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Carmen Li

CEO

Silicon Data & Compute Exchange

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Carmen Li, Silicon Data & Compute Exchange

Carmen Li

CEO Silicon Data & Compute Exchange

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(INTRO)

John Furrier

>> Welcome back, I'm John Furrier with Howard Shue, my co -host here, also AI leader in the industry. This is our third annual CUBE and the NYSE Wired AI Leaders Summit here in our studios. And of course we have our third annual pool party gathering, 180 people coming together, all leaders, talking about the future, what they're working on, sharing their information. As the market continues to grow, the ecosystem is growing as well. Of course, the data really matters, and Carmen Lee is here, she's the founder and CEO of Silicon Data, for the Compute Exchange. Congratulations on all your momentum, great to see you again.

>> Good to see you again, John, this is great.

John Furrier

>> When you first started on the queue, We've been on a fellow journey together with NYSE Wired, so great to see you. First of all, what's the update? I've been seeing a lot of talks, so you're on Bloomberg recently. A lot of great action. Give us the update.

>> Yeah, so compute change, we launched the compute forward packages as well as our refurbished markets two weeks ago. So it's been, we see an amazing trend where people start transacting secondary GPU servers, which is great, right? because sometimes you might need the latest and greatest GP300, you might not want the H200, but someone else can use that for different reasons. So we want to see a robot secondary market to really solve the shortage problem that we're facing today for the GPU service.

John Furrier

>> So people need GPUs still as a supply chain problem, you're filling that with your market.

>> That's right, so through compute change you can get reserved contracts, forward contracts, which means things happen for the future, as well as secondary transactions, meaning you're looking to get secondary GPUs, which are certified, refurbished by us and our partners.

John Furrier

>> You've also been working on silicon data and getting all the data on the pricing.

>> That's right.

John Furrier

>> But you know I always ask the question, what's the current price of a GPU?

>> Exactly.

John Furrier

>> What's the status of pricing, durability, seeing any fluctuations, what's the data tell you?

>> I mean, if you ask me, remember last year, everyone was like freaking out, saying, well why do we need the indices, right? The compute price will keep going down forever. And last year the price did come down across all chips. So since January this year, all GPUs, we're talking about A100, H100, B200, all went up between 15 -20 % here today for on -demand NeoCloud GPUs. Hyperscale or on -demand prices have been pretty stable for A100, H100, but they were high to begin with. So when NeoClouds all went up 15 -20%, if you look at the term structure for all different GPUs, they all elevated, all the curves went up, right? So we see huge robust demand across all different chips globally.

John Furrier

>> So we see production of Vera Rubin. Does that skew the data, or is that just an outlier? Is that tracking, what's with Vera Rubin?

>> So for the Vera Rubins, most of them are, you know, people putting purchase orders. Hard to get. Exactly, exactly. So people are not actually, they've seen them in the quantity we're seeing yet, not because people don't want to.

>> Because they get their hands on them. Exactly.

>> So I would say another two months, potentially, we will start seeing restore of data coming in. We'll see forward contracts start being placed. But right now, we don't see much data. The GB300, yes, it's selling.

>> So in the GPU, the price go up and down. That makes a lot of sense. But you also kind of have this exchange. How fungible the GPU is? Like a GPU in one data center with certain network is totally different when it comes to token economics. So how to think about it?

>> You're 100 % correct. So that's why ComputeChange, we're doing full contracts on specific GPU servers, right? So you ask for, hey, I need 200 B200 nodes in US East with this particular specification. The CPU, this RAM, this disk, you have to find everything, the storages, right? Network storage. Everything. Software stack. Exactly. It's not just a... No, it's not just, hey, I need a H200, right? You have to tell me the specs. Oh, you don't care. Maybe you don't care, right? So then they have more options if you don't care about something. And then what we do is we give you a rubric spec and tell you, hey, here's the things satisfied, what you're looking for. Here's the specs. Here are the benchmark results. Because to your point, those machines might be three years old. The performances might not be the same as the specs. So we tell you the third -party benchmark results based on the performances, throughputs, flops, memory bandwidth, all the things, thermal degradation, and then you make a decision, right? Supposedly, better performing GPU can trade higher prices, right? I mean, today, you're lucky you get what you're looking for, right? That's your location you're looking for. But most of the time, you look at specs, you pick one, trade cleared, right? That's the way to go.

