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It's because it reduces data dimensionality. From 2d to 1d and from 1d to 0d (scalar).
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It always messes with me: reducing across a specific axis always takes O(whole tensor) time, because there's no difference between "iterate over all dims, then collapse the final one" versus "iterate versus the first dim and do some cursed tensor accum" (and likewise for between)

Maybe there's just a better way to think about it and I'm still thinking about it way too much like a programmer

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No, reduce has exactly the same time complexity as map and filter.
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Sorry I changed problems a bit and started talking about me trying to understand matrices lol
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