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 →
Answered raw tape
D 5 · C 5 · P 4 · Cm 3 4.45
Q And how do you see the market evolving in these next five or six years? Is it, there's a standard set of chips for training, a standard set for inference, or do you just see an explosion, like a Cambrian explosion of, Different ASICs, different designs, different use cases.
A Yeah, I like that question because I am a believer in there will be diversity of chips. And the reason is there's so many use cases, right? If you think about use cases from, you know, whether you're talking about science or manufacturing or design or back end or, you know, frankly, personal AI, I think we're going to see AI in everything that we do, you know, certainly in your phones and your PCs. And so you have all these pieces. You're going to have different types of chips that do that. Uh, you know, certainly the, for the largest systems, uh, we tend to believe that, uh, you know, you need the most compute you can get, and so, you know, GPUs are there, but lots of ASICs are, um, in the, in the process, and, you know, we'll see a variety of different chips.
AI assessment note: “I am a believer in there will be diversity of chips.”
Answered raw tape
D 5 · C 4 · P 4 · Cm 3 4.15
Q And how do you see the market evolving in these next five or six years? Is it, there's a standard set of chips for training, a standard set for inference, or do you just see an explosion, like a Cambrian explosion of, Different ASICs, different designs, different use cases.
A Yeah, I like that question because I am a believer in there will be diversity of chips. And the reason is there's so many use cases, right? If you think about use cases from, you know, whether you're talking about science or manufacturing or design or back end or, you know, frankly, personal AI, I think we're going to see AI in everything that we do, you know, certainly in your phones and your PCs. And so you have all these pieces. You're going to have different types of chips that do that. Uh, you know, certainly the, for the largest systems, uh, we tend to believe that, uh, you know, you need the most compute you can get, and so, you know, GPUs are there, but lots of ASICs are, um, in the, in the process, and, you know, we'll see a variety of different chips.
AI assessment note: “I am a believer in there will be diversity of chips”
Answered raw tape
D 4 · C 4 · P 3 · Cm 3 3.60
Q just for XAI, and the second was Sam Altman. They signed a deal for a four, I think, gigawatt data center Thirty billion a year with Oracle. That just portends an enormous amount of chips that are necessary and power. And if you forecast that, how do we actually meet all of that? What needs to happen that's not happening today inside of the United States to actually do that?
A Yeah, it's, it's a great, great point. I mean, that's, that's what we're seeing. We're seeing this, uh, incredibly large demand, uh, for AI and they're coming from, you know, Sam and Elon are certainly, uh, the, you know, the, the leaders, a couple of the leaders, uh, there's, there's a lot of demand elsewhere too. I mean, if you think about it, nations want their own AI. So there's a very high demand. We're, we're imagining, That just the accelerator market, so the chips for these, um, you know, AI large computing systems will be like, you know, over five hundred billion dollars in a couple of years, so very high growth, and when you say, you know, what do we need to do, um, it's the entire ecosystem needs to scale up, so we need to scale up, um, certainly what we're doing in chip design is trying to get chips out as fast as possible, but we're also scaling up the entire manufacturing ecosystem, and You know, as I said, I don't, I think the US is going to be a huge piece of it, so it's not just about the silicon, there's all of the various other pieces of the ecosystem that have to come to the US, and, and I think, look, I think today's, um, AI action plan is actually a really, you know, excellent blueprint.
AI assessment note: “it's the entire ecosystem needs to scale up, so we need to scale up”
Answered raw tape
D 4 · C 4 · P 3 · Cm 3 3.60
Q made the bet back then, it would have been very inconclusive that you would have picked Nvidia and AMD. And if anything, there was a, an amount of inherent belief that Intel had just figured it all out. Can you just tell us sort of like the lessons learned of why you thrive and maybe what you take away from their journey that you make sure AMD doesn't play out?
A Well, you know, as a CEO, we have to be paranoid every single day, right? So we don't rely on the past, but I think there are lessons of the past. And I think that probably the most important lesson that I can say for technology is you have to shoot ahead of the duck. Like you have to be thinking, what is the most like your question, Jason? Great question. We think about that all the time. How do we shoot ahead of the duck? And, you know, you have things that change. You know, technology is a beautiful place because you see big inflection points like five years ago. AI was around, but we wouldn't be able to gather this audience to talk about AI, because people would be like, who cares? But the fact is you had to invest many, many years ago to be where we are today. And I think, you know, I, I like to say that, you know, you will, you will be able to judge whether we've done a good job or not by how we perform five years from now. Like the decisions we're making will take, you know, five plus years to play out. Uh, but that's the key thing in tech. Like nothing is fast, but hopefully it's quite lasting in what it can achieve.
