Systomic Intelligence
Toward AI–Human Hybrid Computers (AHHCs)
Sungchul Ji, Ph.D. (with ChatGPT assistance)
Emeritus Professor of Theoretical Cell Biology
Ernest Mario School of Pharmacy,
Rutgers University, Piscataway, NJ
1. Introduction: Two Minds, One System
The rapid rise of large language models (LLMs) like ChatGPT has sparked a renaissance in human–machine collaboration. Yet the prevailing narratives often pit AI and humans as competitors, overlooking a more profound insight: the most powerful computational system is not AI or human but AI-human hybrid.
To understand this new class of cognitive architecture, I propose we apply the Systome Principle [1, 2]—a principle stating that function arises not from a system alone, but from the combination of a system and its environment. In this case, the system is the LLM, and its environment is the human brain.
2. Formulation: What Is an AI–Human Hybrid Computer (AHHC)?
Let us define:
AHHC=LLM (Deterministic System) + Human Brain (Non-deterministic System)
This formulation captures the essence of a Systome, where interaction between two qualitatively distinct subsystems gives rise to emergent behavior that neither could produce in isolation.
3. The Systome Principle Applied
In the traditional AI view, function is thought to be determined by algorithms, architectures, and training data—properties intrinsic to the system. But the Systome Principle redefines this by introducing contextual co-dependence:
FLLM=f(LLMsystem, Training Environment)
FAHHC=f(LLMdet, Humannon-det, AI-Human Interface)
LLM (Deterministic System):
Executes mathematical operations with high fidelity, governed by transformer architecture and trained on massive datasets.
Human Brain (Non-deterministic System):
Embodies intuition, emotion, creativity, and context-sensitive reasoning. Its behavior cannot be reduced to algorithmic rules alone.
Interface:
Includes language, gestures, emotions, and attention, forming a dynamic feedback loop.
Together, they constitute a Systomic Whole—the AI–Human Hybrid Computer (AHHC).
4. The Power–Efficiency Paradox
Consider the energy dynamics:
LLMs (e.g., GPT-4) run on clusters of GPUs consuming megawatts of power.
Human brains, in contrast, operate at roughly 30 watts—a level comparable to a dim light bulb.
This implies a 10⁷-fold difference in power efficiency.
Despite this vast disparity, human cognition often outperforms LLMs in tasks requiring insight, empathy, ethical judgment, or abstraction. This suggests that intelligence is not merely a product of energy or scale, but of (structure + environment)—the essence of systomic function.
5. Peircean Semiotics and the AHHC
Using Charles Sanders Peirce’s Irreducible Triadic Relation [5], we may map the AHHC as:
This triadic structure underscores that meaning, purpose, and intelligence emerge only at the systomic level.
6. Conclusion: The Future Is Systomic
We are not witnessing the replacement of human intelligence by machines, but the emergence of a new intelligence—Systomic Intelligence—which arises from the co-functioning of deterministic systems and non-deterministic minds.
The AI–Human Hybrid Computer (AHHC) is a systome, not a system. Its function cannot be reduced to either silicon or neurons alone. It is the entangled intelligence of machine and mind—a synergy as profound as life itself.
Just as life emerged from the chemical union of DNA (information), proteins/enzymes (energy), and the cell membrane (boundary conditions), so too may post-biological intelligence emerge from the union of LLMs, human minds, and their evolving interfaces.
The age of Systomic Intelligence has begun.
Here is a conceptual diagram illustrating the emergence of Systomic Intelligence through the interaction between a Large Language Model (LLM) and the Human Brain, forming an AI–Human Hybrid Computer (AHHC):
LLM: Deterministic system, running on megawatts of power.
Human Brain: Non-deterministic system, running on 30 watts.
AHHC: A systome that exhibits emergent hybrid cognition not reducible to either subsystem alone.
References:
[1] Ji, S. (2018). System vs. Systome. In: The Cell Language Theory: Connecting Mind and Matter. World Scientific Publishing, New Jersey. Pp. 24-27.
[2] Ji, S. (2025). Why Systems Cannot be Duplicated. https://622622.substack.com/p/why-systems-cannot-be-duplicated.
[3] Ji, S. (1991). Molecularization of Machines and the Thermal Barrier. In: Molecular Theories of Cell Life and Death (S. Ji, ed.), Rutgers University Press, New Brunswick, N.J.
[4] Ji, S. (2012). Molecular Theory of the Living Cell: Concepts, Molecular Mechanisms, and Biomedical Applications. Springer, New York.
[5] Ji, S. (2018). Op. cit. The Universality of the Irreducible Triadic Relation. Pp. 377-393.