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 →

Aditya Agarwal no published score: only 2 usable exchanges 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 5 · C 5 · P 5 · Cm 4 4.85

Q In SPC, in the US, in the Bay Area especially, has existed for the last seven years, right? What are the some of the known companies that have come out of SPC, if you can share those journeys?

A Yeah, yeah, absolutely. You know, one of our companies, um, thinking back to, like, Fund One, um, this was, you know, some of our earliest companies that, you know, um, we're proud of our, like, great companies, Base 10. So this is kind of one of the leading kind of like AI inference companies that really kind of built out, um, I mean, like a great product that is allowing enterprises to really kind of like deploy a variety of kind of models within kind of like their applications. And you've seen phenomenal growth. They recently kind of raised around valuing them at like, you know, multi billions of dollars. Like I think a two billion dollar valuation that, you know, I think has been announced publicly. Another one of our great companies is render. So this is kind of building a, Uh, a modern kind of like essentially, uh, platform as a service kind of app. So basically instead of building on bare metal, kind of like on the cloud services, you can use Render to manage kind of your compute and your data, your databases and so on. Also doing phenomenally well. Um, another one of our early companies is Pilot. So this was started by Waseem. Um, Waseem, Jessica, and Jeff, and they're basically building out , they started doing this a while ago, but like what would AI enable bookkeeping look like, right? Like from scratch. Um, we also have in our portfolio Gamma, which is one of a very…

AI assessment note: “one of our earliest companies that, you know, we're proud of our, like, great companies, Base 10.”

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

Q And you were saying what, how has AI impacted you internally?

A All across the board, right? Like if you kind of think about, you're also an investor, so hopefully this will resonate, but if you kind of break down the act of investing, right? Like if you break it down into like, okay, I need to go find people, amazing people at the top of the funnel, right? Then from those set of people, I need to go find people who are resonant with our kind of investment theses and our investment areas to then kind of like, how do I then support them, right? Like when they're actually a portfolio company or they're in the community, how do we support them with You know, early go to market introductions, introductions to engineers, like post kind of like fundraising support, right? Uh, and like all of, and then with marketing as well, obviously, right? So all of those areas, um, we kind of decided about 18 months ago that much like we want to invest in AI companies, and all our companies today are able to get a lot done with fewer people, we need to be as AI forward as all of our companies. So in each of those areas, We're actually investing quite a large amount of, I would say, tooling, functionality, process, like also like AI just like pushing each other. We're gonna be like, why are you doing this manually? Like, you know, have you thought about using an LLM? That's a great example, right? Like, One of the, you know, we wake up every morning, uh, to ki…

AI assessment note: “Now, like AI summarizes all of that and kind of is able to call out”

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