John Furrier

>> How has been the competition? Because I've been seeing a lot of people coming in, a lot more Neo clouds coming in, standing up infrastructure, a lot of build outs going on. Does that impact supply and or is it competition? Are they competition, are they frenemies? How do you view that?

>> We love all new clouds. We love all capacity coming in. So Compute Exchange doesn't own anything. We work with everybody and we - You're asset light, you have no asset light. We're asset light, so think about like NASDAQ, right? You guys, NYSE, right? NYSE doesn't own any stocks, you work with everybody, right, so similar Compute Exchange, work with everybody on your cloud. So the way we work is we integrate with them, so inventory tracking, so we know who has what.

John Furrier

>> You're not renting, are you renting GPUs or you're selling?

>> We have real -time inventory matching, so we know who has what.

>> Okay, exchange.

>> Exactly, so people, the RFQ comes in, or auction comes in, we'll match with, hey, John has this, or Howard has this, right? Pick one, right? I honestly don't care who they picked, as long as we can satisfy the AFQs, and then you find clients, and that's how we make money. It's transaction -based fee, right? So to your point, we see, which is amazing, a lot more new clouds coming in. You, I would like to think there's a lot more demand, and supply for the future, but we haven't really seen as much as I thought we could see by now. At least things get just soaked up very quickly thanks to all the open source models, I guess.

John Furrier

>> And the CapEx build out's been massive.

>> Exactly, globally too, right? Not just here, we were at a race summit and everything in Europe also sold out.

John Furrier

>> What else is going on in your world? There are new things on your radar. Obviously, the Vera Rubins are in full production now. We heard that from NVIDIA. ARM has Helios. They got some GPUs now. Now you're starting to see, you know, Tensor Wave, others come out. Argentum is a hot neocloud we covered recently. Core, we had the first workload. We launched them here in our studio on June 30th. So everything's hitting this next wave. What's it like?

>> So we're exploring a few interesting mechanism for the markets. To your point, today you can reserve GPU at certain prices with some of the names you mentioned. One of the things people are facing is token prices, right? So if you're calling token, people assume, which is kind of true, is you can only do on -demand pricing for tokens. Whatever API charges you, you get charged for that. Unlike GPU, you can reserve prices for next year minimum. For token, we're exploring ways to do token -forward packages, meaning you can lock in prices for the next year token given model family. So that's something we're exploring with our clients and our suppliers. So then give more people an option. sure, you care about GPU, do GPU reserve packages. If you care about token, which probably more people I would like to think care about tokens more than GPU, honestly, you don't care about what GPU underneath that. You care about token and do token for packages. You're locking token prices ahead of time, probably better for CFOs.

>> So for instance, Opus 4 .8, you reserve that for one year for the price, but one challenge is 4 .8 may be decommissioned in six months, how do you handle those?

>> So that's why we call it suppliers. So as a supplier, you have to commit to say, hey, I'm going to support this family because for that particular package, you'll be promised a support. The term for the four packages is not that long. We're not running like five -year long packages, like six months to 12 months. So give people enough room to upgrade or change their mind per se.

>> This is a minor digression, at least for this revolution, the AI revolution, compared to the past enterprise business Joe and I are so familiar with. is the backward compatibility is not a thing for those AI guys. They don't care, right? When Grog came out with 4 .5, they just decommissioned the API from the previous generation so fast. And that never happened when HP, IBM of the world were reigning. They care about the API or the service they provided six years ago. So it's a very different world.

>> So we don't support deprecated models as long as people are using them, as long as our providers, and you can factory support them.

>> But they don't always do that.

>> And people still support OSS, people using that for summarization, it's a very good model for that particular task. So for Silicon Data Compute Exchange, we don't really have a model that is latest, greatest, or deprecated, as long as people are using it, we love to support them.

John Furrier

>> Okay, explain, folks don't know, there's obviously more than NVIDIA out there, you've got AMD, you've got a big showing last week in San Francisco, Intel back in the game, inference is hot, are there other products in there? How do you view that as more exchange for you? Or how do you view that?

>> We're agnostic, so for compute change, we had a few deals on AMD, which is amazing. We will actually have an event with a few AMD new clouds coming up, which we're excited about. Silicon data have MI300 indices that's up and running. The price has been going up as well, just like other GPU indices, right? So we do cover all different design houses. Obviously, now, you know, the media still the sort of the majority of new cloud backbone, right? But in general, yeah, we work with that.

