Decision models show embedding models are ripe for disruption as the Pareto frontier of model form factors stays unexplored
adhamelarabawy · x · 2026-10-12
Using @typesafeai's decision models as an example, the author argues the Pareto frontier of model form factors is largely unexplored: deliberate tradeoffs for specific capabilities can yield order-of-magnitude gains in speed, throughput, and reliability, reopening use cases like computer use and production classification.
Embedding models, he contends, are ready for disruption—they offer pre-computable, compact representations with O(N) storage and O(log N) interaction, and can even be initialized from (M)LLMs. But even instruction-tuned embeddings leave much expressivity unused at the query interface. There's no reason intelligence can't be shifted into retrieval the way it has been into classification.
Related event: Decision Models May Disrupt Embedding Models, Argues Viral Thread(2 posts)→
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