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- [00:01]
Ryan Reynolds
Introducing B of A Rewards A new loyalty program with rewards for every ambition. From cash back deals on brands you know and love to a credit card rewards bonus. From fueling up to rewards that fuel your goals. It all starts with a Bank of America checking account and grows from there. What would you like the power to do? Bank of America Open or enroll your account@bankofamerica.com V of A rewards bank of America corporation. All rights reserved. So there's a lot of noise about AI, but time's too tight for more promises. So let's talk about results. At IBM, we work with our employees to integrate technology right into the systems they need. Now a Global workforce of 300,000 can use AI to fill their HR questions. Resolving 94% of common questions, not noise. Proof of how we can help companies get smarter by putting AI where it actually pays off. Deep in the work that moves the business. Let's create smarter Business.
- [01:00]
Chase for Business Representative
IBM Small businesses are the pulse of every community. They bring people together, create opportunities and drive growth. Chase for Business helps business owners like you with personalized guidance and convenient digital tools all in one place. With that guidance and your determination, you can take your business farther and help build a brighter future for your community. Learn more@chase.com business chase for business Make More of what's yours the Chase Mobile app is available for select mobile devices. Message and data rates may apply JP Morgan Chase Bank NA Member FDIC Copyright 2026 JPMorgan Chase Co.
- [01:40]
Joe Weisenthal
Bloomberg Audio Studios Podcasts Radio.
- [01:56]
Tracy Alloway
Hello and welcome to another episode of the oddlaws Podcast. I'm Tracy Alloway.
- [02:00]
Joe Weisenthal
And I'm Joe Weisenthal.
- [02:02]
Tracy Alloway
So Jo, 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 his new venture, right?
- [02:27]
Joe Weisenthal
And so his new venture is one that actually there's quite a bit of competition in 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 and could I sell the 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, it's tradable instruments, is very tbd.
- [03:10]
Tracy Alloway
Yeah. And the analogy that everyone always uses is compute is the new oil.
- [03:14]
Joe Weisenthal
Right.
- [03:15]
Tracy Alloway
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.
- [03:27]
Joe Weisenthal
The same chip at one data center might not be equal to the same chip at a different data center.
- [03:32]
Tracy Alloway
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?
- [04:26]
Carmen Lee
Yeah, so thank you for the great intro and great audience. Before I do that, actually going to call back to six months ago in the DOM podcast you did, you asked them the 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, but 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 on CME in a couple of months, pending CPC approval, obviously. 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. 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.
- [05:42]
Joe Weisenthal
Let's talk about the variety of options that we have to financialize 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 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?
- [06:21]
Carmen Lee
It's a great question. So the computer market right now for Compute Exchange we have all our provider mostly are new clouds around the world. That's one side. Another site is a big variety for a 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. They don't own GPUs but they provide open source, open weights model support for other use cases. We'll see 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. Right. So for contracts you basically lock in deliverables for next whatever month, starting September maybe or starting November. February. Rubens. Right. So this all comes because of market condition. So computer change cover that the physical GPU procurement also token. So we would 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 WTM brands, right? The PPU WTM 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 put options. If you naturally short oil American line. Right. They want to control their cost volatility. They want to obviously use future options as well. Simple to compute your new cloud or your anyone have the servers. Ideally you want to have predictable revenue streams.
- [08:42]
Joe Weisenthal
So the new cloud would be the shell in this example.
- [08:44]
Carmen Lee
Exactly. You have GPUs, right? Or the banks where GPU is on your balance sheet, 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, then depends how much you use. If you want to control your cost volatility, you want to use future to hatch as well.
- [09:11]
Tracy Alloway
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?
- [09:25]
Carmen Lee
This is a great question, so I'm going to flip the slide if you don't mind.
- [09:30]
Tracy Alloway
Oh yeah, we have visuals more slides.
- [09:32]
Carmen Lee
So I usually don't like to use slides, but this time because you mentioned really good questions. So we actually call it GPU lottery. We published a paper early this year at GP GPO conference with Jefferson Lab on GPU performances. We can have you create a link to the audience later on. This is a 100, by the way. I know we didn't put Tag on a 140 gigabytes memory bandwidth. We proved this 38% performance variance for the same chip. And then we decomposes into the chip itself, intra provider and interprovider. And there's many reasons for that. To your point, you don't know until you get your GPUs. We have a Plex for GPU, Carfax for GPU. Depends how you look at it. So in compute change you actually verify the GPU before delivered to you. So Basically you can RFQ for say, Hey, I want a 200B 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.
- [10:57]
Joe Weisenthal
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 company.
- [11:19]
Carmen Lee
It's an amazing company. Yep.
- [11:20]
Joe Weisenthal
Yeah. But there are 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 H100 index will be good enough as a hedging instrument.
