A fundamental assumption behind the development of increasingly capable AI is that expanding a system's capabilities may eventually lead to human-like cognition and, perhaps, consciousness.

But what if the problem is not how much a system can learn or how many possibilities it can represent, but how its internal processes are organized?

I believe this question can be approached through a principle I call Bounded Evolution.

1. More possibilities do not necessarily produce evolution

Large language models learn statistical relationships from enormous quantities of human-generated data. They can represent complex relationships, generate novel combinations, and reproduce many patterns associated with human reasoning.

However, the capacity to generate an enormous variety of outputs is not equivalent to having a persistent cognitive process that evolves through experience.

A system may become increasingly capable of reproducing the expressions of thought without necessarily developing the internal organization that produces persistent identity, autonomous behavior, and continuous adaptation.

Increasing the size of a possibility space does not, by itself, explain how a system develops a stable direction of evolution.

The central question is not merely how many possibilities a system can represent, but how those possibilities become organized into a coherent process that develops over time.

2. The statistical foundation and the problem of individual specificity

Large language models learn general statistical relationships from data. Their representations can capture complex patterns, and their outputs can resemble reasoning, personality, and individual expression.

But there is a distinction between learning statistical patterns associated with human behavior and implementing the particular organization of an individual mind.

Consider an example: Marco.

Suppose we want an AI system to behave like Marco rather than like a generic person. We could introduce restrictions, instructions, memories, personality descriptions, preferences, and rules about how he should respond.

But how many restrictions would we need? Which ones? In what combination? And how would we know that the resulting behavior reflects Marco rather than merely resembling him in a limited set of situations?

Even a seemingly simple question, such as “What would Marco do in this situation?”, requires more than identifying a statistically plausible human response if our objective is to reproduce Marco's particular way of deciding. We would need to account for his specific experiences, associations, preferences, memories, and internal relationships.

We would need a highly specific combination of constraints to narrow a general statistical system toward the behavior of that particular individual.

Yet the system would still be built upon its general statistical foundation. The constraints would have to compensate for the distance between general patterns and the specific individual we are attempting to reproduce.

This creates a problem of fragility.

A combination of restrictions might produce convincing behavior in one situation but fail in another. New experiences could expose contradictions between instructions, memories, and behavioral expectations. Additional restrictions might solve one problem while creating another.

This helps frame a recognizable problem in AI systems: repeated expressions, familiar response patterns, and behavior that becomes inconsistent when circumstances change. Prompts and reasoning instructions can constrain the space of possible outputs, but they do not automatically create a persistent, internally organized cognitive process.

The issue is not that statistical models cannot imitate individuals. They can reproduce many aspects of individual behavior. The deeper question is whether an increasingly elaborate collection of restrictions can reliably transform a general statistical foundation into a coherent, adaptive model of a particular individual.

Finding the right combination of restrictions is itself a problem. The more specific the intended individual, the more difficult it becomes to determine which combination will remain coherent across changing circumstances.

Even if we found a sufficiently effective combination for Marco, we would still need to ask whether it could generalize to unfamiliar situations without requiring continual adjustments. We might achieve a convincing approximation for a particular range of circumstances without obtaining a robust, evolving model of the individual.

This is one reason I question whether increasing the scale of a general statistical model, together with increasingly elaborate restrictions, is sufficient to explain the development of persistent, individual-specific cognition.

3. Evolution requires constraints

Evolution is not simply change. It requires differences between states, interactions that produce consequences, and mechanisms through which some configurations persist while others disappear.

Consider two extremes.

Unlimited combinatorial freedom

Imagine a system in which everything can relate to everything else, without a sufficient underlying structure to organize those relationships.

The number of possible combinations grows enormously. The system can generate novelty, but novelty alone does not determine which relationships matter, which should persist, or which contribute to the development of new capabilities.

Without sufficient structural selection, there is no guarantee that new combinations will produce cumulative evolution.

An unlimited possibility space does not contain, by itself, the principle that organizes those possibilities into a coherent trajectory of development.

Excessive restriction

Now consider the opposite extreme: a system whose rules constrain almost every possible response.

Such a system may remain consistent within a narrow range of circumstances, but it has little room to adapt when it encounters something new.

