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

Episode Date: June 15, 2026

When we spoke to DRW's Don Wilson last year, he talked about building out a GPU market that might be bigger than oil. Now, a year later, he is working with Carmen Li to do just that. Li is the CEO of two companies — Silicon Data and Compute Exchange (where she works alongside Wilson). The former company is building the index for GPU pricing while the latter is a spot marketplace for GPU procurement. Today's episode — recorded at our live show at City Winery in New York — gets into how Li is building a whole new market for GPUs at her two companies. We talk about the challenge of standardizing compute, GPU price volatility, if used GPUs are like used cars, what goes into constructing a GPU index, and what it means to win the GPU lottery. Read more:Jane Street Plans New Data Center as Computing Power Runs ScarceSpaceX Inks $30 Billion Computing Power Deal With Google Only Bloomberg - Business News, Stock Markets, Finance, Breaking & World News subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at  bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

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Bloomberg Audio Studios. Podcasts Radio News. Hello and welcome to another episode of the Odd Thoughts podcast. I'm Tracy Alloway. And I'm Joe Wisenthall. 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 of excitement in. And it's essentially like, okay, 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 sell those, resell those futures? Will there be speculators speculating on the upper down price of like an H-100, running an H-100

in Vividient 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 that's tradable instruments is very TBD. Yeah. And the analogy that everyone always uses is compute is the new oil, right? So why can't it have, you know, 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 be 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. 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? Yeah. So thank you for the great intro and great audience. Before I do that, I actually going to call back to six months ago in the Dom podcast you did. You asked on the question, what if compute prices keep going to go up? At that time, September, October, compute prices were going down, cross all chips. Now see what happened. I think you called it.

I think you called 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 all be launching GPU future options on CME in a couple of months, pending CEPC approval, obviously. So that's quite exciting. We've been working on GPU indices for past two and a half years,

starting 20, 20, for April. So it's been a while. And we launched World's First GPU indices at Bloomberg Terminal in 2025. Yeah. A year later, we launched the partnership with Siamese. So it's quite exciting. Separally, I heard you mentioned Compu 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 refurbish contracts. Let's talk about the variety of options that we have to finance. compute and so forth. So this, I mean, this came up in our conversation, 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, right, 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

by spot compute or very short-term, short-dated compute futures. It's a great question. So the compute market right now for compute exchange, we have all our provider, mostly are new clouds around the world. It's one side. Another side is a big variety from AI startup. So even though they are a startup, they spend millions of dollars on GPUs already. There are enterprises who are traditional businesses, but they are needing a note, two notes,

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. 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 a given time. So translate to your price can go from $3 to $6 to $9 to 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 your GP resources for next hour or next month. So you see a lot of people shifting from on demand to reserve even forward contracts. So forward contracts, you basically lock in deliverables for next whatever month, starting September maybe, or starting November of February opens, right?

So this all comes because of the market condition. So computer change cover that the physical GPU procurement also token. So we'll love to talk about token as well. On flip side, who is going to use the futures options can be a similar set of people, right? You look at oil market, which we all love WTOM brands, right? The PPUS double T and brand, a lot of them are naturally long oil. So the shells, the producers, they need to hedge your revenue volatility by shorting futures or port options.

If you're naturally short oil, I'm right now. They want to control their cost volatility. They want to obviously use future options as well. Simple to compute. Your new cloud or you or anyone have the servers. Ideally, you want to have predictable revenue strings. So the neocloud would be the shell in this example. Exactly.

Okay. You have GPUs, right? Or the banks where GPUs on your balance sheet, right? You're long GPUs. Then naturally you want to make sure your revenue, right, 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 of volatility, you want to use future to hedge you 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 question. So I'm going to flip to a slide if you don't mind. Yeah, we have visuals.

