Carmen Li's Plan to Build a Futures Market for Compute

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2:01Don Wilson:Hello and welcome to another episode of the Odd Thoughts Podcast. I'm Traci Alloway. And I'm Joe Weisenthal. So Joe, we're still continuing our series recorded from the live show in New York. We had a bunch of great conversations. A couple of them were building off of discussions that we had had previously. And one of those discussions was in Chicago at another live show about six or seven months ago, back in October. We spoke with Don Wilson of DRW about the trading environment, but also about his new venture. Right. And so his new venture is one that actually there's quite a bit of competition and quite an excitement.

2:38Don Wilson:in. And it's essentially like, OK, GPUs, we know they're very important for the AI boom, et cetera. The question is, can GPU capacity, which is scarce, can it become a tradable commodity such that I can buy futures to lock in my price of access to compute power? Could I resell those futures? Will there be speculators speculating on the up or down price of like an H100, running an H100 NVIDIA chip for an hour. This is a big question. We know there's a lot of interest in the actual compute, but whether there's interest in compute futures as tradable instruments is very TBD. Yeah. And the analogy that everyone always uses is compute is the new oil.

3:20Don Wilson:So why can't it have a market structure that looks somewhat like the oil market? And there are challenges. Fungibility is a big one. Like one chip might not necessarily be equal to another chip. Or one chip, the same chip at one data center might not equal to the same chip at a different data center. Exactly. And so even if you're not interested in AI, what I say here is like the market structure questions and the idea of building an entirely new market is really fascinating to me. And I think others will find it interesting, too. And we really do have the perfect guest. We're speaking with Carmen Lee.

3:56Don Wilson:She is the CEO of Compute Exchange and Silicon Data. These are the two companies that Wilson is invested in. And they've already announced that they're doing futures with the CME. So really the perfect person to speak to. So take a listen. Last October, we spoke with Don Wilson of DRW Fame, and he was talking to us about his new project, which was basically building out this compute exchange. Now we're here with you six months later. You're actually the one leading it. How far are you in this endeavor? and remind us what exactly are you trying to do here?

4:32Carmen Li:Yeah, so thank you for the great intro and great audience. Before I do that, I'm actually going to call back to six months ago in the Don podcast you did. You asked him a question, what if compute prices keep going to go up? At that time, September, October, compute prices were going down across all chips. Now see what happened. I think you called it. I think you called it when the market called it. So I'm the founder CEO for Silicon Data. So that's the index provider for GPU indices. We recently announced partnership with CME. So we will be launching GPU future and options from CME in a couple of months, pending CFTC approval, obviously.

5:14Carmen Li:So that's quite exciting. We've been working on GPU indices for past two and a half years, starting 2024 April. So it's been a while. And we launched world's first GPU indices at Bloomberg Terminal in 2025. A year later, we launched the partnership with CME. So it's quite exciting. Separately, I heard you mention Compute Exchange before. So thank you for doing that, Joel. I'm the CEO for Compute Exchange, which is spot marketplace for GPU procurement. So we do reserve contracts, forward contracts, as well as refurbished contracts.

5:47Don Wilson:Let's talk about the variety of options that we have to financialize compute and so forth. So this came up in the first conversation we had with Ian Dunning. Who is the type of buyer who would want to buy compute on a spot market? Because you talk about typically we think it's like these multi-year contracts where some entity enters into a contract with a data center or a new cloud, whatever, and they have this for a while. So who is the buyer or the user of these instruments that might want to buy spot compute or very short-term, short-dated compute futures?

6:27Carmen Li:It's a great question. So the compute market right now for Compute Exchange, we have all our provider, mostly our NeoClouds around the world. That's one side. Another side is a big variety from AI startup. So even though they are startup, they spend millions of dollars on GPUs already. There are enterprises who are traditional businesses, but they are needing a node, two nodes, a few servers here and there for their inferencing or, I don't know, other deployment needs. They are providers. They are inferencing providers, right? They don't own GPUs, but they provide open source, open weights, model support for other use cases.

