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 →

Garry Tan no published score: only 6 usable exchanges on raw tape, and a fair score needs 8+ · coarse estimate ≈4.0/5 from 6 raw tape exchanges 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 4 · Cm 4 4.60

Q think, um, uh, respected for like the scrappiness culture, right? Hacker culture. And, uh, you know, one question is like, do you expect companies in YC to do pre-training and do their own foundation models in any way? Because the, you know, there is a, um, compute driven narrative that you need, A hundred million, and then a billion in next generation, closer to 10, in order to compete there.

A Totally. Um, I think people are starting to do it, and then I guess what, you know, it's kind of like when Cruz came through YC, here's this giant, like, mega research project, and you need a hundred million dollars to do it, and then you work backwards from that. Like, what are the milestones that we can get to so that, you know, we can draw a line from, you know, we have nothing, to we have something, to we really have something. You know, that's sort of the question always. So we absolutely have companies that are building foundational models. Um, you know, Diffuse Bio is doing over on the bio side. Uh, you know, uh, there, you know, there's a company working on, like, robotics foundational models. There's so many things that you could do. And then, uh, the half a million dollars, you know, during the three or four months, it's sort of what, like, Kyle did for Cruise. He had to figure out, well, how do I make a 10,000 dollar, um, automated driving Attachment to an Audi A four. And how do I get people, get people to pay for it? How do I, you know, all he did was actually make a demo that, uh, funny, funny enough resembles full self-driving in any Tesla car today, but this was in 2013. You could drive up and down one on one. It didn't do streets. It didn't do cities. It only did, um, uh, you know, driving up and down the highway. And that was enough to sort of Show that, and t…

AI assessment note: “we absolutely have companies that are building foundational models”

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

Q 152 hundred startups. And to some extent you could argue each startup is in some sense a vote on what's interesting in AI. If you kind of aggregate up all those startups, are there common themes that are emerged? Is there sort of things that are more topical or interesting from a startup perspective? I almost view it as, like, a lens of, like, a giant, um, founder voting machine.

A Yeah, totally. So I, I'd say, like, 70% are somehow related to AI, and then two-thirds of them, um, you would sort of argue are, uh, SaaS wrappers, actually. Like, wrapper is, like, sort of the pejorative, but I definitely believe that the, the sort of, uh, I think wrappers work. ChatGPT wrapper thing is actually just wrong. It's like saying that, uh, all of SaaS and cloud are basically, you know, MySQL wrappers. It doesn't make any sense. Like this is just a pure technology that can be implemented. And then essentially it's a concentrated form of intelligence. Um, and then the funny thing about it is like, it's probably only 85 IQ actually. Like if you look at who would you actually hire in your real workplace, like.

AI assessment note: “70% are somehow related to AI, and then two-thirds of them... are SaaS wrappers”

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

Q And I think there's a very strong shipping culture. Um, where does that come from? Is it the partners that you have? Is it originally from Paul Graham? I'm just sort of, I think there's a really strong culture of a lot of these things that really help drive progress forward in the earliest stages of a company.

A Yeah. I mean, I think it was sort of like the blend of a whole lot of different things. Like, I, I actually don't, I think Paul Graham in particular was always about helping people see how big the idea could be. Like you're doing this thin edge of the wedge, but, but if you do that really well and you do these other intermediate things like, wow, you could be a ten billion dollar, a hundred billion dollar company. And so, um, I think Paul Buchheit, like creator of Gmail, he was one of the first people to make a group office hours, which is sort of using social pressure. It's almost like, uh, Alcoholics Anonymous as like, but for founders where it's like, uh, you really don't want to You, I mean, it's a mild form of coopetition. Like, you're in competition with your friends. Like, you want them all to succeed, but at the same time, like, if you come and you don't come correct, you're gonna feel bad that week. And, you know, our realities are very socially.

AI assessment note: “Paul Buchheit, like creator of Gmail, he was one of the first people to make a group office hours”

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

Q How do you think about, you know, being in the SF ecosystem?

