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 →

Jeff Chung no published score: only 1 usable exchange 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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1exchanges match
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Answered raw tape D 5 · C 4 · P 4 · Cm 4 4.30

Q Yeah, and, and, and maybe, uh, Jeff, so there's a competition sort of head on in terms of product, there's a competition in terms of, like, certain talent, as, uh, you know, uh, are you guys finding that those great companies, uh, just, uh, you know, hire all the tough people?

A Yeah, I mean, just, I just had lunch, uh, with one of our companies out here, um, who's also in the AI space, uh, a lot of frustration around the ability to attract talent, not only in the industry, but if you go straight to academia and try to pull them out of, even if it's, you know, undergrad, they're getting seven figure offers from Google, Facebook, um, to go work on, whether it's core, Deep learning tech, AI, or better ad targeting. I mean, it's, it's, it's, the benefits of Google and, and the, the, the frustrations are both there. Um, but it, it is a real issue, which is why I think when we look at any team, um, especially on a, in a seed stage company, it really needs to be a strong foundational team. It can't, it can't miss a single piece, um, Typically the teams that we're looking at today are generally two or three. That's just the nature of how technically challenged these things are, and it is a challenge, you know, whether you're here in New York or you're out there in San Francisco. But more and more I'm finding that the teams that we're funding are coming out of academia, because whether they worked on it together at Uh, you know, their postdoc, uh, as a research effort, they're both missionly driven to solve this issue because they've been thinking about it for the past four or five years. Um, we've found that that is a really interesting area where you find re…

AI assessment note: “they're getting seven figure offers from Google, Facebook”

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