John Furrier

>> One trend I walked away from AMD last week was, is that the trend is, oh, a year and a half ago we had 500 GPUs, now we have 5 ,000, then 50 ,000. So you started to see the increase in GPUs. That's right. As their business and their services start to get adopted. Are there learnings that you have over the past 12 months about the sea change around that kind of growth? Has that changed the dynamics? What have been your key learnings over the past 12 years?

>> It's been amazing. So I feel like I've been telling people that we can all calculate how much the supply is supposed to be online based on the news and the capital spends, right? How much supply going to come online for AMD, for Nvidia, for CoreWeave. But no one, I don't think I can predict the demand side. Like I don't know how much, what we're going to use next quarter. I don't know when another country going to take off with AI adoption. option. So demand side is a big unknown. That translates to your point earlier, why GPU price is going all over the place? Why it was so volatile? Why price is going up and down all the time? Because no one can really control the demand side.

>> Open cloud probably contributed to that.

>> Some of that, right? Potentially. Maybe people love K3. I mean, I don't think anyone already has support of K3 quite yet, right? Not today, not right now. But that can be a potential potential boom for the B200s or 7 -page 200s, which will kind of support K3. So the demand side, I don't know. That's why the prices, it can be all over the place. I think people have a predetermined sort of view, say, hey, all the GPU costs are going to hop down, right? I mean, maybe, could be, but again, we don't know the demand side. I will argue that even in the US, East Coast, West Coast probably adopting more than other parts of the country. Other parts of the world, maybe not quite yet, right? So that's the adoption will take place.

John Furrier

>> Every day is a new model and they got the open weights, that's going to change maybe some of the other inventory. I'm sure you got your eye on that too. What are you optimizing for this year? What's new for you? What's your focus these days?

>> We are very excited for obviously the CMEs futures launch on my indices in the fall. We're working with all different natural hedgers. So it's pretty interesting. I'm helping probably a dozen market participants set up their compute desks, so people are actually helping their client to hatch. So think about if your client, your client, if you're a bank, your client can be the hyperscaler or neoclouds, they have a long exposure, they try to help them manage the futures and volatility for their revenues. Or your bank, your client can be the massive AI company, they're going to consume a lot of either GPO token, so they help them manage the short exposure. So we're helping those banks to hatch their volatility. facility.

John Furrier

>> It was covering the Argentum rise from zero to 10 billion in revenue. And one of the things I wrote in my post, Howie, was that Jensen says we're bounded by energy. It's the low level of his five -layer cake. But I added a new dimension, finance. And at a lot of cities, setting up a credit desk, banks, the financing side of it is bounded. So you have a financial element, not just cost for tokens, which we know in the enterprise is a little bit inflationary right now in terms of the costs. That'll get reined in, that's on the enterprise side, but then on the cloud and neocloud and hyperscaler side, there's a huge financing vehicle. Amazon's taking on debt, so is Google. So financial and energy are the bounding functions.

>> I mean, 100 % so we're top of capital. Obviously, banks' capital is supposed to be cheapest, right? And then you get more expensive capital come in from asset management of the world, right? So people have different risk appetite. they have to manage their rates differently, right? So for us it's all about bringing transparency to people.

John Furrier

>> Like the Moody's rating for GPUs.

>> Which is interesting you mention that. We start working with the credit rating agencies, right? Because we do do server fair price evaluation. So imagine a balance sheet, tons of servers, right? Someone has to evaluate performances and as well as evaluation for those servers. And they're not all zero, right? It's like if your airplane, aircraft carrier, not all your airplanes worth zero, by the end of year three, someone will have to come in and do a fair market evaluation, that's what we do as well.

John Furrier

>> Carmen, great to see you, thanks for coming on theCUBE in our third annual, you've been part of us the whole way. Appreciate your time and congratulations, we'll be keeping in touch. Get that data from Silicon Data, we'll put it on SiliconANGLE anytime.

>> Yep, yep, yep, sounds good, it's great.

John Furrier

>> Thank you for having me. I'm John Furrier, we're here for the third annual AI Leader Summit here in our Palo Alto, the CISO of theCUBE at NYC Wired. Of course, the event tonight, the leaders getting together, sharing their knowledge and of course interacting as the ecosystem continues to grow. We're doing our part bringing all the data to you here in theCUBE. Thanks for watching.

