AI has a GPU pricing problem. Silicon Data wants to fix it.

The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has become the single biggest cost for anyone building AI products. But for all that spending, there still isn’t a straightforward way to put a price on compute — or for firms to hedge their exposure when the price changes. Silicon Data just closed a $30 million Series A to change that. The startup aims to become the reference price for GPU rental and an index that a Wall Street futures contract would settle against. The company plans to launch its compute futures trading on the CME October 5th, pending regulatory approval. On this episode of TechCrunch's Equity podcast, Rebecca Bellan is joined by Steve Hou, head of research at Silicon Data, to discuss the health of the AI buildout, and why the data is telling a different story than the doom and gloom headlines about depreciating chips and stalled data centers. Listen to the full episode to hear more about: What happens if OpenAI or Anthropic can’t make good on the massive compute commitments they’ve already promised, and whether “compute default” becomes a risk. Why rental demand for older A100 chips is holding up, even as newer and more powerful GPUs hit the market. If compute futures could make it easier for neo-clouds to finance expensive data centers by locking in future rental revenue. What rising GPU rental rates across every contract length since March say about AI demand. Subscribe to Equity on YouTube, Apple Podcasts, Overcast, Spotify and all the casts. You also can follow Equity on X and Threads, at @EquityPod. Chapters: 00:00 Intro 0:59 Futures contracts explained 4:01 Can GPU compute actually be a commodity? 7:10 Who's really trading these: hyperscalers, AI labs, and market makers 14:32 Locking in prices to make data center debt easier to swallow 18:21 Inside the pricing data 20:25 The A100 mystery: why an old chip is still in high demand 22:56 Do GPUs depreciate as fast as everyone thinks? 23:20 Making sense of the Texas and New York data center pauses 25:40 What the futures curve says about where prices are headed 28:01 Silicon Data's $30M Series A and what's next 30:36 Outro Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  This episode of Equity is brought to you by Schneider Electric. Schneider Electric, advancing energy tech.

• 0:05Rebecca Bellan?

  Hello and welcome back to Equity Tech Crunch's flagship podcast about the business of startups. I'm Rebecca Bellan and this is the episode where we bring on an industry expert to help us explore a trend in the tech world and dive deep. The AI buildout shows no signs of slowing with billions of dollars a year going into data centers and GPUs. Compute has become the single biggest cost for anyone building AI products. But what happens to those billions if the price of renting a GPU crashes or spikes?

• 0:38Rebecca Bellan?

  Today, we're joined by Steve Howe, Head of Research at Silicon Data, the company trying to become the reference price for GPU rental and the index that a Wall Street futures contract would settle against. Steve, welcome to the show.

• 0:50Speaker 02

  Thank you very much for having me.

• 0:52Rebecca Bellan?

  So you are new to Silicon Data. You joined in May this year as Head of Research. So it's only been a few months, but I'd love to hear just like a little bit of your background. I think you did quant research at Bloomberg, was it?

• 1:04Speaker 02

  At Bloomberg Indices. So I was previously a senior researcher in charge of our systematic equity index research benchmarks at Bloomberg Indices, which is actually, I think, new to people because most people think of Bloomberg as either the news business or the terminal business, the data platform. And the index business is actually a separate business, one of the many vertical pillars that is a diversifying attempt from the terminal itself.

• 1:32Rebecca Bellan?

  So you and I met at a party in New York and a soiree. And I was really interested because your friend that you were with described what you're doing at Silicon Data as data centers futures, right? And so for me, I'm thinking futures trading, that's like gold and corn and grain and things like that. So it's commodities. So I would love to hear, before we kind of get into the nitty gritty of what Silicon Data is doing, like for those who aren't aware, can you briefly explain futures contracts, simply for listeners who don't trade?

• 2:05Speaker 02

  Yeah, futures, actually, it sounds complicated. And indeed, I mean, most average investors, retail investors probably don't use financial derivatives futures. But it's actually one of the most ancient financial instruments in the world, right? The whole idea is that in the ancient days, suppose you were planting crops, right? And let's say your cost of planting crops is a dollar and currently crops sell for $2. You make a profit of a dollar, but you worry that by the time you harvest, let's say, six months or a year from now, the price of corn may actually plummet to be below your cost and you may end up with a loss.