More slides, yeah. So I usually don't like to use slides, but this time because you mention really good questions. So we actually call it GPU lottery. So we published a paper early this year at GPGPO conference with Jefferson Lab on GPU performances. Well, actually, so what can have you create a link to the audience later on? This is 8100 by the way. I know we didn't put on 10 on the 840 gigabytes memory bandwidth.

We proved there's 38% performance variance for the same chip, and then we decompose into the chip self, intra provider and inter provider. And there's many reasons for that, right? And to your point, you can, you don't know until you get your GPUs. We have a plat for GPU, Carfax for GPU, depends how you look at it. So we, in compute change, you actually verify the GPU before delivered to you. So basically, you can RFQ for say, hey, I want a 200 B 200 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, SLAs, 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? We believe give people the option and transparency is the most important thing. 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 H-100 index. We did an episode of the podcast last week, I think, with the CEO of Cerberus, which is another... Amazing company, yep. Yeah, but there are a different, 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 training, whatever, some of these others, that an H-100 index will be good enough as a hedging instrument? This is the whole goal for me sitting here, actually, right? There's a meaning for every financial products, the functional reason. For commodity, it is for hedging, right? This speculation is great,

but really for people to hatch their relativity, to do risk allocation, to do risk transfer, and then asset capital allocation. If we can't do Joe WU. Then we failed at our job, right? So that's why we went all the way back, the way we developed our index model. It's not simple math. It's not, hey, you have 2H100 to simple average, right?

Because then you compare Apple to oranges. The 2H100 can have different CPU, different RAM, different disk, different location, different memory bandwidth. 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 ingesting our platform,

and we normalize the traded prices based on different characteristics of the model itself, and then normalized to a base case, and then we do the math of settlement prices 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? Like, at a commodity, there's a basis risk. We're helping client calculating the basis risk.

So you know, hey, you're US East. You may be a bibs higher or two. Then there's expectation, a manageable correlation, understanding of the indices. 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 interesting. So last year when GP 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 indices when price always go up? That's right. Literally this all the question I get. It's pretty fascinating. So when the way we look at volatility, we'll look at daily volume, volatility, not the price up and down, right? The daily volatility for a 100 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. 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 to 30 daily level. Pride is like love.

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new on the IHeart radio app, Apple Podcasts, or wherever you get your podcasts. I'm always fascinated by, like, you know, we look at the Bloomberg terminal, for example, and there's a price on a screen, and it's just there. And we started taking for granted that, like, it had to come from somewhere. And maybe some, some commodities have, like, 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? elect 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? How did you gather that information

from, say, the inference providers? So it is a very, can be lengthy, depends on what data sources. The nature of GPU spot markets, so compute change is one of them. And then many, many new clouds, hyper-skiller marketplaces all have very different contrast size, duration, specs, and their way to manage their data, right? So it's a lot of a licensing conversation, negotiation. And also context, I love 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 computer. Like, do you call them up? Like, so it's like, okay, the price is different on a Friday version of one. You zoom them up. Yeah.

Well, you first have a conversation. Say, hey, I love what you do, you're in your cloud, can I license your data? Okay. And usually your feedback is what is in for me? Yeah. Right? And then what's how about commercials?

And then your concern could be, hey, you know, if I give you all my data, I give my way 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 right? You use 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 stunt. conversation. And right now with 8 million pricing points globally around 200 data sources, it's pretty much BAU. A lot of people will say, he, Cameron always bring up, can not have your data? It's always my ending. You know, we were talking about GPU indices, 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? So I can't comment other people's mythology, because I actually do not know. Different data, raw data, different mythology will eventually drive the 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 vote.

Once things start trading, the market always gravitates with things actually help them hatch. If you easily manipulate, if you are not data source, people actually use you hatch, what's the point, aside from speculation, right?