Carmen Li, Silicon Data & Compute Exchange

search

(INTRO)

John Furrier

>> Welcome back, I'm John Furrier with Howard Shue, my co -host here, also AI leader in the industry. This is our third annual CUBE and the NYSE Wired AI Leaders Summit here in our studios. And of course we have our third annual pool party gathering, 180 people coming together, all leaders, talking about the future, what they're working on, sharing their information. As the market continues to grow, the ecosystem is growing as well. Of course, the data really matters, and Carmen Lee is here, she's the founder and CEO of Silicon Data, for the Compute Exchange. Congratulations on all your momentum, great to see you again.

>> Good to see you again, John, this is great.

John Furrier

>> When you first started on the queue, We've been on a fellow journey together with NYSE Wired, so great to see you. First of all, what's the update? I've been seeing a lot of talks, so you're on Bloomberg recently. A lot of great action. Give us the update.

>> Yeah, so compute change, we launched the compute forward packages as well as our refurbished markets two weeks ago. So it's been, we see an amazing trend where people start transacting secondary GPU servers, which is great, right? because sometimes you might need the latest and greatest GP300, you might not want the H200, but someone else can use that for different reasons. So we want to see a robot secondary market to really solve the shortage problem that we're facing today for the GPU service.

John Furrier

>> So people need GPUs still as a supply chain problem, you're filling that with your market.

>> That's right, so through compute change you can get reserved contracts, forward contracts, which means things happen for the future, as well as secondary transactions, meaning you're looking to get secondary GPUs, which are certified, refurbished by us and our partners.

John Furrier

>> You've also been working on silicon data and getting all the data on the pricing.

>> That's right.

John Furrier

>> But you know I always ask the question, what's the current price of a GPU?

>> Exactly.

John Furrier

>> What's the status of pricing, durability, seeing any fluctuations, what's the data tell you?

>> I mean, if you ask me, remember last year, everyone was like freaking out, saying, well why do we need the indices, right? The compute price will keep going down forever. And last year the price did come down across all chips. So since January this year, all GPUs, we're talking about A100, H100, B200, all went up between 15 -20 % here today for on -demand NeoCloud GPUs. Hyperscale or on -demand prices have been pretty stable for A100, H100, but they were high to begin with. So when NeoClouds all went up 15 -20%, if you look at the term structure for all different GPUs, they all elevated, all the curves went up, right? So we see huge robust demand across all different chips globally.

John Furrier

>> So we see production of Vera Rubin. Does that skew the data, or is that just an outlier? Is that tracking, what's with Vera Rubin?

>> So for the Vera Rubins, most of them are, you know, people putting purchase orders. Hard to get. Exactly, exactly. So people are not actually, they've seen them in the quantity we're seeing yet, not because people don't want to.

>> Because they get their hands on them. Exactly.

>> So I would say another two months, potentially, we will start seeing restore of data coming in. We'll see forward contracts start being placed. But right now, we don't see much data. The GB300, yes, it's selling.

>> So in the GPU, the price go up and down. That makes a lot of sense. But you also kind of have this exchange. How fungible the GPU is? Like a GPU in one data center with certain network is totally different when it comes to token economics. So how to think about it?

>> You're 100 % correct. So that's why ComputeChange, we're doing full contracts on specific GPU servers, right? So you ask for, hey, I need 200 B200 nodes in US East with this particular specification. The CPU, this RAM, this disk, you have to find everything, the storages, right? Network storage. Everything. Software stack. Exactly. It's not just a... No, it's not just, hey, I need a H200, right? You have to tell me the specs. Oh, you don't care. Maybe you don't care, right? So then they have more options if you don't care about something. And then what we do is we give you a rubric spec and tell you, hey, here's the things satisfied, what you're looking for. Here's the specs. Here are the benchmark results. Because to your point, those machines might be three years old. The performances might not be the same as the specs. So we tell you the third -party benchmark results based on the performances, throughputs, flops, memory bandwidth, all the things, thermal degradation, and then you make a decision, right? Supposedly, better performing GPU can trade higher prices, right? I mean, today, you're lucky you get what you're looking for, right? That's your location you're looking for. But most of the time, you look at specs, you pick one, trade cleared, right? That's the way to go.