• 2:44Speaker 02

  So if there exists a futures market which allows you to deliver that bushel or the amount of crop at that point of harvest at the price that's currently trading at, say, above your cost, you might be inclined to lock it in, right? Now, why would that be such a contract? Why would someone offer you such an arrangement? Someone else must have a different point of view, right? And someone may actually believe that, oh, actually, based on my expectation of weather and whatnot, by the time harvest comes around, corn actually will be more than $2. I can actually make even more by selling you to buying it from you from 22 and selling it on to whatever I think it's going to be, right?

• 3:28Speaker 02

  So market is always full of people with different opinions. Some people are bearish, some people are bullish, some people are risk averse. Even if they are, have an expectation of things could be good, but they just want you to lock in the safety. So that's how futures contracts historically have always worked. It works for shipping. You can have cases where someone goes overseas and brings something back and there's uncertainty about what thing is going to be. By the time some events materialize and obviously, oil is now one of the biggest futures markets, same idea, right? Airlines want to lock in prices. So that's basically futures and you can apply that concept more very broadly.

• 4:07Rebecca Bellan?

  Right, so the CME, which is essentially, as I understand it, the New York Stock Exchange, but for futures trading, or one of the platforms where you can trade futures. Plans to launch Silicon Data linked compute futures on October 5th, pending regulatory approval. What I don't get is like, you mentioned oil. Oil is storable, right?

• 4:28Rebecca Bellan?

  You know, compute's not. NH100 can become technically or technologically obsolete. Data centers have a different networking, they have different power constraints. Like what makes you confident that this will be a commodity?

• 4:39Speaker 02

  So a couple of things, right? I think there is a casual, understandable presumption that there needs to be storability or there needs to be some sort of tangible existence for something to be a commodity. In reality, one of the biggest contracts traded on the CME on futures contracts today is actually just the imminent the futures on the S and P 500. Obviously, it's all cash settled and you don't take physical delivery of a company. It's a basket of companies, the S and P 500 index, right? And all you do is, at expiry, you look at the P and L, whether or not the number that you sell or bought the futures at is above or below, whatever the underlying ends up at. And if it's favorable, you get a profit.

• 5:22Speaker 02

  And if it's unfavorable, you get a loss and you settle at the P and L. And in fact, if you look at oil, the vast majority of oil futures contracts do not physically settle, right? In fact, the reason why during COVID in March 2020, whenever the oil futures became negative, is it was exactly because people didn't want to take physical delivery. So they were paying someone else to take the future contracts away from them at a loss, right? And that's exactly the reason why, because the world had run out of storage facility. So people who were about to take physical delivery didn't mean to, they just wanted to P and L. So with regards to compute, the reason we feel confident that this should be something that exists

• 6:05Speaker 02

  is because the sheer size of physical exposure this year, if you do some simple tally, just the hyperscalers, right? The Googles, AWS and Amazons of the world are spending some $750 billion on compute. Next year, they're slated to spend something like a trillion. And we're not even counting the non-hyperscaler spending in the rest of the world and so on. Within this number, let's say we say $150 billion of worth of compute changing hands because somebody needs compute at any given moment and somebody has access. That already creates a lot of, I think, the demand for trading. And the people that have exposure with compute that they're bringing onto the market that has a lot of,

• 6:47Speaker 02

  they may want to actually share some of that exposure or even hedge, right? Whatever they may be, the same idea we had discussed, the idea of crops, right? You may want to lock in, right? How much you can earn from the compute? Because while you have a certain expectation about how much compute you're going to need or how the market is going to want, you may actually have some uncertainty. Or in any event, you may actually want to generate some cash from your holdings of compute.

• 7:15Rebecca Bellan?

  So who's actually trading these then? Is it, so it's compute providers like hyperscalers and NeoClouds who might want to hedge downside versus like AI labs and inference platforms who might want to hedge upside? And then just like anyone in between? Retail, futures traders, et cetera?

• 7:32Speaker 02

  So we can sort of think this through, right? So there are any market, any financial markets that three sides. Typically, you think buyer and seller is actually three sides. There are the people that are natural longs, right? And there are people who are natural shorts and then there are people who are in between. You think about, say, the cloud providers, people who are actually either selling compute, have available compute that's available for rent, or people who are building data centers, small and big and small. They may have the desire to lock in rental income, inference rental compute income today, right? So they are going to be the ones that will want to naturally sort of sell short and generate income from the futures contracts, right?