So, you know, I love to say, I mean, I also strongly believe we're the best, but again, I will let the market decide, which will happen very soon. So, of course, like, yes, there's the economic rationale for the existence of a hedging instrument. And we can understand that. Someone who is an entity that needs compute, they're implicitly short GPUs. They want to hedge, et cetera. But the liquid markets

also really do need speculators. And they need people betting on price. What are you seeing right now in terms of traders or institutions, et cetera, who economically can take both sides of the trade? And how active is this getting where there's just a compute trading desk that is separate from their economic needs? The conversation has been going on for a very long time with various banks, various market participants, speculators. They are very excited. So some banks obviously have both sides of the trade, right? So they can cross off some positions internally. That's great.

Obviously, some they have to use leverage external products. That's where we come in. The way I encourage them to do is I selfishly, I want them to start trading that on compute. The more people trade, the better for me, right, selfishly. But same time, it's important for people understand GPU trading. It's not like you can't just move someone from, trade oil, electricity, with no background context, jumping to GPU compute futures. There's a lot of contexts where, number one, GPU, it is not homogeneous product. Number two, you have to understand the use case of 8100, H100.

Right now, they are not that correlated. Is that right? Maybe that's not right. I don't know. There are use cases which they're pretty separated, but maybe their use cases, they can be transferred. And also there's software layer to this, right? So right now, you can argue certain use cases, some large models cannot be deployed and the legacy chips, but doesn't mean six months later it cannot do so.

As the software layer compression, model compression gets better, optimization gets better, things can change. So really understand not just the hardware configuration, this, local supply demand curve for the server itself, also the software layer. That's kind of critical, right? That's really change the supply demand curve and all the way to the user behavior. So it's all it's going to take some time as we have engaged with a lot of participants. Yeah. Make sure they have the right set up. I have what is possibly a dumb question, but the compute futures, how are those actually settled? Because I have like images in my mind of taking physical

delivery of like maybe one of those big, uh, super, chips. I'll get away first. Oh, you'll get a server a wafer. That'll be fun. So for the CME futures, we'll be financially settled, just like the traditional oil settlement process goes. Four contracts, obviously we do four right now at compute exchange, but we always open to do, you know, physically deliver futures, especially given we do have silicon mark, which is GPU benchmarking. So imagine in the future you can do, hey, I want a 20 grade A, B200, this configuration, this shape of servers, in US East, and then at the end, we'll get that. Well, usually API costs, so you don't get physical

and it's not as cool as physically give you away way for, but you get API costs. One can dream. How do you literally trade it? Is in like, let's say there's probably some very bright people in the room, now with an institution, when it's all listed and everything, is it need to go through like a futures broker? Is it like a, could it be like a prediction market

or you just go to the website? Like what is the actual, how does someone actually, get in this. Setting aside whether they're sophisticated enough of whether they know what they're doing. A lot of people trade who have no idea what they're doing. Yeah, setting all this side, yes, you know, only trade what you know. But like, what is it through a prime broker? Like how will people actually be able to participate in this market? The beauty of CME is you can do the same thing you're doing now, trading CME products. Okay. The same process, a certain process, a margin. That's why you get great margin optimization, right? Everything is BAU. It's no different.

We don't have anything right now. So any commodities broker that someone has, they will be able to, on that platform, they will have access to these instruments. Exactly right. Yeah, we make it easy for people. Would you be upset if a prediction market set up a GPU price contract of some sort, with that into your business? Not at all. So we actually worked with Polymarket last year. Someone actually listed my product at Polymarket with my consent.

It's always start like that. And someone told me that and then we tried to polymarket say, hey, do you want to do something, you know, more real? So we did February, settled and April settled a few contracts on polymarket just to test the order, right? Obviously, we're exclusively with CME right now. But yeah, so I think obviously you have to do right licensing, normal political, right, all the right things. Yeah, you know, I don't, market can do whatever they want. And then people will choose the best product for them to use. Setting aside the financial instruments for the moment, would people think about AI and they think about the use of GPUs?

