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.
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Answered raw tape
D 5 · C 5 · P 5 · Cm 5 5.00
Q And, and, and just tell us, give us that quick thumbnail on semi-analysis today. Like what is the business?
A Yeah. So today we are a semiconductor research firm, AI research firm. We, Service companies, our biggest customers are all hyperscalers, uh, the largest semiconductor companies, uh, private equity, as well as hedge funds, and we, uh, sell data around where every data center in the world is, how, what the power is in each quarter, what, how the build outs are going. Um, we sell data around, uh, fabs. We track all 1500 fabs in the world. For your purposes, only 50 of them matter, but like, you know, all 1500 fabs around the world. Uh, same thing with, uh, the supply chain of, like, whether it be cables, or servers, Or boards or transformer substation equipment. We try and track all of this on a very, uh, number driven basis as well as forecasting. Um, and then we do consulting around those areas.
AI assessment note: “today we are a semiconductor research firm, AI research firm.”
Answered raw tape
D 5 · C 5 · P 5 · Cm 4 4.85
Q You said if they stand still there, they would, they would have competition. Like what, what. Would be their area of vulnerability or, or what would have to play out in the market for other alternatives to take more share of the workloads?
A Yeah. So, so the main thing for NVIDIA is, you know, hey, this workload is this big, right? It's, it's, it's well over a hundred billion dollars of spend. Um, for the biggest customers, they have multiple customers that are spending billions of dollars. I can hire enough engineers to figure out what, how to run my model on other hardware, right? Now, maybe I can't figure out how to train on other hardware, but I can figure out how to run it for inference on other hardware. So NVIDIA's moat in, in inference is actually A lot smaller on software, um, but it's a lot bigger on, hey, they just have the best hardware. Now, what is, what does the best hardware mean? It means capital cost, and it means operation cost, and then it means performance, right? Performance, TCL. Yes. Um, and Nvidia's whole moat here is if they stand still, the performance CCO doesn't grow. Um, but interestingly, they are, right? Like with Blackwell, not only is it way, way, way faster, anywhere from 10 to 15 times on really large models for inference, because they've optimized it for very large language models, they've also decided, hey, we're gonna cut our margin, too, somewhat, because I'm competing with, uh, Amazon's, you know, chip, and TPU, and, and AMD, and all these things. They've decided to cut their margin, too. So, so between all these things, they've, they've decided, That they need to push perfo…
AI assessment note: “NVIDIA's moat in, in inference is actually A lot smaller on software”
Answered raw tape
D 5 · C 5 · P 5 · Cm 4 4.85
Q Um, it's not just like- Broaden out beyond Broadcom. I'm talking NVIDIA and everybody else. Like, you know, we've had these two massive years. Right, of tailwinds behind this sector. Is 2025 a year of consolidation? Do you think it's another year that the sector does well? Just kind of
A Yeah, I think, I think the plans for hyperscalers are pretty, uh, firm on, they're, they're gonna spend a crap load more next year, right? And therefore, the ecosystem of networking players, of ASIC vendors, of, uh, systems vendors is gonna do well, whether it be NVIDIA or Marvell or Broadcom or AMD or, you know, generally, you know, some, some better than others. The real question that people should be looking out to is, twenty-twenty-six, um, do, does the spend continue, right? We are not, the growth rate for NVIDIA is gonna be stupendous next year. Right, and that's gonna drag the entire component supply chain up. It's gonna bring so many people with them, but twenty-twenty-six is like where the reckoning comes, right? Um, but, you know, will, will people keep spending like this? And it's, it's all points to where, will the models continue to get better? Because if they don't continue to get better, um, in my opinion, will get better faster, in fact, next year, then there will be a big, you know, sort of clearing event, right? Um, but that's not next year, right? Um, you know, the other aspect I would say is there is consolidation In the NeoCloud market, right? There are 80 NeoClouds that we're tracking, that we talked to, um, that we see how many GPUs they have, right? The problem is, nowadays, if you look at rental prices for H-one hundreds, they're tanking, right? Not jus…
AI assessment note: “they're gonna spend a crap load more next year, right? And therefore, the ecosystem”
Answered raw tape
D 5 · C 5 · P 5 · Cm 4 4.85
Q a development sandbox world to a optimization world. Is that likely to happen? Is there an equivalency here or not? And if you could touch on why the, the, the back end is so steep and cheap, like, you know, just, you, you go a model, you know, behind, or you, you, like, the, the token, the price you can save by just backing up a little bit is nutty.