• 8:17Speaker 02

  On the other hand, you're going to have AI labs, right? That actually buy compute, contrary to some people's casual intuition. It's actually not enterprises that will necessarily buy compute. Enterprises may actually just directly buy AI tokens and model tokens. But their supplier, the AI labs, whether it's Anthropic, OpenAI, the closed frontier labs, or the inference platforms that will post train open source models that will sell on the tokens onto these enterprises, they are the ones who are naturally buying compute. And they will have an incentive to lock in prices of our compute if there's expectation that compute prices could actually keep on going up. So in fact, and then there is everyone in between, so to speak, the market makers,

• 9:02Speaker 02

  someone who is going to try to create that double coincidence of one. Because someone who wants to necessarily go long compute futures may not happen at the same time. Someone who wants to go short compute futures and someone is going to be in between. That will be basically your typical market makers in DRW, Jane Street, HRT. It's a typical market makers, including the banks, right? That could actually be intermediating. And then, of course, there'll be others in narrowness in which things may not be cutting so cleanly. You can have someone who has exposure that wants to hedge, that may not necessarily align up exactly with one of these three parties. Suppose you have a large financial institution with a significant exposure to AI, right? And they're underlying AI data center income.

• 9:47Speaker 02

  They may actually want to hedge some of their existing exposure. So. So they actually, it's a composition of financial barrier.

• 9:57Rebecca Bellan?

  So is it essentially just like a way for a lot of the companies that have skin in the game to hedge their bets and earn a sort of a revenue or potential loss off of something that is not the actual build out itself, but trading on these futures, trading on something that's really just intangible?

• 10:20Speaker 02

  That's right. And it doesn't necessarily have to take this shape of a betting. Like I understand people these days think of financial security, they immediately think of someone's betting on an outcome. It could just be that you want to adjust your exposure. Maybe you have too much, maybe you have too little. And you want to dynamically adjust it. I'll give you a different example. Suppose you have several apartments in New York City. Yeah, you think that's too much exposure. You worry about a downturn in New York City real estate. Instead of outright selling a portion of your real estate by selling one of the apartments, you can suppose there was an instrument that gives you access to an index on the New York real estate index and those futures come from that, you can actually try to short that futures

• 11:01Speaker 02

  or try to get exposure that way and shave off some of your real estate exposure and take that cash to buy some other exposure, say equities or bonds or commodities or what have you, right? Basically, at any given moment, people always have a reason to adjust their portfolio composition that may or may not necessarily mean that they're betting on some outcome.

• 11:22Rebecca Bellan?

  Yeah, like if you have skin in the game, like let's say you were, going back to the landlord example, I have too many Manhattan apartments. What if there's a downturn? What if COVID happens again, something like that? How can I, so are you essentially, I know you don't like the word betting, but are you essentially betting that the price will go down?

• 11:37Speaker 02

  You're hedging against that eventuality, yes.

• 11:39Rebecca Bellan?

  You're hedging against it?

• 11:40Speaker 02

  Sometimes people do that just for psychological comfort. They may not think that it's going to happen, but it helps them sleep better. And you'd be surprised how often that actually happens, right? You want to shave off some of that risk, not because you expect it to happen or the worst case to happen, just because you don't want the worst case scenario for you to actually feel terribly.

• 12:01Rebecca Bellan?

  Oh yeah, no, I have a strong disposition towards anxiety and superstition, so I get it. I would totally do the same. So, okay, so like what other, what new behaviors does this enable otherwise? Like do NeoCloud sell futures against GPU fleets to finance data centers? Like do hedge funds start expressing a view on whether the AI boom is real by shorting compute? Like this could have some serious follow on effects.

