Human language is holding AI back: the case for LLMs thinking in a native meta-language
Robert__Sinclair · reddit · 2026-09-07
The author argues human language constrains LLMs: the same logic problem yields different results depending on prompt language, because natural languages are ambiguous and cluttered — "like asking a supercomputer to do calculus with Roman numerals."
- Under the hood, models already think in math: text becomes high-dimensional vectors, the true native tongue of neural networks
- The bottleneck is chain-of-thought reasoning, still forced to write internal monologue in human grammar, wasting compute on sentence structure
- Vision: a hyper-optimized internal meta-language for logic, with a final layer translating conclusions into any human language — human language as display screen, not engine
- Obstacle: all human knowledge is recorded in human text, making training such a model hard
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