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An essay published by CipherTalk argues that buyers and lenders lack a reliable way to compare what AI inference services actually deliver. Common measures such as tokens per second or cost per GPU-hour, it says, do not capture the whole technology stack or verify performance after deployment.
Why it mattersThe author proposes a benchmark built around delivered work, full-stack disclosure and enforceable terms. It is a proposal, not an established standard, but it puts a useful question on the table: what should count as value when AI is sold by the inference?
Discuss: Should AI inference benchmarks prioritise verified work delivered, even if that makes comparisons slower and more complex, or are simpler measures such as tokens per second good enough for buyers?
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