Wayne Nelms of Ornn, co-founder and chief technology officer, CTO, discusses Ornn's approach to alleviating graphics processing unit, GPU, scarcity by operating a compute exchange that matches supply and demand and by developing a data business that produces the Ornn Compute Price Index, OCPI. On theCUBE Research, hosts Gemma Allen, John Furrier and Dave Vellante examine Ornn's market-making strategy the role of financing in hardware deployment and the implications for artificial intelligence, AI infrastructure and enterprise adoption. Nelms explains plans to institutionalize compute pricing with Intercontinental Exchange, ICE through the OCPI to enable financiers to hedge NVIDIA GPU exposure. They also outline Ornn's focus on serving undercapitalized AI labs and neoclouds and on improving transparency in the compute market.
Key takeaways include Ornn securing approximately $40 million in a seed round led by Andreessen Horowitz Crypto and Galaxy Digital and the company's emphasis on financial instruments to support hardware deployment. Nelms details how institutionalized pricing via the OCPI and collaboration with ICE can let financiers hedge NVIDIA exposure and facilitate capital allocation across AI projects. Analysts highlight persistent demand pressure and supply constraints that shape the emerging compute market.
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Wayne Nelms, Ornn
Wayne Nelms of Ornn, co-founder and chief technology officer, CTO, discusses Ornn's approach to alleviating graphics processing unit, GPU, scarcity by operating a compute exchange that matches supply and demand and by developing a data business that produces the Ornn Compute Price Index, OCPI. On theCUBE Research, hosts Gemma Allen, John Furrier and Dave Vellante examine Ornn's market-making strategy the role of financing in hardware deployment and the implications for artificial intelligence, AI infrastructure and enterprise adoption. Nelms explains plans to institutionalize compute pricing with Intercontinental Exchange, ICE through the OCPI to enable financiers to hedge NVIDIA GPU exposure. They also outline Ornn's focus on serving undercapitalized AI labs and neoclouds and on improving transparency in the compute market.
Key takeaways include Ornn securing approximately $40 million in a seed round led by Andreessen Horowitz Crypto and Galaxy Digital and the company's emphasis on financial instruments to support hardware deployment. Nelms details how institutionalized pricing via the OCPI and collaboration with ICE can let financiers hedge NVIDIA exposure and facilitate capital allocation across AI projects. Analysts highlight persistent demand pressure and supply constraints that shape the emerging compute market.
>> Palo Alto Studio Connection, Silicon Valley and Wall Street.
Wayne Nelms
>> I'm John Furrier, co-host 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 all things AI factories, specifically what happens when AI infrastructure becomes its own financial market. Joining me now for a conversation on exactly that is Wayne Nelms, CTO and co-founder of Ornn. Welcome, Wayne.
Wayne Nelms
>> It's great to be here.
Gemma Allen
>> So you've been on the show a couple of times this year. We've met in a couple of continents. We've definitely had a few chats, Wayne, but it seems as though every time I meet you, something new and exciting and revolutionary is happening. So maybe fill us in on what's been going on with you and Kush and the team at Ornn since we last had you on in May.
Wayne Nelms
>> Yeah, so I think we last talked in May, like you mentioned. A lot of things have happened since then, both in the news and for our company. I guess most notably we had announced our seed round fundraising led by Andreessen Horowitz Crypto and the Galaxy Digital team, which is an incredible opportunity for us. I think being able to raise a lot of capital was great, of course, for the signaling reasons, but also allowed us to really scale the business, which is super valuable for us at this stage, just in terms of hiring, in terms of image and brand. But also, I think even more important is how the market has evolved since we last spoke.
Gemma Allen
>> Yeah, $40 million seed round, right?
Wayne Nelms
>> Roughly $40 million.
Gemma Allen
>> Yeah.that's a big seed. in the world I grew up in, that was a very big seed. Nowadays, things are changing rapidly. But let's talk about where that money is being spent and the— I guess how you prioritize your businesses because you actually have two separate businesses in some respects, right? You have a marketplace where GPU supply meets demand.. And you also have a data business. And I know we're going to talk a little about what's happening with ICE in that business, but which is essentially around creating an index or a commodity for
Wayne Nelms
>> GPUs.