- [11:45]
Carmen Lee
This is the whole goal for me sitting here actually. Right. There's a meaningful every financial products in my. The functional reason for commodity it is for hedging. Right. This speculation is great, but really for people to hatch their volatility, to do risk allocation, to do risk transfer and then asset capital allocation. If we can do Joe, what you said, then we fail at our job. So that's why we went all the way back. The way we develop our index model is not a simple math. It's not, hey, you have two H100 to a simple average because then you compare Apple to oranges. The two H100 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 trader prices ingest our platform and we normalize the trader 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. Right? So then this price will be highly correlated, ideally as much as it can to the price you pay at a NEO cloud, for example. However, it won't be the same. Just like basis trading. Right. Like every other commodity is a basis risk. We're helping client calculating the basis risk. So you know, hey, you us east, you may be a bit higher or two. Then there's expectation and manageable correlation understanding of the indices.
- [13:25]
Tracy Alloway
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 lineup or steady line down, like it's going to be a kind of boring market.
- [13:42]
Carmen Lee
So it's 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 done? And this year is why do you want to indices when price always go up literally is all the question I get. It's pretty fascinating. So when we will look at volatility, we'll look at daily volatility movement, not the price up and down. Right. The daily volatility for a 100H100 is around 20 to 30. It's a very healthy commodity volatility range. So I don't manage volatility. Just happened to be that volatility that can 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 80% volatility, some chips with over 100. Because normalization of indices you actually get a very healthy 2030 daily volume.
- [14:48]
Ryan Reynolds
So there's a lot of noise about AI. But time's too tight for more promises. So let's talk about results. At IBM, we work with our employees to integrate technology right into the systems they need. Now a Global workforce of 300,000 can use AI to fill their HR questions. Resolving 94% of common questions, not noise. Proof of how we can help companies get smarter by putting AI where it actually pays off. Deep in the work that moves the business. Let's create smarter business.
- [15:17]
Public/Advertising Voice
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- [16:18]
Chase for Business Representative
business takes everything you've got. But with Chase for Business, you're not alone. They bring together local support and a broad range of resources to more than 7 million customers with a deep understanding of your day to day needs. They provide products and guidance built to help you thrive right now. Earn $500 when you open a new Chase Business Complete Checking Account for new Business Checking customers with qualifying activities. Offer Expires June 18, 2026 Chase Business Complete Checking has the flexible tools you need to accept payments, make deposits and manage your finances with confidence. Learn more@chase.com PodcastBizOffer Chase make more of what's yours. These may apply to Chase Business Complete checking accounts. The $500 offer is available for new business checking accounts with qualifying activities through June 18, 2026. Eligibility and qualification requirements must be met. Additional restrictions may apply. Please speak with a business banker for more information. JPMorgan Chase Bank NA member FDIC I'm
- [17:18]
Joe Weisenthal
always fascinated by like you know, we look at the Bloomberg terminal for example and there's a price on the screen and it's just there and we sort of take it for granted that like it had to come from somewhere and maybe some, some commodities have like a, you know, 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? How did you gather that information from say the inference providers.
- [17:57]
Carmen Lee
So it is a very can be lengthy depends on what data sources, the nature of GPU spot market. So configuration is one of them. And then many, many neoclouds, hyperscaler marketplaces all have very different contract size, duration, specs and they are a way to manage their data right. So it's a lot of a licensing conversation negotiation. Well and also context myself I don't know I was used for Bloomberg data so I was in databases 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
- [18:34]
Joe Weisenthal
do you call them up? So it's like okay the price is different on a Friday versus suit them up.
- [18:39]
Carmen Lee
Well first you have conversations say hey I love what you do. You create new cloud can I license your data and usually your feedback is what is in for me and I will tell our commercials and then your concern could be hey, if I give you all my data I give away all my secrets and I will Go through traditional licensing agreement. What 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 running my API to yours? Are you writing to mine? It's a lot of conversation. It's actually pretty standard conversation. 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, karma always bring up can I have your data? That's always my ending.
- [19:25]
Tracy Alloway
You know, we were talking about GPU indices and you're not the only one doing GPU price indices for sure.
- [19:32]
Carmen Lee
Not anymore.
- [19:33]
Tracy Alloway
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?
- [19:47]
Carmen Lee
I guess so I can comment other people's mythology because I actually do not know. Different data, raw data, different methodology 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 vote once those things start trading. The market always gravitates with things action help them hatch, right? If you easily manipulatable, if you are not data source people acting actions, you hatch. That was the point aside from speculation, right. So I love to say, I mean I also strongly believe we're the best, but. But again, I will let the market decide which will happen very soon.
- [20:36]
Joe Weisenthal
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 from time that needs compute, they're short, 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.
