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 →

Dillon Erb 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.

clear all ✕
1exchanges match
1on raw tape
0redirected or not addressed
Partly raw tape D 3 · C 4 · P 3 · Cm 3 3.30

Q From you guys, ah, maybe tell us a little bit about the business aspect of this, the commercial, the go-to-market, who's an ideal customer, or how did you start build the customer base?

A So we, ah, I mean, I kind of mentioned at the beginning, we came at this from running GPUs in data centers, largely, um, and, you know, over the course of the last few years, the biggest group that has kind of come, or basically the, the two biggest groups were, ah, you know, crypto miners and, Machine learning practitioners, ah, one of them actually really needed compute, the other one just pretended to need compute. Um, and so, you know, we've been, for the last three years, kind of, seen this space evolve, and, you know, I, I do believe that the challenges here are mostly infrastructural. So, our team is not really, ah, you know, deep AI practitioners. It's mostly systems engineers, network engineers, you know, people that are, that are kind of looking at best practices from software development methodologies, and trying to apply that to this emerging space that has, you know, Data scientists, mathematicians, statisticians, and people that aren't, you know, traditionally versed in, ah, you know, software development methodology, which is different. I mean, I think you see a lot of vertical AI companies coming from, ah, you know, a PhD in AI with a vertical focus that will do really well, ah, and what we're trying, and that was sort of the point about wide and deep. You know, I think there are companies that are going to go super deep, and I think we're interested in sort of …

AI assessment note: “the two biggest groups were, ah, you know, crypto miners and, Machine learning practitioners”

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