A I mean, I think being in person just makes sense. Going from zero to one, uh, you need to be with, like, the smartest people around you, and then, um, I actually think there's a lot of value in just, like, the dinner discussions that are happening around San Francisco. You know, only here can you, you know, can the tool makers and the foundational model builders and the researchers literally sit down and break bread with the people way out on the edge, like, trying to practically Deploy these things to every industry and every type of knowledge work in the world. And those are literally sort of the most high value conversations on the planet right now, because, you know, on the one hand, like, maybe HGI will happen, like, relatively soon. On the other hand, like, it might be longer, and then, uh, we're gonna be sort of trying to squeeze, like, the most out of, uh, you know, rag, out of, like, how do we make embeddings work? How do we actually Uh, make this workflow and test work, and, like, people don't know. Like, you know, the, the difference between something that is a very good AI, uh, you know, app and, like, something that's outrageously stellar is actually the devil's in the details and all of that stuff, and those people are literally coming up with the concepts and the writing the open source and, like, sharing it on, you know, the, the forums are global, but, like, th…

AI assessment note: “I actually think there's a lot of value in just, like, the dinner discussions”

Answered raw tape D 3 · C 3 · P 3 · Cm 2 2.85

Q You're widely credited externally as somebody who's really bringing a lot of, um, almost like founder energy back into YC again and helping to really drive, um, future directions. How do you think about where you want YC to be over the next couple of years? Like five years from now, looking back, what do you want to have changed or built or accomplished?

A I'm sort of assuming that there's a scenario where, um, there will always be a need for people who understand how this stuff works to continue to build the next version. Um, if we're in a scenario where that's not true, like, I have no intelligent thoughts on, like, what's gonna happen there? Um, on the, on the flip side, like, I think that, I mean, There's no, there's no substitute for human, like, intent, and ingenuity, and belief, and frankly, ideology. Like, I think that, ah, you know, what I love about YSEED is sort of like the defining magnet for people who are ambitious and optimistic about technology and smart. And, you know, those are the people who we really desperately need to save from working at Goldman Sachs. Like, sorry, my friends at Goldman Sachs, but like, You know, sort of saving people from investment banking, from consulting, from middle management, from working in big tech, like, you know, we're not numbers, we're free people. Like, we need to escape and, uh, create. And, you know, if, if it's one thing I've learned, like, that's sort of, um, the responsibility and the obligation, uh, you know, to whom much is given, much is expected. And, you know, what I want YC to be is like sort of that, Like, beacon and institution. Like, you know, if you have an idea, if there's a problem in the world to solve, it could be, if it could be solved through capitalism te…

AI assessment note: “what I want YC to be is like sort of that, Like, beacon and institution.”

Redirected raw tape D 2 · C 3 · P 3 · Cm 2 2.55

Q So still, um, Posterous Age Gary, when do you start making angel investments?

A Oh gosh, it was much later. I mean, let's see. Posterous was funny because we had a chance to be Instagram, but we didn't know it at the time. Obviously, no, like, nobody did. I mean, you, you were working in the social space too, a lot. Like, that was a really interesting moment in internet history. I think it, like, kind of parallels what people are probably experiencing right now with AI, isn't it? Like, we all knew that something very different was happening with the way humans were sort of communicating. Um, you know, there was six degrees. There were like, I mean, all of the same parallels. Like, there were a whole generation of social networks that failed, and then those were the obvious ones to point to and say, oh, this, the whole trend is bunk. Like, it's not gonna work. You know, six degrees didn't work. That was a, you know, good enough team. What happened to that? Or like, oh, blogger, you know, It's funny because it's sometimes the same characters, right? Like, Evan Williams worked on Blogger, it was mostly forgotten at Google, and then he just never really, like, lost the bug to work on this social space. Um, probably the craziest thing was, uh, do you remember what Facebook, like, meeting Facebook people was like back then? Like, it was like meeting cult members.

AI assessment note: “Oh gosh, it was much later. I mean, let's see. Posterous was funny”

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