theCUBE + NYSE Wired: AI Factories - Data Centers of the Future | Paweł Czech & Cole Crawford, NATIVX

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Gemma Allen

Host, theCUBE + NYSE Wired

theCUBE

Pawel Czech

CEO

Starfield

Cole Crawford

Founder

Synova Global

play_circle_outline Energy as durable underlying asset versus GPU depreciation and silicon generational change

play_circle_outline Fungibility Benchmarking: Tokens per Watt per Dollar and Goodput Across Locations, SKUs, and Accelerators

play_circle_outline NeoClouds' role: democratize access, offer optionality, compete on execution and SLAs

play_circle_outline Financialization of compute: futures, swaps, liquidity, hedging for supply and demand

play_circle_outline ICE Futures on NxCI: Tokenized Compute (COIL) with Physical Delivery and On-Chain Attestation

play_circle_outline Need for industry standards and community adoption to establish the commodity

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Paweł Czech & Cole Crawford, NATIVX

Gemma Allen

Host, theCUBE + NYSE Wired theCUBE

HOST

Pawel Czech

CEO Starfield

Cole Crawford

Founder Synova Global

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Cole Crawford

>> Palo Alto Studio, connecting Silicon Valley and Wall Street. I'm John Furrier, co-host of theCUBE here with Dave Vellante, my co-host.

Gemma Allen

>> Welcome back to theCUBE studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired. And today we are talking AI factories. We talk all things AI. It's a race for models, chips and compute. But what if compute itself is becoming a commodity? Oil is a global market because we figured out how to measure it, price it, trade it, and ultimately build financial markets around it. NATIVX is betting we can do something similar with compute. Joining me now to unpack that are Cole Crawford, founder of Synova Global and co-founder and CEO of NATIVX, and Paweł Czech, co-founder of NATIVX and CEO of NativelyAI. Welcome, folks.

Pawel Czech

>> Thank you.

Pawel Czech

>> Thank you very much.

Gemma Allen

>> So I'm very excited to unpack this company. I'm going to ask you guys some questions. But first, this is a special story for NYSE Wired because the overall thesis and mission of NYSE Wired is to connect great minds, builders, breakers, disruptors, build a community, get people in the right rooms. And that was Brian Baumann and John Furrier's ultimate mission. You guys are actually a live test case.

Cole Crawford

>> Living the dream.

Pawel Czech

>> Living the dream.

Gemma Allen

>> Talk us through how this all came to be.

Pawel Czech

>> So Brian effectively was speaking to me about what will a compute marketplace look like. And he immediately said, okay, I have somebody that you have to meet. There's no question. This is the best person in the world that's working on how markets will evolve and what technology will be used and how will that impact how we create markets and introduced me to Cole. And Cole got a call from Brian in a similar way. I flew down to meet him in Spain. He picked me up at the airport and we spent the day in a restaurant and drawing up the concept on a napkin, literally, which we still have, and then a receipt from the restaurant. And that was in December. And today we're here, we're going live.

Gemma Allen

>> Well, NVIDIA's story started on a napkin in a Denny's, I believe, right?

Cole Crawford

>> That's right.

Gemma Allen

>> So maybe we're heading the same journey. Cole, you've built your career in open source. That has been your wheelhouse. You have been singing that song for a long time. NATIVX, what— break this down for me. is this really about trying to financialize the world of open

Cole Crawford

>> source?no, no, it's actually the same mission as open source. So the point, the reason I was always excited about open source is, number one, there's a little bit of meritocracy. Associated with the code that gets contributed. Now AI has really replaced a lot of that, that currency, if you think of code as currency in open source. But really what open source is about is transparency. And NATIVX is really the culmination of work that I was doing on sort of tokenized real-world assets and building the banking rails and all of the software as a service capabilities. So you could take a commodity, an RWA, put that on chain and start settling against that atomically with a two-sided market because two-sided markets are great. I, for one, have thought that these closed-door sort of backroom— you can argue circular finance, we can maybe talk about that later, but you can argue that this is not a transparent marketplace today. And NATIVX is very much in the same vein as open source. About trying to increase the transparency and build a true two-sided market.

Gemma Allen

>> Okay, let's talk about why it's non-transparent, though. Let's unpack some of the norms, right? Because there is legacy thinking in place here. Some folks argue it's not fungible. You cannot measure an H100 in a data center in Poland and an H100 in an NVIDIA InfiniBand center in San Jose.

Cole Crawford

>> We agree on that.

Gemma Allen

>> We agree. So help me understand how you truly make it fungible. What is the universal benchmark that location performance, everything can be measured against?

