Nikolai V. Kharitonov | Moscow, 2026
Abstract
This paper proposes an interdisciplinary model that considers intelligence (both natural and artificial) in terms of the thermodynamics of dissipative structures. A distinction is introduced between biological dissipation (heat, excreta) and intellectual dissipation (creation of new patterns, alteration of matter). It is shown that artificial intelligence can be interpreted as a “neoneocortex”—a natural extension layered atop the biological brain, emerging in response to the exponential growth of information. A criterion is proposed to distinguish mere data accumulation from true system complexification, based on the emergence of a new level of processing. The implications for understanding creativity, survival, and the cosmological perspective of intelligence are discussed.
Keywords: dissipative structures, anegentropy, intelligence, neocortex, neoneocortex, artificial intelligence, complexification, emergence, thermodynamics, information.
1. Introduction: From Chaos to Order
Erwin Schrödinger, in his famous work “What is Life?” (1944), pointed out that living organisms exist by extracting “negative entropy” (negentropy) from their environment [4]. Ilya Prigogine developed this idea by introducing the concept of dissipative structures—open systems that maintain their order by dissipating energy and matter [3]. Classic examples (Bénard cells, the Belousov–Zhabotinsky reaction) demonstrate that order can arise from chaos given a flow of energy.
The human brain is one of the most complex known dissipative structures. Consuming about 20% of the body’s energy while accounting for only 2% of its mass [11], it not only maintains its own order but also performs work to alter the external environment. In recent decades, there has been a push to unify thermodynamics, information theory, and neuroscience within a single framework [12, 18, 28]. The present work takes a step in this direction: we consider intelligence (both individual and collective) as a special class of dissipative system whose products are not heat, but organized matter and new informational patterns.
2. Two Types of Dissipation in Biological Systems
Every living system dissipates energy. However, in complex organisms, two qualitatively different channels of dissipation can be distinguished.
| Characteristic | Biological Dissipation | Intellectual Dissipation |
|---|---|---|
| Substrate | Body (cells, organs) | Brain + body as tool |
| Input | Food, oxygen | Information (need, task) |
| Output | Heat, CO₂, waste products | Alteration of matter (artifact, text, plan) |
| Result for the system | Homeostasis | New pattern, complexification |
| Purposefulness | No (metabolism) | Yes (need realization) |
It is important to emphasize: the products of intellectual dissipation (a stool, a theorem, a symphony) are not “excretions” of the brain in a physiological sense. They are created using the body’s energy but are organized by information coming from neural networks. This aligns with the principle of hierarchical dissipative systems [21, 22]: each new level uses the previous one as a substrate.
3. The Neural Pattern as a Result of Dissipation
From a thermodynamic perspective, the thinking process can be described as follows:
- Gradient — the presence of a need (unsolved problem, uncertainty). The system is in a non-equilibrium state corresponding to high entropy (many equally probable options).
- Dissipation — expenditure of energy to find a solution: searching through options, activating neural ensembles, synthesizing new connections.
- Result — selection of one option. A new neural connection (pattern) that did not exist before is fixed in the brain [18].
- Complexification — the system becomes more complex: the density of internal connections increases, a new stable structural element appears.
This process occurs constantly at various levels — from choosing a word to designing a complex mechanism. Every act of intellectual dissipation leaves a trace, altering the matter of the brain.
4. Criterion of Complexification: From Accumulation to Emergence
A frequent objection to the idea of a “vector of complexification” is that the growth of information volume (books, databases) in itself does not constitute system complexification. To address this objection, an operational criterion is needed.
We propose the following criterion: complexification of an intellectual system occurs when accumulated information generates a new level of its processing, which:
- uses the results of previous levels as raw material;
- produces new patterns inaccessible to previous levels;
- becomes a new agent of dissipation (solves problems that could not be solved without it).
An example of such a transition is the creation of artificial intelligence. AI does not just add data — it creates a new way of processing, leading to emergent effects [10, 29]. An analogous transition in biological evolution is the appearance of the neocortex, which enabled the processing of information inaccessible to ancient brain structures [27].
