Noise Instead of Signal: The Content of Large Language Models

This essay explores how large language models invert Claude Shannon’s classical distinction between signal and noise in information theory. Where Shannon sought to isolate meaningful signals from noise, LLMs are trained on the entire “noise” of human textual production—errors, contradictions, and redundancies included. The essay argues that these models don’t transmit meaning but approximate it through statistical correlation, transforming noise into seemingly coherent signals. Drawing on Shannon’s mathematical theory of communication and contemporary developments in generative AI, the paper demonstrates that meaning in LLMs emerges not from reference to reality but from pattern recognition within linguistic noise itself. This represents a fundamental philosophical shift in how we understand machine-generated language. The essay concludes that our task is not to silence this noise but to maintain critical distance, recognizing that understanding begins with doubt, not fluency, and that meaning is not what survives the noise but what resists becoming it.