• 12:24Speaker 02

  Oh, absolutely. So just like as we have seen with the recent Iran conflict in the oil futures market, you know, it's basically the major focal point. Everyone is expressing a point of view, you know, that may or may not exactly even be related to oil itself through oil futures. The existence of a futures contract and financial derivatives in general is to provide liquidity. And this is actually one of the things that this country does best, right? The existence of financial derivatives helps make the underlying physical exposure more liquid and therefore giving you higher, higher degree of confidence about engaging in the expenditures, right? The spending. I remember there was a famous moment when Daria Modic went on to the podcast with Dork Cash,

• 13:09Speaker 02

  where he was asked, if you are so confident about your building this country of geniuses, how come you just don't build way, way more compute, right? And the answer was, the difference between getting this wrong can be between sort of having a ton of computer, making tons and tons of money or going bankrupt because you misalign the timing. And there's a certain degree in which you make that risk aversion adjustment by scaling back your real investment. Now imagine that on the contrary, you could actually, if you have liquid financial instruments, you can actually build more and adjust that exposure dynamically subsequently with the help of financial derivatives. And historically, this is exactly what this country has always done best with every single rounds of industrialization.

• 13:52Speaker 02

  Wall Street helps the capitalization and financialization of physical investments.

• 13:58Rebecca Bellan?

  Yeah, we've had CoreWeave and Nebius report earnings recently. They've been doing really, they did really well. They reported some good revenue numbers, which we can get into a little bit more later. But a lot of investors have been concerned this year that, you know, there's such a huge capex to build out these data centers. They're essentially landlords for GPUs, right? They're just renting the GPUs out. They don't really own them. And to build it out, they have to, at least CoreWeave has to borrow the money to do that. So if there's a future in which there's declining rental streams, like could futures let NeoClouds lock in those declining rental streams and make the infrastructure debt easier to underwrite?

• 14:39Speaker 00

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• 15:05Speaker 02

  I mean, that is the idea. I think we are all rowing in the same direction, alongside of what Jensen is doing with these financing platforms with the help of financial institutions and the rise of futures. We're all trying to find different ways in which we can make the financing of these very expensive capital assets a bit easier, a bit cheaper. And the existence of a futures market will just bring about more pricing transparency. And everyone can actually observe with greater certainty. And as you exactly say, right now, I think the data centers, a lot of them, they do different things. Some of them actually proactively synthetically lock in prices by pre-selling.

• 15:50Speaker 02

  So they only build if there is pre-commitments of a certain amount of compute. And that way, you're essentially locking it in, right? You're saying, I'm only building this because so and so is committing to buy this much computer at this price from me. But at which point you worry about, oh, what if there's counterparty risk? And a part of the market has been worried about is like OpenAI or Anthropic has made all these commitments about I'm going to buy all this compute, but they themselves, are they actually necessarily always in the end of the day credible? Will they actually be there by the time the compute capacity arrives? So how do you ensure against their counterparty risk credibility, right? Is there such a thing as like complete default, right? So I think this is where I like to liken the part of the world we're dealing with

• 16:34Speaker 02

  is the rise of a huge but young industry, like a giant baby, right? Like the thing is very young, but it's also enormous that the things that you need are not here soon enough.

• 16:47Rebecca Bellan?

  Yeah, I'm just picturing a giant baby. Yeah, seriously. Okay. Well, you know, it's interesting because we're talking futures trading, but Silicon Data doesn't do forecasting, right? Like why, why not?

• 16:59Speaker 02

  Well, I mean, it's not like we cannot do forecasting. It's just that we're not necessarily any better at it. Like so you say, the S and P 500 and S and P Global publishes an index on the, and is an index provider. Just because you publish an index about what's happening today, it doesn't make you any better at forecasting what will be in the future, right?

• 17:18Rebecca Bellan?

  Okay, so tell me a little bit briefly where you get your data from. You have, you source it from two places, right?

• 17:25Speaker 02

  For renting a GPU, just like renting an apartment, we observe contracts from two types of general sources. One is we look at public available published prices. People tell you how much you can run their GPU or how long and so on. Another part is we get it from data partners, including our own sister exchange, compute exchange, on which we should observe actual transactions, right? Of rental transactions that is being facilitated on the exchange.

• 17:52Rebecca Bellan?

  Yeah, okay.

• 17:53Speaker 02

  We observe rental contracts, sort of like of different lands, different geolocation, different memories, back CPU and so on and so forth. And you've got to sort of normalize them so that they're apples to apples. Just like if you say had furnished, unfurnished, single bedroom apartments in different parts of Manhattan City and different square footage, different rental lengths, three, six, 12 months, 24 months, whatever. The per day rate is not going to be the same unless you make the appropriate adjustment to comfort the fact that they are all different. But there is something that you can do. So we use machine learning statistics. We essentially adjust for these essential features that make them different and create an apples to apples comparison and consolidate these observations.

