Intelligence is Compression With an Explainability Floor
zakelfassi · x · 2026-07-20
By comparing the Token consumption of different models performing similar tasks, the author proposes a new perspective for evaluating AI efficiency: the Semantic Yield Rate.
- Beyond Surface Metrics: Current model evaluations often focus only on output quality, price, or latency, neglecting how much valuable semantic information a model retains per unit of inference burden (including Tokens, compute, time, and cost).
- Explainability Floor: Compression does not equal intelligence. If a system hides its reasoning process, assumptions, and uncertainties just to be brief, it becomes hard to debug and trust. Therefore, a reliable model must have an "explainability floor," retaining enough visible intermediate reasoning steps based on the task's risk level.
- Redefining the Model Race: The cheapest model isn't a good system component if it unnecessarily consumes three times the Tokens, nor is the highest-scoring model if it buries results in massive amounts of text requiring manual review. Future evaluations need to balance the semantic compression rate with explainability.
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