Cole Crawford

>> Sure. And it's pretty straightforward. We actually have a hedgeable, durable, deep bench of commodities that we know how to trade today. That is actually the entire back half of the forward curve for accelerated compute, and that's energy. Energy is something that's truly scarce. We might be in a synthetic scarcity situation today because Micron is catching up to the demand and Samsung and Seagate and other component part manufacturers are catching up to the demand. And if you remember, I built Open Compute, so I was privy to some of the supply chain and demand side of what was happening when Facebook and others were building infrastructure at a pretty fast pace. And guess what? They caught up, right? The supply and the demand, they caught up. So it's one thing to say on a GPU hour an H100 has gone up for a month. That's to me a little bit like weather. Weather can swing, you can have cold to hot in one day, but climate is a little more consistent over a little bit longer time period. And the transparency for a two-sided market in a commodity like this needs a common durable underlying that lasts across the capital outlay for the entire thing. It can't just be on the GPU because the GPUs, they do depreciate. Even if today they're up by some marginal percent, they depreciate over years. You look, for example, at OpenAI yesterday announcing Jalapeño actually being a real thing at half the power and 1.9 to 2x based on SemiAnalysis, based on their testing. Half the power, 1.9%— excuse me, 1.9x the performance. What would that do? What will that do to the futures of an H100 when the substation and the data center actually make up roughly 60% of the capital outlay of what— at least NVIDIA, but more to the point, what the world today calls an AI factory.

Gemma Allen

>> So let's just get into that for a second. You mentioned SemiAnalysis we had Jordan Nanos on the show last week. Him and I spoke a little bit about this world of GPU per hour versus goodput. Very interesting. The ClusterMAX philosophy that those guys are developing, right? Again, create that independent verification. But at the end of the day, who truly verifies, right? If we think about how other commodities have been built— oil, gas— they're built over time unilaterally. People see some sort of ubiquitous value that they can measure against, right? We don't— do we have that level of data? Who is measuring this?

Cole Crawford

>> We sort of do. And you have it more generally. And I think the beauty of NATIVX is like we can actually abstract some of that away because there's a lot of independent testing that goes on from SemiAnalysis and goodput and other firms. In fact, you yourself can benchmark this. There's really good tools built into Llama and other harnesses that will give you kind of the token output. And there's nothing wrong with having a standardized benchmark in performance per watt per dollar, where in accelerated compute performance is tokens per watt per dollar. That's a great question. It's not a great hedgeable market. So an H100, which by the way, there's multiple H100s. A GB300 can be PCIe-based, it can have NVLink, it doesn't have to have NVLink. So think about how many SKUs. Now you have Jalapeño, you've got SambaNova, you've got Groq, you've got Cerebras, you've got Positron. You have a number of silicon companies that are all going to introduce models and generational change as, I guess we're calling this Huang's Law. As the performance of accelerators gets better every 9 months. This is not like Moore's Law. Where it was 18 months. So it's fast. And you can actually look inside of your own harness and say, this is how many tokens per watt per dollar I'm seeing on this card. And you can easily build standardization around this pretty transparently. The challenge comes when you try and hedge that against the overall capital outlay of the data center, the substation, the racks, the rectifiers, the networking components, the CPUs that do a lot of the interfacing. You're missing 60% of the asset if you're just looking at GPUs.

Gemma Allen

>> So, Paweł, I go to you for a second. People want optionality, right? And in a market like this, that's this hot, where supply isn't really necessarily able to meet demand, optionality is a challenge. You have built a career around community builders, all sorts of peripheral players, right? Not necessarily the guys inside the Nvidia data center, folks that are building apps. On the edge all over the world. When we think about the model for this, who could really benefit from this? Talk me through that. And I want to challenge you a little bit on the NeoCloud Framework, but first I want to hear from you.

Pawel Czech

>> Sure. So very much like you compare it to a commodity like oil, right, that we learned how to trade and create different types of contracts around people that are going to be off-takers, they're going to be off-takers of a specific type of compute, right? So it can be edge compute, it can be compute that has a specific, source of power, it can be compute that has a green offset, right? It's producing a specific amount of tokens. So it's not homogeneous. You need to create that common denominator. And our experience right now is that for different use cases, people want different type of compute. you don't have to power up the sun to cook something in the microwave. And this is what we're doing today. And we're always buying the sun. We're always paying that maximum premium. And this is because nobody knows how much is your capacity actually worth. What's the value of that compute? So number one for us, As a NeoCloud, as a community builder, it was very important to bring a tool, bring a system into existence, into the market that will allow more democratic access. It is about being able to build the AI economy and make it more accessible for everybody. It's not only people that can make a $20 million purchase from Google, right? Or somebody that has to pay $100 billion like we know that there are deals between NVIDIA and OpenAI, right, for $100 billion to access specific compute. That's not the totality of the market. That's not what the average company, the average builder, the average enterprise buyer wants.