Gemma Allen
>> Yes.Let's start in the market-based
Gemma Allen
>> business.
Gemma Allen
>> Okay.A lot happening there. We just today had SemiAnalysis on the show. We had one of the lead analysts, Jordan Nanos. He talked a lot about the hype, the bubble, the fact that there is so much demand, not enough supply. And we don't really know when that's going to slow down. What are you seeing? Where are you guys sort of sourcing from? Who are you matching?
Wayne Nelms
>> Sure.
Gemma Allen
>> Talk about the buyers and sellers that you're connecting here.
Wayne Nelms
>> Yeah. So on the exchange side, we have a compute exchange like you mentioned, and that sits opposite of the data business that we can go into. But I think on the compute exchange side, what we're really focused on is solving the immediate need. And the immediate need in this industry is that there is just, like you mentioned, so much demand for compute capacity. There's so many smart people raising a lot of capital to train their models, to run inference workloads, to really deliver AI and the power of AI to enterprise and the individual level, right through application layer, etc. And there's— with that growing demand, there's just not enough supply of compute to keep up. Right. It's in today's age, it's not trivial to build a new cloud or to stand up a GPU cluster. Right. Not only are there bottlenecks across the supply chain, but in our opinion, one of the biggest unknown bottlenecks or one of the biggest unmentioned bottlenecks until recently is this kind of ability to finance the hardware itself, right? So historically how it's worked is everyone that's financing a GPU cluster, call it a neocloud or hyperscale, etc., needs to finance that GPU against the committed contract of the offtaker, right? The person that's buying capacity on that piece of hardware. And more specifically, financiers tend to be a little wary of AI risk, at least today, and they only look to finance projects that are backed by either backstops by NVIDIA, Google, etc., or committed contracts from Amazon, Meta, some of the larger hyperscaler players. Right. And if you can imagine in that sort of environment, financing can be very difficult. So what we're hoping to do on our exchange side business is really grow the market for people that can access this compute.
Gemma Allen
>> And who do you think is being somewhat left out or left out in the cold by this cycle? We hear a lot about the favoritism economy, right? Folks are getting into bed together fast. In certain spaces within tech. You know, there's a pecking order even for GPU access for supply. What are you seeing? What sort of short tail and longer tail opportunities are you guys considering?
Wayne Nelms
>> Yeah, certainly. So there's a huge unserved market in this space, and that unserved market is kind of who we target right now. It's those that have raised significant capital to buy compute capacity yet don't have the balance sheets or the credit history to off-take that capacity quite yet. Right. So you could think of AI Labs, neoclouds, you could think of startups and those that just need access to compute, whether it's bare metal or through some virtualized layer. These are all the players that have potentially the capital and potentially the interest in compute. But again, they might have only been around for the last 3 to 6 months. Or under a year. Underwriters might not be comfortable underwriting a billion-dollar-plus cloud facility to one of these or a few of these off-takers in a multi-tenant system. So what opportunity there is for us to bridge that gap, we try to solve and help. And then, how can we help actually manage this risk? I think that leads us to the data business and a bit of the index side. But I'll let you take us there.
Gemma Allen
>> Let's go there, because one thing we know underwriters really don't like is uncertainty, right? They want financial predictability. They want to know if you are underwriting a loan or investment against CapEx in a data center business that they know what it's going to cost them 3 years out and that that CFO or that company know what it's going to cost. And there's a lot of uncertainty and volatility in that space. Talk about this movement that you guys are building and the relationship that you're developing with ICE, because really what you're saying is GPUs are a commodity, right? There should be a predictive pricing index.
