The Exchanges

Every argument clarity score on this site is built from rows on this page. Each question and answer was assessed with names hidden, the host's own answers included, on four things from 1 to 5: directness (does it answer the question asked), coherence (do the ideas follow), precision (concrete details and clear references), compression (says a lot per word). The weighted mix (30/30/25/15) is the exchange score. A person's published score averages their exchange scores on raw tape only, at least 8 of them, shrunk toward the cohort mean. Full method →

Aaron Levie no published score: only 2 usable exchanges on raw tape, and a fair score needs 8+ record → ← everyone

Every exchange below was scored with names hidden, four dimensions each from 1 to 5. An exchange's score is 0.30·directness + 0.30·coherence + 0.25·precision + 0.15·compression. The published score averages the raw tape exchange scores and shrinks small samples toward the cohort mean, so five great answers can't beat twenty good ones. Produced feed rows count only toward coarse estimates, never toward a full score.

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2exchanges match
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Answered raw tape D 4 · C 4 · P 4 · Cm 4 4.00

Q Aaron, we were talking about the Meter paper, and it was, the paper suggested that their developers were actually less productive with AI, but that doesn't square with your experience talking to a lot of different startups, seeing a lot of different startups, and how they're so much more productive. So, why don't you talk about where, where you're seeing startups say they're more productive, and why is it happening?

A Yeah. So I'll, I'll first just represent our own case study and then, and then there's the really extreme version. So, uh, our own case study is we've adopted a few different kind of, um, AI coding tools. Um, uh, you know, cursor being a, a super, super popular one internally. And I, you know, as I talk to people, let's say in the hallway who have, you know, yeah, maybe they're trying to get me excited by AI, but like, I think they know I'm, I'm bought in. So, so the, the kind of Qualitative answers I get from, from people, and then I'll give you our, our internal metric. You know, some, some people say I'm getting, you know, a 20, 30% productivity gain. Other people will say 75%. Um, interestingly, I have not been able to pinpoint the demographic difference, uh, on the answers.

AI assessment note: “we've adopted a few different kind of, um, AI coding tools. Um, cursor”

Partly raw tape D 3 · C 4 · P 4 · Cm 3 3.55

Q Aaron, we were talking about the Meter paper, and it was, the paper suggested that their developers were actually less productive with AI, but that doesn't square with your experience talking to a lot of different startups, seeing a lot of different startups, and how they're so much more productive. So, why don't you talk about where, where you're seeing startups say they're more productive, and why is it happening?

A Yeah. So I'll, I'll first just represent our own case study and then, and then there's the really extreme version. So, uh, our own case study is we've adopted a few different kind of, um, AI coding tools. Um, uh, you know, cursor being a, a super, super popular one internally. And I, you know, as I talk to people, let's say in the hallway who have, you know, yeah, maybe they're trying to get me excited by AI, but like, I think they know I'm, I'm bought in. So, so the, the kind of Qualitative answers I get from, from people, and then I'll give you our, our internal metric. You know, some, some people say I'm getting, you know, a 20, 30% productivity gain. Other people will say 75%. Um, interestingly, I have not been able to pinpoint the demographic difference, uh, on the answers.

AI assessment note: “some people say I'm getting, you know, a 20, 30% productivity gain.”

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