Gemma Allen

>> And this isn't based on any price list, right? This isn't based on AWS or Azure. It's not based on a pricing index. It's based on essentially how much AI can I get for my token?

Cole Crawford

>> but that's in delivered buckets. So it has a great comparable in oil. So, in Brent crude, the standard is a barrel. A barrel happens to be 42 gallons. I don't know why it's 42 gallons, but it's 42 gallons. If we kind of look across the industry, it made sense from an economic perspective, from a dollar perspective, to do this based on a guaranteed delivered 20 kilowatt hours of compute. It's energy normalized. We call this energy normalization. So now it's not based on what a very good or successful salesperson sold an H100 for. That feels a lot like LIBOR to me.

Gemma Allen

>> Yeah, for sure. And I want to talk about the marketing side of this business because I feel like we don't talk about it enough, right? In the whole world of AI factories, NVIDIA is a phenomenal company, right? What they build with CUDA. How they've locked folks in. It's unbelievably impressive, right? They're also marketing maestros, though. if we're— if we think about it frankly, so is Anthropic. we're seeing that with OpenAI. They are very, very good at leading with the message. When you think about the appetite and the enthusiasm around having something that has a standard commoditized interface, I asked a gentleman on the show last week, one of your guests, somewhat of a peer in this industry, you think Jensen wants this? And he said, yeah, absolutely, Jensen wants this. I kind of beg to differ on that, right? I think that there is a lot of ambiguity in this marketing model that we're in right now, and it serves the titans of industry in some respects. The neo-cloud— and you can challenge me on this, I'm interested to know your take— the neo-cloud model, though, is an interesting one because it allows them to hedge capital, right? If you have predictability around cost outcomes and it also gives them some sort of competitive advantage on the performance layer. What do you think about both of those things? how do you respond to— does Jensen want, a price index for GPUs? And what is the unique advantage of this working for the world of neo clouds broadly, not just your own, but the CoreWeave of this world?

Cole Crawford

>> I would say anytime you're in a position to play sort of kingmaker because you have the supply and you create the demand, and that demand comes from the fact that you control the supply, that's not a two-sided market. That's a one-sided market for sure. And so, does any person in that position want transparency? I don't know. Okay, I guess it depends on your motive. If your motive is purely financial, then absolutely no, you don't want that. But all of your competitors do. And this is a part of why open source exists is because that was exactly Microsoft.

Gemma Allen

>> Microsoft Corporation. Just like my pension. Okay.

Cole Crawford

>> But 25 years ago, they had a closed source ecosystem. They gave priority to their biggest customers. Linux was created in part as an alternative to that, the transparency to make the code do what you want. You're now seeing competitors. And OpenAI became a big competitor to NVIDIA yesterday. Cerebras, from a performance per watt per dollar perspective is one of the best chips on the planet right now. I think your competitors, when you are as big as they are, people start gunning for you because they see market opportunity. This is just— this is capitalism. that's great.

Gemma Allen

>> And in the new cloud space, your CoreWeave, your Nebius, you're selling H100s, at a standard price, right? What are you competing on then? You're competing on the performance. walk me through like 5 years from now, if we have a really clear global commodity around GPUs, how you have competitive advantage in that market as a NeoCloud.

Cole Crawford

>> I think— I'm sorry, Paweł, I want to— I actually would like you to talk to it, but I do believe that in this particular case, AI coding, agentic coding has largely taken away software as a moat and now it becomes execution. And so from a NeoCloud perspective, what's your uptime? What's your time to response? What are your SLAs? How do you support your customer on the missions that they're on? Can you fine-tune LLMs or the harness for their specific use cases? This is an execution-based business now, not we have a SaaS differentiation because you can create that in minutes.

Gemma Allen

>> interestingly, people say that the NeoCloud bubble— there are skeptics that argue it's a bit of a real estate play, right? So in some respects, this really challenges, it's where the rubber meets the road. If that was to be true, how can it not be?

Pawel Czech

>> Yeah, yeah, absolutely. One way to think about it is, are you actually optimizing the use of your, let's say, real estate or the amount of capacity that you have? So if you create, number one, a market where everybody can buy and sell right? You create liquidity, and that compute becomes tradable instantly, right? You can settle, you can have physical delivery of the compute as well, right? Then the question becomes, okay, do you actually have megawatts or bragawatts? Yeah, somebody said it.

Gemma Allen

>> I love that.

Pawel Czech

>> So, you need effective capacity. Effective capacity means that it also has to be delivered, at a latency that is acceptable for your use case. It has to be, in the, uh, jurisdiction that is relevant for you to actually generate the tokens. So when we're talking about an AI factory and we're saying a sovereign AI factory is something that can be attested, delivered, right? It has to be available to you at a latency that you're willing to accept.

Pawel Czech

>> Mm-hmm.