John Furrier

>> How has been the competition? Because I've been seeing a lot of people coming in, a lot more Neo clouds coming in, standing up infrastructure, a lot of build outs going on. Does that impact supply and or is it competition? Are they competition, are they frenemies? How do you view that?

>> We love all new clouds. We love all capacity coming in. So Compute Exchange doesn't own anything. We work with everybody and we - You're asset light, you have no asset light. We're asset light, so think about like NASDAQ, right? You guys, NYSE, right? NYSE doesn't own any stocks, you work with everybody, right, so similar Compute Exchange, work with everybody on your cloud. So the way we work is we integrate with them, so inventory tracking, so we know who has what.

John Furrier

>> You're not renting, are you renting GPUs or you're selling?

>> We have real -time inventory matching, so we know who has what.

>> Okay, exchange.

>> Exactly, so people, the RFQ comes in, or auction comes in, we'll match with, hey, John has this, or Howard has this, right? Pick one, right? I honestly don't care who they picked, as long as we can satisfy the AFQs, and then you find clients, and that's how we make money. It's transaction -based fee, right? So to your point, we see, which is amazing, a lot more new clouds coming in. You, I would like to think there's a lot more demand, and supply for the future, but we haven't really seen as much as I thought we could see by now. At least things get just soaked up very quickly thanks to all the open source models, I guess.

John Furrier

>> And the CapEx build out's been massive.

>> Exactly, globally too, right? Not just here, we were at a race summit and everything in Europe also sold out.

John Furrier

>> What else is going on in your world? There are new things on your radar. Obviously, the Vera Rubins are in full production now. We heard that from NVIDIA. ARM has Helios. They got some GPUs now. Now you're starting to see, you know, Tensor Wave, others come out. Argentum is a hot neocloud we covered recently. Core, we had the first workload. We launched them here in our studio on June 30th. So everything's hitting this next wave. What's it like?

>> So we're exploring a few interesting mechanism for the markets. To your point, today you can reserve GPU at certain prices with some of the names you mentioned. One of the things people are facing is token prices, right? So if you're calling token, people assume, which is kind of true, is you can only do on -demand pricing for tokens. Whatever API charges you, you get charged for that. Unlike GPU, you can reserve prices for next year minimum. For token, we're exploring ways to do token -forward packages, meaning you can lock in prices for the next year token given model family. So that's something we're exploring with our clients and our suppliers. So then give more people an option. sure, you care about GPU, do GPU reserve packages. If you care about token, which probably more people I would like to think care about tokens more than GPU, honestly, you don't care about what GPU underneath that. You care about token and do token for packages. You're locking token prices ahead of time, probably better for CFOs.

>> So for instance, Opus 4 .8, you reserve that for one year for the price, but one challenge is 4 .8 may be decommissioned in six months, how do you handle those?

>> So that's why we call it suppliers. So as a supplier, you have to commit to say, hey, I'm going to support this family because for that particular package, you'll be promised a support. The term for the four packages is not that long. We're not running like five -year long packages, like six months to 12 months. So give people enough room to upgrade or change their mind per se.

>> This is a minor digression, at least for this revolution, the AI revolution, compared to the past enterprise business Joe and I are so familiar with. is the backward compatibility is not a thing for those AI guys. They don't care, right? When Grog came out with 4 .5, they just decommissioned the API from the previous generation so fast. And that never happened when HP, IBM of the world were reigning. They care about the API or the service they provided six years ago. So it's a very different world.

>> So we don't support deprecated models as long as people are using them, as long as our providers, and you can factory support them.

>> But they don't always do that.

>> And people still support OSS, people using that for summarization, it's a very good model for that particular task. So for Silicon Data Compute Exchange, we don't really have a model that is latest, greatest, or deprecated, as long as people are using it, we love to support them.

John Furrier

>> Okay, explain, folks don't know, there's obviously more than NVIDIA out there, you've got AMD, you've got a big showing last week in San Francisco, Intel back in the game, inference is hot, are there other products in there? How do you view that as more exchange for you? Or how do you view that?

>> We're agnostic, so for compute change, we had a few deals on AMD, which is amazing. We will actually have an event with a few AMD new clouds coming up, which we're excited about. Silicon data have MI300 indices that's up and running. The price has been going up as well, just like other GPU indices, right? So we do cover all different design houses. Obviously, now, you know, the media still the sort of the majority of new cloud backbone, right? But in general, yeah, we work with that.

