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 4 4.60
Q So as the head of global tech for BlackRock, what, what brings you here? How did you get involved with Raise?
A So I got involved last year. One of my companies I was involved with, uh, SambaNova, Lipu was supposed to be one of the speakers, um, and, uh, he couldn't make it, so I decided to fill in for that, and, um, and then I, you know, I, I saw Ray's, um, this thing in Paris at the Louvre, and I was like, oh, this is interesting. It's, uh, it was the second year of Development that, uh, Henri, uh, had kind of pioneered and built this, uh, this event. Um, I saw something there. I saw a lot of my, uh, my colleagues and friends from San Francisco all congregating here in Paris, and, uh, I said, well, I'd like to, uh, uh, help foster this, uh, get it going, and so last year I, I was here, um, and then this year, uh, it's, I don't know, probably tripled again in size. It seems to be Europe's biggest or most targeted, uh, AI conference. So, uh, yeah, so I continue to, uh, to help and, uh, do what I can to help build an AI presence for, uh, in Europe.
AI assessment note: “So I got involved last year. One of my companies I was involved with”
Answered raw tape
D 5 · C 5 · P 4 · Cm 4 4.60
Q So between all of the panels that you're doing, you're doing four panels. What are the through lines and the macro themes?
A Through lines, macro themes. Okay. Well, I, I think one, one of the big ideas, obviously, clearly is, um, and we see that in the stock market, and we see it in the investment market, is that, uh, the primacy of compute, and how the, actually, you're seeing it already, the, how much the stock market And the capitalization, uh, in Silicon Valley has changed. And, um, we went from a, let's call it a, uh, A software-centric world to a compute-centric world. And, and, and you're, and you're seeing the emergence of companies, um, so that's number one, this, this move to compute. And, you know, to me, in my opinion, you know, the models and the compute are kind of like, are symbiotic and, and, uh, synonymous with each other. So, the primacy of compute. Within that, secondly, Is, um, since now that is the dominant, uh, theme, it is a dominant, where the capex, where the money, where the capitalization is all gone, it then, uh, engenders a whole rethink of the data center. And, uh, so I think there is a redesign of the data center, and we're going through stages of the data center rebuild. And so we had, think of data centers pre-AI, Kind of like the scramble to build data centers today where there's a massive shortage of compute. But on the other hand then, you know, we're hitting the laws of physics are, are, are driving a yet another transformation of data center design going forward…
AI assessment note: “one of the big ideas... is that, uh, the primacy of compute”
Partly raw tape
D 3 · C 4 · P 4 · Cm 3 3.55
Q we have entered the new era of AI. This has increased an entire rebuild Of everything that's going on in tech because we need inference. We need things faster and agents are now coming to market. It's no longer just chat. So I'm curious from your standpoint on the investor side, how do you think and how has your strategy evolved to now play offense on this type of field?
A Uh, I like your framing. It is a complete rebuild. So, let's start with that, and then, and then how we play offense as investor. So, you're absolutely right. It is a complete rebuild. So, the internet as we know it was built, let's just say, 2000 to 2000 twenty-ish. In one framework, which is basically around, basically the birth and the dawn of cloud computing. And, um, and cloud computing, you know, necessitated a certain kind of data center. You remember the good old classic data center, megawatts, not gigawatts, right? So we had an order of magnitude increase today. And then these data centers were, were small, and, and, and they were, um, At the end of the day, cloud computing, this is a big, everyone says it's software, but it was really reselling CPUs with hard drives. That was the compute stack. A CPU with a hard drive. And it was considered a commodity. And server prices were tens of thousands of dollars, thousands of dollars, and now those compute servers are 1,000,010 of millions of dollars. So compute was an afterthought. And so when you, when you think of, the other way, the other way I think about it is that, and then these clouds built these classic compute stacks, and then they resold that as platform services, databases, and then SAS built on top of that. And, and, and so, but the, if you think about the base unit to create a cloud was relatively small. CPUs a…
AI assessment note: “let's start with that, and then, and then how we play offense as investor.”
Answered raw tape
D 3 · C 3 · P 3 · Cm 2 2.85
Q And with these new chips and building them specific for models. So like, how do you think about that and how do you weigh the different kinds of risks that come along with it?
A You know, um, the investor portfolio manager, You know, at the end of the day, you're allocating capital, right? So, you know, you have only so many bullets. And so, you know, my, I'm a public investor, I'm a private investor, I do both. And, but at the end of the day, you're allocating capital. And, and the creation of whatever portfolio you're creating for your For your mandate, for your clients. Then they, you're trying to arbitrate between risk, like you said, what is, what is the today, and what is the tomorrow? And like a lot of the things around, around this AI today, you know, there's a lot around today. Even the three year, the three year duration mismatch of let's say DRAM and foundries. So I, I call that kind of like the now, right? This is the now. The vortex of AI is like the now. And, and so, you know, if I, in, in this kind of three year window, That is probably where 90 plus percent of my investment, well, ninety-ish percent, something like that, you know, the majority is going in. Um, you know, within this three year window, what, who's winning, who's losing, what is on the ascendancy, what is on the decline, what is stagnating, and then you're arbitrating between these ideas. So that's one. The second thing, or even in this three year window, let's call it, Is, is there, is there, is there life after the three years? So you, you kind of have to believe that th…
AI assessment note: “you're trying to arbitrate between risk, like you said, what is the today, and what is the tomorrow”