Pawel Czech

>> And to your point, at a cost that is relevant for your business. Mm-hmm. And this is where one of the biggest challenges of the industry today lies is the lack of profitability, the lack of return on that real estate. So our claim is twofold. Number one, our goal is to deliver that capacity instantly at fair market value that is determined by an actual market. And then secondly, to create a financial, I would say, fintech approach to compute where you can actually create products like futures contracts, like swaps, things that—

Gemma Allen

>> yeah, markets love regulation, right? They like predictability. So guys, a lot has happened for you in what is now 8 months. You're here today. I know there's some exciting stuff happening. I think you're also about to announce a deal with ICE. Can you talk us through that? talk us through what has been happening? there's clearly an appetite here. Bring us up to speed on the progress thus far.

Cole Crawford

>> We— I think that was announced. We announced that ICE was going to be building a futures contract based on the NxCI, just the NativX Compute Index that is backed by COIL, Compute Oil.

Gemma Allen

>> Love that. And actually, you know what? I have it here. I think I have one of the tokens.

Cole Crawford

>> There is your COIL. That is worth 20 kilowatt hours of attested and delivered compute.

Pawel Czech

>> So as you see, it does say COIL, Compute Oil, on it, right? and it's trademark pending. Trademark pending. It is a representation, right? It is for people to have an easier understanding that it is physical delivery of a commodity, much like you can take delivery of a barrel of oil. You can take delivery of this.

Cole Crawford

>> And again, this is— you said something really important, Paweł. This is transparent, settled because there was a fair market value. There was a two-sided market and a fair market value because someone put this up, as you know better. we're sitting here at the epicenter of capitalism at the NYSE. What makes the money move? A matching engine. So you put up an order and that order crosses. This is the same thing. So we do run a spot exchange where you can buy that capacity, those orders can cross, and then that becomes instantly deliverable as a spot. What's great about this model specifically is everything is on-chain. So you publicly and cryptographically sign that you've made that available, and anybody, any of the Big Four audit firms, any private equity bank, any hedge fund can go replay that publicly. It's all there. So there's no LIBOR-like, qualitative deal done here. There's compiled software that sits and cryptographically signs the energy-based accelerated compute that gets delivered on something that people know how to hedge and trade today, that will survive the depreciation cycle of silicon, which is energy.

Gemma Allen

>> So it's all on-chain. Okay. So you guys are doing something very interesting in that you're selling a commodity in one space, but you're also convincing the world of the value of that commodity in another, right? So it's both a marketplace and a movement.

Cole Crawford

>> And a movement largely based on the fact— sorry.

Gemma Allen

>> Yeah. And marketplaces need customers and movements need believers, right? How are you attacking this? talk me through what you guys have been doing. It's a fascinating challenge.

Pawel Czech

>> So number one, today, right, we're here inviting the industry and I mean anybody that is dealing with the financial industry and the Neo Clouds coming together and effectively being able to start trading on the exchange. So this is today. This is why we're here. We're going to have an amazing event thanks to ICE, thanks to the NYSE, the team here. And so people are so interested. The demand is so high. We've heard that it very likely is the highest demand for any type of financial asset in history. So I believe that the market in itself wants to participate. Yes. So we've— whenever we speak to anybody in the world so far for the last year, nobody has said, I don't need this, I don't want this, this is not something that is gonna work and function on a day-to-day basis. So we're extremely confident in that. At the same time, it is a question of now the industry coming together and agreeing on a standard, right? And the closer we are to that energy-normalized standard, the better the outcome. So one of the things that we're doing as NativelyAI is that we are also taking the responsibility to deliver compute, right, based on what people buy and sell. So we're making sure that you can actually take physical delivery of this. So it's a question of confidence. It's a question of managing the risk, right, as well, and it becoming a tradable asset.

Gemma Allen

>> And above all, I'm sorry, can I

Cole Crawford

>> just—yeah, just 10 seconds. there's all of the neoclouds. The reason why We're all in this kind of boat, in this movement. I think the movement built itself, right? Largely because everyone's in this race. I think the frontier folks would tell you we're in this race to AGI, but I think the bankers would tell you we're just in a race to capitalize on the opportunity. And those are probably not mutually exclusive. So noble goals on one side and financial goals on the other. But there's nothing wrong with that. I think the point is that in order for this to succeed as a movement, you need confidence on both sides. So the supply side needs to go back to their LPs, to go back to their investors, go back to the ETFs and the mutual funds and the sovereign wealth funds that are saying, okay, we're going to put money into your fund. And then on the neocloud side, or on the hyperscale cloud side, there is $4.6 trillion sitting in SPVs off balance sheets today. Like that number is going to grow. So I think it's solving both sides. Supply side gets better transparency. neoclouds get better interest rates and better deals, maybe supply chain advantage. And I think at the end of the day, this very quickly— and over the next 12 to 18 months— will start looking like a revenue-based business and not a growth-based business. And that's what we intend to help with.