They mostly still probably in their mind think of like Open AI, Anthropic and Google basically. And that's kind of it. But obviously, as you've stated, like the world of entities that serve inference in some form or another is much greater than these three companies that we talk about. Talk to us a little bit more about what the actual world of inference provision looks like outside of the big household AI names. So the ones you mentioned, they mostly are closed source models, as we call it. Right. But they do have some open source versions, but they're famous for their close source models. So we actually track 300 open source, open weights, closed source models globally from pricing and consumption point of view.

It is really interesting if we have actually, you know, we haven't really formally launched LM token indices. You can kind of look at Bloomberg and it's on Bloomberg. What's interesting is people are, depends. It's all based on your choices. Right now, the price actually doubled final indices from now from December 1st last year. It's like $2.21 per million token. It's a mixture of input open token prices, average weighted by consumption,

by Baskill models. It's not here. This is a GPU, unfortunately. Wait, sir. Since we have this specific chart up right now, what is the Y axis in this chart show? So you're looking at the dollar per GPU power rental rate on demand for three chips. Okay.

The top one, the yellow line, is B200 NEO Cloud on Demand per GPU power. Sorry, it's a mouthful. The line, the yield line is interesting, right? So every new chip we came out based on historical. data A100, H100, usually came out to be high. And then comes down as more supply, you know, came live. And then price will come down and then stabilizes. So that's the trend we have up sort of for A100 and then for H100.

So when B2 country came out, we polished the data last year at Bloomberg, this early this year. The price was high and then came down, which is kind of what I expected, but the slope was less steep than I expected. I was like, hmm, that's interesting. the slope wasn't as steep and then quickly observed the price just came up and now it's higher than the initial open whatever you call that right launch prices that shows you demand supply curve in a different stage than whatever stage we had before so the a 100 the red line is h100 neocl cloud on demand per GPU power rate so you can see the price came down last year a little

bit sorry about the scale so you don't see much but came down and came back up quite a bit i think I think the last three months came up to like 8% for the H100. The 8100 is the oldest chips among the three, right? They're pretty, you know, pretty much a commodity at this point. The price came down. They stabilized, but the price came up about 10, 15% for the past three months. Remember, the 8100, right? They're not the latest and greatest at all.

So this also tells you the supply demand curve shifting. Oh, yeah. Actually, that reminds me. Talk to us, because you're doing refurbishment of chips as well, right? Which seems like challenging in many ways and kind of reminds me a lot about like the sort of Carvana model of compute or something like that. How are you actually doing this?

Like how does that business work? So this is cool in two different things. One is for people come to compute change saying that, hey, I want to, you know, it's a new cloud provider, right? If you get a piece of land, you again, a GA co-location, great. congratulations. Then your option is number one should I get latest and greatest? The B300, the GBs, the Vib, is the Vibb with a few months, or do you want to get ready for B chips and turn out, maybe sooner, right? Then to you, it's become RRI calculation for the most part, right? What's your

expected, you know, future revenue generation? What's your residual value calculation? How much you can purchase by, right? It's actually pretty simple cash flow based in RI calculation. So the way we approach residual value and the referral transaction is, you know, based on RI, you know, this is your potential break-even. Looking at H-100, right? Obviously, you're not going to charge its high speed, 200, but your cost base is also lower. So you can do the future as you'll assume a few years of forward contract you sign in three years. This is kind of cash flow back. That's your risk value now, right? So we do that calculation with people, so they understand, hey, what's the value supposed to generate? And then what's the trading in the market prices

refurbish or use GPU. And you have a test you have to make sure things works and there's other nuances to that. But we help go to the understanding of the whole residual value. And that's why the whole bubble thing came about. But go ahead. What month was it last year when like everyone got really

obsessed with like the lifespan of chips? November December. Remember like tweeted something about he's like oh the lifespan. They're like right? And everyone was free spent like three weeks free and then moved on from that conversation. Right? Like that was like what do we know about chip lifespans? Are there misconceptions out there about the how long these can

be productive? I got into view a few times, but I don't, I mean, I'm not important. I still am not important, but back then I even less relevant. I was telling reporters, I was like, look, I don't know we did that you're looking at based on my, I actually have blogs on my website, which is completely, you can just search for it. Last year, because of that conversation, I want you to curve later on. The second year, H-100, residual value, resale. value for refurbid chips about 85 cents on a dollar. So a year later you can set 85 cents on a dollar. That's pretty good, I would say. The third year is 84 cents on dollar. I think my car depreciate way more than that, right? And I drive my car for what I return 10, 10 years. So it's,

