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 →

James Manyika 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 3 4.15

Q within Google and then labs went away for a bit. Not that labs was the only bit of experimentation within the company, but then labs was revived. And, uh, and it seems like we're starting to see many more experimental projects come out of Google proper in a way that we hadn't seen In a long time. So how responsible is labs for that? And why, why is labs back?

A Oh, Labs is so much fun. Uh, so, so what actually happened was three years ago, uh, you know, uh, you know, this is a kind of a inspired, you know, Sundar moment said, let's reboot Labs. And, you know, we're in this AI moment. How do we kind of explore and experiment and build these AI first, uh, AI products that are totally AI first. So the idea with Labs is let's take the most amazing research coming out of Google DeepMind and Google research and any other place, quite frankly, in the company, Where there's incredible research and focus primarily on how do we build experimental AI first products? I think what most people probably know of the most is what's now, you know, notebook LM, you know, the, the way that started by the way is incredible. Cause it was, I remember when I first encountered.

AI assessment note: “we're in this AI moment. How do we kind of explore and experiment”

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