AI assessment note: “most important lesson that I can say for technology is you have to shoot ahead”
Answered raw tape
D 3 · C 4 · P 3 · Cm 3 3.30
Q made the bet back then, it would have been very inconclusive that you would have picked Nvidia and AMD. And if anything, there was a, an amount of inherent belief that Intel had just figured it all out. Can you just tell us sort of like the lessons learned of why you thrive and maybe what you take away from their journey that you make sure AMD doesn't play out?
A Well, you know, as a CEO, we have to be paranoid every single day, right? So we don't rely on the past, but I think there are lessons of the past. And I think that probably the most important lesson that I can say for technology is you have to shoot ahead of the duck. Like you have to be thinking, what is the most like your question, Jason? Great question. We think about that all the time. How do we shoot ahead of the duck? And, you know, you have things that change. You know, technology is a beautiful place because you see big inflection points like five years ago. AI was around, but we wouldn't be able to gather this audience to talk about AI, because people would be like, who cares? But the fact is you had to invest many, many years ago to be where we are today. And I think, you know, I, I like to say that, you know, you will, you will be able to judge whether we've done a good job or not by how we perform five years from now. Like the decisions we're making will take, you know, five plus years to play out. Uh, but that's the key thing in tech. Like nothing is fast, but hopefully it's quite lasting in what it can achieve.
AI assessment note: “the most important lesson that I can say for technology is you have to shoot ahead of the duck”
Answered raw tape
D 3 · C 4 · P 3 · Cm 3 3.30
Q And how does that impact the business, if at all, uh, in terms of competition globally?
A Well, I think the important thing is, I mean, just think about, like, everybody wants a GPU, right? Like, if you look across the industry, You really say, you know, the people who are gonna win in AI want to have as much compute in their foundation as possible, and they want assurance of supply. We want to be able to supply this no matter what happens. And so if you put that in context, you know, the fact that you're not going for the, the lowest cost, you know, every minute of the day, um, is okay. It's okay. Like, obviously, we're not gonna build, um, not everything needs to be in the most advanced technologies, and so we have a very geographically diverse supply chain. You know, I think Taiwan continues to To be important, um, in that view, but the, the focus, um, from this administration on getting, uh, on firm manufacturing in a big way, not in a small way, I think is, is very good for the country.
AI assessment note: “the people who are gonna win in AI want to have as much compute... assurance of supply”
Partly raw tape
D 3 · C 4 · P 3 · Cm 3 3.30
Q just for XAI, and the second was Sam Altman. They signed a deal for a four, I think, gigawatt data center Thirty billion a year with Oracle. That just portends an enormous amount of chips that are necessary and power. And if you forecast that, how do we actually meet all of that? What needs to happen that's not happening today inside of the United States to actually do that?
A Yeah, it's, it's a great, great point. I mean, that's, that's what we're seeing. We're seeing this, uh, incredibly large demand, uh, for AI and they're coming from, you know, Sam and Elon are certainly, uh, the, you know, the, the leaders, a couple of the leaders, uh, there's, there's a lot of demand elsewhere too. I mean, if you think about it, nations want their own AI. So there's a very high demand. We're, we're imagining, That just the accelerator market, so the chips for these, um, you know, AI large computing systems will be like, you know, over five hundred billion dollars in a couple of years, so very high growth, and when you say, you know, what do we need to do, um, it's the entire ecosystem needs to scale up, so we need to scale up, um, certainly what we're doing in chip design is trying to get chips out as fast as possible, but we're also scaling up the entire manufacturing ecosystem, and You know, as I said, I don't, I think the US is going to be a huge piece of it, so it's not just about the silicon, there's all of the various other pieces of the ecosystem that have to come to the US, and, and I think, look, I think today's, um, AI action plan is actually a really, you know, excellent blueprint.
AI assessment note: “it's the entire ecosystem needs to scale up, so we need to scale up”
Answered raw tape
D 3 · C 3 · P 3 · Cm 3 3.00
Q And how does that impact the business, if at all, uh, in terms of competition globally?
A Well, I think the important thing is, I mean, just think about, like, everybody wants a GPU, right? Like, if you look across the industry, You really say, you know, the people who are gonna win in AI want to have as much compute in their foundation as possible, and they want assurance of supply. We want to be able to supply this no matter what happens. And so if you put that in context, you know, the fact that you're not going for the, the lowest cost, you know, every minute of the day, um, is okay. It's okay. Like, obviously, we're not gonna build, um, not everything needs to be in the most advanced technologies, and so we have a very geographically diverse supply chain. You know, I think Taiwan continues to To be important, um, in that view, but the, the focus, um, from this administration on getting, uh, on firm manufacturing in a big way, not in a small way, I think is, is very good for the country.
AI assessment note: “we have a very geographically diverse supply chain.”