7:07Carmen Li:So what's the big variety? Most North American firms, they do a variety of combination of contracts. obviously on demand give you the most flexibility. You don't pay when you don't use it. However, you're also at the mercy of demand supply curve at any given time. So translate to your price can go from$3 to$6 to$9 depends on demand supply curve shifting. So that doesn't help when you can have a predictable margin. And also in terms of scarce, you're not guaranteed for GPU resources for next hour or next month. So you see a lot of people shifting from on-demand to reserve, even forward contracts.

7:48Carmen Li:So forward contracts, you basically lock in deliverables for next whatever month, starting in September maybe, or starting in November, February opens. So this all comes because of market condition. So Compute Change cover that, the physical GPU procurement, also token. So we would love to talk about token as well. On the flip side, who's going to use the futures options can be a similar set of people. You look at oil market, which we all love WTM brands. The people use WTM brand, a lot of them are naturally long oil. So the shells, the producers, they need to hedge their revenue volatility by shorting futures or port options.

8:30Carmen Li:If you're naturally short oil, American line, they want to control their cost volatility. They want to obviously use future options as well. Simple to compute. Your NeoCloud or anyone have the servers. Ideally, you want to have predictable revenue streams.

8:47Don Wilson:So the NeoCloud would be the shell in this example.

8:50Carmen Li:Exactly. You have GPUs, right? Or the banks where GPUs own your balance sheet, right? Your long GPUs. Then naturally, you want to make sure revenue is stable to a certain degree. And then you want to use future to do so. If you are naturally short GPU, which is everybody in this room, unless you tell me you have GPUs, right? then you net depends how much you use if you want to control your cost volatility you want to use

9:16Don Wilson:future to hedge as well just on the compute exchange side of things if someone is buying like off the spot market how do you guarantee i'm not sure quality is the right word for this but how do you guarantee they're getting what they expect this is a great um question so i'm gonna

9:33Carmen Li:flip to a slide if you don't mind oh yeah we have visuals more slides yeah so i usually don't like few slides, but this time because you mentioned a really good question, so we actually call it GPU lottery. So we published a paper early this year at GPGPU conference with Jefferson Lab on GPU performances. We can have you create a link to the audience later on. This is A100, by the way. I know we didn't put a tag on. This is A100, 40 gigabytes memory bandwidth. with. We proved there's 38 % performance variance for the same chip and then we decompose into the chip itself, intra-provider, and inter-provider.

10:12Carmen Li:And there's many reasons for that, right? And to your point, you don't know until you get your GPUs. We have a PLATS for GPU, CAR FACS for GPU, depends how you look at it. So in ComputeChange, you actually verify the GPU before it's delivered to you. Basically, you can say, hey, I want 200 B200 nodes. Obviously, we'll give you specs back and the commercial back. Same time, independently verify the performances on flops, memory bandwidth, tokens, and other information, SLA and other things. And as a user, you can decide, is price your most important criteria? Maybe it is. Or maybe you're willing to pay a premium for geolocation or the performances that you care more about on latency, right?

10:59Carmen Li:We believe give people the option and transparency is the most important thing.

11:03Don Wilson:Let's stick with the oil analogy for a second. You know, there's a few benchmarks that we all know about. There's Brent, there's WTI, there's others, but those are the two that we talk about. If we transpose this to chips for a second, okay, we say you have an H100 index. We did an episode of the podcast last week, I think, with the CEO of Cerberus, which is another... It's an amazing company, yep. Yeah, but they're another type of chip for inference. Is your assumption that these indices are going to be close enough to the cost such that if you're, okay, I'm running inference maybe on some Cerberus or TPUs or whatever, some of these others, that an H100 index will be good enough as a hedging instrument?

11:50Carmen Li:This is the whole goal for me sitting here, actually, right? There's a meaning for every financial product. The functional reason. For commodity, it is for hedging, right? This speculation is great, but it's really for people to hedge their volatility, to do risk allocation, to do risk transfer, and then asset capital allocation. If we can't do just what you said, then we fail at our job, right? So that's why we went all the way back. The way we developed our index model is not a simple math. It's not, hey, you have 2H100 to a simple average, right? Because then you compare Apple to oranges. The 2H100 can have different CPU, different RAM, different disk, different true location, different memory bandwidth.

