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 →

Ara Kharazian 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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Answered raw tape D 4 · C 5 · P 4 · Cm 4 4.30

Q Yeah, I mean, you're the economist. I don't know. Is there an economic theory that explains why people would stick with a vendor, ah, that has constant interruptions, even if there's another one with, um, you know, just as good or on par capabilities that doesn't have the turbulence?

A Well, what we found is that these models are a little bit stickier Than we thought they would be. And we talk about them being these commodities that, oh, you can just switch between one model and the other. And maybe at the model level, you really can think of things like that. But in terms of how many employees at firms are actually using AI, you know, better models don't necessarily create the switching for, for employees and users. It's about the product experience around those models. You know, Claude code was so successful, not because it was powered by the models of Claude, but because it was, uh, uh, it was integrated into your workflows in a very effective and agentic way, that it was the first model and first experience that allowed a engineer to execute on multi-step tasks without, you know, babying a chatbot the entire time. Uh, and so, such that incremental improvements of the model are important and helpful, but they weren't the whole story for, uh, the actual growth of Cloud Code. Uh, and so you can, you can imagine that to be driving some of the stickiness between Claude Code and Codex, OpenAI and Anthropic, is that people get really used to the tools, the software that they're in. Uh, they like the experience that one provides over the other. I also do think there is going to be A, a sort of branding effect where Anthropik's AI safety posturing, you know, love …

AI assessment note: “It's about the product experience around those models.”

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