The Closed Box Problem: Why LLMs Might Never Think Like Humans

For the first time in history, machines can generate essays, write code, explain philosophy, and answer questions with remarkable fluency. Systems based on large language models (LLMs) can appear almost conversationally intelligent.

But beneath this impressive capability lies a fundamental limitation.

Large language models operate inside what we might call a closed box. They process symbols about the world without ever experiencing the world itself.

Humans, on the other hand, are embedded within a rich loop of perception, action, body chemistry, and environmental interaction. Intelligence for us is not merely computation—it is the outcome of a deeply integrated biological system.

This difference raises a fascinating question:

Can intelligence truly emerge from pure computation alone, or does it require a body, a chemical system, and continuous interaction with reality?

To explore this, we need to understand how human intelligence works and how it differs from the architecture of modern AI.

Intelligence in Humans Is Not Just Computation

When people describe the brain as a “neural network,” they are not entirely wrong—but they are also dramatically oversimplifying the story.

The human brain does use neurons connected through synapses, and these networks process electrical signals. However, the brain is not simply an electrical circuit.

It is also a chemical computer.

Neurons communicate through neurotransmitters—chemical molecules such as dopamine, serotonin, and glutamate. These chemicals alter how signals propagate through the brain and influence how we perceive, learn, and make decisions.

For example:

- Dopamine influences motivation and reward learning.

- Serotonin affects mood and emotional regulation.

- Cortisol changes cognition under stress.

- Oxytocin influences trust and social behavior.

These biochemical signals dynamically reshape how the brain processes information.

In other words, human cognition is not just about firing neurons. It is about a constantly shifting chemical environment that alters how those neurons behave.

Large language models have no equivalent to this.

They run fixed mathematical operations across billions of parameters, but their internal state does not evolve through biochemical feedback.

The Brain Is Connected to a Body

Another critical difference is that the brain is not an isolated processor.

It is part of a system that includes the entire body.

The brain constantly receives signals from:

- vision

- hearing

- touch

- smell

- temperature

- balance

- internal organs

- muscle position (proprioception)

These signals create a continuous stream of information about the environment and the body’s state within it.

For instance, your brain knows:

- whether you are hungry

- whether you are tired

- whether your heart rate is increasing

- whether the room temperature is changing

These signals influence cognition in subtle but profound ways.

Stress can narrow attention. Hunger can change decision-making. Fatigue can alter reasoning ability.

In humans, intelligence is therefore embodied. It emerges from the interaction of brain, body, and environment.

An LLM, by contrast, exists entirely inside a computational container. It receives tokens and produces tokens.

It does not feel temperature. It does not perceive gravity. It does not experience time passing.

Its “world” is made entirely of text.

Intelligence Is a Continuous Feedback Loop

Human intelligence operates as a loop.

The cycle looks something like this:

Perception → Prediction → Action → Feedback → Learning

We see the world, predict outcomes, act within the environment, observe the results, and update our internal models.

A child learning to walk, for example, constantly runs this loop:

- Attempt a movement

- Fall or succeed

- Receive sensory feedback

- Adjust motor control

Over time, the brain refines its internal model of balance and movement.

This type of learning is deeply tied to physical interaction with the world.

Large language models do not participate in such loops.

Their learning happens during training when they process massive datasets of text. After training, they generate responses based on statistical relationships between tokens.

They do not test hypotheses in the real world.

They do not take actions and observe consequences.

They operate entirely in the domain of symbolic representation.

Language Is Not the Same as Understanding

One of the classic problems in cognitive science is known as the symbol grounding problem.

It asks a deceptively simple question:

How do symbols acquire meaning?

For humans, words are grounded in sensory experience.

When someone says the word “fire,” we recall:

- heat

- light

- smell

- danger

- past experiences

Our understanding of the word is tied to real-world interactions.

An LLM, however, understands “fire” only through patterns in text. It has read countless sentences containing the word, but it has never experienced heat or seen flames.

Its knowledge is therefore derivative.

It is based on how humans describe the world rather than direct interaction with it.

This is the essence of the closed box problem.

LLMs manipulate symbols about reality without direct contact with reality itself.

Why LLMs Are Still Extraordinary

Despite these limitations, large language models are remarkable tools.

Their strength lies in their ability to capture patterns across enormous amounts of human knowledge.

They excel at tasks such as:

- summarization

- code generation

- translation

- reasoning over text

- knowledge retrieval

In many domains, they augment human capabilities in powerful ways.

But their intelligence is fundamentally text-bound.

They operate within the linguistic universe humans have already created.

The Future: Opening the Box

If intelligence truly requires interaction with the physical world, the next stage of AI may involve systems that combine language models with sensory and motor capabilities.

This direction is often called embodied AI.

Such systems would integrate:

- vision

- touch

- spatial reasoning

- physical action

- environmental feedback

Robotics research is already moving in this direction.

Instead of learning purely from text, future AI systems may learn by interacting with the world—much like humans do.

This could gradually open the box that current AI systems operate within.

Intelligence Beyond Computation

The rise of large language models has sparked renewed debates about the nature of intelligence.

Are we witnessing the early stages of machine reasoning?

Or are we observing a sophisticated form of pattern recognition that merely imitates reasoning?

The answer may lie in recognizing that intelligence is not a single mechanism.

It may emerge from the integration of multiple systems:

- computation

- perception

- action

- chemical regulation

- environmental interaction

Humans possess all of these.

Large language models currently possess only one.

And until that gap narrows, AI may remain extraordinarily capable—but fundamentally different from the intelligence that evolved inside the human brain.