Gemma Allen

>> Well, folks, it's certainly about commodification and confidence, but it's also about community. And again, shameless plug for NYSE Wired, but what a great story. Delighted to have you guys here. Looks like great events happening upstairs. Hope to make it up. Thanks so much for joining us on NYSE Wired.

Pawel Czech

>> Thank you. It was a pleasure. Thank you very much.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's AI Factories. Thanks for watching.

Paweł Czech & Cole Crawford, NATIVX

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Cole Crawford

>> Palo Alto Studio, connecting Silicon Valley and Wall Street. I'm John Furrier, co-host of theCUBE here with Dave Vellante, my co-host.

Gemma Allen

>> Welcome back to theCUBE studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired. And today we are talking AI factories. We talk all things AI. It's a race for models, chips and compute. But what if compute itself is becoming a commodity? Oil is a global market because we figured out how to measure it, price it, trade it, and ultimately build financial markets around it. NATIVX is betting we can do something similar with compute. Joining me now to unpack that are Cole Crawford, founder of Synova Global and co-founder and CEO of NATIVX, and Paweł Czech, co-founder of NATIVX and CEO of NativelyAI. Welcome, folks.

Pawel Czech

>> Thank you.

Pawel Czech

>> Thank you very much.

Gemma Allen

>> So I'm very excited to unpack this company. I'm going to ask you guys some questions. But first, this is a special story for NYSE Wired because the overall thesis and mission of NYSE Wired is to connect great minds, builders, breakers, disruptors, build a community, get people in the right rooms. And that was Brian Baumann and John Furrier's ultimate mission. You guys are actually a live test case.

Cole Crawford

>> Living the dream.

Pawel Czech

>> Living the dream.

Gemma Allen

>> Talk us through how this all came to be.

Pawel Czech

>> So Brian effectively was speaking to me about what will a compute marketplace look like. And he immediately said, okay, I have somebody that you have to meet. There's no question. This is the best person in the world that's working on how markets will evolve and what technology will be used and how will that impact how we create markets and introduced me to Cole. And Cole got a call from Brian in a similar way. I flew down to meet him in Spain. He picked me up at the airport and we spent the day in a restaurant and drawing up the concept on a napkin, literally, which we still have, and then a receipt from the restaurant. And that was in December. And today we're here, we're going live.

Gemma Allen

>> Well, NVIDIA's story started on a napkin in a Denny's, I believe, right?

Cole Crawford

>> That's right.

Gemma Allen

>> So maybe we're heading the same journey. Cole, you've built your career in open source. That has been your wheelhouse. You have been singing that song for a long time. NATIVX, what— break this down for me. is this really about trying to financialize the world of open

Cole Crawford

>> source?no, no, it's actually the same mission as open source. So the point, the reason I was always excited about open source is, number one, there's a little bit of meritocracy. Associated with the code that gets contributed. Now AI has really replaced a lot of that, that currency, if you think of code as currency in open source. But really what open source is about is transparency. And NATIVX is really the culmination of work that I was doing on sort of tokenized real-world assets and building the banking rails and all of the software as a service capabilities. So you could take a commodity, an RWA, put that on chain and start settling against that atomically with a two-sided market because two-sided markets are great. I, for one, have thought that these closed-door sort of backroom— you can argue circular finance, we can maybe talk about that later, but you can argue that this is not a transparent marketplace today. And NATIVX is very much in the same vein as open source. About trying to increase the transparency and build a true two-sided market.

Gemma Allen

>> Okay, let's talk about why it's non-transparent, though. Let's unpack some of the norms, right? Because there is legacy thinking in place here. Some folks argue it's not fungible. You cannot measure an H100 in a data center in Poland and an H100 in an NVIDIA InfiniBand center in San Jose.

Cole Crawford

>> We agree on that.

Gemma Allen

>> We agree. So help me understand how you truly make it fungible. What is the universal benchmark that location performance, everything can be measured against?

