Synthèse
The 2nd Law of Thermodynamics teaches us that every energy transformation introduces inefficiencies. Some energy inevitably becomes less useful for doing work. The same pattern appears in enterprise AI: Not every token contributes equally to business outcomes. Repeated prompts Oversized context windows Redundant reasoning Hallucinations requiring correction Multiple agents performing the same work Expensive models solving simple problems Those tokens are not “lost.” They simply pr
The 2nd Law of Thermodynamics teaches us that every energy transformation introduces inefficiencies. Some energy inevitably becomes less useful for doing work. The same pattern appears in enterprise AI: Not every token contributes equally to business outcomes. Repeated prompts Oversized context windows Redundant reasoning Hallucinations requiring correction Multiple agents performing the same work Expensive models solving simple problems Those tokens are not “lost. ” They simply produce very little business value.
I think of this as token entropy. Every enterprise deploying AI will experience it. The goal is not to eliminate token entropy completely — that would be unrealistic. The goal is to continuously identify it, measure it and reduce it. Because every unnecessary token represents an opportunity to improve both cost and business performance.
Source originale
en.wikipedia.org
CORTEX