12:33Carmen Li:You cannot do simple math. What we do is we usually collect six months of historical trading data from over 100 data sources, and we see which factor drive the price differentiation. So every day, over 150 ,000 traded prices ingest in our platform, and we normalize the traded prices based on different characteristics of the model itself and normalize to a base case, and then we do the math of settlement price calculation. So then this price will be highly correlated, ideally, as much as it can, to the price you pay at a neocloud, for example. However, it won't be the same, just like basis trading, right?

13:16Carmen Li:Like every other commodity, there's a basis risk. We're helping clients calculating the basis risk. So you know, hey, you're US East, you may be a bit higher or two, then there's expectation, a manageable correlation, understanding of the indices.

13:31Don Wilson:You mentioned volatility just then. I mean, the reason people need to hedge is because of volatility. are you seeing enough of that in GPU prices that like this model makes sense because if it's just a steady line up or steady line down like it's going to be a kind of boring market so it's

13:48Carmen Li:interesting so last year when GPU price all going down the big conversation is why do you need indices for something price will always go down and this year is why do you want to indices when price always go up that's right literally is all the question I get it's pretty fascinating So when we look at volatility, we look at daily volatility movement, not the price up and down, right? The daily volatility for A100, H100 is around 20 to 30. It's a very healthy commodity volatility range. So I don't manage volatility. It just happened to be that volatility. That can't change. It's all because we normalize it.

14:22Carmen Li:If you look at each individual chip configuration at different geolocation, the volatility are different. There are some chips with 8 % volatility, some chips with over 100. Because normalization of indices, you actually get very healthy 20 or 30 daily ball.

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17:28Don Wilson:Additional restrictions may apply. Please speak with a business banker for more information. JPMorgan Chase Bank, N.A., member FDIC. I'm always fascinated by, like, we look at the Bloomberg terminal, for example, and there's a price on the screen and it's just there. And we started taking for granted that it had to come from somewhere. And maybe some commodities have like a, there's an existing exchange and a public price. And then there's also a lot of commodities, just bilateral trades. What is the actual process by which you collect the most recent data? So if you say, okay, an hour of H100 usage costs X, right, whatever it is right now, how did you assemble that number?

18:12Don Wilson:How did you gather that information from, say, the inference providers?

18:16Carmen Li:So it is a very, can be lengthy, depends on what data sources. The nature of GPU spot markets, so compute changes is one of them, and then many, many new clouds, hyperscale or marketplaces all have very different contract size, duration, specs, and their way to manage their data. So it's a lot of licensing conversation, negotiation. And also context a lot myself. I don't know. I was used for Bloomberg data. So I was in data business for a period of time. So everything is pretty intuitive to me. It's very important to get a variety of data sources, especially for computing.

18:53Don Wilson:Do you call them up? So it's like, okay, the price is different on a Friday versus one.

18:57Carmen Li:You zoom them up. Yeah. Well, first you have a conversation. Say, hey, I love what you do. You're a great new cloud. Can I license your data? And usually your feedback is, what is in for me? And then we'll talk about commercials. And then your concern could be, hey, if I give you all my data, I give away all my secrets. And then we'll go through traditional licensing agreement. Can I disclose what I want from you, what I do not want from you? What's the pipeline look like? Are you a straight bucket job? Are you writing my API to yours? Are you writing to mine? It's a lot of conversation. It's actually pretty standard conversation.

19:32Carmen Li:And right now we have 8 million pricing points globally around 200 data sources. It's pretty much BAU. A lot of people will say, hey, Carmen, always bring it up. Can I have your data? That's always my ending. You know, we were talking about GPU indices.

19:46Don Wilson:And you're not the only one doing GPU price indices for sure. Not anymore. Yeah, not anymore. But when you look at some of the other ones, like sometimes they show different numbers or even different longer term trends. What accounts for the discrepancy there? What are you doing differently or what are they doing differently, I guess?

20:06Carmen Li:So I can't comment on other people's methodology because I actually don't know. Different data, raw data, different mythology will eventually drive to different prices. So the way I would look at this is, you know, it's always smart for anyone to look at multiple data sources and then figure out what is the actual decision you have to make, which data source do you trust. The market always vaults. Once things start trading, the market always gravitates with things that actually help them hatch, right? If you're easily manipulatable, if you are not data source, people actually do hedge. What's the point, aside from speculation?