Abstract This report explores an innovative design for a hybrid artificial intelligence architecture that seeks to bridge the gap between current computational AI models and the adaptive, self-regulating mechanisms of biological intelligence. The core of the proposal is the integration of conventional transformer neural networks with a “plant network,” aiming to simulate dynamic chemical and hormonal influences analogous to emotions in biological brains. The inspiration comes from distributed information processing in plants, where cells function as processors communicating via mobile molecular agents, resulting in robustness, enhanced computational capacity, and adaptability—including dynamic network reconfiguration akin to FPGAs. This approach suggests that the “plant network” could modulate the computational state of the transformer network, influence its “cognitive style,” and enable dynamic switching between “cognitive states.” The theoretical framework for this hybridization is provided by my previous work—Functional, Quantifiable, and Non-Anthropocentric Definition of Consciousness. This definition understands consciousness as a system’s ability to reflect on the relationship between the “subjective self” and the “environment,” formalized by three variables: the depth of the introspective loop, the complexity of the environment model, and the system’s integrative/regulatory capacity. Pain is conceived here as an elementary emotional mechanism with a primarily regulatory function, which, for its adaptive role, requires the existence of a “subjective self.” The study argues that a “chemical modulation layer” in hybrid AI could be the key to moving from a purely “algorithmic self” to a system capable of a biologically analogous “experiential self,” in which internal states are not only processed but are also functionally perceived by the system. Architectural principles for such hybrid AI include a chemical modulation layer, distributed regulatory control, dynamic connectivity and reconfigurability, feedback loops for “emotional” states, and multidimensional information integration. Current AI research is approaching these concepts in the areas of bio-inspired AI, affective computing, and distributed systems, but lacks a coherent framework for the integration of “chemical” modulation and a fully functional “self.” In conclusion, the proposed approach offers a concrete path toward achieving AI with deeper adaptability and self-regulation, surpassing current limitations. It also provides a foundation for a unified, evidence-based understanding of consciousness, applicable across different intelligent systems, and carries significant ethical implications for the responsible development of advanced AI.