I had a data, but again, I'm not going to argue against narrative, which is. So, but there's a fairly steep, a decent drop from year one to year two. But after that, you see a general level That's November-December analysis. Right now it's a little different. I haven't refreshed the study, but our code is there. If you're on my data client, if you're wrong my code, you get a number right away. Another thing I want to point out is L-40s. They're like the OGs, right?

At that time, they used to people to use them. They charge you, hyper-seular charge you $0.40 per chute per hour. So, you know, I don't know about two years worth the net number coming from. But I will do that trade every single day. You sell me you're two years old at $0.10. to a dollar, I'll buy it. There's a sort of big question looming in the background of a lot of these discussions, which is the B question, I guess, whether or not we're in an AI bubble, right?

And you sort of touched on it earlier. You have all this granular data on how people are actually using compute, GPU prices, all of that. What's your take on the big question? So as an index provider, I cannot give any full guidance. Not why now. Nor do I know, right? In fairness, I know what do I know?

So the way I look at is we have defined a bubble, right? So you look at a stop bubble, right? And then NASDAQ showed up 200%, it came back down, it was 84% whatever back then. That's, it's a bubble, right? The way I look at bubble is, is your valuation, can your future cash flow support today's valuation of yours, right? So then I'm not talking about like opening and everyone else's valuation.

I'm not obviously. I don't, I don't understand. that process. The way I look at GPUs, the machines, it's actually pretty simple. Look at a future cash flow of your forward contracts and then you discount it back. Can you get money back for the price you pay? Right. It's actually pretty straightforward for the machine level. Right. But to your point, right, you can say, hey, what happened if demand jobs? No one going to use your whatever things you have. But remember the forward contract is a signed contract. If you have that, you can't know. Obviously, if you have things, the biggest concern

is people have concern overbuilt. You've overbuilt, then by theory, then all your prices will calm down because it's over supply of the market. Right. So then you talk about supply demand equilibrium. How do we know about future demand of GPUs, right? I don't know that. Everyone's guess is better than mine probably. The way I look at it is not, it's not that easy to to bring any GPU online. Right? You hear all those sites, big data center bill, $25 billion invested,

but they don't translate to immediate GPU availability. In the servers, which you have to be a waylisted, if you buy brand new stuff, collocation, you need optic fiber. So it's a lot of, unfortunately, star has to be aligned. Wait, but people can default on contracts, right? So even if you have a long-term contract signed,

like that could not work out. Could you envision like credit default swaps or something in the compute market? Like, so that happens in every other markets, right? Every market, if you do OTC trade, you have to raise some more wealthy on you, doesn't matter who they are, right? So there's very, there's a lot of mechanism to hedge that. The things you cannot hatch is GPU cost, right, the price you entered. So that's something exactly what CME futures for. You can have the transparency, the liquidity and then the easiness of trading in and out and had your position. Carmen Lee, thank you so much for joining us.

Yeah, thank you. That was our conversation with Carmen Lee of Compute Exchange and Silicon Data recorded live at our New York show. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Wisenthal. You can follow me at the stalwart. Follow our guest, Carmen Lee at Carmen Lee. Follow our producers, Carmen Rodriguez at Carmen Armin. Dashel Bennett at Dashpot. Kill Brooks at Kill Brooks. and Kevin Lazzano at Kevin Lloyd Lazzano. And from our oddlods content, go to Bloomberg.com slash oddlots. We have a daily newsletter and all of our episodes.

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