• 18:36Speaker 02

  And we have, we believe, one of, I think by far, the most comprehensive look at all the available price points that exist globally. And we create these benchmark indices that we publish daily on our website and some of which you also, you can find them on data platforms such as the Bloomberg Premier.

• 18:54Rebecca Bellan?

  So you've got visibility into the prices across essentially this entire market. Like what is, what is your data telling you today that you think the conventional AI infrastructure narrative is getting wrong?

• 19:04Speaker 02

  The conventional use narrative is always changing. I think people, I think depends on, the narrative always follows the price. If the stocks are going up, people think there's never enough compute forever. But then price stock could be going down because of some story coming from China or elsewhere. What we see is that what data informs us is the follows is that I like to say I'm looking at cross section and I look across the term structure. When I look in the cross section, I mean that I look at not just the price of a single chip. People like to look at the H100 copper chip, right? And if that thing goes up or down, people think that means more demand, less demand. I look at not just H100, I look at B200, which is a black well. I look at A100, which is Ampere.

• 19:48Speaker 02

  They all these are named after famous past physicists. So Ampere 100 was launched and released in May 2020. So this is an ancient chip, right? And if you look at the rental rate on this chip, it was actually going down somewhat over the course of last year. And then since late last year through now, it's been steadily increasing and holding strong. So what has caused that, right? That's actually just the rise of agentic AI demand. The fact that you are now able to have AI calling other AI and using tools such that the amount of AI demand just exploded. And the fact that AI also has become more powerful. Every single token has become more intelligent and cheaper, right?

• 20:32Speaker 02

  Intelligence has actually become, artificial intelligence, machine intelligence has become cheaper over time. That has actually also led to way more usage. I like to tell people that this year really was the first time that AI has become useful. So in this context, if I track the A100 rental rate. Uh, that the fact that this has helped strong despite being such an old chip that tells me that inference demand is really strong, right? That people are using.

• 20:58Rebecca Bellan?

  Because A100s are good at memory, right? Like, they have a lot of memory storage? Is that correct?

• 21:02Speaker 02

  Uh, no, A100 is good at nothing. A100 is just old. It's not very good.

• 21:06Rebecca Bellan?

  It's just old. Okay, so why is it good for inference? Because I, I, I can't remember if it was the A100 chip or if it was the H100.

• 21:12Speaker 02

  So, A100 is, is an older chip, right? You know, and it's the less powerful chip. The fact that it's being used to the maximum and rented out to the maximum tells you that there is just so much demand that people are willing to use something that is not so powerful.

• 21:28Rebecca Bellan?

  I guess the lesson, though, like, if we're talking about conventional wisdom, a lot of the conventional wisdom right now around data centers is, why is it not going to work? Because they're getting loans out to build data centers off of depreciating assets, right? But are the assets depreciating? Like, maybe not. Maybe, like, because there's just so much demand, older chips, you know, you find a use for them. I think I was thinking of the H100s that are really good for memory stor- they're really good for inference because they're really good for memory storage, and when you have more agentic tasks, you need to have a longer context window, et cetera, et cetera.

• 21:58Speaker 02

  So every generation, they tend to have more memory and they tend to be more powerful. H100 was the frontier chip, you know, at some point, it was, it was a few years ago, and then now we have the Blackwell and the next, you know, Bear Rubin and so on. There's always going to get more memory and the, the demand is going to basically be addictive, right? And the old narrative was that these chips are all quickly depreciate. Within the two, three years, they all either break or stop working and they'll lose their value. People have this rough mental image of like an iPhone, right? iPhone 15 comes out, iPhone 14 immediately lose all of its value because that one was on the iPhone 15. It turns out, there is so much demand for phone usage that even iPhone 14s are being fully used.

