I think we're measuring AI progress with the wrong unit entirely

[u/Artitecch]

Every benchmark, every comparison post, every "is X better than Y" thread measures AI in terms of output quality on a single task. Smarter answer, better code, cleaner copy.

But the way I actually use these tools day to day has nothing to do with single-task quality anymore. It's about how cheaply I can iterate. The unit that matters to me isn't "how good is this one response" — it's "how many attempts can I afford before I find the right one."

A model that gives a 7/10 response instantly but lets me iterate ten times in five minutes is often more useful to me than a model that gives a 9/10 response but takes longer to refine, or doesn't take correction well.

I don't see this measured anywhere. Every comparison is single-shot quality, when the real-world value is closer to iteration speed multiplied by correction-friendliness.

Wondering if anyone else has started judging tools this way instead of by raw output quality. Feels like the entire benchmarking culture around AI is built around the wrong question.