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 →
Alina Cohen no published score: no usable exchanges on raw tape, and a fair score needs 8+ · coarse estimate ≈4.5/5 from 3 produced feed 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 produced feed
D 5 · C 5 · P 4 · Cm 5 4.75
Q We, we mentioned a quote from Gary earlier, and I got one from Alexis too, and he gave me a fantastic quote that you've said before, and I love it, and it's, AI today is maybe more artificial than it is intelligent. So talk to me, what do you mean by this, and what's the thesis behind that?
A Sure, uh, well, let's think about it as a black box. Let's say I have this person here, and in order to get this person to learn anything, it's not that I need to teach Some rules or give them a few examples. It's not even enough to give them a thousand examples. It's that I need to give them a thousand thousand examples, millions of examples, and that's the current state of the art in AI. It's done a lot better because of the amount of data available, but nothing changes the fact that it's just relying on a really large amount of data to get anything usable out of it, and what you get is fairly narrow. It's very specific to what you trained on, so that Doesn't feel very intelligent. That doesn't bring us any closer to the ability to reason, to create, or to do anything else that we associate with actual intelligence. And that's not to say that these neural nets and feedback loops aren't useful development tools. They can be, but they're most useful for the companies who already have the most and the best quality data. Machine learning is a leveraged play on data, so So it's the Facebooks, the Apples, the Googles of the world that are getting the most benefit from it. It's not the early stage startups. It's the companies that already have a monopoly and who are already generating all
AI assessment note: “That doesn't bring us any closer to the ability to reason, to create”
Answered produced feed
D 5 · C 4 · P 4 · Cm 4 4.30
Q I think, I think they're going to get a ton of follow-on investment as a result. I'm really intrigued, because you said about kind of LogDNA and the founders contemplating an acquisition there, and you spoke about your acquisition with Facebook. What advice would you give to founders who are contemplating acquisitions and the questions that they maybe should ask when thinking about this?
A Yeah, I think taking an Transition from being your own game master to being a player in somebody else's game. And that's not to say the answer is always no. I think there's plenty of times when a company can be more efficient within a larger company, but you have to understand what you're getting into. In the case of LogDNA, they were going to gut a lot of the product and make it into something that was not what the founders wanted it to be. And things like that, there's always going to be opportunities. Most of the founders are so talented that there's just going to be acquisitions Offered to them because of their secrets, right? Whether it's the deep technical knowledge, whether it's their PMing ability, and you really have to make sure that you're comfortable, that you're ready to Be taking this next step.
AI assessment note: “Transition from being your own game master to being a player in somebody else's game.”
Answered produced feed
D 4 · C 4 · P 4 · Cm 3 3.85
Q I think, I think they're going to get a ton of follow-on investment as a result. I'm really intrigued, because you said about kind of LogDNA and the founders contemplating an acquisition there, and you spoke about your acquisition with Facebook. What advice would you give to founders who are contemplating acquisitions and the questions that they maybe should ask when thinking about this?
A Yeah, I think taking an Transition from being your own game master to being a player in somebody else's game. And that's not to say the answer is always no. I think there's plenty of times when a company can be more efficient within a larger company, but you have to understand what you're getting into. In the case of LogDNA, they were going to gut a lot of the product and make it into something that was not what the founders wanted it to be. And things like that, there's always going to be opportunities. Most of the founders are so talented that there's just going to be acquisitions Offered to them because of their secrets, right? Whether it's the deep technical knowledge, whether it's their PMing ability, and you really have to make sure that you're comfortable, that you're ready to Be taking this next step.
AI assessment note: “you have to understand what you're getting into”
