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 →
Partly raw tape
D 3 · C 4 · P 3 · Cm 2 3.15
Q On the incentives drive outcomes, I loved something you also said. You said, why hiring people who give a shit is harder than it sounds. What do you mean? And how do you think about that when hiring?
A You know, it sounds so simple when you, when you really boil it down, but if you hire people who, you know, we say give a shit internally, but who really, really care, you know, they really, really care about the, their, their work product. They really, really care about the quality of their work. They really, really care about the organization. They care about making sure that the company has an impact. You know, they just, they just really care. And what that means, how that manifests is, you know, they're willing to sweat every single detail. And if they get roadblocked or there's like something in their way, They'll spend the extra, they'll go the extra mile to make sure that they get through those things. You know, that ends up being, that's, that's how startups work fundamentally, is that you have these small teams of people who each care 10 times more than the, than the average employee, 10 or a hundred times more than the average employee inside a big company, and so they end up, you know, you end up just solving so many more problems than, um, than the big company can.
AI assessment note: “who really, really care, you know, they really, really care about”
Redirected raw tape
D 2 · C 4 · P 3 · Cm 3 3.00
Q So how do we codify and capture the data that's not codified already? As you said there with the fraud analyst, the thought process, the analysis, the discussion that goes on in internal meetings that's not codified in data sets. How do we capture that to enable us to do the work?
A Our thesis or what I really believe is what we need from now forward is frontier data. And we need to, we need to basically have, uh, data abundance of frontier data. We're right now we're in a sort of data scarcity mindset or, uh, we're hitting a data wall. And this frontier data is exactly what we're talking about. Frontier data in my mind is, you know, complex reasoning chains, complex discussion, um, you know, uh, reasoning chains of models going, agent chains of models going and looking up a piece of data, doing some reasoning, looking up another piece of data, um, maybe correcting if it has an error, um, tool use, you know, all of the, All of the key components that we would think of as of an agent being able to do, that all needs to be encapsulated into the frontier data to power the forward capabilities of these models.
AI assessment note: “what we need from now forward is frontier data”
Not addressed raw tape
D 2 · C 4 · P 3 · Cm 3 3.00
Q So how do we codify and capture the data that's not codified already? As you said there with the fraud analyst, the thought process, the analysis, the discussion that goes on in internal meetings that's not codified in data sets. How do we capture that to enable us to do the work?
A Our thesis or what I really believe is what we need from now forward is frontier data. And we need to, we need to basically have, uh, data abundance of frontier data. We're right now we're in a sort of data scarcity mindset or, uh, we're hitting a data wall. And this frontier data is exactly what we're talking about. Frontier data in my mind is, you know, complex reasoning chains, complex discussion, um, you know, uh, reasoning chains of models going, agent chains of models going and looking up a piece of data, doing some reasoning, looking up another piece of data, um, maybe correcting if it has an error, um, tool use, you know, all of the, All of the key components that we would think of as of an agent being able to do, that all needs to be encapsulated into the frontier data to power the forward capabilities of these models.
AI assessment note: “what I really believe is what we need from now forward is frontier data.”
Partly raw tape
D 3 · C 4 · P 2 · Cm 2 2.90
Q On the incentives drive outcomes, I loved something you also said. You said, why hiring people who give a shit is harder than it sounds. What do you mean? And how do you think about that when hiring?
A You know, it sounds so simple when you, when you really boil it down, but if you hire people who, you know, we say give a shit internally, but who really, really care, you know, they really, really care about the, their, their work product. They really, really care about the quality of their work. They really, really care about the organization. They care about making sure that the company has an impact. You know, they just, they just really care. And what that means, how that manifests is, you know, they're willing to sweat every single detail. And if they get roadblocked or there's like something in their way, They'll spend the extra, they'll go the extra mile to make sure that they get through those things. You know, that ends up being, that's, that's how startups work fundamentally, is that you have these small teams of people who each care 10 times more than the, than the average employee, 10 or a hundred times more than the average employee inside a big company, and so they end up, you know, you end up just solving so many more problems than, um, than the big company can.