John Furrier

>> One trend I walked away from AMD last week was, is that the trend is, oh, a year and a half ago we had 500 GPUs, now we have 5 ,000, then 50 ,000. So you started to see the increase in GPUs. That's right. As their business and their services start to get adopted. Are there learnings that you have over the past 12 months about the sea change around that kind of growth? Has that changed the dynamics? What have been your key learnings over the past 12 years?

>> It's been amazing. So I feel like I've been telling people that we can all calculate how much the supply is supposed to be online based on the news and the capital spends, right? How much supply going to come online for AMD, for Nvidia, for CoreWeave. But no one, I don't think I can predict the demand side. Like I don't know how much, what we're going to use next quarter. I don't know when another country going to take off with AI adoption. option. So demand side is a big unknown. That translates to your point earlier, why GPU price is going all over the place? Why it was so volatile? Why price is going up and down all the time? Because no one can really control the demand side.

>> Open cloud probably contributed to that.

>> Some of that, right? Potentially. Maybe people love K3. I mean, I don't think anyone already has support of K3 quite yet, right? Not today, not right now. But that can be a potential potential boom for the B200s or 7 -page 200s, which will kind of support K3. So the demand side, I don't know. That's why the prices, it can be all over the place. I think people have a predetermined sort of view, say, hey, all the GPU costs are going to hop down, right? I mean, maybe, could be, but again, we don't know the demand side. I will argue that even in the US, East Coast, West Coast probably adopting more than other parts of the country. Other parts of the world, maybe not quite yet, right? So that's the adoption will take place.

John Furrier

>> Every day is a new model and they got the open weights, that's going to change maybe some of the other inventory. I'm sure you got your eye on that too. What are you optimizing for this year? What's new for you? What's your focus these days?

>> We are very excited for obviously the CMEs futures launch on my indices in the fall. We're working with all different natural hedgers. So it's pretty interesting. I'm helping probably a dozen market participants set up their compute desks, so people are actually helping their client to hatch. So think about if your client, your client, if you're a bank, your client can be the hyperscaler or neoclouds, they have a long exposure, they try to help them manage the futures and volatility for their revenues. Or your bank, your client can be the massive AI company, they're going to consume a lot of either GPO token, so they help them manage the short exposure. So we're helping those banks to hatch their volatility. facility.

John Furrier

>> It was covering the Argentum rise from zero to 10 billion in revenue. And one of the things I wrote in my post, Howie, was that Jensen says we're bounded by energy. It's the low level of his five -layer cake. But I added a new dimension, finance. And at a lot of cities, setting up a credit desk, banks, the financing side of it is bounded. So you have a financial element, not just cost for tokens, which we know in the enterprise is a little bit inflationary right now in terms of the costs. That'll get reined in, that's on the enterprise side, but then on the cloud and neocloud and hyperscaler side, there's a huge financing vehicle. Amazon's taking on debt, so is Google. So financial and energy are the bounding functions.

>> I mean, 100 % so we're top of capital. Obviously, banks' capital is supposed to be cheapest, right? And then you get more expensive capital come in from asset management of the world, right? So people have different risk appetite. they have to manage their rates differently, right? So for us it's all about bringing transparency to people.

John Furrier

>> Like the Moody's rating for GPUs.

>> Which is interesting you mention that. We start working with the credit rating agencies, right? Because we do do server fair price evaluation. So imagine a balance sheet, tons of servers, right? Someone has to evaluate performances and as well as evaluation for those servers. And they're not all zero, right? It's like if your airplane, aircraft carrier, not all your airplanes worth zero, by the end of year three, someone will have to come in and do a fair market evaluation, that's what we do as well.

John Furrier

>> Carmen, great to see you, thanks for coming on theCUBE in our third annual, you've been part of us the whole way. Appreciate your time and congratulations, we'll be keeping in touch. Get that data from Silicon Data, we'll put it on SiliconANGLE anytime.

>> Yep, yep, yep, sounds good, it's great.

John Furrier

>> Thank you for having me. I'm John Furrier, we're here for the third annual AI Leader Summit here in our Palo Alto, the CISO of theCUBE at NYC Wired. Of course, the event tonight, the leaders getting together, sharing their knowledge and of course interacting as the ecosystem continues to grow. We're doing our part bringing all the data to you here in theCUBE. Thanks for watching.