- [21:12]
Carmen Lee
The conversation has been going on for a very long time with various banks, various market participants, speculators, they all 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. So that's where we come in the Way I encourage them to do is I selfishly I want them to start trading desk on compute. The more people trade, the better for me. Right? Selfishly. But same time it's important for people to understand GPU trading. It's not like you can't just move someone from all your electricity with no background context job into GPU compute futures. There's a lot of context where number one GPU it is not homogeneous product. Number two, you have to understand the use cases for a 100H100 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 and large amount of 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 service itself. Also the software layer that's kind of critical, right? That's really changes supply demand curve and all the way to the user behavior. So it's going to take some time. Since we have engaged with a lot of participants make sure they have the right setup.
- [22:48]
Tracy Alloway
I have what is possibly a dumb question, but the compute futures, how are those actually settled? Because I have images in my mind of taking physical delivery of maybe one of those big I'll give you a
- [23:00]
Carmen Lee
wafer chip, I'll give a server away for it. That'll be fun. So for the CME futures will be financially settled just like the traditional oil settlement prices goes four contracts. Well obviously we do four right now at Compute Exchange, but we always open to do physical delivery features. Especially given we do have silicon mark which is GPU benchmarking. So imagine 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 calls so you don't get physical and it's not as cool as physically give you away wafer, but you get API calls.
- [23:40]
Tracy Alloway
One can dream.
- [23:41]
Joe Weisenthal
How do you literally trade it as 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, does it need to go through like a futures broker? Is it like a. Could it be like a prediction marker? 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 and whether they know what they're doing, A lot of people trade who have no idea what they're doing. Yeah. Setting all this. I. 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?
- [24:15]
Carmen Lee
The beauty of CME is you can do the same thing you're doing now, trading CME products.
- [24:21]
Joe Weisenthal
Okay.
- [24:21]
Carmen Lee
The same process in margin. That's why you get great margin optimization. Right. Everything is bau. It's not no different. We don't have anything right now.
- [24:32]
Joe Weisenthal
So any commodities broker that someone has, they will be able to. On that platform, they will have access to these instruments.
- [24:39]
Carmen Lee
Exactly right. Yep. We make it easy for people.
- [24:42]
Tracy Alloway
Would you be upset if a prediction market set up a GPU price contract of some sort with that into your business?
- [24:49]
Carmen Lee
Not at all. So we actually worked with Polymarket last year. Someone actually listed my product at polymarket without my consent. It's always start like that. And then someone told me that. And then we try 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 water. Right. Obviously we're exclusively with CME right now. But yeah. So I think obviously you have to do. Right, right. Licensing, nominal P. Right. All the right things. Yeah. I, you know, I don't. Mark can do whatever they want and then people will choose the best product for them to use.
- [25:29]
Joe Weisenthal
Setting aside the financial instruments for the moment, when people think about AI and they think about the use of GPUs, they mostly still probably in their mind think of like OpenAI, 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.
- [26:06]
Carmen Lee
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 closed source models. So we actually track 300 open source, open weights, whole source models globally from pricing and consumption point of view. It is really interesting if we have actually we haven't really formally launched LM token indices. You can currently 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 from our indices from now, from December 1st last year. It's like $2.21 per million token. It's a mixture of input, output, token prices, average weighted by consumption by basket models. It's not here. This is gpu, unfortunately.
- [26:59]
Joe Weisenthal
Since we have this specific chart up right now. What is the Y axis in this chart?
- [27:06]
Carmen Lee
So you're looking at the doll per GPU per hour rental rate on demand for three chips.
- [27:12]
Joe Weisenthal
Okay.
- [27:13]
Carmen Lee
The top one, the yellow line is B200 Neo Cloud on demand per GPU per hour. Sorry, it's a mouthful. The line, the yield line is interesting. Right? So every new chip came out based on historical data. A100 H100 usually came out to be high and then comes down as more supply came live and then price came down and then stabilizes. So that's the trend we have observed for a 100 and then for H100. So when B2 country came out, we published the data last year at Bloomberg, this early this year, the price was high and it came down, which is kind of what I expected. But the slope was less steep than I expected. I was like, that's interesting that 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 A1, the red line is H100 neocloud 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 the last three months came up to like 8% for the H100. The A100 is the older, 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 A100, right. They're not the latest and greatest at all. So this also tells you supply and demand curve shifting.
- [28:48]
Tracy Alloway
Oh yeah. Actually that reminds me. Can you talk to us? Because you're doing refurbishment of chips as well, right. Like, which seems like challenging in many ways and kind of reminds me a lot about like sort of Carvana model of compute or something like that. How are you actually doing this? Like, how does that business work?
