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Chapter 1: What is the main topic discussed in this episode?
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Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Weisenthal.
And I'm Tracy Alloway.
Tracy, I'm envisioning this future where like we have to do a state of the sort of AI inference market episode like once a month, you know, like where it's like things are moving so rapidly and there's so much change either in terms of what models are using or what they're being used for, etc.,
That in the same way we would do like, you know, the occasional regular stock market episode or whatever, we would just do, okay, what are we seeing right now in AI inference trends? Because it just feels like the moment we do an episode a few weeks later, it may be out of date.
We should just bite the bullet and do a weekly episode. Transform lots more into a market update on compute.
We could do inference. I don't know. We'll have to workshop.
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Chapter 2: What has changed in the compute market since CoreWeave's IPO?
I'm going to use the most advanced model to do that, et cetera. I have a theory, and we will get into this with our guest, that one of the things that will ā and we've talked about this with Goldman's Marco Argenti. But one of the things I predict is that companies are like clearly ā they're going to keep using it more and more would be my guess ā
But there were probably a lot of investment made in sort of like optimal model routing because some models are like a hundredth per query of what a frontier model is. Probably a lot of people don't know like what is the sort of like efficient frontier model usage.
And so actually routing the query to the sort of most efficient model, I have a feeling we're going to see a lot of investment in that area specifically. Yeah.
Well, there's also just the question of whether or not the models get cheaper overall as they advance. Right. And we have seen some I think NVIDIA has a new system or chip out or something that is supposed to reduce token usage. We can get into that as well.
And, you know, we did that live episode recently with Ian Dunning of Hudson River Trading. And he said a lot of interesting things in that. But one of the things he said is that. The scarcity is increasingly like just the real estate component.
Finding a suitable place to plug in your GPUs, at least from his perspective right now, is as much, if not more so of a challenge than securing GPUs themselves.
Which is different to what it was like three years ago.
Yeah. So just like where you plug it in, we know there's all the like the anti-data center politics out there. So it's like, yeah, we got to take the pulse of this market.
All right. Consider this our inference update.
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Chapter 3: How are companies managing their AI compute budgets?
This is something we've been talking about for a while as it relates to the infrastructure side of things as well. Because you don't need that latest model for everything. Accordingly, you don't need the latest piece of infrastructure to support every single inference or training query that's out there.
You can kind of conceptualize this matrix of different sizes of workloads relative to different sizes of GPUs. And all of a sudden, that tells you, my God, H100s could last six, seven, eight years. A100s are going to last longer. And it totally changes the entire conversation around depreciable life of infrastructure. as that was a really popular topic during 2025.
People were saying like, oh, this stuff will last two years. It's worth zero afterwards. And like, we've never seen any semblance of that because of the point you guys are accurately making, which is users are going to need to find a way to use the appropriate model for their prompts. And that'll be solved by model routing, to your point.
But that just further enables this concept that infrastructure is going to be used longer. And we see that every day in our portfolio, extending all the way back to A100s.
I just want to ask a specific question about the broadening out of the customer base. And you mentioned, for example, financial services clients. When you talk about, say, a financial services client as being distinct client from one of the major AI labs, does that mean what you're saying? So it's like I'm just making it up. Let's just say I don't know if these relationships exist.
Let's say a Citigroup has an enterprise license with an anthropic. Does that count as Anthropic as a customer or Citi as a customer? And when you talk about this broadening out, are there essentially more types of entities who are building some type of model, not necessarily an LLM per se, but some type of internal house specific model from which they want to run inference?
It's a great question. The scenario you presented, Anthropic would be our client. Okay, got it. So what I'm highlighting, I want to correct a number I said earlier. Our financial service clients, and this is direct to those financial services, they're approaching $10 billion in backlog. So this would be a good example of this, and that's something we made recently, is with Jane Street.
That's not Jane Street coming through OpenAI or Anthropic to get to us. That is Jane Street coming directly to us and using our platform.
For a model that they're building. So it's a Jane Street- Right.
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Chapter 4: What are the current challenges in securing GPUs and infrastructure?
We really don't see demands on a material basis for anything but that NVIDIA compute. And that's what we are building today.
Obviously, just to push back on this a little bit, and I'm not really in any position to push back. I can only relay what past guests have said in my own reading. So what one of our guests said is that absolutely, NVIDIA has the lock.
on model training, that if you want to train a model that, yes, Nvidia chips are the only game in town, but that for inference, the really, his view, this is Ian Dunning again, his view is there really were options. And then of course, we had someone who was much more biased. We interviewed the CEO of Cerebros, the company that makes the gigantic plate and, or sorry, the The gigantic chip.
The gigantic player.
And, of course, he did. But, I mean, of course, he was going to say, yeah, the CUDA mode is vastly overrated for inference. It barely exists. Now, of course, of course, he's going to say that. So, like, you know, he's in a competitor. But we've also heard it from a user of inference. And intuitively, it makes sense, like training is very complicated and all that stuff.
But what you're saying is that from the customer standpoint, you see the demand for NVIDIA on both the training and the inference as being steady and that you perceive that advantage to be consistent through both aspects.
So I believe in our last quarterly report, our CEO, Mike, qualified that. Inference workloads represent well in excess of 50% of infrastructure utilization on our platform. It's the exact same infrastructure that you use for training as well. Going back to my comment of it's very fungible between those different types of workloads. Those customers are choosing NVIDIA to work with on inference.
I think what you're going to see is people will want to try at small scale other types of silicon. But the reliable, proven, and remains, from our perspective, most efficient infrastructure to use is NVIDIA today. Does that change over time? Who really knows? But I think we've seen NVIDIA battling this concept for years.
And every year they show up and they remain the de facto choice for AI infrastructure. I think we're going to be one of the first people in the market to see it. Because that will be a tone shift change from our clients asking us to run something else. That hasn't happened.
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Chapter 5: How is CoreWeave diversifying its customer base?
How would you characterize, I guess, the difference between the U.S. and the Chinese market at the moment? I'm sure this is something you think about even though you don't participate in the Chinese market directly.
Yeah.
Tracy's asking for questions. It's like questions that we can ask people when we're over there.
Yeah. That's likely going to be my response. Tracy is like, we just do not participate in that market. I think that there's opportunity for us to be expanding. As you guys know, we, we operate in Canada, Europe. I think moving further East makes a lot of sense for us, but we're trying to be very methodical in the way that we expand and
So unfortunately, I'm not going to be able to help you with specific questions in that market, but I would imagine you're going to encounter a lot of the same things that you're seeing in the US, which is just insatiable, unrelenting demand for AI. And we just kind of keep coming back to this. It's like, there is no solution in sight for being able to satiate demand, right?
There's just too many supply chain markets There's no path to solving demand in the near term or even the medium term, frankly.
You mentioned, so Tracy asked you about land use. You said that really was an issue. But like the first time we talked to you in 2023 or whenever that was, there was not a major growing movement of people who are just like anti-data centers anymore. Maybe there were a few fringe people, but it was not something that was on the minds of politicians and activists and so forth.
And you do see these headlines, you know, about some projects really having been shelved. It was like a big one. Northern Virginia is a huge hotspot for it. And there was a big project that was they pulled the plug on due to some they couldn't get an agreement with the local government. That must affect you. What are you seeing in terms of like your capacity to build?
How has it changed specifically in light of or have you seen a change? Would you be able to build faster in a world where this had never become a political hot button issue?
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