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.
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Answered raw tape
D 4 · C 4 · P 4 · Cm 4 4.00
Q Do you feel like you're competing with build internally versus buy? Or buy versus buy?
A Yeah, so sorry, this was all to build up to your question. So one of the Notion engineers told me that they'd sat and Probably on a napkin, like drawn out, like, why hasn't anyone built this? And then they saw terrible performance like, well, literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about, can we build it? It's about, do we have time to build it? And I think they, like, I think they felt like, okay, if this is a team that can do that and they feel enough of like an extension of our team, well, then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the test, right? Like we've had some very, very long nights to, to, to do that POC, and they were really our biggest, our second big customer after Cursor, um, which also was a lot of late nights, right?
AI assessment note: “AI has also changed the buy versus build equation in terms of”
Partly raw tape
D 3 · C 4 · P 4 · Cm 3 3.55
Q I'm curious, like how you evaluate it. Okay. I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy. It's like an interesting technical problem. I should just build it within Cursor, and then they don't have to encrypt all this stuff, they don't have to obfuscate things, like, was that on your mind at all, or?
A Before taking the, the small check from Locky, I did have like a hard, like, look at myself in the mirror of like, okay, do I really want to do this? And because if I take the money, I really have to do it, right? And so the way I almost think about it is like, you kind of need to have, like, you kind of need to be, like, fucked up enough to want to go all the way, And that was the conversation where I was like, okay, this is going to be part of my life journey to build this company and do it in the best way that I possibly can. Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously and maybe I take it too seriously. I don't know, but that was like a very intentional moment. And so then it was very clear, like, okay, I'm I'm not gonna do this, and I'm gonna give it everything.
AI assessment note: “Before taking the, the small check from Locky, I did have like a hard”
Partly raw tape
D 3 · C 4 · P 3 · Cm 3 3.30
Q Which is like, they, I mean, Notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like, yeah, just like talk through that kind of.
A Last time I was in San Francisco, I was talking to one of the engineers, actually, who, who was one of our champions, um, at, at Notion. And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed You know, like, it's like the way I think about is like, I have to earn a return on whatever the clouds charge me, and then my customers have to earn a return on that. And it's like very simple, right? And so there has to be gross margin all the way up, and that's how you build the product. And so then our customers have to make the right set of trade-offs that Turbo Puffer makes, and if they're happy with that, that's great.
AI assessment note: “they were just trying to make sure that the, you know, per user cost matched”
Redirected raw tape
D 2 · C 4 · P 3 · Cm 3 3.00
Q And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data? Is this like closer to ragged into like a XR, like a public search thing? Like how do you segment like the different types of search?
A The way that I generally think about this is like, there's a lot of database companies. And I think if you want to build a really big database company, sort of, you need a couple of ingredients to be in the air. We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database multiple times. You look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database, right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later, it's, or even more than that. There's not a company on earth that doesn't indirectly Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right? I don't think you're going to find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connected to AI. So you need that new workload. Like you need something to be happening where there's a new workload that causes that to happen. And that new workload is connecting very large amounts of data to AI. The second thing you need The second condition to build a big database company is t…
AI assessment note: “if you want to build a really big database company, sort of, you need”