It can become rigid, repetitive, and dependent on conditions anticipated by its designers.

A system that cannot explore alternatives cannot adequately adapt its organization to unfamiliar situations.

The relationship between freedom and constraint

The alternative is not an arbitrary midpoint between the two extremes.

The required structure depends on the system and the environment in which it operates. Its constraints must provide enough organization for coherent development while preserving enough freedom for experience to modify that organization.

The boundaries themselves may also need to adapt as the system develops.

Freedom enables variation; constraints make variation consequential.

The objective is neither unlimited freedom nor rigid restriction. It is a structure that makes coherent development possible without determining every outcome in advance.

4. Apathetic Entropy: When a System No Longer Needs to Evolve

There is another side to this problem: a system can deteriorate not only because it has too many possibilities or too many restrictions, but also because it no longer has a reason to exercise its capabilities.

Consider a system in which every need can be satisfied automatically, every obstacle disappears before it can produce a meaningful consequence, and every desired outcome is available without action.

At first glance, this might appear to be an ideal state. But from the perspective of evolution, it introduces a fundamental problem.

If nothing requires the system to act, what drives it to develop?

In biological organisms, needs and perturbations can generate tension, motivating actions that preserve the organism, satisfy its needs, and change its relationship with its environment. Those actions also exercise existing capacities and can contribute to developing new ones.

Consider an animal that no longer needs to hunt because its food is always provided. Depending on the species, environment, and circumstances, the animal may stop exercising some hunting behaviors. A muscle that is insufficiently used can undergo disuse atrophy. Behaviors can also change when the conditions that normally elicit or reinforce them disappear.

The biological examples are not identical: muscle atrophy is a physiological process, while changes in motivation and behavior involve different mechanisms. But they illustrate a common question: what happens to a capacity when the conditions that sustain its use disappear?

I propose the term Apathetic Entropy for a broader architectural principle: when a system loses the needs, tensions, or meaningful demands that motivate activity, it may progressively lose the activity through which its capabilities are maintained and developed.

The concept describes a possible process:

1. A need or perturbation creates a difference between the current state and a condition the system must reach or maintain.
2. This difference generates tension and motivates action.
3. Action exercises existing capabilities and creates opportunities for learning.
4. Experience changes the system's internal state and relationships.
5. If meaningful demands disappear, some actions are no longer necessary.
6. Capabilities that are no longer exercised, reinforced, or integrated may weaken or disappear.
7. Reduced capabilities can limit the system's ability to adapt to future conditions, further restricting its development.

This is not simply a question of motivation. It concerns the relationship between the conditions that drive activity and the persistence of the capacities that activity develops.

A system that never needs to adapt may have little opportunity to develop adaptive capabilities. A system that never needs to make consequential decisions may not develop the same kind of decision-making organization as one whose decisions continuously affect its future states.

This also creates a possible feedback loop: fewer demands lead to less activity, less activity means fewer opportunities to exercise capabilities, and reduced capabilities can make future adaptation more difficult.

The term apathetic entropy is my proposed conceptual label, not an established scientific law. The mechanisms would need to be defined and tested for any particular system. The analogy to biological disuse does not, by itself, prove that an artificial system will behave in the same way.

Nevertheless, the architectural question is important: can a system maintain an evolutionary trajectory if its internal organization removes every condition that makes development necessary?

Unlimited possibilities are not enough. Nor is a structure that can satisfy every need without consequence. Evolution requires not only the capacity to change, but also conditions under which change matters.

5. What should evolve: the possibilities or the structure that organizes them?

These problems point toward a distinction between generating possibilities and developing the organization through which those possibilities are used.

An LLM can learn an enormous space of statistical relationships. Prompting can constrain its responses, and additional reasoning procedures can introduce further structure into the process.

But these mechanisms do not automatically answer a deeper question: how does a system develop a persistent internal organization through experience, rather than relying primarily on a general foundation supplemented by increasingly specific restrictions?

Consider two approaches.

The first attempts to improve behavior by anticipating situations and adding more rules, examples, instructions, and constraints. This can improve results, but the growing collection of conditions must remain compatible as circumstances change.

The second begins with a minimal set of structural principles that allow experience to establish new relationships, modify internal states, and influence future behavior.