- [29:08]
Carmen Lee
So this is Cool. In two different things. One is four people come to compute exchange saying that hey, I want to you know as you new cloud provider, right. If you get a piece of land you again a GA colocation. Great, congratulations. Then your option is number one. Should I get the latest and greatest the B300, the GBS, the Varian, wait for a few months or do you want to get refurbishmentships and turn around? Maybe sooner. Right. Then to you it's become ROI calculation for the most part. What's your expected future revenue generation? What's your residual value calculation, how much you're going to purchase by? This is actually pretty simple cash flow based ROI calculation. So the way we approach residual value and the refurbish transaction is based on ROI. This is your potential breakeven. Look at the H100, right. Obviously you're not going to charge as high as B200 but your cost base is also lower. So you can do the future as you assume a few years of forward contract, you sign in three years, discount the cash flow back. That's your risk 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 the market prices for refurbished or used gpu. Again you have to test you to make sure things works and there's other nuances to that but we help people to the understanding of the whole residual value and that's why hold the whole bubble thing came about. But go ahead.
- [30:32]
Joe Weisenthal
What month was it last year when like everyone got really obsessed with like the lifespan of chips. Remember December Bury 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. What do we know about chip lifespans? Are there misconceptions out there about the how long these can get be productive.
- [30:58]
Carmen Lee
I got interview a few times, but I don't. I mean I'm not important still. I'm not important. But back then I even less relevant. I was telling the reporters, I was like look, I don't know what data you're looking at based on my. I actually have blogs so my website which is completely. You can just search for it Last year because of that conversation, I want you a curve later on the second year H100 residual value of resale value for refurbishments about 85 cents on a dollar. So a year later you can sell 85 cents on $1. It's pretty good. I would say the third year is 84 cents, $1. I think my car depreciate way more than that, right. And I drive my car for whatever, 10, 10, 10 years. So it's ahead of data. But again, I'm not going to argue against narrative, which is so.
- [31:40]
Joe Weisenthal
But there was, there's a fairly steep, a decent drop from year one to year two. But after that you see a general
- [31:47]
Carmen Lee
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 my data client, you can run my code. You get a number right away. Another thing I want to point out is L40s, they're like, you know, the OGs, right? At that time, people still use them. They charge you hyper seal, charge you 40 cents per GPU, per hour. So, you know, I don't know about two years where it's that number coming from. But I will, I will do that trade every single day. You sell me, you're two years old at 10 cents to a dollar, I will buy it.
- [32:17]
Tracy Alloway
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?
- [32:39]
Carmen Lee
So as an index provider, I cannot give any forward guidance disclaimer, nor do I know, right? In fairness, I know what do I know. So the way I look at is we had defined a bubble, right? So you look at style bubble, right? The Nasdaq shoot up 200, it came back down 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 valuation. I'm not vc. I don't understand that process. The way I look at GPUs, the machines, it's actually pretty simple. Look at future cash flow of your forward contracts and then you discount it back. Can you get your money back for the price you pay? Right. It's actually pretty straightforward for the machine level. But to your point, right, you can say, hey, what happened if demand dropped? No one can use your whatever things you have. But remember, the forward contract is a signed contract. If you have that, you kind of know, obviously if you have Things the biggest concern is people have concern overbuilt. If overbuilt, then by theory then all your prices will come down because it oversupply of the market. So then you talk about supply, demand, equilibrium. How do we know about the future demand of GPUs? I don't know that everyone's guess is better than mine. Probably the way I look at it is it's not that easy to bring any GPU online. You hear all those say big Data center bill, $25 billion invested, but they don't translate to immediate GPU availability in the servers, which you have to be waitlisted. If you buy brand new stuff, colocation, you need optic fiber. So it's a lot of unfortunately star has to be aligned.
- [34:39]
Tracy Alloway
But people can default on contracts, right? So even if you have a long term contract signed that could not work out. Could you envision credit default swaps or something in the compute market?
- [34:52]
Carmen Lee
So that happens in every other market, right? Every market if you do OTC trade, you have the risk someone will default you. Doesn't matter who they are, right? So there's various, there's a lot of mechanism to hedge that. The things you cannot hedge 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 hedge your position.
- [35:18]
Joe Weisenthal
Carmen Leaf, thank you so much for joining us.
- [35:20]
Carmen Lee
Thank you. This is great.
- [35:36]
Tracy Alloway
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.
- [35:45]
Joe Weisenthal
And I'm Joe Eisenthal. You can follow me @thestop Stalwart. Follow our guest Carmen Lee at Carmen Lee. Follow our producers Carmen Rodriguez at Carmen Armand, dashiell Bennett at Dashbot, Kale Brooks Kale Brooks and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content go to bloomberg.com oddlods we have a daily newsletter and all of our episodes and you can chat about all these topics 24. 7 in our Discord Discord, GG Oddlaws
- [36:12]
Tracy Alloway
and if you enjoy Odd Lots, if you like it when we do these live shows and talk about the birth of of a new market, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad free. All you need to do is find the Bloomberg Channel on Apple Podcasts and follow the instructions there. Thanks for listening.
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