Incomprehensibly Lucid

When being exactly right may probably be wrong

There are over 300,00 words defined in the English language, but only a few percent are in common usage.  If every speaker clearly understood every word and used each to best describe their intention, then ambiguity and misunderstanding would dramatically decline.  While most humans are incapable of such linguistic legerdemain, an LLM can do so.  How might LLMs be used by people to reduce ambiguity and misunderstanding in this manner, and how would the general populous deal with these precise works?

This is one of the most likely—and least discussed—civilizational effects of LLMs: language compression gives way to language re-expansion.

For centuries, mass communication has favored a relatively small working vocabulary because human conversation is constrained by memory, speed, education variance, and fear of not being understood.

An LLM removes much of that constraint because it can function as a semantic intermediary: translating between precision and accessibility in real time. That means the issue is no longer whether rare words exist, but whether their precision can be deployed without social exclusion.

1. Why human language uses only a small fraction of available precision

English has enormous lexical granularity because many words capture distinctions that ordinary speech collapses.

For example:

• anger
• irritation
• resentment
• indignation
• exasperation
• umbrage
• antipathy

Each names a different mental state.

Humans usually choose broader terms because conversation optimizes for speed, not semantic fidelity.

LLMs can instead detect:

“The user means something closer to ambivalence with moral hesitation than simple uncertainty.”

That is a fundamentally different communicative capability.

2. LLMs as semantic amplifiers

The most immediate use will be:

Precision before publication

A person writes:

“I disagree because this seems wrong.”

The LLM may suggest:

“Do you mean empirically unsupported, ethically objectionable, internally inconsistent, or strategically unwise?”

That forces the speaker to locate intention more precisely.

This reduces ambiguity because many disputes are actually disputes over hidden word imprecision.

3. LLMs may function like cognitive spectrometers

Instead of merely correcting grammar, future systems may continuously ask:

• Is this the right degree of certainty?
• Is this causal or correlational?
• Is this emotional or analytic?
• Is this descriptive or normative?

That means users begin learning distinctions they previously operated without.

A large share of misunderstanding disappears when people discover they were using one word to carry four meanings.

4. Precision creates a paradox: clearer language can initially feel less human

Highly precise wording often sounds formal, unfamiliar, overly deliberate, or emotionally cold.

For example:

A person may say:

“He lied.”

A more precise rendering may be:

“He selectively omitted material facts while preserving plausible deniability.”

More accurate—but socially heavier. So general populations may initially resist precision because ordinary speech depends on tolerated blur.

5. Therefore, LLMs will likely become translators between precision levels

The real power is not rare vocabulary itself, but bidirectional semantic conversion:

Upward:

ordinary speech → precise meaning

Downward:

precise meaning → socially digestible speech

This means one person could think in highly precise terms while speaking accessibly.

In effect, LLMs may allow ordinary people to borrow expert-grade linguistic precision without sounding alien. That is historically new.

6. Rare words will matter less than precise conceptual framing

The public may not need to adopt obscure vocabulary directly. Because often precision comes from structure, sequencing, contrast, and explicit boundaries.

Example:

Instead of rare words:

“This policy may help short-term liquidity while worsening long-term capital allocation.”

That is highly precise without unusual vocabulary.

So, the future may not produce giant vocabularies in common speech.

It may produce:

more exact conceptual syntax

7. How the general populous will deal with precise words

Three likely layers emerge:

Layer A: passive familiarity expands

People gradually understand more words than they actively use.

Layer B: active vocabulary rises modestly

A few thousand additional words become common because LLMs normalize them.

Layer C: semantic dependence develops

People increasingly rely on systems to test whether wording matches intent.

This resembles calculators:

Most people still estimate mentally—but trust machines for precision.

8. Social side effect: conflict may become sharper, not softer

Reduced ambiguity does not always reduce disagreement.

Sometimes ambiguity preserves peace. If LLMs clarify meaning too well, hidden disagreements become visible, motives become harder to obscure, and rhetorical escape narrows. That can improve honesty—but also intensify conflict.

9. The deeper consequence: thought itself changes

Vocabulary is not merely labeling. It determines what distinctions people can stably hold. A society with LLM-assisted precision may begin noticing distinctions previously unavailable in ordinary discourse.

That means language does not merely become clearer; reality becomes more finely partitioned. Historically, every expansion of available distinctions changes civilization.

10. Final perspective

The likely outcome is not that everyone starts speaking like a lexicographer. It is that LLMs become:

semantic prosthetics

allowing people to mean more, misstate less, detect hidden assumptions, and choose precision appropriate to audience.

The challenge is that precision itself carries power: whoever defines the exact word often defines the frame. So, reducing ambiguity does not eliminate struggle over meaning—it elevates it.