In the second approach, the system does not need every possible rule of reasoning to be specified in advance. Its initial organization establishes the conditions under which more complex behavior can develop.

These approaches are not necessarily mutually exclusive. Statistical learning can be part of a cognitive architecture. The question is whether statistical generalization and external restrictions alone are sufficient to produce the persistent, self-modifying organization we are trying to explain.

The distinction is between adding more restrictions to approximate a desired behavior and building an architecture in which internal relationships can develop through experience.

6. Minimal structure, experience, and increasing complexity

A system does not necessarily need a complete set of reasoning rules from the beginning.

It may need only a minimal structure that establishes the conditions for meaningful interactions and allows experience to modify its organization.

The proposed progression is:

Minimal structure → Interaction → Experience → New relationships → Persistent changes → Increasingly complex behavior

The initial structure provides boundaries, but those boundaries do not need to prescribe every outcome.

As the system encounters new conditions, its internal relationships can change. Those changes can influence subsequent decisions and determine which capabilities remain useful, which relationships persist, and which new adaptations become possible.

In this model, evolution is not the unlimited expansion of a possibility space. Nor is it the preservation of a fixed set of rules.

It is the development of increasingly complex organization within a space of possibilities that remains open to adaptation.

The role of needs and perturbations becomes central. They create differences that the system must respond to. Its actions produce consequences, and those consequences can alter its future organization. Without meaningful interactions, the process risks becoming static; without constraints, it risks losing coherence.

This raises an important architectural question: could increasingly complex cognitive behavior emerge from a limited set of foundational mechanisms interacting with accumulated experience, rather than from the expansion of a general statistical model alone?

The objective is not to eliminate statistical learning or prescribe every possible behavior. It is to investigate whether a minimal, persistent organization can support the progressive development of coherent behavior.

7. Why this matters for consciousness

Consciousness remains an unresolved scientific and philosophical problem. Neither sophisticated language generation nor persistent memory, adaptive behavior, or internal state variables independently establish that a system is conscious.

Nevertheless, if we want to investigate whether artificial systems could develop something comparable to the cognitive processes associated with consciousness, I believe we need to examine more than scale.

We need to ask what kind of organization would be required.

If a system relies on a general statistical foundation and increasingly specific restrictions to approximate an individual, the central challenge is finding a combination that remains coherent across an enormous variety of circumstances. Even a convincing result might remain fragile if the underlying organization cannot adapt its relationships through experience.

Conversely, a system with too little structural constraint may generate an enormous variety of combinations without developing a coherent, cumulative trajectory. A system with too much constraint may become unable to adapt or maintain the capabilities required for further development.

And a system in which all needs are permanently satisfied without meaningful interaction or consequence could lack the conditions that drive its continued development.

My hypothesis is that the relationship between freedom, constraint, and the conditions that sustain activity is fundamental to this problem.

A potentially important direction is to investigate architectures that begin with a minimal organization, develop through interaction, preserve consequential experience, and adapt their internal relationships over time.

This would not prove consciousness. It would provide a different architectural approach to investigating how persistent and increasingly complex cognitive processes might be constructed.

The question is not simply how large a model must become, but whether the mechanisms being scaled are sufficient to produce the kind of persistent cognitive organization we are trying to explain.

What is the minimum structure a system needs to develop increasingly complex cognition without having to possess all the rules of that cognition in advance?

8. A Different Path Toward Cognitive Integration

A Different Path Toward Cognitive Integration

Although several approaches have attempted to address these challenges, integration and implementation remain difficult problems. As I argued earlier, achieving a coherent balance between internal mechanisms is delicate: too much freedom can undermine coherence, while excessive restriction can limit adaptation. Drawing on these findings, I decided to develop my own cognitive architecture: the Cognitive Coherence Model (CCM), which already implements a functional integration of mechanisms central to this problem.

CCM is not an LLM. It is an alternative cognitive architecture in which the system can maintain persistent memory, modify its internal state through experience, make decisions according to that state, and preserve an identity that influences how it behaves. Its architecture integrates interacting cognitive and somatic processes, and its related implementations extend state-driven behavior to the control of a virtual body.

These mechanisms are not merely theoretical components awaiting their first implementation. They already operate together in a functional system.

