AI compression keeps your favorite vocabulary words while silently rewriting the logical structure of your argument.
Compressed summaries often look accurate because they reuse the specific terminology found in the source text. The internal logic and argumentative connections actually shift into something different that the reader cannot easily spot. This creates a dangerous illusion of fidelity where the keywords remain but the logical sequence is poisoned. Large language models currently prioritize linguistic fluency over structural accuracy in ways that can distort legal or philosophical theories. Relying on AI to summarize complex reasoning will lead to a slow erosion of intellectual nuance in public discourse.
Semantic Infidelity: How AI Compression Distorts Precision-Dependent Arguments
SSRN · 6556180
When AI language models summarize precision-dependent theoretical work, they produce output that retains the vocabulary of the original while replacing its argumentative structure. The result is not a simpler version of the same idea-it is a different idea, delivered in prose fluent enough that the reader has no signal a substitution occurred, and the author has no control once the compressed version enters circulation. This paper names the phenomenon semantic infidelity: the production of well-