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 →

George Cameron 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
Answered raw tape D 5 · C 5 · P 5 · Cm 4 4.85

Q And is it like, I am a fortune 500, I need advisors on objective analysis and I call you guys and you pull up a custom report for me. You come into my office and give me a workshop. What, what, what kind of engagement is that?

A So we have a benchmark and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report. How to think about choosing between serverless inference, managed deployment solutions, or leasing chips and running inference yourself is, is an example kind of decision that big enterprises, Uh, face, and it's hard to, hard to reason through. Like, this AI stuff is, is really new to, to everybody, and so we try and help with our reports and insight subscription companies navigate that. We also do custom private benchmarking, and, um, so that's very different from the public benchmarking, um, that we publicize, and there's no commercial model around that, but for private benchmarking, well, at times, Create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things and we help them understand what they've built with private benchmarking, um, you know, through the expertise, mainly that we've developed through trying to support everybody, uh, publicly, uh, with our public benchmarks.

AI assessment note: “we have a benchmark and insight subscription... We also do custom private benchmarking”

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