• 22:41Speaker 02

  That's the A100 analogy for you, like, basically, if everyone is walking around using an iPhone 12, even though there is iPhone 17 released, that tells you, there's something going on with demand for phones, right? There is not enough phones being produced. There is something along that line, right? So part of what I think market may be getting wrong is that these things may not depreciate as quickly as people thought and may have actually a longer lifespan as people had previously budgeted for. But obviously, market is not dumb, right? Market catches on to these things and the prices in these things, but then there will be other factors that drive some, you know, equity valuation, you know, has to having to do with the fact that equity is prices in not the first derivative, but the second derivative, right? The speed of increase, right?

• 23:23Speaker 02

  Maybe things don't increase exponentially, but, you know, just super linearly and then people freak out too.

• 23:29Rebecca Bellan?

  Okay, so, so this really contradicts some narratives that I've heard that, like, maybe the AI buildout is slowing, right? Because there have been reports about data center pullbacks and pauses, like Texas halted new data centers as the governor called for audits, New York also halted construction of all new data centers. I guess, how are you seeing the market weighing out this pushback against data centers across the country, but it seems like it's not really stopping anything?

• 23:54Speaker 02

  This is kind of funny, right? I think there are a few different dimensions all mixed meshing all together, and AI has become this all-consuming thing, right? AI could be stopping and pausing because people think this is not such a good idea. The return on investment is not going to be very high and there won't be very much strong demand. That's one angle that people often worry about. There's, the other angle is that there is a local political pushback, right? People worry about how AI, you know, with all this buildout is going to cause electricity prices to go up, cause inflation, cause water pollution, cause noise, uh, and, you know, obviously that will feed through the political angle. And there's also just a hard physical constraint that there is not enough electricity, right, in the country. So whatever people may want to build, they cannot find the land, they cannot find the

• 24:38Speaker 02

  premise, they cannot find the electrical power. And, you know, in order to have that power, you need to bring your own electricity, which means that you have to build your own power plants, which means that you have to go to source, you know, generator of power, but then you turn around and realize there are actually no turbines and whatever gas turbines you can strip off of the, so, old Polish airplane or Iraqi ship, they all been stripped out already.

• 24:59Rebecca Bellan?

  Yeah.

• 25:00Speaker 02

  Uh, and then you realize you don't have transformers, not the model, but the physical transformer that takes structures from high voltage to lower voltage, like, those things are all sold out for years. So, so there are different dimensions in which things can pause or slow down. At any given moment, someone is worried about one of the channels. I think the Texas news, the pause, that may have something more to do with the fact that there's just been so much buildout in Texas. Everything has been building out in Texas because of a more favorable political attitude towards building out that maybe there is some sort of, oh, let's hold on, let's slow this down. But obviously, that creates, even like, any slowdown is going to cause people to wonder, oh, does this have an impact? Maybe we'll have a whole bunch of ships that sitting around waiting for a site to be deployed

• 25:44Speaker 02

  at and they don't find one. So, there is endless worries.

• 25:50Rebecca Bellan?

  Yeah. I want to ask you a little bit, so I, I was reading some of your data curves that you guys posted in July and showed B200 prices were 8% lower, 36 months out, H100s 13% lower, and A100s 15% lower. So you, you kind of touched on this earlier, but like, the market is pricing compute to get cheaper. So what assumptions are built into that? Is that, you know, more supply, better chips?

• 26:13Speaker 02

  So, that one tells you that, oh, like, basically, this curve is actually downward sloping in a pretty significant way because people were still being given a pretty significant discount for renting long-term reserve contracts, right? Locking in this long-term reservation. And over time, over the subsequent three to six months, we see the entire curve essentially moved upward, which is exactly consistent with what we were saying before about rising agentic AI demand. At every term length, at every contract length, the rental rate has gone up, right? You have to pay more to rent a GPU per hour, right? And that, if you saw that in the rental market, that every lens, rental lens, you know, for a single bedroom apartment in New York has gone up, you would immediately know that,

• 26:55Speaker 02

  oh, this just means that a lot more people are renting, there's a lot more demand. And what's interesting is that if you track the ensuing four months from March, you know, 2026, and on that chart that you may want to put on the thing later, and, you know, it's actually just been monotonically increasing, right? It's been steadily going up and up.

• 27:14Rebecca Bellan?

  So high level, like, what, what are we pulling away from this high level? What can we learn about this?

• 27:18Speaker 02

  Yeah.

• 27:19Rebecca Bellan?

