Large language models (LLMs) appear to be one of the most important discoveries of the twenty-first century. The word discovery is more appropriate here than invention, because even the creators of these models do not fully understand what exactly they have produced. They know the technical details: how the graph of tokens is structured, how linear algebra is applied, and how computations can be efficiently parallelized using graphics processing units. They know how to optimize the training process. But in essence, they cannot give an unambiguous answer to the question: “What exactly is our model a model of?”
Modern language models should not be considered solely through the prism of technical solutions; their philosophical aspects must also be taken into account. In the twenty-first century, we have encountered a situation in which “technology is explained through nineteenth-century metaphors,” creating an enormous gap between scientific and technological achievements and our understanding of their meaning. This is why language models should be approached not only from the perspective of algorithms and architectures, but also from the standpoint of the history of philosophy and epistemology.
From the Cartesian Subject to a Network Conception of Thought
To understand why language models provoke so much controversy, it is useful to recall the origins of modernity. René Descartes, who is often called the “father of modern philosophy,” introduced the proposition: “I think, therefore I am.” In doing so, he made human consciousness the unconditional center of being, implicitly equating it with something divine: it is the only thing of which we can be completely certain. Historically, this gave rise to a close association between the concepts of reason, will, and life—they came to be perceived as inseparable from one another.
The fantastic literature of the nineteenth and early twentieth centuries—for example, Mary Shelley’s Frankenstein—supported the idea that “artificial intelligence” would necessarily have to be an entity resembling a human being, endowed with the capacity to love, suffer, and empathize. This approach implicitly follows the Cartesian line: intelligence was conceived only as a reflection of human consciousness and will.
However, already in the first half of the twentieth century, traditional philosophical ideas began to give way to what is now often called postmodernism. An “attack” was launched against the classical subject–object relationship and, along with it, against the conventional understanding of signs and meaning. If, in modernity, meaning was located inside the “thinking subject,” now culture, language, and mythology themselves were said to “think.” Meaning does not originate from an isolated observing “I,” but emerges from a complex web of interrelations. Thus, the Aristotelian logic of “A or not-A” gives way to the idea of a multidimensional graph of concepts, in which meaning arises through the combination, intersection, and overlapping of different contexts.
Language Models and the Reconsideration of “Intelligence”
Modern large language models are often called “artificial intelligence,” which, in my view, creates additional confusion. People are still inclined to associate intelligence with consciousness and emotional-volitional qualities—an association formed as early as the time of Descartes and Romantic literature. As a result, one often encounters the objection: “How can this be intelligence if it cannot love or suffer?”
Technically, however, LLMs are far removed from modeling the human brain in any literal sense. Terms such as “neuron” and “neural network” are indeed used to describe them, but instead of actual nerve cells, we find ordinary matrices, multidimensional vectors, and operations of linear algebra. “Neural networks” began as an attempt to imitate biological neurons, but today the term is better understood as a practical name for classes of architectures capable of processing vast amounts of data and discovering statistical dependencies within linguistic corpora.
This gives rise to an interesting parallel with postmodern philosophy: LLMs really do “think” not through a rigid subject–object framework, but through the large-scale reading and modeling of relationships within language. Moreover, if the idea of the “world as text” became popular toward the end of the twentieth century, large language models inadvertently embody this proposition. They treat reality—or at least its verbal dimension—as an endless body of “text,” in which meaning is formed through the structure of relationships between elements.
From the “World as Text” to a New Kind of Thinking
This model suggests that human consciousness is not the only “pure source” of reason. Language, culture, and texts may themselves “think”—that is, they may generate new meanings and construct conceptual connections. At the level of LLMs, this becomes visible in the model’s ability to generate meaningful texts, answer questions, and even discover subtle relationships between concepts without necessarily possessing “consciousness” in the Cartesian sense.
We are therefore confronted with a profound philosophical question: might our familiar conception of consciousness and reason be only one possible way of explaining the phenomenon of thought? And might it now be easier to describe thinking as “text”—as a system of relationships within an enormous body of data? Perhaps the centrality of consciousness has indeed been exaggerated—or, more precisely, was specific to the modern era—while the development of language models offers us a new way of looking at what we call “intelligence.”
Conclusion
Large language models are not merely an engineering innovation, but also a serious challenge to our familiar philosophical ideas about reason. Like every major discovery, they force us to reconsider fundamental assumptions about consciousness, knowledge, and the nature of reality. To reduce them either to simple “imitation of the human being” or, conversely, to primitive statistics is to overlook their unique nature. We will probably continue to live for a long time at the intersection of different paradigms, where technology races far ahead while philosophy attempts to provide it with a new and more adequate metaphorical and conceptual framework.
And if the idea of the “world as text” once seemed merely a radical postmodern proposition, the emergence of large language models has made it far more tangible—and perhaps central to further reflection on what intelligence is and what forms it may take.