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 →
Peter Deng no published score: only 3 usable exchanges on raw tape, and a fair score needs 8+ · coarse estimate ≈4.0/5 from 3 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.
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q Well, Peter, can I push you on that? That is the maybe URMI narrative, but there was a study I saw recently that said that AI is so hated, it's actually hated more than Jeffrey Epstein. By the general public, right? It's like, it is absolute, it is the worst brand like you could possibly imagine. No one trusts it.
A I'm not disputing that. What I'm saying is that that barometer is going to shift over time. I was just on a call with a bunch of creatives, and maybe Scott, I'd love to hear your thoughts on this because you're in the thick of it, who, you know, they're writers who just started saying like how awesome it's been for them to build portfolios for their, you know, friends with cloud code that they were unable to do before. That's like not a conversation I would have had with them Six months ago. I'm not saying where the trust is right now, Sam. I'm saying that that shifts as people get more comfortable with it, and it's just a matter of time.
AI assessment note: “I'm not disputing that. What I'm saying is that that barometer is going to shift”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q like not have that happen. I want to pivot one or two more times before I lose you guys, and I really appreciate you, you joining for this fun conversation. OpenAI Exec Exodus. Brad Lightcap out. Kevin Weill, who we used to work with, out. Peter, you've been out for, like, years, so, like, you were just, you're a forebearer on this. Like, why and, like, what, what's going on?
A My take on this is it's actually a feature, not a bug. It's a feature of having insanely great talent. I think Sam is N of one at recruiting with really ambitious, talented people. And in a time when you can build anything, you should expect the churn. These are people who want to go and kind of build something really, really insanely great. And that's not a talent problem. That's just kind of a natural output of what he's optimized for. I mean, you're seeing this everywhere. And also the other thing is that it's not just having the models. You have to point the models with Sense of obsession at a problem. And, you know, like something like periodic labs, uh, Liam left, uh, open AI. We were the first check investors into that. They're doing material science. It requires a different level of obsession on building a wet lab that just doesn't exist at a bigger companies, right? So the companies out there like applied compute, core automation, other new labs, there's just that there's a point of view that you want to go chase. And if you're ambitious, you're going to go out and chase that. That's largely what I see from this. You have to point the models and it's a great time to build. And so that's what everyone is really doing.
AI assessment note: “it's actually a feature, not a bug. It's a feature of having insanely great talent.”
Partly raw tape
D 3 · C 4 · P 4 · Cm 4 3.70
Q They're all going to do that. They're all going to do that. Right. Like, and they're already doing that. I think the question is, I think was what Peter started with, which is like, at some point, like you do have to expose something to them or you just build your own, but like, who do you trust? Right.
A A lot of companies are building their own. It's not rocket science to build it. And, you know, Josh, I'm on the same side as you. I'm a board member at Arena. We see every week the models change in who's the, who's the leader. And not only that, Arena has a great insight into exactly for each use case, accounting or whatever, what is the actual leaderboard for it. And quite frankly, you don't need to drive a Ferrari to do the task of a certain smaller job. You're going to see a lot more of this, like, I, I believe in a multimodal world, and I believe there's going to be a lot of customization And tuning and you got companies like apply compute. That's doing this many others as well. So I think it's going to be a pretty tough road for something like a router. I just, I feel like either roll your own or you've got to find another way to make a model your own.
AI assessment note: “A lot of companies are building their own. It's not rocket science to build it.”