  The difference between NeoCloud rental prices versus like what you might pay for a hyperscaler?

• 27:24Speaker 02

  Let's separate those two things a little bit, right? So, what we were just looking at, that chart only contain the rental prices you will get from NeoClouds, right? NeoClouds are, so to speak, companies that only provide AI compute, you know, as a service. Hyperscalers tend to actually price their compute at a persistent, significant premium, maybe two to three times the cost of a NeoCloud because it's a bundled service, right? When you come to a hyperscaler, you tend to already have been a customer of AWS, you are enterprise, there is trust, compliance, you know, data security, you know, different software analytics, and so on and so forth. So it's a different product, you know?

• 28:01Rebecca Bellan?

  Right, because your H100 index had NeoCloud capacity at $2.70 an hour versus hyperscalers at $7.28.

• 28:08Speaker 02

  Yeah.

• 28:09Rebecca Bellan?

  Yeah, okay, so that makes sense because it's all bundled in together.

• 28:11Speaker 02

  That's right. Like, you wouldn't put Bim and Air's row apartment rental in the same category.

• 28:19Rebecca Bellan?

  Yeah. You know, we talked about this, that you guys are, Silicon Data is going to launch their compute futures in October, hopefully, if there's, you know, right, no regulatory issues, and you'll have an H100 and a B200 rental index futures, is that correct?

• 28:34Speaker 02

  Yes.

• 28:34Rebecca Bellan?

  Okay, and so, apart from the news that you guys are trading on the CME in October, Silicon Data just raised a Series A round. Can you talk a little bit about that?

• 28:44Speaker 02

  We announced that we had, had initial posing of 30 million, uh, you know, it's a Series A round led by, uh, Valor and Trade's AI Fund. Um, so Antonio Garcia and Gavin Baker being the, the headline leaders of those two. And we have also had participation from many other financial institutions, you know, F Prime, Fidelity. I guess CME itself has a venture fund participated. We also had Samsung Next, which is the venture arm of Samsung. There are others, I, I have criminally left out, Tectonic and others. The reason we have actually gathered this group of investors because we want to build our ecosystem to actually help create an, a financial instrument to facilitate a physical AI compute financing, right?

• 29:28Speaker 02

  And actually, eventually, uh, we had discussed at the top of the hour how these futures contracts don't have a physical underlying. We are going towards it. We want to have physical delivery of compute. We want to build indices that is with greater compute. We have this thing called Silicon Mark, which actually gives individual GPUs, individual data centers that can visit and give it a grade so that you can actually take instance of on-demand or reserved AI compute at expiration of a futures contract. That is something we are going, you know, towards in the future. We believe that with enterprise adoption of AI and inference being a much, much, much bigger, right, portion of AI, AI demand, inference is going to make compute more interchangeable, right?

• 30:12Speaker 02

  You can, in theory, take your inference job in, at two different places and your, you know, sort of inference platforms of the world could put together compute from different providers of compute also using software. It's such a case that you're going to be able to have a certain degree of fungibility, so to speak, not perfect fungibility, but fungibility nevertheless, that you can basically say, I can have cheaper to deliver our compute provided a point, you know, of expiration for futures contracts. So that is actually what will be, to be a much, much bigger, you know, plan for the future.

• 30:42Rebecca Bellan?

  Wow, okay, so we'll all stay tuned, but in the meantime, we'll keep an eye out for October the 5th and see you guys on the CME.

• 30:49Speaker 02

  Thank you very much.

• 30:49Rebecca Bellan?

  Steve, it was so great chatting with you. We're about out of time. Can our listeners connect with you online anywhere?

• 30:55Speaker 02

  Yeah, so I'm pretty active online on Twitter especially, so Steve Ho, I'm on X, and people can find me there. You can also email me at S H O U at silicondata.com. Um, so, um, look forward to hearing from your audience.

• 31:09Rebecca Bellan?

  Okay, great, and thanks again for joining us. To our listeners, you can find me on X and LinkedIn, and you can find Equity at Equity Pod on X and Threads. Talk to you next time. Equity is hosted by TechCrunch senior reporters and produced by Teresa Locan Solo with editing by Cal. Subscribe on YouTube or wherever you get your podcasts and find out what's next at techcrunch.com/events. Thanks so much for listening and we'll talk to you next time.