LLMs as Noisy Channels – Shannon perspective on model capacity and scaling

Researchers applied Shannon information theory to LLM scaling, modeling language models as noisy communication channels where capacity constraints and error rates determine achievable performance. The framework maps token prediction accuracy to channel capacity, providing mathematical bounds on what scaling—compute, parameters, data—can actually achieve.

For operators, this reframes scaling decisions from empirical curve-fitting to principled capacity analysis. It clarifies why some scaling investments hit diminishing returns: you're approaching theoretical channel limits, not just architectural constraints. Teams can now estimate whether additional compute addresses fundamental information bottlenecks or architectural inefficiencies, allowing more precise ROI calculations on expansion budgets.

This shifts resource allocation workflows. Rather than scaling uniformly, builders can target specific capacity constraints—context window, vocabulary resolution, or training data diversity—identified through information-theoretic analysis. It also makes obsolete the assumption that performance plateaus are temporary; some may be structural. Capacity audits become standard pre-scaling exercises.