Cole Crawford

>> Sure. And it's pretty straightforward. We actually have a hedgeable, durable, deep bench of commodities that we know how to trade today. That is actually the entire back half of the forward curve for accelerated compute, and that's energy. Energy is something that's truly scarce. We might be in a synthetic scarcity situation today because Micron is catching up to the demand and Samsung and Seagate and other component part manufacturers are catching up to the demand. And if you remember, I built Open Compute, so I was privy to some of the supply chain and demand side of what was happening when Facebook and others were building infrastructure at a pretty fast pace. And guess what? They caught up, right? The supply and the demand, they caught up. So it's one thing to say on a GPU hour an H100 has gone up for a month. That's to me a little bit like weather. Weather can swing, you can have cold to hot in one day, but climate is a little more consistent over a little bit longer time period. And the transparency for a two-sided market in a commodity like this needs a common durable underlying that lasts across the capital outlay for the entire thing. It can't just be on the GPU because the GPUs, they do depreciate. Even if today they're up by some marginal percent, they depreciate over years. You look, for example, at OpenAI yesterday announcing Jalapeño actually being a real thing at half the power and 1.9 to 2x based on SemiAnalysis, based on their testing. Half the power, 1.9%— excuse me, 1.9x the performance. What would that do? What will that do to the futures of an H100 when the substation and the data center actually make up roughly 60% of the capital outlay of what— at least NVIDIA, but more to the point, what the world today calls an AI factory.

Gemma Allen

>> So let's just get into that for a second. You mentioned SemiAnalysis we had Jordan Nanos on the show last week. Him and I spoke a little bit about this world of GPU per hour versus goodput. Very interesting. The ClusterMAX philosophy that those guys are developing, right? Again, create that independent verification. But at the end of the day, who truly verifies, right? If we think about how other commodities have been built— oil, gas— they're built over time unilaterally. People see some sort of ubiquitous value that they can measure against, right? We don't— do we have that level of data? Who is measuring this?

Cole Crawford

>> We sort of do. And you have it more generally. And I think the beauty of NATIVX is like we can actually abstract some of that away because there's a lot of independent testing that goes on from SemiAnalysis and goodput and other firms. In fact, you yourself can benchmark this. There's really good tools built into Llama and other harnesses that will give you kind of the token output. And there's nothing wrong with having a standardized benchmark in performance per watt per dollar, where in accelerated compute performance is tokens per watt per dollar. That's a great question. It's not a great hedgeable market. So an H100, which by the way, there's multiple H100s. A GB300 can be PCIe-based, it can have NVLink, it doesn't have to have NVLink. So think about how many SKUs. Now you have Jalapeño, you've got SambaNova, you've got Groq, you've got Cerebras, you've got Positron. You have a number of silicon companies that are all going to introduce models and generational change as, I guess we're calling this Huang's Law. As the performance of accelerators gets better every 9 months. This is not like Moore's Law. Where it was 18 months. So it's fast. And you can actually look inside of your own harness and say, this is how many tokens per watt per dollar I'm seeing on this card. And you can easily build standardization around this pretty transparently. The challenge comes when you try and hedge that against the overall capital outlay of the data center, the substation, the racks, the rectifiers, the networking components, the CPUs that do a lot of the interfacing. You're missing 60% of the asset if you're just looking at GPUs.

Gemma Allen

>> So, Paweł, I go to you for a second. People want optionality, right? And in a market like this, that's this hot, where supply isn't really necessarily able to meet demand, optionality is a challenge. You have built a career around community builders, all sorts of peripheral players, right? Not necessarily the guys inside the Nvidia data center, folks that are building apps. On the edge all over the world. When we think about the model for this, who could really benefit from this? Talk me through that. And I want to challenge you a little bit on the NeoCloud Framework, but first I want to hear from you.

Pawel Czech

>> Sure. So very much like you compare it to a commodity like oil, right, that we learned how to trade and create different types of contracts around people that are going to be off-takers, they're going to be off-takers of a specific type of compute, right? So it can be edge compute, it can be compute that has a specific, source of power, it can be compute that has a green offset, right? It's producing a specific amount of tokens. So it's not homogeneous. You need to create that common denominator. And our experience right now is that for different use cases, people want different type of compute. you don't have to power up the sun to cook something in the microwave. And this is what we're doing today. And we're always buying the sun. We're always paying that maximum premium. And this is because nobody knows how much is your capacity actually worth. What's the value of that compute? So number one for us, As a NeoCloud, as a community builder, it was very important to bring a tool, bring a system into existence, into the market that will allow more democratic access. It is about being able to build the AI economy and make it more accessible for everybody. It's not only people that can make a $20 million purchase from Google, right? Or somebody that has to pay $100 billion like we know that there are deals between NVIDIA and OpenAI, right, for $100 billion to access specific compute. That's not the totality of the market. That's not what the average company, the average builder, the average enterprise buyer wants.

Gemma Allen

>> And this isn't based on any price list, right? This isn't based on AWS or Azure. It's not based on a pricing index. It's based on essentially how much AI can I get for my token?

Cole Crawford

>> but that's in delivered buckets. So it has a great comparable in oil. So, in Brent crude, the standard is a barrel. A barrel happens to be 42 gallons. I don't know why it's 42 gallons, but it's 42 gallons. If we kind of look across the industry, it made sense from an economic perspective, from a dollar perspective, to do this based on a guaranteed delivered 20 kilowatt hours of compute. It's energy normalized. We call this energy normalization. So now it's not based on what a very good or successful salesperson sold an H100 for. That feels a lot like LIBOR to me.

