𝗧𝗵𝗲 𝗚𝗮𝗽 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗛𝘂𝗺𝗮𝗻 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗮𝗻𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗧𝗲𝘅𝘁

 

In recent years, machines have learned to write with remarkable fluency. Essays, reports, poems, and even philosophical reflections can now be generated in seconds. This has led many to ask a deeper question: Do machines understand what they write? The short answer is no—and the reason lies in a fundamental gap between human concepts and machine text.

Meaning Before Language

Human thinking is conceptual before it is linguistic. We form ideas from perception, experience, emotion, and social interaction. Language is merely a tool we use to express these pre-existing concepts. When a human speaks of justice, fear, or responsibility, the words are anchored in lived reality, values, and consequences.

Machines, by contrast, encounter language without experience. They do not start with concepts and then choose words. They start with words and derive statistical relationships between them.

Text Without Understanding

A language model operates by identifying patterns in massive amounts of text. It predicts which word is likely to follow another, based on probability. This process can produce coherent, persuasive, and contextually appropriate sentences—but it does not produce understanding.

For a machine:

• Words do not refer to the world; they refer to other words.

• Statements are not true or false; they are more or less likely.

• Meaning is simulated, not grasped.

This is the core of the gap: humans attach language to reality, machines attach language to language.

The Illusion of Comprehension

Fluency is deceptive. When a system writes confidently about ethics, economics, or human emotions, it is easy to project understanding onto it. This is a cognitive bias. We equate well-formed language with thought because, in humans, the two are inseparable.

In machines, they are not.

A model can describe grief without ever having lost anything. It can explain courage without risk. It can argue morality without values. What appears as insight is, in fact, an advanced form of imitation.

Correlation vs. Causality

Another aspect of the gap lies in reasoning. Humans reason causally: we ask why things happen and what consequences follow. Machines, in their current form, reason correlationally: they detect that certain ideas often appear together, not that one causes the other.

This limits true understanding, especially in domains that require judgment, ethics, or responsibility.

Why This Still Matters

None of this diminishes the practical power of AI. On the contrary, recognizing the gap allows us to use these systems more effectively and safely. Machines are excellent at:

• Articulating ideas

• Summarizing knowledge

• Exploring alternatives

• Supporting human decision-making

They are not substitutes for human intent, accountability, or wisdom.

A Closing View

The gap between human concepts and machine text is not merely a technical limitation; it is a philosophical boundary. It may narrow through better grounding, multimodal learning, and real-world interaction, but it does not disappear without consciousness, experience, and agency.

AI can speak about meaning.
Only humans can mean what they say.

—
Mr. Ejup Qerimi