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 →

Arthur Mensch 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
1redirected or not addressed
Redirected raw tape D 2 · C 3 · P 3 · Cm 3 2.70

Q it like running the company and building a large language model in Europe? Obviously there's regulations and all kinds of considerations. Privacy. The French are known for protecting privacy. In the United States, we're known for taking it away. How is the landscape there? And what do you have to deal with there that maybe you wouldn't have to deal with in America? And what's the pros and the cons?

A First, we have 25% of our business in the US and 25% of our researchers are actually here. So I actually spent a lot of time here as well as in France, as well as in the UK, in Singapore, where we are. So, of course, it's different markets. It's markets where you have language, which is a topic, where there's much more manufacturing. Manufacturing is a bigger piece of the cake than it is here. And I'd say our strength has been to also work with European companies That are a bit lagging behind, uh, and that wants to adopt the technology to, to leap forward. And we've been able to do that through a forward deployment engineering engagement for our Forge product, for our studio product that allows to deploy agents that do end-to-end automation. But on top of that, the thing that we have announced today, like Forge is something that is actually being used today, uh, with customers in the U S because they come to us with, uh, needs for post-training for making models specifically good at financial services. And, uh, what's happening is that we have this product and we can bring the models to specialize them as well.

AI assessment note: “First, we have 25% of our business in the US and 25% of our researchers”

page 1
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 460 episodes transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.