Gemma Allen

>> Yeah, for sure. And I want to talk about the marketing side of this business because I feel like we don't talk about it enough, right? In the whole world of AI factories, NVIDIA is a phenomenal company, right? What they build with CUDA. How they've locked folks in. It's unbelievably impressive, right? They're also marketing maestros, though. if we're— if we think about it frankly, so is Anthropic. we're seeing that with OpenAI. They are very, very good at leading with the message. When you think about the appetite and the enthusiasm around having something that has a standard commoditized interface, I asked a gentleman on the show last week, one of your guests, somewhat of a peer in this industry, you think Jensen wants this? And he said, yeah, absolutely, Jensen wants this. I kind of beg to differ on that, right? I think that there is a lot of ambiguity in this marketing model that we're in right now, and it serves the titans of industry in some respects. The neo-cloud— and you can challenge me on this, I'm interested to know your take— the neo-cloud model, though, is an interesting one because it allows them to hedge capital, right? If you have predictability around cost outcomes and it also gives them some sort of competitive advantage on the performance layer. What do you think about both of those things? how do you respond to— does Jensen want, a price index for GPUs? And what is the unique advantage of this working for the world of neo clouds broadly, not just your own, but the CoreWeave of this world?

Cole Crawford

>> I would say anytime you're in a position to play sort of kingmaker because you have the supply and you create the demand, and that demand comes from the fact that you control the supply, that's not a two-sided market. That's a one-sided market for sure. And so, does any person in that position want transparency? I don't know. Okay, I guess it depends on your motive. If your motive is purely financial, then absolutely no, you don't want that. But all of your competitors do. And this is a part of why open source exists is because that was exactly Microsoft.

Gemma Allen

>> Microsoft Corporation. Just like my pension. Okay.

Cole Crawford

>> But 25 years ago, they had a closed source ecosystem. They gave priority to their biggest customers. Linux was created in part as an alternative to that, the transparency to make the code do what you want. You're now seeing competitors. And OpenAI became a big competitor to NVIDIA yesterday. Cerebras, from a performance per watt per dollar perspective is one of the best chips on the planet right now. I think your competitors, when you are as big as they are, people start gunning for you because they see market opportunity. This is just— this is capitalism. that's great.

Gemma Allen

>> And in the new cloud space, your CoreWeave, your Nebius, you're selling H100s, at a standard price, right? What are you competing on then? You're competing on the performance. walk me through like 5 years from now, if we have a really clear global commodity around GPUs, how you have competitive advantage in that market as a NeoCloud.

Cole Crawford

>> I think— I'm sorry, Paweł, I want to— I actually would like you to talk to it, but I do believe that in this particular case, AI coding, agentic coding has largely taken away software as a moat and now it becomes execution. And so from a NeoCloud perspective, what's your uptime? What's your time to response? What are your SLAs? How do you support your customer on the missions that they're on? Can you fine-tune LLMs or the harness for their specific use cases? This is an execution-based business now, not we have a SaaS differentiation because you can create that in minutes.

Gemma Allen

>> interestingly, people say that the NeoCloud bubble— there are skeptics that argue it's a bit of a real estate play, right? So in some respects, this really challenges, it's where the rubber meets the road. If that was to be true, how can it not be?

Pawel Czech

>> Yeah, yeah, absolutely. One way to think about it is, are you actually optimizing the use of your, let's say, real estate or the amount of capacity that you have? So if you create, number one, a market where everybody can buy and sell right? You create liquidity, and that compute becomes tradable instantly, right? You can settle, you can have physical delivery of the compute as well, right? Then the question becomes, okay, do you actually have megawatts or bragawatts? Yeah, somebody said it.

Gemma Allen

>> I love that.

Pawel Czech

>> So, you need effective capacity. Effective capacity means that it also has to be delivered, at a latency that is acceptable for your use case. It has to be, in the, uh, jurisdiction that is relevant for you to actually generate the tokens. So when we're talking about an AI factory and we're saying a sovereign AI factory is something that can be attested, delivered, right? It has to be available to you at a latency that you're willing to accept.

Pawel Czech

>> Mm-hmm.

Pawel Czech

>> And to your point, at a cost that is relevant for your business. Mm-hmm. And this is where one of the biggest challenges of the industry today lies is the lack of profitability, the lack of return on that real estate. So our claim is twofold. Number one, our goal is to deliver that capacity instantly at fair market value that is determined by an actual market. And then secondly, to create a financial, I would say, fintech approach to compute where you can actually create products like futures contracts, like swaps, things that—

Gemma Allen

>> yeah, markets love regulation, right? They like predictability. So guys, a lot has happened for you in what is now 8 months. You're here today. I know there's some exciting stuff happening. I think you're also about to announce a deal with ICE. Can you talk us through that? talk us through what has been happening? there's clearly an appetite here. Bring us up to speed on the progress thus far.

