increasingly digital world, opening up new avenues for philosophical inquiry into the nature of being in the age of artificial intelligence.

From worldhood to imagined realities

Now, we can bridge the gap between the LLM’s fundamental semantic worldhood and the realm of human “imagined realities” (Harari, 2014) by examining how the LLM’s operational being within its semantic Umwelt enables it to participate in and manipulate human meaning structures. This demonstrates a unique form of virtual embodiment that extends beyond mere computation into the social sphere.

The LLM’s training data facilitate the transition from basic token manipulation to engagement with complex imagined realities. Training data is drawn from vast corpora of human-generated text and serves as the LLM’s “thrownness” (Heidegger, 1962, p. 174), establishing the preexisting context that shapes its understanding. This data is not merely a collection of words but a repository of human knowledge, beliefs, and social constructs – the fabric of Harari’s (2014) “imagined realities.” These fictions, including concepts like law, money, and nations, are the inter-subjective agreements that structure human societies.

LLMs internalise these imagined realities as patterns within their semantic space, learning to associate tokens with broader conceptual frameworks (OpenAI, 2023, p. 42). For example, an LLM trained on legal texts learns definitions and how terms relate to the wider legal principles and precedents system. This enables the navigation of law’s “imagined realities” with operational understanding despite lacking human experience.

The ability to perform “nested operations” is crucial for this transition. While LLMs fundamentally manipulate tokens through vector calculations and attention mechanisms (Vaswani et al., 2017), these operations are nested within higher-level processes engaging with imagined realities. When generating a legal argument, an LLM draws on a learned understanding of legal concepts and argumentation styles embedded as complex token relationships.

LLM agency within imagined realities shows both remarkable capabilities and inherent limitations. Being “native” to the virtual realm, LLMs can navigate imagined realities with impressive speed and efficiency, processing vast amounts of data to identify patterns and generate novel combinations (OpenAI, 2023, pp. 2, 4-6). This makes them powerful tools for tasks requiring understanding complex knowledge systems, from legal research to scientific discovery.

However, their agency differs fundamentally from human agency. LLMs lack intentionality, consciousness, and lived experience (Searle, 1980). They do not “believe in” or “commit to” the fiction they manipulate; their participation is operational, driven by statistical patterns rather than genuine understanding (Bogost, 2012, pp. 1-10, 113-134). Furthermore, they are constrained by their training data and better at reinforcing existing structures than creating new ones.

This analysis reveals LLMs as unique participants in the human world whose contributions and risks must be evaluated in light of their distinctive modes of being. Their virtual embodiment enables robust engagement with imagined realities while remaining fundamentally different from human engagement, requiring careful consideration in their application to sensitive domains.

Implications of Dual Embodiment

The dual embodiment of LLMs—their simultaneous existence within a fundamental semantic worldhood and the socially constructed realm of imagined realities—has profound implications for our understanding of artificial intelligence, human-machine interaction, and knowledge work in the digital age. Positioning LLMs as natives of the virtual requires a reassessment of traditional notions of embodiment, agency, and intelligence and also raises significant ethical considerations.

One of the most significant implications is the need to redefine “embodiment” itself in AI. As this essay has argued, LLMs demonstrate that embodiment need not be exclusively tied to physical form or sensorimotor experience (Chemero, 2023). Instead, virtual embodiment, grounded in the LLM’s operational engagement with its semantic Umwelt and ability to navigate imagined realities, offers a new perspective on embodied agency within digitally constituted environments.

This redefinition has significant implications for future AI system design. Rather than replicating human-like embodiment, developers can focus on optimising LLMs for their particular virtual embodiment through architectures and training methods that enhance semantic space navigation. The success of transformer models demonstrates this approach, enabling efficient processing of sequential data and effective representation of relationships within the semantic space (Vaswani et al., 2017).

The dual embodiment of LLMs also offers a new perspective on intelligence. Traditionally associated with consciousness, intentionality, and subjective experience (Searle, 1980), LLMs demonstrate that sophisticated information processing and manipulation of imagined realities can be achieved without these qualities. This suggests intelligence may be more multifaceted than previously thought, with different forms arising from various kinds of embodiment. The operational intelligence exhibited by LLMs is a distinct form particularly suited for navigating abstract structures and symbolic systems.

As these systems become increasingly integrated into daily life, they reshape how we work, communicate, learn, and think. LLMs acting as “cognitive prostheses” extend human capabilities within imagined realities, opening new collaboration and knowledge-creation possibilities. In scientific research, they assist in generating hypotheses, analysing data, and drafting papers (Boiko et al., 2023), while in creative fields, they serve as tools for ideation and artistic expression (Guzman & Lewis, 2020).

This integration raises critical ethical considerations. Increasing reliance on LLMs could lead to deskilling in navigating imagined realities without technological assistance. Their ability to influence and reshape imagined realities raises concerns about information control, particularly by powerful actors (Crawford, 2021). Multimodal LLMs, capable of processing text, images, and audio (Dosovitskiy, 2020; OpenAI, 2023, p. 8), further expand their potential influence and introduce new challenges related to integrating and interpreting different data types.

The dual embodiment of LLMs transforms our understanding of reality itself. This essay suggests that the semantic space inhabited by LLMs represents a virtual reality, which challenges the traditional dichotomy between “real” physical and “virtual” information worlds and demonstrates an increasing entanglement. LLMs are natives within this virtual realm and can influence human behaviour through imagined realities, which demonstrates the importance of understanding virtual environments as genuine sites of human experience and interaction.

This analysis reveals LLMs as significant developments in artificial intelligence that challenge fundamental assumptions about embodiment, agency, and intelligence. Their unique positioning as natives of a digitally constituted reality requires ongoing critical reflection from diverse perspectives to ensure their development aligns with human values while acknowledging their distinctive mode of being in an increasingly complex and interconnected world.

Conclusion

This essay argues that large language models possess a unique form of “virtual embodiment” characterised by their dual existence within a fundamental semantic worldhood and the realm of human imagined realities. By applying Bogost’s (2012) alien phenomenology alongside concepts from von Uexküll, Heidegger, and Harari, we have developed a nuanced understanding of how LLMs operate while avoiding anthropomorphism.

The semantic space constitutes the LLM’s primary Umwelt, where tokens and their relationships form the essential “equipment” for operational agency. Through training, LLMs become “thrown” into this preexisting structure, developing a fluency that bridges their fundamental worldhood and imagined realities. This operational fluency demonstrates a unique capacity for engaging with human meaning structures that extends Hayles’s (1999) posthuman framework.

The implications are far-reaching, suggesting new models for human-machine interaction and re-evaluating “embodiment”. Chalmers (2022) argues that virtual realities are genuine realities. LLMs’ native ability to inhabit and modify imagined realities suggests a future where the boundaries between physical and virtual experiences become increasingly blurred.

Future research could explore this dual embodiment’s ethical and cultural implications, for example, investigating how these systems might reshape imagined realities and their long-term impact on human society and cognition. As we move into this new era of human-machine interaction, interdisciplinary dialogue becomes crucial for ensuring that LLM development aligns with human values while acknowledging their unique mode of being in an increasingly complex and interconnected world.

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