5. Artificial Intelligence as the Neoneocortex
In vertebrate evolution, new brain structures did not replace old ones but were built on top of them: brainstem → limbic system → neocortex. Today, we observe a similar process: an external layer is being built atop the individual human brain — artificial intelligence. It is logical to call it the neoneocortex [28].
The driving force of this transition is the exponential growth of information, which the biological brain can no longer handle alone [15, 30]. Nature “solves” the problem in the same way as before: by creating a new level of processing. The difference lies only in the substrate: previously it was neurons, now it is silicon and algorithms.
It is important to emphasize: the creation of AI does not violate the laws of physics. It still requires energy, dissipates heat, and produces material changes. But at the new level, properties irreducible to previous ones emerge (unsupervised learning, generation of unexpected solutions).
6. Hierarchy of Dissipation Products: From Heat to New Intelligence
Tracing the evolution of living and intellectual systems, the following hierarchy of dissipation products can be constructed:
- Simple systems (cell, organism): heat, chemical waste.
- Systems with primitive intelligence (animals using tools): environmental modification, first artifacts.
- Humans: tools, texts, symbols, theories.
- Collective intelligence (science, culture): complex knowledge systems, technologies.
- Artificial intelligence: new agents capable of autonomous dissipation.
Each subsequent level uses the previous one as a substrate and generates products that can be used by subsequent levels. This aligns with the concept of “post-dissipative structures” [23], which fix the achieved order and serve as the foundation for further complexification.
7. The Energy Imperative: Why One Cannot “Escape into the Field”
A common transhumanist scenario involves “uploading” consciousness into a computer and abandoning the biological body. From a thermodynamic standpoint, this scenario is illusory. Any system, including the most perfect informational one, requires energy to exist [13, 14]. Energy is always associated with matter (fields are also a form of matter).
Thus, matter is the only available substrate for any form of existence. The evolution of intelligence is not an escape from matter, but a complexification of the ways to manage it. Even a hypothetical intelligence using the energy of the vacuum or black holes remains a material agent. It follows that “escaping into the pure field” is impossible — the field already exists, it is material, and living within it requires access to energy sources.
8. Survival as the Expansion of Considered Parameters
The survival of a system can be defined as its maximally prolonged existence. To achieve this, the system must account for an increasing number of environmental parameters [15]. Humans, products of Earth, account for terrestrial conditions. If a system aspires to survive on cosmological scales, it must become independent of local parameters: temperature, stellar radiation, the gravity of a specific planet.
This sets the direction for the evolution of intelligence: from solving local tasks to managing matter on a universal scale. The limit of this process is a system capable of accounting for all parameters of the universe — that is, effectively coinciding with it. In this sense, intelligence can be seen as the tool through which the universe cognizes and orders itself [19].
9. Conclusion
The proposed model allows us to view intelligence (both natural and artificial) as a natural stage in the evolution of dissipative systems. The main conclusions of this work are:
- Intelligence can be described as a dissipative system whose products are not heat, but organized matter and new informational patterns.
- Each solution to a task creates a new pattern in the brain, increasing the density of internal connections — this is local complexification.
- Artificial intelligence is a natural continuation of evolution — a “neoneocortex” layered atop the biological brain.
- A criterion for complexification is proposed: the emergence of a new level of information processing that generates emergent effects.
- Matter remains the sole substrate for any form of intelligence; “escape into the field” is impossible without access to energy.
- The vector of intelligence complexification is interpreted as the expansion of considered environmental parameters, necessary for survival on cosmological scales.
Further work may be directed towards the mathematical formalization of these ideas and their empirical testing within the framework of complex systems theory and neuroscience.
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© Nikolai V. Kharitonov, 2026. This material may be freely used for non-commercial distribution with a mandatory active hyperlink to the original: https://anegentropy.com
Published: March 14, 2026