Wayne Nelms
>> Yeah. So I think certainly financiers hate excess volatility, especially in markets where they're underwriting huge deals. I think for us, right, what we've always seen in AI and AI markets is that uncertainty is rampant, right? When we first entered the space roughly last year, what we wanted to solve initially was the fact that no one knew what was happening with AI markets, right? Where is the future of AI? What is the future of cost of compute? What is the future cost to deploy GPU and what does demand and supply look like in the future? And I think we've seen historically since we started the business that demand has been nothing but rampant. The growth and the adoption of AI has just been on a tear, especially recently. And I mentioned before, supply just can't keep up. However, that's not— that story might not be good enough for a lender in a one-off project, right? They want to see committed capacity in their specific investment, of course. And so rather than necessarily finding a long-term contract, right, what we hope the future looks like is not only finding committed off-takers for a project, but also for the financier, for the lender, for financial players to be able to hedge some of their exposure on financial markets. I think we took a look at how all financial markets have developed, especially in the commodity space. If you're, let's say a corn farmer, you're able to pre-sell your corn before you've even planted the seed.
Wayne Nelms
>> Right.
Wayne Nelms
>> And I think in the future, what we hope to make a reality is the ability to sell future capacity, potentially even before deploying the GPU.
Gemma Allen
>> Well, let's stay on corn for a second, and that's used as a good example, right, for what's fungible and what's not. Because there is a value to output, right? There is a market price. There's an expectation of what you spend versus what you consume. We think about the world of GPUs and compute. It's very different. Some folks say, it's not fungible. And some of the metrics that are being used right now to develop a level of fungibility, like GPU cost per hour, etc., aren't really accurate because they don't take into account things like latency, performance, overall efficacy. What are your thoughts? What's your response to that?
Wayne Nelms
>> Yeah, certainly. I think at Ornn, we've always believed that compute is, of course, a commodity in our eyes, but it can be different.
Wayne Nelms
>> Right.
Wayne Nelms
>> But I don't think those two things are necessarily mutually exclusive. I think you look at a lot of commodities markets, right? For example, corn. It's hard to say that all corn is the same, right? However, we've implemented benchmarks and standards for what a traded commodity should look like. Right. It should meet these grades and should meet these characteristics. You know, what we try to do at Ornn is something similar, right? So when we compile our index, people always wonder what is our index comprised of? How do we calculate it? And all of our methodology is available online on our website. But what we end up doing is a kind of a very standard volume weighted average pricing metric. It's what you would naively assume an index to be. We ingest so much data on pricing for a certain quality class of compute. We look at only compute capacity that's been sold that hits a certain minimum across a few different specs, call it memory, networking, performance, etc. And after we compile all that, all those prices, we just output the average, right? And effectively what we want to do is really represent what the current market pricing for GPU hour is across all these NVIDIA chips. And I think that is not only something that we are focused on, right? We want to have the most representative index. But of course, in order to bring in lots of liquidity, I think a lot of the market participants are looking for such an index that does track reality.
Gemma Allen
>> Let's stay on NVIDIA for a second, right? If we think about a comparison of Exxon and Brent crude, right? It provides a level of democratization too for buyers, for sellers, predictability. That's great, right? Did Exxon want that to develop as it did develop? And what are your thoughts in the perspective of, there is a lot of ambiguity out there in this market. It has been very beneficial to some of these titans of industry like Jensen and the team at NVIDIA? Do you think they want to see a level of indexing, financial predictability? Yeah. What are your thoughts?
Wayne Nelms
>> So we really believe that our product is super beneficial for NVIDIA specifically. So, in the last 2 weeks or so, NVIDIA and Jensen released a statement regarding the financialization of NVIDIA Compute. And I think what you saw in that piece was the introduction of traditional financing players, financing giants step in and say, look, we're happy to commit capital to help finance this revolution, to help finance the clouds that are deploying NVIDIA GPUs and hardware. And I think that really brings to the forefront of our minds the real value of not only NVIDIA products, but the NVIDIA ecosystem as a whole. I know there's a lot of conversation about the strength and the dominance of NVIDIA. Across the software, across the hardware performance. And we certainly agree. We see that, of course, in terms of adoption in the compute markets. A lot of people are deploying NVIDIA chips, but the additional moat that NVIDIA has today is that because of their adoption, financiers are happier underwriting the NVIDIA GPU hardware.
Wayne Nelms
>> Right.
Wayne Nelms
>> They've just had more reps. They understand potentially how this GPU trades over time and how the compute itself trades over time. And so what we actually see is that when we are launching Ornn Compute Price Index (OCPI), what we're allowing people to do is hedge NVIDIA exposure and NVIDIA GPU compute exposure, which, if anything, should allow financiers to better underwrite this equipment and in theory allow more people to access NVIDIA hardware and continue to spread the NVIDIA ecosystem.