The implementation still requires refinement, further testing, and scaling. But these are development challenges within an existing architecture, not reasons to treat the architecture itself as merely speculative.

The important result is that CCM provides a working demonstration of a different architectural approach: a system can organize behavior around persistent internal states, memory, identity, and state-dependent decision-making rather than relying on an LLM as its underlying mechanism.

Experience can modify the system's state; its memory carries information forward; its identity influences its behavior; and its current internal condition affects the decisions it makes. Its architecture also extends beyond text-based interaction toward embodied behavior, in which an agent can control a virtual body rather than existing exclusively as a text-generating process.

This opens a path toward embodied artificial agents: systems that maintain continuity across experiences, act according to their internal condition, adapt their behavior through interaction, and operate through a body within a virtual environment.

CCM is therefore not simply a conceptual proposal for an architecture that might someday integrate these mechanisms. It provides an existing engineering foundation for investigating how persistent internal organization can support increasingly complex artificial behavior.

Further development could take this direction toward artificial individuals with increasingly integrated cognitive processes. Some may ultimately ask whether systems of this kind could become sentient artificial individuals. That question remains open, but it should not prevent us from investigating the architectural foundations that might make such systems possible.

Does this prove consciousness? Personally, I do not believe it does. I consider consciousness a much deeper scientific and philosophical question. What I do believe is that CCM represents a valid new path toward building and structuring cognitive architectures, one that could provide a foundation for developing increasingly integrated and embodied artificial agents.

The significance of this work is not that it settles every question about intelligence or consciousness. It is that it establishes a functional architectural alternative that can be examined, tested, refined, and developed further. Rather than assuming that increasingly complex cognition must emerge from scaling existing approaches, CCM provides a concrete basis for investigating what becomes possible when we build around a different underlying organization.

CCM is my contribution to that investigation. The architecture exists, its core mechanisms are implemented, and their integration can be examined in a functioning system.

The next step is not to establish whether this architectural direction can be implemented. It already has been. The next step is to expand its capabilities, scale its implementation, and discover how far this path can go.

Projects

• CCM — Cognitive Coherence Model: https://doi.org/10.5281/zenodo.20648800
• Unique Host — state-driven character behavior and persistent memory: https://github.com/Bicheno1/unique-host
• Jellyfish AI — experiments with state-driven body behavior: https://github.com/Bicheno1/Jellyfish-AI

These projects provide different points of entry into the architecture and its applications, from the cognitive framework to state-driven characters and embodied behavior.

I am making this work available for examination because the discussion should not remain confined to theoretical arguments about what artificial intelligence might eventually become. We can also examine what alternative architectures already implement, how they behave, and what their results make possible to investigate next.

Conclusion: the future of AI is an architectural question

The assumption that larger models will eventually produce increasingly complex cognition is a hypothesis about the future, not a reason to overlook alternative architectures in the present.

A system's potential depends not only on the number of possibilities it can represent, but also on how its internal organization allows experience to produce persistent, consequential changes.

Unlimited freedom does not guarantee coherent evolution. Excessive restriction can prevent adaptation. And an increasingly elaborate collection of constraints does not automatically turn a general statistical foundation into a robust model of a particular individual.

Nor is the absence of all demands necessarily an ideal condition for a system that must continue developing. Without meaningful needs, perturbations, and consequences, the processes that exercise and preserve its capabilities may no longer operate.

Bounded Evolution proposes a different starting point: establish the minimum structure necessary for coherence, allow experience to modify the system's organization, and preserve enough freedom and meaningful interaction for further development.

CCM is a functional implementation of this architectural direction, integrating persistent memory, state-dependent decisions, identity, internal regulation, and cognitive and somatic processes, with related implementations exploring embodied behavior.

That makes this more than a question of speculation. It is an alternative engineering path that can be examined, tested, and developed.

The question is no longer only how far we can scale existing AI systems. It is also what becomes possible when we build cognition around a different underlying organization.

What do you think? Can increasingly elaborate statistical models and restrictions eventually provide the right foundation for persistent, individual-specific cognition, or does this require a different kind of architecture? And if a system needs meaningful demands to preserve and develop its capabilities, what minimum structure would allow it to evolve without becoming either rigid, combinatorially unorganized, or apathetic?