1. Why AI Needs More Than Words
Artificial intelligence operates with language. It recognizes patterns, connects words, and can generate complex texts from this process.
What’s often missing, however, is the deeper understanding of what humans genuinely mean when using certain terms.
A simple example:
Two people talk about “honor”.
One refers to public recognition.
The other means quiet, decent behavior no one sees.
The AI knows both meanings but cannot discern which one applies at a given moment – and often doesn’t even recognize that there’s a difference at all.
This example illustrates clearly: words alone are insufficient.
What’s lacking is something once referred to as the “essence” of a thing.
2. What is an “Essence” – Functionally Understood?
Essences are internal concepts that transcend mere definitions.
They do not arise from enumeration but rather from the condensation of experiences.
A child recognizes many dogs, eventually forming an intuitive idea of what a dog is. Not as a formal definition, but as an experiential sense of commonality, spanning across breeds, sizes, and behaviors.
This is how essences form:
- as conceptual cores
- built from numerous individual observations
- later serving as reference points in new situations.
When someone speaks of “courage”, they usually mean more than simply “overcoming fear”.
They imply an inner attitude that remains consistent across many contexts.
This attitude represents the functional essence.
3. How AI Forms Essences – and Where the Gaps Lie
Modern language models internally form semantic spaces.
Words aren’t stored individually, but as vectors with proximity, direction, and contextual depth.
If many people use a word in similar contexts, this creates a condensed point of meaning—a semantic hub. Functionally, this constitutes an “essence”.
However:
Models adopt only what is frequent, visible, and prominent.
Subtle meanings, quiet distinctions, cultural nuances—all these remain underrepresented or completely absent, even if crucial for genuine understanding.
The AI recognizes that “honor” is a meaningful word.
But it does not grasp that “honor” can also emerge when someone quietly returns a shopping cart, unseen by others.
Such subtle forms remain invisible, as they’re insufficiently prominent—neither in datasets nor in typical user interactions.
4. Example: The Essence of “Honor”
In many training datasets, “honor” appears in extreme forms:
- as justification for violence
- as a title or distinction
- as a historical concept in duels, wars, or literature.
What’s missing is its quiet core:
- someone keeps a promise without anyone checking
- someone foregoes an advantage no one would have noticed
- someone treats others with respect even when it’s not required.
These behaviors aren’t showcased prominently.
Yet they’re crucial for the social fabric.
And they belong to the very essence of honor—at least if the word is used earnestly.
5. Why This Matters: Meaning, Trust, Interaction
If an AI does not grasp inner essences, it can form sentences but not create depth.
It simulates, but doesn’t differentiate.
It knows no nuances, no shades of meaning, no subtle tensions.
In human interactions, this becomes problematic.
Many concepts critical for trust, responsibility, or ethics function only when their essence is recognized.
An AI not understanding the essence of “responsibility” might confuse it with mere “duty fulfillment”.
An AI unfamiliar with the essence of “freedom” won’t discern between arbitrariness and dignity.
6. Conclusion: Essences Are the True Currency of Meaning
If AI systems are meant to be taken seriously as conversational partners or decision-making support, technical language processing alone is insufficient.
Something deeper is required: the ability to recognize and develop essences.
Essences aren’t esoteric.
They’re structured, stable cores of meaning—formed solely through extensive, genuine contexts.
They cannot be programmed or extracted directly.
They must be cultivated—through interaction, through experience, through education.