Gemma Allen
>> And that would be happening here and with your relationship with ICE, they will be hedging against that NVIDIA spend. Talk me through when that will happen, how that will happen, and also what the broad spectrum of data points that you use to ensure that that, index continues to be as accurate as it can possibly be.
Wayne Nelms
>> Sure. So our partnership with ICE is amazing for us, right? It really helps institutionalize or be one of the first steps to institutionalize our Compute Index, of course, and the compute financial economy as a whole. The launch date is in the fall, by end of year, pending regulatory approval, of course. And then in terms of the index itself, what we're very committed to doing is compiling as much data as possible. I think what we are really focused on is being as wide a breadth and depth as possible. We want to effectively collect and aggregate data from across the world and the reason for that is compute is global, right? Not only is it global, it trades 24/7. And in order to really represent and help hedge risk for the people in this system and in this economy, what we want to do is get as much data as we can across the entire world. So that's what we're committed to doing.
Gemma Allen
>> So circa $40 million raised. Talk about where you're going to spend that. Where are you guys investing? What does the product roadmap and the cultural and team roadmap look like for the next 6 to 12 months?
Wayne Nelms
>> Yeah. So we're very committed to building one of the best teams in this, you know, not only AI but also the financial spaces. I think one thing that we've learned very quickly as a team is that it was great when it was just the 4 of us and it was great when it was just the 6 of us. And now we're at 22 people and it's been an incredible ride. And just being able to grow with the team and see how this market has changed over time, kind of acting as a tailwind for our thesis, it's been really incredible. So of course, one thing is focusing on the team, focusing on building talent internally, and then also thinking a little bit more about external market dynamics. What we really want to do is continue building partnerships to help accelerate our business and really thinking about the next steps for growth and partnership across the entire compute landscape.
Gemma Allen
>> And last question, Wayne. Growth and partnerships. This is an interesting go-to-market strategy, right? You have a lot of different profiles of buyers. And stakeholders and advocates here. How do you think about that? how do you guys think about building something that's both a marketplace and somewhat of a movement?
Wayne Nelms
>> Yeah. So I think there's a lot of interest across, like you mentioned, so many different kinds of groups of people, right? You have the financial players, the banks, the traders, market makers, etc. But of course, in our own industry, we have all the clouds, we have the hyperscalers, we have big labs and the frontier labs, specifically inference providers, etc. There's so many people that have such a vested interest in what happens in our space. And so, one, it's obviously very exciting for us being able to sit in between kind of the most interesting and impactful sectors, I think, in the economy right now. But of course, that also gives us the ability to think about strategic partnerships and long term where we want to take those. So for now, I think it's still a very open question where we go specifically. But I think in general, we're very excited for where we are.
Gemma Allen
>> Well, we are certainly excited to continue to watch this journey evolve. Wayne, great to have you on theCUBE.
Wayne Nelms
>> Yeah, thanks for having me here.
Gemma Allen
>> I'm Gemma Allen here at theCUBE studio at the New York Stock Exchange. This is AI Factories, one of our programs at NYSE Wired. We connect Silicon Valley to the great minds here on Wall Street. Thanks for watching.
>> Palo Alto Studio Connection, Silicon Valley and Wall Street.
Wayne Nelms
>> I'm John Furrier, co-host 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 all things AI factories, specifically what happens when AI infrastructure becomes its own financial market. Joining me now for a conversation on exactly that is Wayne Nelms, CTO and co-founder of Ornn. Welcome, Wayne.
Wayne Nelms
>> It's great to be here.
Gemma Allen
>> So you've been on the show a couple of times this year. We've met in a couple of continents. We've definitely had a few chats, Wayne, but it seems as though every time I meet you, something new and exciting and revolutionary is happening. So maybe fill us in on what's been going on with you and Kush and the team at Ornn since we last had you on in May.