AI assessment note: “what that means, how that manifests is, you know, they're willing to sweat every single detail.”
Not addressed raw tape
D 2 · C 4 · P 3 · Cm 2 2.85
Q So how do we codify and capture the data that's not codified already? As you said there with the fraud analyst, the thought process, the analysis, the discussion that goes on in internal meetings that's not codified in data sets. How do we capture that to enable us to do the work?
A Our thesis or what I really believe is what we need from now forward is frontier data. And we need to, we need to basically have, uh, data abundance of frontier data. We're right now we're in a sort of data scarcity mindset or, uh, we're hitting a data wall. And this frontier data is exactly what we're talking about. Frontier data in my mind is, you know, complex reasoning chains, complex discussion, um, you know, uh, reasoning chains of models going, agent chains of models going and looking up a piece of data, doing some reasoning, looking up another piece of data, um, maybe correcting if it has an error, um, tool use, you know, all of the, All of the key components that we would think of as of an agent being able to do, that all needs to be encapsulated into the frontier data to power the forward capabilities of these models.
AI assessment note: “what we need from now forward is frontier data”
Redirected raw tape
D 1 · C 4 · P 3 · Cm 3 2.70
Q billion dollars in revenue from Generative AI and OpenAI was obviously two billion dollars. How do you think about, I'm just intrigued with Scale AI today and working with some of the largest enterprises, a services component, the learning and adoption curve is challenging for large enterprises. Do you see that as a core part of your business in the next few years as we scale the education curve? Yeah.
A I mean, I think that, um, first of all, I think you're right. I think that the, um, there's so much value to be generated from AI for sure, but then there's this, there's this very natural question of, uh, where's the value capture going to be, right? And, you know, there's this fascinating thing, you know, um, if you go back and read a high output management by Andy Grove, there's like, there's these chapters around like, oh, you know, for Intel, like, hey, we thought that First, we thought that, like, this is where the value capture is going to be, but then we realized it was going to be in this other part of the stack, and so we had to migrate to that part of the stack, and then we had to migrate again, and it's this incredible case study. You know, I remember reading that, and I read it maybe a decade ago in an, in a different era of tech, and I was like, this is weird. This doesn't feel very relevant. And then now in AI, you're, you're seeing it once again, where it's, I think, you know, it's so new and so nascent where exactly where in the stack value will accrue, Feels like it's constantly moving. And I agree with you. I think that the, the models themselves, um, there's so much competition there, um, that I don't know if, I don't know how much value accrues at, at literally the model itself, but the everything above the model and everything below the model, I, I feel ve…
AI assessment note: “there's this very natural question of, uh, where's the value capture going to be”
Not addressed raw tape
D 1 · C 4 · P 3 · Cm 3 2.70
Q It's almost unfair though. Can you imagine if someone said, hey, I'm going to do scale AI, but I don't care if we lose money. You'd be like, oh, fuck. How do I compete with that?
A Yeah, but, but I think it's, um, It's pretty stark. You know, I've received more fair treatment testifying in front of Congress than I have from, uh, various media outlets over the years. And, um, it's, it, it, it feels like this totally ridiculous statement, but I think we're in this perverse state of, of a lot of traditional media where, um, the, the system itself, you know, because of this sort of like very click, uh, oriented approach versus a genuine educational approach, you know, it, it, um, It almost has no way of being, uh, you know, fully fair to the companies. And so I think it's the, the imperative is on the companies themselves to properly tell their story through direct channels and through podcasts and through, um, avenues where their, their message won't be, won't be altered.
AI assessment note: “I've received more fair treatment testifying in front of Congress than I have from, uh, various media outlets”
Not addressed raw tape
D 1 · C 4 · P 3 · Cm 3 2.70
Q It's almost unfair though. Can you imagine if someone said, hey, I'm going to do scale AI, but I don't care if we lose money. You'd be like, oh, fuck. How do I compete with that?
