On the Cognitive Limits of Artificial Intelligence: Wonder and Contradiction in a Dialectical Perspective

Abstract

Objective. Objective. The study aims to demonstrate that the capacity for wonder is a necessary condition of thought in the dialectical-logical sense, and that the transformer architecture of large language models (LLMs) structurally excludes this capacity through the absence of the constitutive conditions of wonder as a cognitive act.
Methods. The methodological basis of the study is dialectical logic in the tradition of Kant and Hegel. The analysis proceeds through the categories of determination, negation, contradiction, and sublation, which allow the structure of the cognitive act to be described independently of questions concerning subjective experience. The transformer architecture is approached not as a technical object but as the realization of a particular mode of activity with concepts, which is compared with what dialectical logic describes as thinking. This comparison is made possible by the fact that contemporary technical documentation allows a conceptually precise description of the model's operations during generation.
Results. The study shows that wonder is not a psychological state but a structural moment of cognition that requires three constitutive conditions. First, a stable concept capable of entering into conflict with reality — as opposed to the statistical probability distribution with which the transformer operates. Second, the ability to retain previous determinations as sublated moments in the sense of Hegel's Erinnerung — as opposed to a Markovian context window that restarts with each session. Third, the ability to transform the discovered contradiction into an impulse toward a more concrete concept — as opposed to the neutralization of contradiction through the selection of a statistically coherent continuation.
Scientific novelty. For the first time, a systematic analysis of the cognitive possibilities of LLMs has been carried out through the lens of dialectical logic, using the concept of wonder as a structural moment of cognition. Unlike existing approaches — which appeal to the absence of consciousness, or which, as in Woods, acknowledge partial logical determination in the machine — the proposed approach allows a qualitative rather than merely quantitative boundary to be drawn between computation and thinking. This boundary is shown to be immanent, following from the very concept of the transformer architecture, rather than an external technical characteristic subject to future correction.
Conclusions. The transformer architecture of LLMs structurally excludes all three conditions of wonder, and this exclusion is immanent rather than technical. Determining judgment is realized by the transformer with an efficiency and scale unattainable for any human. Reflecting judgment — the formation of a new universal for a given particular, beginning with wonder at contradiction — remains a human task not for technical but for architectural-logical reasons. This distinction is the adequate basis for understanding what modern artificial intelligence is — and what it is not.