Wayne Nelms
>> Yeah, so I think we last talked in May, like you mentioned. A lot of things have happened since then, both in the news and for our company. I guess most notably we had announced our seed round fundraising led by Andreessen Horowitz Crypto and the Galaxy Digital team, which is an incredible opportunity for us. I think being able to raise a lot of capital was great, of course, for the signaling reasons, but also allowed us to really scale the business, which is super valuable for us at this stage, just in terms of hiring, in terms of image and brand. But also, I think even more important is how the market has evolved since we last spoke.
Gemma Allen
>> Yeah, $40 million seed round, right?
Wayne Nelms
>> Roughly $40 million.
Gemma Allen
>> Yeah.that's a big seed. in the world I grew up in, that was a very big seed. Nowadays, things are changing rapidly. But let's talk about where that money is being spent and the— I guess how you prioritize your businesses because you actually have two separate businesses in some respects, right? You have a marketplace where GPU supply meets demand.. And you also have a data business. And I know we're going to talk a little about what's happening with ICE in that business, but which is essentially around creating an index or a commodity for
Wayne Nelms
>> GPUs.
Gemma Allen
>> Yes.Let's start in the market-based
Gemma Allen
>> business.
Gemma Allen
>> Okay.A lot happening there. We just today had SemiAnalysis on the show. We had one of the lead analysts, Jordan Nanos. He talked a lot about the hype, the bubble, the fact that there is so much demand, not enough supply. And we don't really know when that's going to slow down. What are you seeing? Where are you guys sort of sourcing from? Who are you matching?
Wayne Nelms
>> Sure.
Gemma Allen
>> Talk about the buyers and sellers that you're connecting here.
Wayne Nelms
>> Yeah. So on the exchange side, we have a compute exchange like you mentioned, and that sits opposite of the data business that we can go into. But I think on the compute exchange side, what we're really focused on is solving the immediate need. And the immediate need in this industry is that there is just, like you mentioned, so much demand for compute capacity. There's so many smart people raising a lot of capital to train their models, to run inference workloads, to really deliver AI and the power of AI to enterprise and the individual level, right through application layer, etc. And there's— with that growing demand, there's just not enough supply of compute to keep up. Right. It's in today's age, it's not trivial to build a new cloud or to stand up a GPU cluster. Right. Not only are there bottlenecks across the supply chain, but in our opinion, one of the biggest unknown bottlenecks or one of the biggest unmentioned bottlenecks until recently is this kind of ability to finance the hardware itself, right? So historically how it's worked is everyone that's financing a GPU cluster, call it a neocloud or hyperscale, etc., needs to finance that GPU against the committed contract of the offtaker, right? The person that's buying capacity on that piece of hardware. And more specifically, financiers tend to be a little wary of AI risk, at least today, and they only look to finance projects that are backed by either backstops by NVIDIA, Google, etc., or committed contracts from Amazon, Meta, some of the larger hyperscaler players. Right. And if you can imagine in that sort of environment, financing can be very difficult. So what we're hoping to do on our exchange side business is really grow the market for people that can access this compute.
Gemma Allen
>> And who do you think is being somewhat left out or left out in the cold by this cycle? We hear a lot about the favoritism economy, right? Folks are getting into bed together fast. In certain spaces within tech. You know, there's a pecking order even for GPU access for supply. What are you seeing? What sort of short tail and longer tail opportunities are you guys considering?
Wayne Nelms
>> Yeah, certainly. So there's a huge unserved market in this space, and that unserved market is kind of who we target right now. It's those that have raised significant capital to buy compute capacity yet don't have the balance sheets or the credit history to off-take that capacity quite yet. Right. So you could think of AI Labs, neoclouds, you could think of startups and those that just need access to compute, whether it's bare metal or through some virtualized layer. These are all the players that have potentially the capital and potentially the interest in compute. But again, they might have only been around for the last 3 to 6 months. Or under a year. Underwriters might not be comfortable underwriting a billion-dollar-plus cloud facility to one of these or a few of these off-takers in a multi-tenant system. So what opportunity there is for us to bridge that gap, we try to solve and help. And then, how can we help actually manage this risk? I think that leads us to the data business and a bit of the index side. But I'll let you take us there.
