Large language models are getting better at sounding like they understand.
This essay looks at why that fluency is convincing—and why it can be misleading.
When Fluency Detaches explores what changes when language improves without being forced to answer to consequence. Using examples from programming, learning, and everyday AI use, it argues that fluency normally signals prior contact with reality—but in LLMs, that cost is often never paid.
The result isn’t deception or hallucination, but something subtler: abstraction that no longer has to return to constraint. The essay asks how we tell the difference between understanding and performance—and what it means when nothing pushes back if an answer is wrong.
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