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The AI cost problem may start in the data layer, not with the model, argues a 9 November opinion piece in SDxCentral. It points to fragmented databases, retrieval that uses more tokens than needed, and the challenge of keeping persistent memory up to date.
Why it mattersThe article’s proposed fixes are to consolidate specialist databases, reduce system hand-offs and use multiwriter storage to ease single-writer bottlenecks. For teams building AI agents, that is a useful reminder to inspect the plumbing as well as the model bill. But these are the author’s recommendations, not evidence that any one architecture will cut costs.
Discuss: When an AI system’s costs rise, should teams first scrutinise the model and inference bill, or the data architecture around it?
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