Gemma Allen
>> Let's go there, because one thing we know underwriters really don't like is uncertainty, right? They want financial predictability. They want to know if you are underwriting a loan or investment against CapEx in a data center business that they know what it's going to cost them 3 years out and that that CFO or that company know what it's going to cost. And there's a lot of uncertainty and volatility in that space. Talk about this movement that you guys are building and the relationship that you're developing with ICE, because really what you're saying is GPUs are a commodity, right? There should be a predictive pricing index.
Wayne Nelms
>> Yeah. So I think certainly financiers hate excess volatility, especially in markets where they're underwriting huge deals. I think for us, right, what we've always seen in AI and AI markets is that uncertainty is rampant, right? When we first entered the space roughly last year, what we wanted to solve initially was the fact that no one knew what was happening with AI markets, right? Where is the future of AI? What is the future of cost of compute? What is the future cost to deploy GPU and what does demand and supply look like in the future? And I think we've seen historically since we started the business that demand has been nothing but rampant. The growth and the adoption of AI has just been on a tear, especially recently. And I mentioned before, supply just can't keep up. However, that's not— that story might not be good enough for a lender in a one-off project, right? They want to see committed capacity in their specific investment, of course. And so rather than necessarily finding a long-term contract, right, what we hope the future looks like is not only finding committed off-takers for a project, but also for the financier, for the lender, for financial players to be able to hedge some of their exposure on financial markets. I think we took a look at how all financial markets have developed, especially in the commodity space. If you're, let's say a corn farmer, you're able to pre-sell your corn before you've even planted the seed.
Wayne Nelms
>> Right.
Wayne Nelms
>> And I think in the future, what we hope to make a reality is the ability to sell future capacity, potentially even before deploying the GPU.
Gemma Allen
>> Well, let's stay on corn for a second, and that's used as a good example, right, for what's fungible and what's not. Because there is a value to output, right? There is a market price. There's an expectation of what you spend versus what you consume. We think about the world of GPUs and compute. It's very different. Some folks say, it's not fungible. And some of the metrics that are being used right now to develop a level of fungibility, like GPU cost per hour, etc., aren't really accurate because they don't take into account things like latency, performance, overall efficacy. What are your thoughts? What's your response to that?
Wayne Nelms
>> Yeah, certainly. I think at Ornn, we've always believed that compute is, of course, a commodity in our eyes, but it can be different.
Wayne Nelms
>> Right.
Wayne Nelms
>> But I don't think those two things are necessarily mutually exclusive. I think you look at a lot of commodities markets, right? For example, corn. It's hard to say that all corn is the same, right? However, we've implemented benchmarks and standards for what a traded commodity should look like. Right. It should meet these grades and should meet these characteristics. You know, what we try to do at Ornn is something similar, right? So when we compile our index, people always wonder what is our index comprised of? How do we calculate it? And all of our methodology is available online on our website. But what we end up doing is a kind of a very standard volume weighted average pricing metric. It's what you would naively assume an index to be. We ingest so much data on pricing for a certain quality class of compute. We look at only compute capacity that's been sold that hits a certain minimum across a few different specs, call it memory, networking, performance, etc. And after we compile all that, all those prices, we just output the average, right? And effectively what we want to do is really represent what the current market pricing for GPU hour is across all these NVIDIA chips. And I think that is not only something that we are focused on, right? We want to have the most representative index. But of course, in order to bring in lots of liquidity, I think a lot of the market participants are looking for such an index that does track reality.
Gemma Allen
>> Let's stay on NVIDIA for a second, right? If we think about a comparison of Exxon and Brent crude, right? It provides a level of democratization too for buyers, for sellers, predictability. That's great, right? Did Exxon want that to develop as it did develop? And what are your thoughts in the perspective of, there is a lot of ambiguity out there in this market. It has been very beneficial to some of these titans of industry like Jensen and the team at NVIDIA? Do you think they want to see a level of indexing, financial predictability? Yeah. What are your thoughts?