A Yeah, but, but I think it's, um, It's pretty stark. You know, I've received more fair treatment testifying in front of Congress than I have from, uh, various media outlets over the years. And, um, it's, it, it, it feels like this totally ridiculous statement, but I think we're in this perverse state of, of a lot of traditional media where, um, the, the system itself, you know, because of this sort of like very click, uh, oriented approach versus a genuine educational approach, you know, it, it, um, It almost has no way of being, uh, you know, fully fair to the companies. And so I think it's the, the imperative is on the companies themselves to properly tell their story through direct channels and through podcasts and through, um, avenues where their, their message won't be, won't be altered.
AI assessment note: “I've received more fair treatment testifying in front of Congress than I have from”
Not addressed raw tape
D 1 · C 4 · P 3 · Cm 3 2.70
Q billion dollars in revenue from Generative AI and OpenAI was obviously two billion dollars. How do you think about, I'm just intrigued with Scale AI today and working with some of the largest enterprises, a services component, the learning and adoption curve is challenging for large enterprises. Do you see that as a core part of your business in the next few years as we scale the education curve? Yeah.
A I mean, I think that, um, first of all, I think you're right. I think that the, um, there's so much value to be generated from AI for sure, but then there's this, there's this very natural question of, uh, where's the value capture going to be, right? And, you know, there's this fascinating thing, you know, um, if you go back and read a high output management by Andy Grove, there's like, there's these chapters around like, oh, you know, for Intel, like, hey, we thought that First, we thought that, like, this is where the value capture is going to be, but then we realized it was going to be in this other part of the stack, and so we had to migrate to that part of the stack, and then we had to migrate again, and it's this incredible case study. You know, I remember reading that, and I read it maybe a decade ago in an, in a different era of tech, and I was like, this is weird. This doesn't feel very relevant. And then now in AI, you're, you're seeing it once again, where it's, I think, you know, it's so new and so nascent where exactly where in the stack value will accrue, Feels like it's constantly moving. And I agree with you. I think that the, the models themselves, um, there's so much competition there, um, that I don't know if, I don't know how much value accrues at, at literally the model itself, but the everything above the model and everything below the model, I, I feel ve…
AI assessment note: “there's this very natural question of, uh, where's the value capture going to be”
Redirected raw tape
D 1 · C 3 · P 3 · Cm 2 2.25
Q billion dollars in revenue from Generative AI and OpenAI was obviously two billion dollars. How do you think about, I'm just intrigued with Scale AI today and working with some of the largest enterprises, a services component, the learning and adoption curve is challenging for large enterprises. Do you see that as a core part of your business in the next few years as we scale the education curve? Yeah.
A I mean, I think that, um, first of all, I think you're right. I think that the, um, there's so much value to be generated from AI for sure, but then there's this, there's this very natural question of, uh, where's the value capture going to be, right? And, you know, there's this fascinating thing, you know, um, if you go back and read a high output management by Andy Grove, there's like, there's these chapters around like, oh, you know, for Intel, like, hey, we thought that First, we thought that, like, this is where the value capture is going to be, but then we realized it was going to be in this other part of the stack, and so we had to migrate to that part of the stack, and then we had to migrate again, and it's this incredible case study. You know, I remember reading that, and I read it maybe a decade ago in an, in a different era of tech, and I was like, this is weird. This doesn't feel very relevant. And then now in AI, you're, you're seeing it once again, where it's, I think, you know, it's so new and so nascent where exactly where in the stack value will accrue, Feels like it's constantly moving. And I agree with you. I think that the, the models themselves, um, there's so much competition there, um, that I don't know if, I don't know how much value accrues at, at literally the model itself, but the everything above the model and everything below the model, I, I feel ve…
AI assessment note: “there's this very natural question of, uh, where's the value capture going to be”