Cole Crawford

>> We— I think that was announced. We announced that ICE was going to be building a futures contract based on the NxCI, just the NativX Compute Index that is backed by COIL, Compute Oil.

Gemma Allen

>> Love that. And actually, you know what? I have it here. I think I have one of the tokens.

Cole Crawford

>> There is your COIL. That is worth 20 kilowatt hours of attested and delivered compute.

Pawel Czech

>> So as you see, it does say COIL, Compute Oil, on it, right? and it's trademark pending. Trademark pending. It is a representation, right? It is for people to have an easier understanding that it is physical delivery of a commodity, much like you can take delivery of a barrel of oil. You can take delivery of this.

Cole Crawford

>> And again, this is— you said something really important, Paweł. This is transparent, settled because there was a fair market value. There was a two-sided market and a fair market value because someone put this up, as you know better. we're sitting here at the epicenter of capitalism at the NYSE. What makes the money move? A matching engine. So you put up an order and that order crosses. This is the same thing. So we do run a spot exchange where you can buy that capacity, those orders can cross, and then that becomes instantly deliverable as a spot. What's great about this model specifically is everything is on-chain. So you publicly and cryptographically sign that you've made that available, and anybody, any of the Big Four audit firms, any private equity bank, any hedge fund can go replay that publicly. It's all there. So there's no LIBOR-like, qualitative deal done here. There's compiled software that sits and cryptographically signs the energy-based accelerated compute that gets delivered on something that people know how to hedge and trade today, that will survive the depreciation cycle of silicon, which is energy.

Gemma Allen

>> So it's all on-chain. Okay. So you guys are doing something very interesting in that you're selling a commodity in one space, but you're also convincing the world of the value of that commodity in another, right? So it's both a marketplace and a movement.

Cole Crawford

>> And a movement largely based on the fact— sorry.

Gemma Allen

>> Yeah. And marketplaces need customers and movements need believers, right? How are you attacking this? talk me through what you guys have been doing. It's a fascinating challenge.

Pawel Czech

>> So number one, today, right, we're here inviting the industry and I mean anybody that is dealing with the financial industry and the Neo Clouds coming together and effectively being able to start trading on the exchange. So this is today. This is why we're here. We're going to have an amazing event thanks to ICE, thanks to the NYSE, the team here. And so people are so interested. The demand is so high. We've heard that it very likely is the highest demand for any type of financial asset in history. So I believe that the market in itself wants to participate. Yes. So we've— whenever we speak to anybody in the world so far for the last year, nobody has said, I don't need this, I don't want this, this is not something that is gonna work and function on a day-to-day basis. So we're extremely confident in that. At the same time, it is a question of now the industry coming together and agreeing on a standard, right? And the closer we are to that energy-normalized standard, the better the outcome. So one of the things that we're doing as NativelyAI is that we are also taking the responsibility to deliver compute, right, based on what people buy and sell. So we're making sure that you can actually take physical delivery of this. So it's a question of confidence. It's a question of managing the risk, right, as well, and it becoming a tradable asset.

Gemma Allen

>> And above all, I'm sorry, can I

Cole Crawford

>> just—yeah, just 10 seconds. there's all of the neoclouds. The reason why We're all in this kind of boat, in this movement. I think the movement built itself, right? Largely because everyone's in this race. I think the frontier folks would tell you we're in this race to AGI, but I think the bankers would tell you we're just in a race to capitalize on the opportunity. And those are probably not mutually exclusive. So noble goals on one side and financial goals on the other. But there's nothing wrong with that. I think the point is that in order for this to succeed as a movement, you need confidence on both sides. So the supply side needs to go back to their LPs, to go back to their investors, go back to the ETFs and the mutual funds and the sovereign wealth funds that are saying, okay, we're going to put money into your fund. And then on the neocloud side, or on the hyperscale cloud side, there is $4.6 trillion sitting in SPVs off balance sheets today. Like that number is going to grow. So I think it's solving both sides. Supply side gets better transparency. neoclouds get better interest rates and better deals, maybe supply chain advantage. And I think at the end of the day, this very quickly— and over the next 12 to 18 months— will start looking like a revenue-based business and not a growth-based business. And that's what we intend to help with.

Gemma Allen

>> Well, folks, it's certainly about commodification and confidence, but it's also about community. And again, shameless plug for NYSE Wired, but what a great story. Delighted to have you guys here. Looks like great events happening upstairs. Hope to make it up. Thanks so much for joining us on NYSE Wired.

Pawel Czech

>> Thank you. It was a pleasure. Thank you very much.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's AI Factories. Thanks for watching.