Wayne Nelms
>> So we really believe that our product is super beneficial for NVIDIA specifically. So, in the last 2 weeks or so, NVIDIA and Jensen released a statement regarding the financialization of NVIDIA Compute. And I think what you saw in that piece was the introduction of traditional financing players, financing giants step in and say, look, we're happy to commit capital to help finance this revolution, to help finance the clouds that are deploying NVIDIA GPUs and hardware. And I think that really brings to the forefront of our minds the real value of not only NVIDIA products, but the NVIDIA ecosystem as a whole. I know there's a lot of conversation about the strength and the dominance of NVIDIA. Across the software, across the hardware performance. And we certainly agree. We see that, of course, in terms of adoption in the compute markets. A lot of people are deploying NVIDIA chips, but the additional moat that NVIDIA has today is that because of their adoption, financiers are happier underwriting the NVIDIA GPU hardware.
Wayne Nelms
>> Right.
Wayne Nelms
>> They've just had more reps. They understand potentially how this GPU trades over time and how the compute itself trades over time. And so what we actually see is that when we are launching Ornn Compute Price Index (OCPI), what we're allowing people to do is hedge NVIDIA exposure and NVIDIA GPU compute exposure, which, if anything, should allow financiers to better underwrite this equipment and in theory allow more people to access NVIDIA hardware and continue to spread the NVIDIA ecosystem.
Gemma Allen
>> And that would be happening here and with your relationship with ICE, they will be hedging against that NVIDIA spend. Talk me through when that will happen, how that will happen, and also what the broad spectrum of data points that you use to ensure that that, index continues to be as accurate as it can possibly be.
Wayne Nelms
>> Sure. So our partnership with ICE is amazing for us, right? It really helps institutionalize or be one of the first steps to institutionalize our Compute Index, of course, and the compute financial economy as a whole. The launch date is in the fall, by end of year, pending regulatory approval, of course. And then in terms of the index itself, what we're very committed to doing is compiling as much data as possible. I think what we are really focused on is being as wide a breadth and depth as possible. We want to effectively collect and aggregate data from across the world and the reason for that is compute is global, right? Not only is it global, it trades 24/7. And in order to really represent and help hedge risk for the people in this system and in this economy, what we want to do is get as much data as we can across the entire world. So that's what we're committed to doing.
Gemma Allen
>> So circa $40 million raised. Talk about where you're going to spend that. Where are you guys investing? What does the product roadmap and the cultural and team roadmap look like for the next 6 to 12 months?
Wayne Nelms
>> Yeah. So we're very committed to building one of the best teams in this, you know, not only AI but also the financial spaces. I think one thing that we've learned very quickly as a team is that it was great when it was just the 4 of us and it was great when it was just the 6 of us. And now we're at 22 people and it's been an incredible ride. And just being able to grow with the team and see how this market has changed over time, kind of acting as a tailwind for our thesis, it's been really incredible. So of course, one thing is focusing on the team, focusing on building talent internally, and then also thinking a little bit more about external market dynamics. What we really want to do is continue building partnerships to help accelerate our business and really thinking about the next steps for growth and partnership across the entire compute landscape.
Gemma Allen
>> And last question, Wayne. Growth and partnerships. This is an interesting go-to-market strategy, right? You have a lot of different profiles of buyers. And stakeholders and advocates here. How do you think about that? how do you guys think about building something that's both a marketplace and somewhat of a movement?
Wayne Nelms
>> Yeah. So I think there's a lot of interest across, like you mentioned, so many different kinds of groups of people, right? You have the financial players, the banks, the traders, market makers, etc. But of course, in our own industry, we have all the clouds, we have the hyperscalers, we have big labs and the frontier labs, specifically inference providers, etc. There's so many people that have such a vested interest in what happens in our space. And so, one, it's obviously very exciting for us being able to sit in between kind of the most interesting and impactful sectors, I think, in the economy right now. But of course, that also gives us the ability to think about strategic partnerships and long term where we want to take those. So for now, I think it's still a very open question where we go specifically. But I think in general, we're very excited for where we are.
Gemma Allen
>> Well, we are certainly excited to continue to watch this journey evolve. Wayne, great to have you on theCUBE.
Wayne Nelms
>> Yeah, thanks for having me here.
Gemma Allen
>> I'm Gemma Allen here at theCUBE studio at the New York Stock Exchange. This is AI Factories, one of our programs at NYSE Wired. We connect Silicon Valley to the great minds here on Wall Street. Thanks for watching.
