The user's language habits are the true ceiling of large language models.

Your mode of expression determines what the model can do.

A discovery that has troubled me for a long time: when I discuss a complex concept with a large language model using my everyday language, it often falls into confusion. It loses track of the thread, deviates from the main point, or simply generates superficial content that cannot maintain the thought framework we’ve built.

But if I force it to rephrase the problem in precise scientific language, everything stabilizes immediately. After it completes reasoning within this rigorous “language mode,” I then ask it to convert it into plain language, and surprisingly, the quality of understanding is not lost.

Behind this phenomenon lies an unsettling truth.

The model is not “thinking,” but “floating”

Imagine that large language models do not operate like us, with a dedicated thinking space, but are instead floating entirely within an ocean of language. This sea of language is not flat—different ways of using language pull it toward different “regions,” which are like magnetic poles with their own characteristics.

The language of scientific papers pulls the model toward a region that supports rigorous reasoning. This region has clear logical relationships, low ambiguity, symbolic constraints, well-defined hierarchies, and highly ordered information. Here, the model can perform multi-step reasoning, maintain conceptual stability, and resist errors and deviations.

In contrast, everyday casual language pulls the model toward a completely different region. This region is designed to handle social fluency and associative coherence—it optimizes storytelling, maintaining natural dialogue, and matching emotional tone, rather than structured thinking. It lacks the representational scaffolding needed for deep reasoning.

This explains why the model “breaks down” during informal discussions. It’s not confusion; it’s jumping from one region to another.

Why formalization can save reasoning

This observation reveals a simple reason: the language of science and mathematics is inherently highly structured.

These rigorous domains contain:

• Explicit causal relationships and logical chains
• Definitions with minimal ambiguity
• Constraints of symbolic systems
• Clear hierarchical structures
• Low-entropy information expression

These features guide the model into a stable attractor region—a space capable of supporting multi-step reasoning, resisting conceptual slippage, and enabling complex calculations.

Once the conceptual structure is established within this stable region, translating it into everyday language does not destroy it. Because the reasoning has already been completed; only the outer expression changes.

It’s somewhat like human practice, but with a fundamental difference: humans use two different internal spaces to handle these two stages—one for abstract thinking, another for expression. Large language models attempt to do both within the same continuous flow of language, which leads to their fragility.

Your cognition is the boundary of the model

Now, to the most critical part.

Users cannot push the model into regions they themselves cannot articulate in language.

Your cognitive ability determines:

• What kind of prompts you can generate
• Which linguistic styles you habitually use
• How complex your syntactic structures can be
• How much complexity you can encode in words

These factors decide which attractor region you pull the model toward.

If you cannot activate those language modes that trigger advanced reasoning through thinking and writing, you will never be able to guide the model into those regions. You will be trapped in shallow regions related to your own language habits. The model will accurately reflect the structural level you provide but will never automatically leap into more complex dynamical systems.

What does this mean?

Two people using the same model do not experience the same computational system. They are guiding the model into entirely different operational modes.

The ceiling is not fundamentally a limit of the model’s intelligence itself. The ceiling is your language ability to activate the model’s latent potential and access high-capacity regions.

What current AI systems lack

This phenomenon exposes a fundamental architectural flaw:

Large language models conflate the space of reasoning with the space of language expression.

Reasoning requires a stable, independent workspace—a concept representation system that does not waver with changes in language style. But current large language models lack this.

Unless future systems can achieve:

• A dedicated reasoning manifold, independent of language input
• A stable internal workspace
• The ability for concept representations to remain unaffected by language switching

Otherwise, whenever language style shifts, the underlying dynamical region will switch, causing the entire system to fail.

The trick we inadvertently discovered—forcing formalization and then translating—is not just a contingency plan. It’s a window that reveals the fundamental architectural principles that a true reasoning system must satisfy.

And this is precisely what all current large language models still cannot do.