A Yeah. Yeah. So, um, today, right? Like, oh, one is stupendously expensive. You drop down to four. Oh, it's a lot cheaper. You jumped down to four Oh Mini. It's so cheap. Why? Because now I'm competing with four Oh Mini, I'm competing against Lama and I'm competing against Deep Seek. I'm competing against Mistral. I'm competing against Alibaba and I'm competing against tons of companies. I think, and in addition, right, there is also the problem of inferencing a small model is quite easy, right? I can run Lama-seventy-b on one AMD GPU. I can run Lama-seventy-b on one NVIDIA GPU, and soon enough there will be on one set of Amazon's Neutronium, right? I can sort of run this model on a single chip. This is a very easy, no, I won't say very easy problem, still hard, but it's quite a bit easier problem than running this complex reasoning or this very large model, right? And so there is, there is that difference, right? There's also the fact that, hey, there's Literally, 15 different companies out there offering API inferences, inference APIs on Lama and Alibaba and Deep Seek and Mistral, like these different models, right?
AI assessment note: “Why? Because now I'm competing with four Oh Mini, I'm competing against Lama”
Answered raw tape
D 5 · C 4 · P 5 · Cm 4 4.55
Q You just did a super deep piece on Tranium. Why don't you do the Amazon version of what you just did with Google?
A Yeah, so, so funnily enough, Amazon's chip is the Amazon, I, I call it the Amazon's basics TPU, right? And the reason I call it that is because, yes, it uses more silicon. Yes, it uses more memory. Yes, the network is, like, somewhat comparable to TPUs, right? It's a six, it's a four by four by four Taurus. Um, they just do it in a less efficient way in terms of, you know, hey, they're spending a lot more on active cables, right? Right, uh, because they're working with, uh, Marvell and Alchip on their own chips versus working with Broadcom, the leader in networking, who then can use passive cables, right, for, for, uh, because their surities are so strong. Like, there's other, there's other things here. Their surity speed is lower. Um, they spend more silicon area. Like, there's all these things about the, the Tranium that are, you know, you could look at it and be like, wow, this would suck if it was a merchant silicon thing, but it doesn't because it's, it's, it's, Amazon's not paying Broadcom margins, right? They're paying lower margins. Um, they're, they're not paying the margins on the HBM. They're paying, you know, they're paying lower margins in general, right? Uh, paying the margins to Marvell on HBM. Um, you know, there's all these different things they do to crush the price down to where their, their Amazon Basics TPU, the Tranium II, right, is very, very cost-effecti…
AI assessment note: “Amazon's chip is the Amazon, I, I call it the Amazon's basics TPU”
Answered raw tape
D 5 · C 4 · P 4 · Cm 4 4.30
Q What are you hearing from the hyperscalers? I mean, they're all out there saying our capex is going up next year. We're building larger clusters. Um, You know, is that in fact happening? Like what's happening out there?
A Yeah, so I think when you look at the streets estimates for capex, they're all far too low. Um, you know, based on a few factors, right? Um, so when we, we, we track every data center in the world and it's, it's, it's insane how much, especially Microsoft and now, uh, Meta and Amazon and, and, and, uh, you know, and many others, right? But those guys specifically are spending on data center capacity. And as that power comes online, which you can track pretty easily if you Look at all of the different, uh, regulatory filings, and use satellite imagery, all these things that we do, you can see that, hey, they're gonna have this much data center capacity, right?
AI assessment note: “when you look at the streets estimates for capex, they're all far too low”
